Access to Dashboard Folders: Permissions control folder access. Users must have certain permissions to access public, hidden, or shared dashboard folders.
Access to Report Folders: Folder access is controlled by permissions. Users must have certain permissions to access public, hidden, or shared report folders.
Intelligent Analytics Apps: Provide your teams with curated paths through your data, plus powerful tools for spontaneous, deep explorations. With an intelligent app, you have a purpose-built set of analyses and insights about a specific area of your business. Answer pressing questions, predict outcomes, and get recommendations on the next best actions with insights infused directly within the flow of work in Salesforce.
Access Levels for Report and Dashboard Folders: Each user, group, or role can have its own level of access to a report and dashboard folder. Viewers can see the data. Editors can determine what data is shown. Managers can control access.
Editor Access to Report and Dashboard Folders: Editor access allows you to view and save the reports or dashboards in a folder, but doesn't determine access to folder contents. Permissions set for the reports and dashboards themselves determine your access to the folder contents. For example, if you have Editor access to a folder and Run Reports permission to a report in the folder, you can run the report but not make changes.
Manager Access to Report and Dashboard Folders: Manager access allows you to view, share, save, rename, and delete the reports or dashboards in a folder, but does not determine access to folder contents. Permissions set for the reports and dashboards themselves determine your access to the folder contents. For example, if you have Manager access to a folder and Run Reports permission to a report in the folder, you can run the report, but not edit or delete it.
Viewer Access to Report and Dashboard Folders: Viewer access allows you to view the reports or dashboards in a folder, but doesn't determine access to folder contents. Permissions set for the reports and dashboards themselves determine your access to the contents. For example, if you have Viewer access to a reports folder and Create and Customize Reports permission to a report in the folder, you can rename or delete the report.
Manage Your Reports and Dashboards on Analytics Home: The Analytics tab provides a modern and cohesive experience for organizing all your analytics content in one place. You can access all your reports and dashboards in a single location, making it easier for you to manage and analyze your data. Some of the enhancements you can experience in the Analytics tab include an improved search experience that helps you find the information you need more efficiently. Another enhancement is the organization of your data with Collections to curate analytics for a project or items that are the most relevant to you.
Stay Organized with Collections: Collections enable you to curate and organize your analytics in a customized way. By creating a collection, you have a personalized set of analytics that’s relevant to your projects or interests. Your analytics can be drawn from different folders, and you can avoid searching different folders to access your most important analytics. In addition, collections can personalize the home page that makes it easier to find and use the information that matters most to you.
Create Reports and Dashboards on the Analytics Tab: Create reports and dashboards right from the Analytics tab. Rather than opening multiple tabs, you can stay in one app and create the analytics you want.
Favorite Your Reports and Dashboards: Favorite your reports and dashboards for quick access, such as My Analytics, and throughout the Analytics tab.
Browsing the Analytics Tab: Find the last report or dashboard that you worked with or see what’s available on the Analytics tab. Browse enables you to quickly locate the items you’re looking for by filtering through different categories.
Analyze Your Data: Unleash the power of data to drive better business decisions. Connect data sources, gain deep understanding of your business and customer data, and deliver timely, actionable insights and predictions to your teams within the context of their work.
Salesforce Analytics Tools: Salesforce offers several tools to help you analyze your data and act on it. Reports and dashboards are built into Salesforce. Connect data and create interactive visualizations with CRM Analytics, a Salesforce-native tool. Tableau is a flexible analytics platform with cloud, on-premises, and desktop applications. Use this matrix to understand each tool’s capabilities.
Reports and Dashboards: Salesforce offers a powerful suite of reporting tools that work together to help you understand and act on your data.
Report and Dashboard Folders: Use report and dashboard folders to organize your reports and dashboards. To share reports and dashboards, you share the folder, not the report or dashboard itself.
User Permissions for Sharing Reports and Dashboards: Each level of access to a report or dashboard folder consists of a combination of user permissions. As an administrator, you can further fine-tune users’ access to dashboards and reports by assigning or removing one or more permissions.
Unified Analytics Experiences: With unified experiences in Analytics tab, mobile, and Slack, you can interact with all of your Lightning reports and dashboards, CRM Analytics assets, Tableau views, and Einstein Discovery models and predictions.
Create a Custom Report Type for Approval History: Before you can run reports on executed and in-progress approval processes and their steps, you must create a custom report type for approval process instances.
CRM Analytics: Unleash the power of data to drive better business decisions. Connect data sources, gain deep understanding of your business and customer data, and deliver timely, actionable insights and predictions to your teams within the context of their work.
Add Tableau View to a Dashboard: Compare and review Tableau and CRM Analytics data side by side in a CRM Analytics dashboard. Embed a published Tableau Cloud or Tableau Server view in a CRM Analytics dashboard to analyze your data without switching between two accounts. For example, keep tabs on your shipment trends from Tableau in the same CRM Analytics dashboard that you track sales orders.
View Semantic Model History: View the semantic models created in previous CRM Analytics Metadata Bridge runs.
CRM Analytics Encryption: The CRM Analytics Encryption solution enhances security for Salesforce customers by extending encryption capabilities to the data at rest that is stored on the Salesforce file system.
Introduction to CRM Analytics Apps for Industries: Get started quickly with the CRM Analytics templated apps specifically designed for industries clouds. The apps provide industry-specific cloud solutions that help businesses in various industries to streamline their processes and enhance customer experiences.
Launch CRM Analytics Assets with URLs: The CRM Analytics mobile app supports deep linking URL schemes, which allow a user to launch the app from a third-party or web application.
Generate the Extended Semantic Model (Beta): Review the data model objects that the CRM Analytics Metadata Bridge adds to the base semantic model, and then generate the extended semantic model.
Review Objects in the Base Semantic Model (Beta): During field mapping, the CRM Analytics Metadata Bridge shows source objects that are already in the base semantic model so you can focus on the objects that the extend flow adds.
CRM Analytics Metadata Bridge Prerequisites: Using the CRM Analytics Metadata Bridge requires the appropriate licenses, org and user account setup, and permissions assigned to user accounts.
Proactively Monitor Data You Care About with Analytics Watch: View daily changes and trends in your data. Select your metrics to watch and the Analytics Watch feed provides trending and anomaly detection. Analytics Watch removes the energy and time required to check dashboards manually to see changes and trends, increasing the likelihood of identifying critical details among the noise.
Event Monitoring Analytics App: The Event Monitoring Analytics App integrates with event monitoring and setup audit trail data to give you insights into your user and org behavior. The app is a built-in way to explore your monitoring data in Salesforce. App creation is easy, and with its prebuilt dashboards and datasets, you can start exploring right away. This app helps you drill into your data and swiftly identify suspicious behavior, slow page performance, and poor user adoption.
Create and Share the Event Monitoring Analytics App: After you enable the correct permissions, set up the Event Monitoring Analytics app. You can create an Event Monitoring Analytics App in one of two configurations. The first configuration incrementally refreshes only new event log file data since the last dataset update. In the second configuration, the app fully refreshes all data with new event log file data.
Create an Event Monitoring Analytics App That Incrementally Refreshes Data: Create an Event Monitoring Analytics app and configure it to incrementally add new event log file data since the last dataset update. This configuration reduces the likelihood of reaching row count limits and your 24 hourly rolling upload limit. It also speeds up dataset refreshes for large datasets.
Event Monitoring Analytics App Prebuilt Dashboards: The Event Monitoring Analytics App comes with a set of prebuilt dashboards so that you can start exploring your data right away. Data from your org is automatically loaded into your Event Monitoring Analytics App datasets when it’s generated.
B2B Marketing Analytics App: Explore your marketing and sales data in one place with B2B Marketing Analytics. Dashboards consolidate Account Engagement and Sales Cloud data so that you can monitor the impact of marketing on revenue and take instant action to drive marketing results.
Share CRM Analytics in Experience Cloud Sites: With CRM Analytics in Experience Cloud sites, external users can view apps shared with them via CRM Analytics dashboards embedded in their site. You can embed dashboards using Experience Builder or Visualforce pages.
Create an App: Create an app to organize and contain any combination of lenses, dashboards, and datasets.
Delete an App: If you have Manager access to an app, you can delete it. Deleting an app permanently removes all of its lenses, dashboards, and datasets from CRM Analytics.
Identify Event Monitoring Analytics App User Types: Identify Event Monitoring Analytics app user types and the tasks they perform to ensure that the setup process meets your team’s event monitoring analytics needs.
Upgrade the Event Monitoring Analytics App: Take advantage of the latest Event Monitoring Analytics App features by upgrading your app every time we release a new version.
Field Service Analytics: The Field Service Analytics app brings the power of Analytics to Salesforce Field Service on any device that supports CRM Analytics.
About the Field Service Analytics App: Learn the benefits of Field Service Analytics before you create and use the app to help manage field service operations.
Set Up and Create the Field Service Analytics App: Set up Field Service Analytics app permissions, make sure your org is enabled to use Field Service, and assign field level security correctly so you can create and share the app.
Analytics for Retail Banking Template: Analytics for Retail Banking is part of a suite of CRM Analytics apps for Financial Services Cloud customers. Its dashboards visualize all the metrics and key performance indicators (KPIs) personal bankers require to grow client relationships.
Financial Services Analytics: Analytics for Financial Services gives financial advisors and personal bankers a comprehensive customer intelligence solution based on the CRM Analytics platform. It includes Einstein Discovery, which lets you create automated AI models on any dataset without writing code.
Manage Templated Apps: CRM Analytics templated apps have multistep processes for installation and upgrades. Use the App Install History page to monitor installations in progress, track the assets created for the app, and view logs to troubleshoot any installation issues. After an app is installed, run or schedule one-click data refreshes.
Set Up Navigation Through Your App: Customize app navigation so that users can easily find the dashboards and lenses that are targeted for them.
Deploy CRM Analytics Industry Apps and Prebuilt Apps: CRM Analytics templated apps give you a fast way to get started with your analytics journey. Create an app from a template to get ready-made dashboards and KPIs that your team can use out of the box or customize to meet your needs.
Create Apps from CRM Analytics Templates: Start Here: Follow these general procedures when you create Apps from any CRM Analytics Template. Before you create an app, see the help for the CRM Analytics Template you want to use for requirements and instructions specific to that template.
Present Live, Interactive Dashboards: Conduct meetings directly from CRM Analytics by presenting dashboards in running apps. With dashboards instead of slides with static images, you have real-time access to your data, and you can showcase dynamic visualizations.
Reconfigure a CRM Analytics App: To restore deleted or altered dashboards or change wizard settings, reconfigure an existing app.
Sales Analytics Team Activities Dashboard: See what your team’s been doing to keep on top of opportunities. Look at their total, completed, and overdue tasks and review each member’s calls, events, emails, and tasks.
Sales Analytics Team Benchmark Dashboard: Shows a ranking of sales rep results across your team’s KPIs, such as closed won deals, best case, and average deal size. Also shows each rep’s performance against team average.
Sales Analytics Executive Overview – Pipeline Performance Dashboard: Sales executives get instant pipeline status. You can view the current amount in the pipe, comparison to the previous period, projected closing, and top deals by lead source, product, account, and region. Also get top-level views of sales and service performance.
Sales Analytics Executive Overview – Sales Performance Dashboard: Sales executives get instant insight into closed business and quickly recognize trends. See bookings for the period, changes from previous period, as well as win rate, lead conversion rate, and top deals. Also see top-level views of pipeline and service business.
Sales Analytics Forecast Dashboard: View quota attainment and forecast for each member of the team, and fine-tune the forecast by reviewing opportunities by stage in a selected period.
Sales Analytics Home Dashboard: Start exploring Sales Analytics with an overview of top-level year-to-date key performance indicators (KPIs).
Sales Analytics Leaderboard Dashboard: Gives sales leaders an overview of team and individual rep performance, including quota attainment, pipe coverage, bookings, pipe generation, closed won business, average sales cycle time, and sales activities.
Sales Analytics Lead Analysis Dashboard: Boost operational efficiency by taking a close look at your team’s lead conversions. Quickly identify leads that convert the most quickly and visualize the team’s conversion rate. And get a view into top leads by source and region.
Sales Analytics Company Overview Dashboard: Intended to give sales leaders and operations staff high in the corporate hierarchy an overview of the sales business and operations.
Sales Analytics Sales Overview Home (Embedded): Designed to be embedded in the Lightning Home page, the dashboard lets sales reps see their KPIs the second they log into Salesforce. They can also quickly access the Sales Analytics dashboards that matter most to them.
Sales Analytics Sales Rep Overview Dashboard: Sales reps: start here to see the status of opportunities you own and get ideas for the quickest ways to make quota. Review quota attainment, bookings, and pipeline for a given time period. Also uncover key opportunities for accelerating deal closing and review the number of activities you’ve completed in the selected period.
Sales Analytics Sales Performance Dashboard (Operations): Sales leaders: Drill into your closed business by geography, account, product, source, or other criteria to get a 360-degree view of the team’s operational efficiency.
Sales Analytics Sales Stage Analysis Dashboard: Shows how deals move through stages of the sales process and if deals are moving smoothly. Also reveals bottlenecks and exposes at-risk opportunities.
Sales Analytics Executive Service Performance: Sales executives get instant insight into customer service performance. See average case duration for a selected period compared with previous periods and case duration by account, service channel, and geography.
Sales Analytics Account Dashboard (Embedded): Get a snapshot of an account, including its average sales cycle, opportunity amount, and win rate. The dashboard is intended for use when embedded in a Salesforce account or opportunity page.
Sales Analytics Trending Dashboards: View company, team, and pipeline changes for a given period, including beginning and end values of the pipe as well as what’s moved in and out.
Sales Analytics Team Whitespace Dashboard: Use this dashboard to uncover resell and upsell opportunities by viewing whitespace. Whitespace means accounts that you can revisit to create business.
Sales Analytics Wizard Data and Feature Check: At the start of app creation, Sales Analytics scans your org. It checks to make sure you can create the app and looks for features and data to add to your app.
How to Answer Custom Wizard Questions: The custom version of the Sales Analytics configuration wizard asks you a series of questions about your data. Follow these general guidelines, and the specifics that follow, to get the best result.
Sales Analytics: Sales Analytics brings the power of Analytics to Sales Cloud on any device that supports Analytics. With intuitive visualizations based on your Salesforce data, Sales Analytics lets you move from insight to action quickly and helps you turn data into smarter sales.
Sales Analytics Calculated Fields: Sales Analytics adds custom fields computed from your Salesforce data to app dataset. The table lists the fields in alphabetical order and shows how they are computed and the dataset that include them.
Create and Share Sales Analytics: Follow these steps to create Sales Analytics and start uncovering the value of your Salesforce data.
Sales Analytics Dashboard Guide: Sales Analytics includes prebuilt dashboard to meet the needs of everyone on the team. Sales executives, managers, and representatives as well as the operations staff all have instant access to the KPIs they need to manage the sales business.
Sales Analytics Datasets: When you create Sales Analytics, CRM Analytics imports your Salesforce data into the app dataset based on your wizard selections. The dataset drive the app’s dashboards and lenses.
Delete Sales Analytics: Delete apps to start app creation all over or to get rid of apps you no longer use.
About Sales Analytics: Learn the benefits of Sales Analytics before you create and use the app to explore your Sales Cloud data.
Sales Analytics Lenses: Sales Analytics lenses augment app dashboards with targeted visualizations of your org’s sales data.
Understand Sales Analytics Limitations: Sales Analytics requires that Sales Cloud include specific data and supports a limited set of Salesforce objects.
Sales Analytics Example .CSV File: Here’s an example of the .CSV file you create to update the Sales Analytics Quota (Target) dataset.
Forecasting and Quotas Data in Sales Analytics: Sales Analytics gives you a choice for how to include your team’s quotas depending on whether you use the Salesforce Forecasting feature to store quotas data.
Edit the Forecast Dashboard Data Source Connection Make Sure Quotas Data Is Accurate: If Sales Analyticsquotas numbers look higher than expected in the Forecast dashboard, it can be because the app counts some quotas twice. To make sure that the dashboard accurately reflects your team’s quotas, specify the dataset field the dashboard uses as its data source
Upgrade Sales Analytics App: Take advantage of the latest Sales Analytics features by upgrading your app when we release a new version.
Service Analytics Wizard Data and Feature Check: At the start of app creation, Service Analytics scans your org. It checks to make sure you can create the app and looks for features and data to add to your app.
Delete Service Analytics: Delete apps to start app creation all over or to get rid of apps you no longer use.
Use the Service Analytics Basic Create Option: Select the basic app creation option when you create Service Analytics for the first time or when you want to create the app quickly.
How to Answer Service Analytics Custom Wizard Questions: After you add features to Service Analytics, the custom version of the configuration wizard asks you a series of questions about your data. Follow these general guidelines, and the specifics that follow, to get the best result.
Service Analytics: The Service Analytics template gets you started fast with Analytics and provides a clear path through your Service Cloud data on any device. Whether you’re a service manager or agent, you get everything you need in one place to uncover key data insights to help you grow your business.
About Service Analytics: Salesforce created the Service Analytics template to make it easy for service managers and agents to use data to drive the success of your service business. Learn the app’s benefits before you create and use the app to explore your Service Cloud data.
Understand Service Analytics Limitations: Service Analytics requires that Service Cloud include specific data, initially supports a limited set of Salesforce objects, and has other miscellaneous limitations.
Reconfigure Service Analytics: To restore deleted or altered dashboards or change wizard settings, reconfigure an existing version of Service Analytics.
Share Service Analytics: Members of your team can only use Service Analytics if you share it with them.
Upgrade Service Analytics: Take advantage of the latest Service Analytics features by upgrading your app every time we release a new version.
Share an App: To enable others to see a lens, dashboard, or dataset, one way to share is by sharing the app it’s in.
App-Level Sharing: CRM Analytics apps are like folders, allowing users to organize their own data projects—both private and shared—and control sharing of dataset, lenses, and dashboard.
Upgrade a CRM Analytics App: Take advantage of a CRM Analytics app’s latest features by upgrading when we release a new version.
Smart Recommendations: The CRM Analytics home page surfaces lists of items suggested for you based on your usage and your org, and the lists are dynamic and update automatically as you work. Two of the lists employ CRM Analytics intelligence and unique view of your org to pinpoint your important analytics, show what’s currently popular, and increase discoverability of analytics you haven’t seen before.
Find Dashboards and Datasets by Field Using Semantic Search: Uncover CRM Analytics items beyond just those matching exact keywords. Semantic search boosts searching with a behind-the-scenes view of object fields and dataset columns across all your org’s data. You can ask to see dashboards using specific sObject or dataset fields, or ask for datasets containing specific sObject fields. Semantic search inspects all your data, but the search results are tailored for you, adhering to user access, sharing rules, and row-level security.
Auto-Installed Apps: Monitor and interact with installed apps on the Apps tab.
Monitor, Update, and Delete Auto-Installed Apps: Templated CRM Analytics apps are automatically installed in Salesforce orgs via managed packages. Use the Auto-Installed App setup page to monitor the status of auto-install requests, view logs to troubleshoot auto-install issues, track app usage statistics, and update and delete auto-installed apps.
Auto-Install Requests: Monitor and interact with auto-install requests on the Requests tab.
Auto-Install Usage: Track the average API calls per hour and the number of dataset rows used per day for all auto-installed apps and for each individual app.
Annotate Data Points with Tooltips and Markers: Create your own tooltips and markers in charts to show details about data points. Create a tooltip to specify which details appear when you hover over any data point. To annotate a specific data point, add a marker and, to draw more attention, make it blink.
Automatically Highlight Data with Conditional Formatting: Don’t delay important decisions because you miss critical changes in your data. With clicks, not code, add rules to automatically format results to quickly catch changes and take immediate action. For example, highlight accounts in red in a bar chart when their CSAT score drops below 80% so that you can get on the phone with those customers.
Handle Missing Values in Line Charts: Charts can display gaps to highlight missing data. Or charts can ignore the missing data and show a continuous line that connects the data points. For periods of missing data in timeline charts, you can make cumulative lines horizontal to indicate that no change occurs.
Format Numbers in Charts to Your Liking: Take advantage of formatting options for chart numbers in lenses and dashboards. Display the entire number value when precision is important, or the compact version for improved readability, or a percentage relative to the total value represented in the chart for easier comparisons.
Customize Chart Segments: Use segment gaps and borders to increase visual separation between adjacent chart segments.
Add Icons to CRM Analytics Charts: Bar charts (but not stacked bars), dot-plot charts, and line charts can have icons along with dimension values as labels. Polar gauge charts can include icons in their centers.
Bubble Map Charts: Use a bubble map chart to visually indicate measure values in specified geographical areas.
Bullet Charts: Bullet charts are perfect for comparing metrics against quantitative benchmarks and references, like current revenue with target revenue.
Calendar Heat Map Charts: Calendar heat maps are useful for visualizing recurring discrete activities, such as closing accounts, over long periods of time. In this chart, you can easily change the granularity of the time-based grouping, such as from month to week or day.
Combo Charts: Use a combo chart to display at least two related series of data, such as discrete grouped values as vertical bars overlaid with a line chart representing an average value.
Custom Map Charts: Use a custom map if you have data with a geographical component that doesn't match any of the standard maps provided by CRM Analytics charts, such as custom regions. You can also use custom maps for areas that can be represented with polygons, such as stadium seat sections, city blocks, or floor plans.
Donut Charts: Use a donut chart when you have a grouping and want to show not only the proportion of a single value for each group member against the total, but also the total amount itself. If you remove the donut's center by selecting 0% from the Center Size menu, this visualization is commonly referred to as a pie chart.
Dot Plot Charts: Horizontal and vertical dot plots use the size and coloring of bubbles to display multiple dimensions and measures. Use dot plots to visualize related data to compare performance or locate unusual values.
Funnel Charts: Use a funnel chart to visualize sequential data that can be broken up into stages, such as a sales cycle.
Gantt Charts: Gantt charts are a powerful way to quickly track activity schedules, task durations, and overlaps.
Gauge Charts: Use gauge charts to track progress along a single measure, such as how much revenue has been realized versus the target.
Geo Map Charts: Geo maps allow you to visualize data that contains geographical coordinates (latitude and longitude). Using coordinates allows you to place visual data elements more precisely on map charts.
Bar Charts: CRM Analytics provides the following types of bar charts: Bar, Column, Stacked Bar, and Stacked Column. Use the Bar or Column chart to provide a quick visual comparison of related values. Use the Stacked Bar or Stacked Column chart to show groups within each bar.
Heat Maps: Use a heatmap to visually enhance high- and low-value data when there's a single measure and multiple dimensions.
Line Charts: Use a line chart when you have one important grouping representing an ordered set of data and one or more values to show.
Map Charts: Use a map if you have data with a geographical component. Maps can shade areas in proportion to mapped values, allowing for visual pop-out of high-value areas. You can find the map chart in the dashboard designer.
Matrix Charts: Use matrix charts to create a table that represents measures visually, allowing you to quickly spot extreme values.
Origami Charts: Use an origami chart to create a striking horizontal visualization for easily identifying high- and low-value data when there's a single measure and a single grouping.
Parallel Coordinates Charts: Use a parallel coordinates chart when you have multiple measures and a single grouping. Parallel coordinate charts are useful for displaying how data elements in a grouping stack up against each other.
Pyramid Charts: Use a pyramid chart to visually highlight relative sizes of stacked dimension values.
Radar Charts: Use radar charts to display a small dataset with one dimension and at least three measure columns.
Rating Charts: Use a rating chart to get a sense of how well a measured quality, such as customer satisfaction, is meeting expectations.
Sankey Charts: Use a sankey chart to visualize the distribution of a measure from one grouping to another grouping.
Scatter Charts: Use a scatter chart to visualize correlation between two groups of data.
Time Bar Charts: A time bar chart is a column chart with the horizontal axis showing a time dimension. Use a time bar chart to visualize changes over time with vertical bars, which is a great way to illustrate relative changes over time and highlight missing data.
Time Combo Charts: A time combo chart shows two or more measures over time. You can display each measure as a line or bar. You can display the charts on the same or separate axes.
Timeline Charts: A timeline chart is a line chart with one axis dedicated to a time dimension. Use a timeline chart to show how a value changes over time.
Treemap Charts: Use treemaps to visualize hierarchical quantitative data, where containing rectangles designate relationship using color, and "leaf" rectangles represent quantity using area.
Column Charts: Use a column chart (also called vertical bar chart) to show relative counts of things, such as leads or dollars. Use a stacked column chart to show groups within each bar.
Waterfall Charts: Use a waterfall chart to show the cumulative effect of sequentially introduced positive or negative values with breakdowns of value totals. Also known as "flying bricks" or "Mario" charts. To include breakdowns of value totals, use a stacked waterfall chart.
Add Multiple Reference Lines to Charts: You can use any number of static or dynamic reference lines to mark up a chart, such as when you indicate points of reference on a timeline before and after a predictive line. Dynamic reference lines are powered by existing queries in the dashboard.
Compare Results for Different Groups with Trellis: Enable trellis on a chart to display a separate chart for each group. Because the charts in a trellis have the same scale, you can quickly compare results across all groups.
Segment Gap and Segment Border: Add visual separation between adjacent chart segments to improve readability and help your charts meet accessibility contrast standards.
Fill the Area Between Lines: You can shade the area between lines in line and timeline charts to accentuate the spread between two measures.
Use a Suggested Chart to Display Results: Using a sophisticated algorithm that analyzes your lens's composition, CRM Analytics suggests chart types that can better communicate the essential message your data is communicating.
Highlight Focus in Custom Tooltips: When using custom tooltips that incorporate charts, it can be difficult to identify which element has focus. Implementing a clear visual indicator for focus within these chart-inclusive tooltips is essential to improve user experience, ensure accessibility, and facilitate seamless data exploration.
Install the Analytics CLI Plugin: Follow these steps to set up the Analytics plugin for Salesforce command-line interface (CLI) for your CRM Analytics development project.
Create and Manage CRM Analytics Templates in Analytics Studio: CRM Analytics gives you a great way to leverage your CRM Analytics app development efforts because it allows you to create a template of your app. Instead of recreating your work, other organizations can use your template to create their own version of your app, built on their own data and org customizations.
What Is a Dashboard?: A CRM Analytics dashboard is a collection of widgets that work together to tell a data story from multiple angles. Depending on what you want the dashboard to show or how to behave, you can add different widgets, such as key performance indicators, charts, tables, filters, and images.
Collaborate with Dashboard Annotations: Annotate dashboard widgets with comments posted in the dashboard and in Chatter. With annotations, you can hold conversations about your data and how it’s visualized, with the dashboard right there for reference.
Build CRM Analytics Dashboards: Build a CRM Analytics dashboard to continuously monitor key metrics of your business, analyzing the results by key dimensions, like region, products, and time period. Add interactive charts that synthesize information into an easy-to-read format. To complement the charts, add tables that show record-level details. Add filters to allow dashboard viewers to change the focus of the results. Create customized layouts to optimize the display of a dashboard on different types of devices, like mobile phones, tablets, and desktops.
Create and Reuse Dashboard Components: Use dashboard components to manage and reuse groups of charts, tables, filters, text, and more in multiple dashboards.
Delete Dashboard Components: If you delete a component that’s used in other dashboards, keep in mind that you’re deleting the contents of the component only. The component will be empty wherever it’s used in other dashboards. To completely delete a component in other dashboards, open and delete the component in dashboards that use it.
Edit Dashboard Components: When you edit a component that’s used in other dashboards, you can edit the original component or make a copy. If you edit the original component, keep in mind that changes are applied wherever the component is used.
Add Lightning Web Components to the Dashboard: Lightning Web Components allow you to create customized reusable extensions for use anywhere in Lightning Experience. Use these same components directly in your dashboards, to create an interactive experience between your custom extensions and your CRM Analytics data.
Manage Datasets in Dashboard Components: You can control whether components inherit data source linking from the dashboard in which the component is created.
Use Parameters to Make Components Dynamic: Parameters are configurable options that adjust a component's behavior and appearance according to specific requirements or preferences without rebuilding the component.
Manage Parameters: You can edit parameters to make minor adjustments or change for a different use, and delete them when they’re no longer needed.
Use a Parameter with a Static Value Type: In this example a parameter is added to the appropriate widget and query values to update a component chart by different groupings.
Use a Parameter to Update a Query: In this example dashboard stakeholders want the flexibility to add filter values and fields to a single column in a table. This type of request can be complicated because it requires passing data to a portion of a query. However, you can insert a parameter into the column filter to be updated with desired values and fields. The parameter can be set up to work as a static or a dynamic value type.
Parameter Value Type: Parameters offer a versatile way to reuse components and manage how updates are implemented according to your requirements.
Copy and Paste Widgets Across Dashboards: Copy widgets such as text and charts, then paste them in another dashboard and still keep the original widget orientation, styling, and queries.
Create a CRM Analytics dashboard with a Template: Dashboard templates speed your analytics development by automatically creating dashboards. Some provide blank layouts that you populate with data, while other “smart” templates create dashboards that require little to no additional configuration.
Create Interactions with Clicks: Interactions, previously called bindings, are the most powerful way to specify relationships between different queries on your dashboard. Creating the right syntax for an interaction can be complex. With the Advanced Interaction Editor, create, edit, and preview your interactions with clicks.
Configure Cross-Dataset Faceting with Connected Data Sources: By default, queries from the same dataset are faceted—automatically filtering each other. To facet queries from different datasets or even custom queries in the dashboard designer, connect them using common fields between them.
Design for Your Audience: Personas and the Path of Inquiry: Before you choose charts or layout, understand who uses the dashboard, what they need to do with it, and whether to structure your app around personas or tasks. Designing for a specific audience keeps a dashboard focused and prevents it from becoming a catch-all.
Download CRM Analytics Dashboards as Documents (Beta): Export your CRM Analytics dashboard as a PDF to share your insights. Preserve your applied filters and the active page, and customize the layout with specific page sizes and data options.
Set Initial Selections in the Dashboard: If the CRM Analytics dashboard contains selection-based widgets (chart, date, list, range, or toggle), you can set initial selections that are applied when the dashboard first opens. For example, you can select a bar in a bar chart and set that as the initial selection. If the underlying query for the bar chart is faceted, other widgets with faceted queries are filtered based on this initial selection. While viewing the dashboard, a user can change the selection in the chart to filter the results differently or remove the selection and filters altogether.
Dynamically Set Initial Selections Based on the Dashboard Viewer: You can set initial filter selections to show information that’s relevant to the logged in user. For example, you can filter a sales dashboard such that each sales rep views only their sales opportunities and performance. You can set initial filter selections based on the user ID, user name, role ID, or role name of the logged in user.
Set Initial Global Filters in the Dashboard: If the CRM Analytics dashboard contains global filters, you can set the initial filters that are applied when the dashboard first opens. If a global filter is unlocked, the dashboard viewer can change the filter while viewing the dashboard. Global filters apply to all queries in the dashboard unless a query is configured to ignore these filters.
Make Real-Time Decisions with an Input Widget: Perform speculative analysis to assess risk and develop recommendations for your business in real time. Use values from the input widget as a data source, and see its effect on your dashboard metrics without manual calculations.
Create an Input Widget: Speculative analysis starts by adding an input widget to the dashboard that captures numeric values to interact with other dashboard widgets.
Layout Properties for Analytics Dashboard: Layout properties specify the layout name, designer grid settings, background settings, and requirements for devices that can use this layout. The properties apply only to dashboards created in the dashboard designer.
Rules for Choosing a Layout for a Device: If multiple layouts are defined for a dashboard, CRM Analytics chooses the optimal layout when displaying the dashboard on a device. To determine the optimal layout, CRM Analytics uses the device properties specified for each layout.
Generate Unique CRM Analytics Dashboard Layouts for Different Devices: After you add widgets to a dashboard, optimize it for viewing on smaller screens. For example, remove widgets from a layout for on-the-go mobile users, and rearrange widgets for users with tablet-sized screens.
Create Dashboard Pages: Make the information on a dashboard easier to digest by chunking the content into multiple pages. And with fewer queries per page, dashboard performance increases. With pages, you can tell a story by creating a dynamic pathway through your dashboard. Depending on how you lay out your pages, you can also create some cool effects as you transition from one page to the next.
Manage Widgets on a Page: You can move widgets to another page. To share them and their queries across pages, add them to other pages. If you no longer need a widget, delete it from the page.
Dashboard Properties: These properties apply to a dashboard designer dashboard. Some of them are the default widget properties that you can apply to each widget to ensure a consistent appearance.
Collaborate on a New Dashboard Version Behind the Scenes: CRM Analytics users can add themselves to the list of dashboard publishers so they can edit and test new versions of the dashboard. Other CRM Analytics users continue to see a live version until a publisher makes the draft the live version.
Collaborate on and Publish a Draft Version of a Dashboard: If you are listed as a publisher, you can review and edit a previous dashboard version. Then save it as the draft version for othesr publishers to review and publish it as the live version that all CRM Analytics users view.
CRM Analytics Quick Sort: Limitations and Guidance: CRM Analytics offers a powerful quick sort feature, enabling users to sort data directly within table and chart widgets by clicking column headers. However, some query configurations can interfere with this functionality. This document outlines known limitations and provides best practices for effective use of the quick sort feature.
Key Elements of a CRM Analytics Dashboard: A dashboard isn’t just for reading. It’s for carrying on a conversation based on one or more datasets that matter to your company. Don’t only look at the pictures—use the tools on the dashboard to dive deeper into the data that you care about. Go ahead, click around, and explore!
Recycle and Restore CRM Analytics Dashboards and Lenses: When you delete a dashboard or lens, it moves to the Recycle Bin instead of disappearing for good. You have 30 days to restore it to its original state before it's permanently gone.
Use One-Click Actions from Repeater Widgets: When you add an actionable dataset field to a text widget in a repeater widget, the text in the widget becomes a one-click action.
Transform Tabular Data with Repeater Widgets: Use repeater widgets to show select fields from a query in a scrollable list in your dashboard. Create a customized layout of text, numbers, charts, and images in a repeater widget, and your dashboard users can scroll through a stylized view of query data.
Highlight Repeater Widget Content with Conditional Formatting: Make key data in repeater widgets stand out by adding conditional formatting rules to text, numbers, charts, or images. For example, highlight countries with opportunity amounts less than 3 million dollars in the Opportunities dashboard that’s set up with a repeater. You can apply the same rule or a separate one for each widget in the repeater based on a value or range.
Create a Repeater Widget: A repeater widget is easy to create because you make just one custom layout using fields from a row of data. For each row of data in a query, the custom layout repeats in a scrollable list.
Add Style to Your Toggles with the Repeater Widget: Liven up your dashboard toggles using a repeater widget instead of a toggle widget. With a repeater widget, you can add images to provide visual cues for the available toggle states, change the text position, and add emoji to the text.
Create Tooltips for Repeater Widgets: For a quick review of related data while reviewing metrics in a scrollable layout, add tooltips to a repeater. You can design a separate tooltip for text, numbers, images, and bullet, gauge, polar gauge, or flat gauge charts in the repeater. The options available vary depending on the widget type. For example, on a number widget, you can choose a default or custom tooltip, while you can include custom text, data point details, and visualizations in a chart widget’s tooltip.
Restore a Previous Version of a Dashboard: CRM Analytics uses version history to back up dashboard versions when you edit them so you can restore a previous version.
Smart Dashboards: Use a smart dashboard template to create a ready-made dashboard that’s prepopulated with your Salesforce data.
Configure Faceting on Related Salesforce Objects: Surface trends between Salesforce objects with parent-child relationships in a single dashboard. For example, on a dashboard that’s set up to monitor Salesforce account records, you can track related opportunities and cases. To facet queries based on parent-child relationships in the dashboard designer, connect them using common fields between them.
Create a Query with a Lens: Manually create a query from the dashboard designer when you’re concerned about the results it returns and not the widget that displays those results. For example, you might build a query that calculates three measures. You can display all measures in a combo chart and display each measure in a separate number widget as well. You can reuse queries in multiple widgets.
Clone a Query: Clone a query to quickly create a query based on an existing one. Cloning is useful when you can reuse an existing query with just a few tweaks.
Manage Filters from Linked or Embedded Dashboard with External Filters: External filters enable you to determine whether dashboard queries can be influenced by filters from linked or embedded dashboards. This means you can selectively apply external filters to specific queries while keeping others isolated.
Dashboard Compatibility Considerations for External Filters: Dashboards created in Summer '25 or later with external filters and targeted faceting can experience errors. These issues commonly occur from inconsistencies during sandbox-to-production org migrations or discrepancies in API versions during deployment.
Optimize a Query: Queries can slow down your dashboard. Run a performance check on the queries to ensure that they’re running optimally. CRM Analytics identifies query bottlenecks and provides recommendations to improve query performance.
Query Properties for Analytics Dashboard: Query properties define the query that returns results to display in the widget. These properties also specify how the widget behaves and interacts with other widgets in the CRM Analytics dashboard. The properties apply to dashboards created in the dashboard designer only.
Create a Query with the Query Editor: Use the query editor to create or customize SAQL or SOQL queries. The query editor gives you additional flexibility over queries created by widget wizards or in the chart and table modes of the lens.
Query Editor Considerations: Keep these considerations in mind when switching to the Query Editor to update a query.
Tips for Working with SAQL Queries in the Query Editor: Queries created or edited in the query editor have unique characteristics compared to other queries, like those queries created in the lens. Review the following tips for working with queries created in the query editor.
Tips for Salesforce Direct Data Queries: Direct Data is an option for querying live Salesforce data without having to set up replication or create datasets. With Direct Data, you can query Salesforce objects in the dashboard designer’s explorer and edit Salesforce Object Query Language (SOQL) queries in the query editor.
Queries and Custom Queries: Widgets that display data require queries. A widget displays the results returned by a query. For example, a query calculates the average opportunity amount, grouped by region. The widget displays the results in a bar chart format with each bar showing the average amount for a region.
Manage Queries for Widgets: Queries return results that are displayed in widgets. For example, a number widget displays the result of a calculation that is defined in a query. Queries can be built on a data source, like a dataset or a Salesforce object. They can also be “custom queries” created with user-defined values.
Create a Custom Query with User-Defined Values: Use custom queries to apply user-defined values to dimensions, measures, and limits of other queries. For example, you can create a toggle widget that filters case records based on the following user-defined values: High Priority Cases, Medium Priority Cases, and Low Priority Cases. To enable these custom values to filter your data, you map them to your data using a binding.
Pivot Table Functional Constraints and Technical Requirements: While pivot tables are essential for summarizing data, their performance and display are subject to specific technical constraints and configuration requirements. The information in the table defines the availability of certain calculation features and the behavior of interactive elements. Understanding these established boundaries makes sure that the resulting pivot tables remain accurate and functional.
Widget Properties for Analytics Dashboard: Widget properties define how widgets appear in the Analytics dashboard. For example, you can apply labels, backgrounds, and borders. To make widgets look consistent, you can set up default widget properties in the layout and then assign them to each widget. Widget properties vary based on the widget type and the designer used to create them. The properties listed here apply only to widgets created in the dashboard designer.
Build and Use Interactions to Navigate Through Product Data: You can add interactions to widgets to guide users through multiple levels of data. For example, start at a large scale, such as a country, and navigate a user through more granular areas, such as a business segment or account owner. You can also use this pattern to create longer, deeper flows, such as configuring a dimension to navigate by, or set a new product’s dashboard to open when you select a product. This example explains how to create a flow that shows the revenue for account owners who work in a specific part of a business, in the United States.
Data Visibility Considerations in Pivot and Compare Tables: Enabling features such as totals, subtotals, row totals, or column totals in pivot or compare tables can alter data display. These added summary rows expand the result set, which can cause certain values to be excluded if the query limit is too low.
Subtotal Considerations for Tables: The ability to expand and collapse rows, often seen in table widget types such as compare and pivot, depends on the presence and proper display of subtotals. This means that the ability to expand and collapse rows within these tables is closely linked to how subtotals are configured and rendered. And, if subtotals are turned off in a table widget, hiding the first row or column can make the entire table disappear.
Add Widgets to the Dashboard: Widgets are the basic building blocks of a dashboard. In the dashboard designer, you can add different widgets to perform functions. For example, widgets can calculate key performance indicators, filter dashboard results, visualize your data using interactive charts, and show record-level details in tables.
Add Chart Widgets to Visualize Data: Charts in a CRM Analytics dashboard allow you to visually summarize and trend your data. For example, a donut chart can show the distribution of opportunities across industry type.
Create and Manage Dashboards with Reusable Components: Dashboard components are a type of dashboard widget that can contain other widgets, pages, and Lightning Web Components. Use dashboard components to manage and reuse groups of charts, tables, filters, text, and more in multiple dashboards. Use Lightning Web Components to bring custom Lightning Experience functionality directly into dashboards.
Add a Dynamic Interaction to a Widget: You can connect a user action to a dashboard action. For example, when you select a bar in an overview chart, a dashboard page with the details of the selection opens. You can create an interaction for a chart, container, image, number, repeater, table, and text widget.
Add a Container Widget to Create Sections in the Dashboard: Use a container in a CRM Analytics dashboard to group related widgets. For example, you can use a container widget to create a filter panel on the left side of the dashboard to store all selection-based filter widgets. To distinguish the filter panel from the rest of the dashboard, you can apply a background color and border to the container. When you move this widget, the contained widgets also move while maintaining their spacing and alignment.
Filter Dashboard Results with Faceting: Faceting makes dashboards interactive. For example, select a sales region in a list widget and see accounts from that region in a table widget. When you enable faceting, selections made on chart, date, list, range, and toggle widgets automatically filter other widgets in the dashboard. You can also use targeted faceting to include or exclude specific queries from your faceting selections.
Add Selection-Based Filter Widgets to Enable Users to Filter the Results: Users viewing the dashboard can make selections in these types of widgets, which filter the results of other faceted widgets. Selection-based filter widgets give users the ability to slice and dice the data to view results from different angles. For example, the dashboard viewer can select a region in a list widget to focus the dashboard results on a specific sales location. Unlike with a filter defined by a query, the viewer can filter the results differently (by making another selection) or remove the filter altogether.
Add a Date Widget to Filter Dashboard Results Based on a Date: A date filter widget allows dashboard viewers to filter the CRM Analytics dashboard results based on a selected date range. The range can use relative dates based on UTC (like 2 years ago), absolute dates (like 1/15/2016), or presets (like Current Fiscal Year To Date). For example, you can add a date widget to filter support cases, showing only cases that closed this month or last month.
Add a List Widget to Filter Dashboard Results Based on a Dimension: A list filter widget allows dashboard viewers to filter the CRM Analytics dashboard results based on a dimension. For example, a list widget can filter results based on a particular case status or region.
Add a Range Widget to Filter Dashboard Results Based on a Measure: A range widget allows dashboard viewers to filter the CRM Analytics dashboard results based on a range of values for a measure. For example, a range widget can show only opportunities with amounts between $100,000 and $900,000.
Make the Dashboard Widgets Interactive: You can make the widgets in CRM Analytics dashboards interactive. For example, you can filter a widget to show only results for the region that’s selected in a list widget. Or, the value of a number widget can change to red when it falls below a threshold. An interaction can also provide progressive navigation and exploration, revealing detailed information with a single click action.
Dynamically Modify the Query, Initial Selection, and Widgets with Bindings: Compared to facets, bindings are more programmatic, and provide more ways to specify interactions. Bindings can dynamically modify a query, set an initial filter selection, or change a widget’s appearance. For example, you can change the measure in a query based on a selection in a custom query. You can initially filter a region-based dashboard based on the country of the logged-in user. Or, you can change the color of the metric in a number widget based on whether its value is high, medium, or low. Each binding is unidirectional, meaning it affects other queries, but other queries don’t affect it.
Add a Link Widget to Link to Other Assets: Take dashboard viewers straight to where they need to go with links to specific assets, like dashboards, pages, queries, lenses, and even web pages. You can also use links to create analysis paths for users to follow, helping them navigate your analytics app, or to perform mass quick actions on record lists.
Add a Navigation Widget to Access Dashboard Pages: Give dashboard viewers an easy way to navigate dashboard pages with a navigation widget. Reuse your navigation widget for a consistent look across dashboard pages.
Add Number Widgets to Include Key Metrics: Key metrics help you track critical measurements of your business. For example, you can show the total number of open cases or total opportunity amount for the last quarter or the northern region.
Project the Same Query Results Differently in Charts and Tables: If charts and tables use the same query, each widget can show a different set of fields from the query results. You can also specify a different field order for each widget. For example, to change the measure that appears on each axis in a scatter plot, reorder the measures for that widget.
Add a Global Filter Panel Widget to Filter Data from the Dashboard: The panel can contain single or multiple global filters. Use a global filter to apply the same filter to multiple queries in the dashboard. For example, a dashboard has two charts comparing two sales reps’ performance. To compare apples to apples, both charts must have the same filters. When possible, use a global filter instead of a selection-based filter to improve dashboard performance. If configured, users can change a global filter while viewing the dashboard.
Single-Click Widget Interactions: Add single-click interactions to your dashboard widgets to curate data journeys for your users. You can change a static dashboard into a dynamic navigation experience that progressively reveals details. A single-click in an overview widget takes you directly to a detailed page, opens a lens for cohesive exploration, or starts a flow.
Add a Table Widget to Show Record Details and Create Calculated Columns: You can add different types of tables to a CRM Analytics dashboard. To view record-level details, add a values table. A values table can show details like how long a case has been open and who owns it. To create custom columns in a table based on calculations from existing fields, add a compare table. If the underlying query contains at least one grouping, you can create a pivot table.
Enrich Your Data with Dashboard Text: Use text widgets to emphasize important data, make your dashboards more scannable, and tell a more compelling data story.
Perform Actions on a Salesforce Record from CRM Analytics: As you gain insights into your business from CRM Analytics, you sometimes perform actions on an underlying Salesforce record. No need to exit CRM Analytics. You can invoke a Salesforce action on the record directly from CRM Analytics. You can also open web pages, such as search the web for the company name listed in a CRM Analytics lens or dashboard.
Set Up Complex Conditional Formatting: You can set up conditions based on a single reference column in the widget properties. However, sometimes, you have to create more complex conditions. To set up rules based on multiple reference columns for number and table widgets, use SAQL.
Bulk Actions Example: You want to add the “Create Opportunities” option in a table widget’s action menu. This action must create an opportunity for each account shown in a table widget.
Define Action Behavior with Apex Controller Class and Methods: Define an Apex controller class and methods that generate a list of accounts shown in the table widget, create an opportunity for each account, and return the progress of each task.
Record Actions Example: Each week, opportunity owners analyze their opportunities using a CRM Analytics dashboard. Sometimes, they must update an opportunity. To help them perform the update without going back and forth between CRM Analytics and Sales Cloud, you decide to set up CRM Analytics actions. To let CRM Analytics dashboard viewers update an opportunity record from CRM Analytics, you add the Update Opportunity action to the Opportunity Name field in a CRM Analytics dataset. The first step is to set up the action in Salesforce and then add the action to the CRM Analytics dataset field.
Set Initial Selections and Global Filters in the Dashboard: Set the initial selections and global filters that appear when the dashboard first opens. To analyze the results from a different angle, the dashboard viewer can change the initial selections and, if configured, global filters while viewing the dashboard.
Optimize Dashboard Performance: Before you finalize the dashboard, run a performance check on the dashboard and its queries to ensure that everything is running optimally. The dashboard inspector identifies different types of bottlenecks, such as slow queries and networks, and provides recommendations to improve performance. Because dashboard layouts can contain different widgets (and queries), run the inspector on each layout. If a dashboard contains multiple pages, you can run the inspector for all pages. The inspector initially provides results only for the current page.
Modify the Query Results Based on the Dashboard Viewer: You can modify a query based on the user viewing the dashboard. For example, you can filter opportunity records to show only those opportunities owned by this user.
Find a Query: You can find all queries defined in the dashboard in the query panel. The panel organizes queries into categories and provides a search bar to help you locate your queries faster.
Edit a Query: When you edit a query, you can change the details, like the query label. These changes affect all widgets that use the query. You can also change the visualization properties, like chart type. Depending on the method that you use to edit the query, CRM Analytics handles visualization changes differently.
Considerations for Queries with Multiple Datasets: When a query has multiple datasets, the query results are formatted using the XMD of the first loaded dataset. If the query is faceted, it receives filters for all of its datasets, but broadcasts selections only from its first loaded dataset.
Facet Queries with Multiple Datasets: You can use a query with multiple datasets in a widget and then facet the query. The query receives filters from all of its datasets, but broadcasts selections from its first loaded dataset.
Troubleshoot Unexpected Query Results: If you have questions about a query’s results, review the query details to learn how CRM Analytics determines the results. Compare the original query to the final query executed by CRM Analytics. If the query is faceted or the dashboard contains global filters, CRM Analytics applies extra filters to query. If the query contains a binding, use the bindings tracer to verify the binding results.
Save Your Filtered View of a Dashboard: No need to reselect the filters every time you open a dashboard because you don’t have permission to save the selections permanently. Change the filters and save your own view. Only you can see your views—other users still see the original dashboards.
Data Template Features: Data templates are similar to app templates, with a few key differences.
Available Data Templates: Data templates provide simple data editing and calculations, advanced data manipulation, and rigorous analysis out of the box. To get started, see the instructions and specific details for each template by following these links.
Create Data Assets from a Data Template: Follow these general procedures when you create data assets from any data template. Before you use a template, see the help for the Data Template that you want to use for requirements and instructions specific to that template.
Get Started Faster with Data Templates: Salesforce Data Pipelines use purpose built data templates for common use case data tasks to accelerate your ROI. These data templates generate the necessary data connectors, data prep recipes, and queries for apps that can be scheduled to run regularly.
Datasets: A dataset is a collection of related data that is stored in a denormalized, yet highly compressed form. There are multiple ways to create a dataset. You can also edit dataset after they’ve been created.
Design a Simple Dataset with Dataset Builder: Use Dataset Builder to create a single dataset based on data from one or more related Salesforce objects. Dataset Builder adds the instructions for building the dataset to the specified dataflow. The dataset is created the next time the dataflow runs, and refreshes each time the dataflow runs thereafter. To edit the dataflow, use the Dataflow Editor.
Upload a CSV File to Create a Dataset: You can upload external data in a .csv format through the user interface. When you upload a .csv file, CRM Analytics infers the metadata about each column in the .csv file. Metadata describes the structure of the data in the file, like the data type, precision, and scale. If you upload a .csv from the user interface, CRM Analytics automatically generates the metadata, which you can preview and change.
Delete a Dataset: Delete unnecessary datasets to reduce app clutter and avoid reaching your org's total row limit for all registered datasets. You can delete datasets from shared apps on which you have at least Editor access, your My Private App, and, with a special user permission, another user’s My Private App.
Edit a Dataset: Edit a dataset to change its name, app, security, or extended metadata (XMD). You can also replace data in a dataset, restore it to a previous version, or delete it. The dataset edit page also provides key information about when the dataset was created and last updated, and where it is used.
Customize the Labels and Colors of Dataset Columns and Values: Choose what shows up in lenses and dashboards. Rename and hide dataset columns. Set the default columns that appear in tables. Set the labels and default colors for dimension values.
Change the Labels and Colors of Dataset Dimension Values: Change the display labels and default colors of dimension values to make them easier to understand and spot. The modifications don’t alter the underlying data stored in the dataset or the API names of the columns. The changes affect only the appearance in the user interface.
Choose the Default Dataset Columns That Appear in a Table: Make tables your own by choosing the default columns. For example, show the most commonly used columns in a particular order. If you don’t set the defaults, the first five measures and five dimensions are selected in alphabetical order.
Develop on the CRM Analytics Platform: The CRM Analytics platform meets a wide range of development needs. Learn about the solutions you can deliver with CRM Analytics platform technologies.
Analytics Development Tools: Customize and deliver robust solutions with Analytics Studio, Data Manager, the Salesforce Command Line Interface (CLI), and Microsoft Visual Studio Code.
Develop with the Analytics Plugin for the Salesforce CLI: The Salesforce CLI is a powerful command-line interface that simplifies development and build automation when working with a Salesforce org. The CLI plugin extends that experience to CRM Analytics. Plugin commands work from either the CLI or Microsoft Visual Studio (VS) Code.
Build Data Assets with Data Manager: Data Manager is the tool for creating, updating, and scheduling data assets—the datasets, recipes, and connectors that power Salesforce insights.
Build Analytics Assets with Analytics Studio: Analytics Studio provides the foundational tool for building CRM Analytics assets—the lenses and dashboards that make up Analytics apps.
Gain Insights on Data Directly at Source: You can explore your data without loading it into CRM Analytics or preparing it in datasets. CRM Analytics Direct Data lets you analyze and explore your data that resides in Salesforce objects, Data 360, Snowflake, and Google BigQuery. You can run queries on the data in real time and also build dashboard widgets based on the queries you run.
Explore Data Directly in Google BigQuery: After you complete setup, CRM Analytics Direct Data for Google BigQuery lets you run queries on the dataset associated with the connection in real time. You can also build dashboard charts and tables based on the queries you run on Google BigQuery data.
Build Queries with Direct Data for Data 360: After you complete setup, CRM Analytics Direct Data for Data 360 lets you create and run queries on Data 360 records in real time. You can also build dashboard charts and tables based on these queries.
Export from Data 360-connected CRM Analytics Assets: Export data from lenses and widgets that use data from Data 360 objects such as data lake objects, data model objects, and calculated insights objects.
Launch Explorations for Data 360 with a Single Click: Analyze and surface customer engagement patterns from Data 360 records without switching to Analytics Studio and creating dashboard queries. With CRM Analytics Direct Data for Data 360, you can now explore calculated insights, data lake objects, and data models from the data object’s record page. Review, profile, and trend data in tables and charts, or run queries on Data 360 right in their business workflow.
Basic CRM Analytics Direct Data for Data 360 Setup: Using CRM Analytics Direct Data for Data 360 requires the appropriate licenses, org and user account setup, and permissions assigned to user accounts.
Analyze Multiple Data Model Objects Using Joins: To explore and draw insights from multiple related data model object (DMO) records simultaneously, join these objects in a single dashboard query. Direct Data for Data 360 supports four types of joins on data model objects with defined relationships between them. You can also filter joined data and highlight the records that matter with conditional formatting.
Unlock CRM Analytics Insights on Data 360: Analyze your unified customer info in Data 360. With CRM Analytics Direct Data for Data 360, you can operationalize data analysis without first loading Data 360, prepping it, or creating live datasets. Embed Data 360 records into CRM Analytics dashboards for any Salesforce object and run queries on the data in real time using single sign-on across both platforms. You can filter Data 360 info, drill deeper, and act on your records directly from the dashboards. And, Accelerated Queries for CRM Analytics runs queries on Data 360 records faster. CRM Analytics ensures that account sharing rules are automatically applied when running queries against Data 360. Also, our systems automatically reuse Data 360 object names and field labels to maintain simplified data mapping between Data 360 and the dashboard assets.
Gain Insights on Data Directly in Google BigQuery: You can explore your data in a Google BigQuery data warehouse without loading data into CRM Analytics or preparing it in datasets. CRM Analytics Direct Data for Google BigQuery lets you set up a live connection to the BigQuery schema you want to explore. You can run queries on the schema associated with the connection in real time. You can also build dashboard charts and tables based on the queries you run on BigQuery data.
Gain Insights on Data Directly in Snowflake: You can explore your data in a Snowflake warehouse without loading data into CRM Analytics or preparing it in datasets. CRM Analytics Direct Data for Snowflake lets you set up a live connection to the Snowflake schema you want to explore. You can run queries on the Snowflake schema associated with the connection in real time. You can also build dashboard charts and tables based on the queries you run on Snowflake data.
Download CRM Analytics Images and Export Filtered Data: Extract detailed insights from CRM Analytics that support collaboration and informed decision-making. You can download images and filtered data from lens explorations and dashboard widgets. Download formats include image (.png), Microsoft® Excel® (.xls), and comma-separated values (.csv) files. With the .xls format, there's also an option to include query metadata.
Explain, Predict, and Take Action with Einstein Discovery: Einstein Discovery augments your business intelligence with statistical modeling and supervised machine learning to identify, surface, and visualize insights into your business data. It uses predictive and prescriptive analysis to predict future outcomes, as well as suggests ways in which you can improve predicted outcomes. Einstein Discovery requires either the CRM Analytics Plus license or Einstein Predictions license, both of which are available for an extra cost.
Introduction to Einstein Discovery: Einstein Discovery augments your business intelligence with statistical modeling and machine learning to identify, surface, and visualize insights into your business data. Einstein Discovery is integrated into your Salesforce environment so that you can quickly operationalize data analysis, predictions, and improvements with clicks, not code. In addition, developers can use the Einstein Prediction Service to programmatically retrieve predictions and write predictions to custom fields. Data specialists can predict outcomes within recipes and dataflows. Tableau users can get Einstein Discovery predictions and improvements for their Tableau data.
Solution Implementation Tasks: The process of implementing Einstein Discovery solutions typically involves a series of tasks.
Supported Use Cases: Einstein Discovery solutions address common business use cases.
Binary Classification Use Case: Binary classification solutions target business outcomes with only two possible results, which are represented as text data.
Multiclass Classification Use Cases: Multiclass classification solutions predict outcomes with 3–10 possible results, represented as text data.
Numerical Use Cases: Numerical solutions target numeric outcomes represented as quantitative data (measures), such as currency, counts, or percentages.
Analyze Your Data with Models and Insights: Create models to analyze your data. Explore the insights that Einstein Discovery revealed in your data using AI, machine learning, and statistical analysis. Insights show you what happened in your data, why it happened, and what could happen.
Build Models in Einstein Discovery: Create models to analyze your data. Define your desired outcome, structure your data source, then explore the insights that Einstein Discovery revealed in your data using AI, machine learning, and statistical analysis.
Create a Model: Create a model from a CRM Analytics dataset or a template. Einstein Discovery thoroughly analyzes your data and generates revealing and meaningful insights for you to explore.
Create a Model from a Dataset: Create a model to uncover the meaningful relationships among variables in a CRM Analytics dataset. Einstein Discovery augments your analysis by quickly sifting through huge amounts of data to uncover statistically significant correlations for you to investigate further.
Create a Model from a Lens: Quickly create a model from a CRM Analytics lens. After creating a lens to explore your data, you can easily get insights directly from the lens. Filters that are applied to the lens get persisted saving you time with model setup.
Create a Model from a Template: Quickly create a model using Einstein Discovery model templates. Each model template provides a starter implementation for a common Einstein Discovery use case. A template gives you what you need to build the app, prepare and load your data, create an initial model, deploy a model, and save predictions into your CRM Analytics dataset.
Recommended Buckets Alert: Indicates that, for a numerical value, Einstein Discovery devised an alternative set of buckets (grouping of data points based on ranges).
Multicollinearity Alert: Indicates that two or more variables are highly correlated (for example, City and Postal Code). These variables can have a duplicate impact on the outcome. This condition is also known as multicollinearity.
Strongest Predictors Alert: Indicates a variable that is so highly correlated to the outcome that it must be examined for possible data leakage. Leakage occurs when the data used to train your model includes one or more variables that contain the information that you are trying to predict.
No Correlation Alert: Indicates that this variable explains no variation in the outcome and has no statistical significance.
Disparate Impact Alert: Indicates that there is a significant discrepancy in the way different classes are being treated, which can signal bias.
Outliers Alert: Indicates the presence of uncommonly large or small numbers, potentially from data entry errors or rare events. Outliers influence averages, which can affect the accuracy of insights, predictions, and improvements.
Proxy Variable Alert: Indicates that one or more variables are highly correlated to a sensitive variable, which means that the variable serves as a proxy variable in your analysis, which can signal bias. For example, a loan applicant's street address can serve as a proxy for ethnicity. Bias can be reflected in adverse approval rates if an institution's lending practices are discriminatory.
Potential Data Leakage Alert: Indicates that a value in an explanatory variable always results in the same outcome value, which can indicate data leakage. Data leakage occurs when your training data contains the information that you’re trying to predict. Leakage results in models that score optimistically high in training but perform less accurately on live data. To produce more realistic models that perform better at predicting outcomes, investigate and remove leaky predictor variables from your model.
Dominant Values Alert: Indicates that most values in a variable are in the same category, which can limit its contribution to the analysis.
Missing Values Alert: Indicates that a variable is missing a high percentage of values, which can lower the quality of your insights or model.
Predictions in Data Prep: Bake predictions into your CRM Analytics datasets with recipes and dataflows.
Get Predictions on Lightning Pages: After a model is deployed, you can use it to display predictions and improvements for a standard or custom object on a Lightning Experience record page.
Ethical AI in Einstein Discovery: Einstein Discovery offers bias detection and model cards to help you practice ethical use of artificial intelligence (AI) in your business.
Einstein Discovery Glossary: Familiarize yourself with terminology that is commonly associated with Einstein Discovery.
Set Up Einstein Discovery: Set up permissions in enable users in your org to use Einstein Discovery. Monitor usage statistics.
Get Descriptive and Diagnostic Insights Programmatically: Use Einstein Discovery's Insights API to embed insights from your model into any website, application, or dashboard. Insights contain values that quantify the impact on the selected model goal, along with narrative descriptions of those values. With the Insights API, client applications can obtain a summary of the top positive and negative insights, query insight details further, and then apply their own narratives to the insight values. You can also query insights by insight type (descriptive or diagnostic).
Compare Subgroups with the Global Average: Explore how a subgroup (category or bucket) relates to a variable’s global average. Isolating a subgroup helps you better understand how it relates to the model’s goal.
Compare Subgroups: Compare subgroups (categories or buckets) to learn how they relate to your model’s goal. For example, you can compare the sales performance between two different territories (such as “North America” and “Europe”). Einstein Discovery displays a waterfall chart to help you visualize the comparison between territories. Comparative insights help you determine whether the patterns you focus on are real and not accidents of the data.
Interact with Data Insights: Interact with insights and drill into details to gain more understanding about your data.
Data Insights Interface: Learn how to navigate insights and become familiar with the user interface.
Explore Bias in Multiclass Outcomes: Bias detection in multiclass models alerts you of the presence of bias so you can mitigate its distorting effects in your analysis.
Navigate Data Insights: Learn about the data insights interface and how to interact with it.
Explore Predictions and Improvements: Explore predicted outcomes and suggested ways to improve those predicted outcomes. Perform interactive, “what if” analyses and change feature selections to see prediction scores, top prediction factors, and top improvements to enhance prediction scores.
Explore Predictions for Multiclass Outcomes: Explore predicted outcomes for multiclass models. Perform interactive, “what if” analyses and change feature selections to see the winning prediction, ranked list of outcomes by probability, model accuracy, and top prediction factors.
Explore a Second-Order Insight: A second-order insight shows how the combination of two explanatory variables explains variation in the outcome variable. In second-order analysis, the combined impact of both variables together on the outcome is sometimes called the interaction effect.
About Einstein Discovery: Use Einstein Discovery to augment your data analysis by exposing relevant facts, themes, and statistical correlations in your data. Einstein Discovery walks you through what has happened and why, what has changed, what is likely to happen, and what you can do about it. Now anyone can tap into the power of data science and conduct statistical analytics and predictive analytics.
Manage and Deploy Models in Einstein Discovery: Einstein Discovery uses models to predict outcomes and suggest ways in which to improve predicted outcomes. You can evaluate model quality and compare models before you deploy and manage them.
About Models: A model is the sophisticated, custom mathematical construct that Einstein Discovery uses to predict a particular outcome. A model accepts inputs (one or more explanatory variables) and produces outputs (a predicted outcome, top factors, and improvements). Create a model and Einstein Discovery generates the predictive and prescriptive analytics.
Deploy Models: Deploy a model so that you can use it to make predictions and improvements.
Customize Prediction Text: Provide custom text for variables that appear as top predictors or suggested improvements on your Salesforce records to make them easier to understand. For example, you can have Einstein explain that a top predictor for time in the sales cycle is “Repeat Purchase” instead of “Previous Closed Won Opportunity Match is True.” You can also have Einstein suggest to “Schedule a meeting” instead of “Set Last Customer Meeting More than 30 days to False.” Your custom text for top predictors and suggested improvements is configured in the model.
Configure Segmentation Filters: Choose whether to use the model to get predictions on all data or on just a segment (subset) of the data. For example, you can focus on a specific product model or a group of customers. A prediction definition can contain multiple models in which each model produces predictions for a different segment.
Map Model Variables: Define the mapping between variables in the model to fields in the Salesforce object or to columns in the supplemental dataset.
Select a Target Prediction Definition or Model: Choose how you want to deploy this model: to a new prediction definition, to an existing prediction definition, or to replace an existing model.
Select Whether to Connect to a Salesforce Object: If you deploy this model to a new or existing prediction definition, decide which Salesforce object, if any, you want to associate with the prediction.
Configure Projected Predictions: If the model is configured to derive a projected prediction, revise time frame settings as needed.
Select Actionable Variables for Suggested Improvements: The suggested improvements from Einstein Discovery are recommendations that users can take action on to improve predicted outcomes. To get suggested improvements, at least one actionable variable must be selected on the model. Actionable variables represent data factors that people can control, such as deciding which marketing campaign to use for a particular customer.
Manage Prediction Definitions and Models: Use the Model Manager to view, configure, and manage prediction definitions and models that have been deployed in your org.
Configure Performance Monitoring for a Prediction Definition: Configure performance monitoring if you want Einstein Discovery to display performance monitoring charts for a prediction definition. Performance monitoring is available for prediction definitions that use automated prediction writeback.
Handle Live Data Alerts: Live data monitoring detects when your production data contains high levels of missing or out-of-bounds values—indications that your model’s accuracy is declining. Model Manager surfaces alerts so that you can easily see what’s happening in your data.
Manage Models: Use the Model Manager to manage the models associated with your prediction definitions.
Configure the Model Card for a Prediction Definition: Model cards help you document and communicate important usage information about your predictions. A model card shows statistics associated with the data that’s used to train the model. It can also show any optional explanations you provide about the prediction’s intended use, design assumptions, target audience, capabilities and limitations, and other relevant information. Disclosing these details helps users to understand predictions and differentiate among multiple predictions. Then they can make ethical, informed decisions about whether a prediction suits their use case.
Enable or Disable a Model: Deployed models are enabled by default. You can disable a model if you want to hide it from Salesforce but still keep it for future use. A disabled model no longer provides scores to Salesforce objects. You can later enable it as needed.
Delete a Model: You can delete a model you no longer need.
Edit Model Settings: You can update model settings that were previously set during model deployment.
View and Manage a Model’s Update History: A model can undergo many updates over the course of its lifecycle. Factors such as altered business circumstances, new and better data, and improved model settings can motivate the creation and deployment of a new model version. To help you keep track of all these versions, examine a model’s version history so that you know exactly when it was updated and by whom, and whether it is scheduled for an upcoming refresh job. For models that aren’t performing as expected, you can revert to a previous version that performs better. You can retrieve and investigate the underlying settings associated with a particular model version.
View a Model: You can view a model deployed in your org.
Configure Model Quality Notifications: Models drift when characteristics in the real world data diverge significantly from the training data used to build them. Einstein can send out alert notifications if your model performance falls below configured thresholds. Significant increases in prediction error rates, missing values, or out-of-bounds values can trigger notifications, as well as detected disparate impact among variables being analyzed for bias. You can set alert thresholds, schedule weekly or monthly performance evaluation cycles, and specify who will receive notifications.
Start Model Manager: Launch Model Manager from the Analytics panel in CRM Analytics Studio.
Configure Automated Prediction Writeback for a Prediction Definition: Automated prediction writeback saves numeric prediction scores to a field in a Salesforce object. Using this feature requires that you deploy a model that outputs a numerical prediction (regression) or score (binary classification) and for which you can map all model fields back to Salesforce fields. You can use an existing AI Prediction field or create a custom field by specifying a label.
Get Predictions With Bulk Scoring: You can score predictions on multiple records at a time. For example, use bulk scoring to refresh all prediction scores after you deploy an updated model. You can also run bulk scoring on historical data to see how well your model performs. With bulk scoring, you can score all records, a segment of the records, or records that have not reached the completed state.
Evaluate Model Quality: Model performance metrics reveal quality measures and associated details for a model. Use model performance to evaluate a model's ability to predict an outcome. When ready, you then deploy a model to Salesforce to predict outcomes in production.
Metrics for Binary Classification Use Cases: The binary classification use case is based on text (categorical) variables with binary outcomes. Model Performance shows quality statistics associated with logistic regression models.
Coefficients Tab for Binary Classification Use Cases: A model uses coefficients to calculate a prediction for a specific observation. You can filter the list of coefficients and also download the data in a CSV file.
Coefficients Tab for Numeric Use Cases: A model uses coefficients to calculate a prediction for a specific observation. You can filter the list of coefficients and also download the data in a CSV file.
Compare Models: Compare metrics for multiple models side by side to see how they stack up against each other. For example, comparing segments in your data can reveal the most important variables in each segment.
Cross-Validation Tab for Binary Classification Use Cases: To test a model’s ability to make predictions, Einstein Discovery uses k-fold cross-validation, a process that reduces sampling bias when validating a model. This tab summarizes the results of the cross-validation process for this model, as well as some of the underlying computational details.
Cross Validation Tab for Numeric Use Cases: To test a model’s ability to make predictions, Einstein Discovery uses k-fold cross-validation, a process that reduces sampling bias when validating a model. This tab summarizes the results of the cross-validation process for this model, as well as some of the underlying computational details.
Cross Validation Tab for Multiclass Classification Use Cases: To test a model’s ability to make predictions, Einstein Discovery uses k-fold cross-validation, a process that reduces sampling bias when validating a model. This tab summarizes the results of the cross-validation process for this model, as well as some of the underlying computational details.
Gain and Lift Charts for Binary Classification Use Cases: The Gain and Lift charts help you evaluate your model’s ability to predict outcomes and understand the benefit of the model. Using a portion of the data that’s scored and ranked for analysis, the charts measure results obtained with the model compared to random guessing without a model. The greater the gain and the higher the lift, the more effective the model.
Metrics for Numeric Use Cases: The numeric use case is based on outcomes that are numeric variables. Model Performance shows quality statistics associated with linear regression models.
Model Evaluation for Numeric Use Cases: The Model Evaluation tab provides information about a model’s performance, cross-validation results, and coefficient values.
Metrics for Multiclass Classification Use Cases: The multiclass classification use case is based on categorical variables with 3–10 possible outcomes. The Model Metrics tabs show quality statistics associated with multiclass models.
View the R Code: The R code contains the transformations made to your CRM Analytics dataset and scoring code. Inspect the code to examine the underlying details of the model that Einstein Discovery produced. R code is available for Binary Classification models.
Threshold Evaluation for Binary Classification Use Cases: Threshold Evaluation helps you optimize the threshold value for a model. The threshold value tells your model how to classify a binary outcome. If the calculated probability is above the threshold value, Einstein classifies the outcome one way (such as True or Positive). If the calculated probability is below the threshold value, Einstein classifies the outcome the other way (such as False or Negative).
On-Demand Predictions: Initiate predictions through Einstein Prediction Service, Lightning Components, Flow Builder, the PREDICT() function, Apex, Slack, or Einstein Discovery in Tableau.
Package and Distribute Your Prediction Definitions and Models: Einstein Discovery supports packaging for prediction definitions and models. You can promote prediction definitions from a sandbox to a production environment. You can distribute prediction definitions using managed or unmanaged packages.
Distribute Prediction Definitions and Models with Managed and Unmanaged Packages: Build and train models using your own data, then distribute them to others—within or outside your company—using managed or unmanaged packages. Others can enjoy the benefits of your models’ predictions and improvements without needing the data to train the models themselves. Einstein Discovery supports both first-generation packaging (1GP) and second-generation packaging (2GP).
Define Your Target Outcome: Start by selecting a business problem you want to solve. Examine the key performance indicators (KPIs) you want to improve. Explore which candidate KPIs could benefit the most from deploying an Einstein Discovery-powered solution.
Get Predictions in Flow Builder: Use the Einstein Discovery core action in Flow Builder to get predictions, top predictors, and improvements.
Predict & Improve Outcomes: After a model is deployed to Salesforce, Einstein Discovery can use it to get predicted outcomes and suggested improvements in different ways. You can display predictions in Lightning record pages and Experience Cloud sites pages. You can get predictions in CRM Analytics Data Prep, process automation flows, and Flow Builder. You can retrieve predictions and improvements programmatically using the Einstein Prediction Service via REST or Apex API calls.
Get Predictions in Apex: Use the ConnectApi.SmartDataDiscovery.predict API to get predictions in your Apex code.
Display Einstein Predictions Using Automated Prediction Writeback: When automated prediction writeback fields are configured for a prediction definition that outputs a numerical prediction (regression) or score (binary classification), Einstein Discovery can save predictions directly to Salesforce records for use in reports and list views. Configure automated prediction writeback in Model Manager. After deployment, Einstein scores newly created or updated records in the Salesforce object and writes the score to the prediction field. Only the prediction is written, not the reasons for the prediction or the suggested ways in which to improve the prediction.
Get Predictions in Process Automation Formulas: Use the Einstein Discovery PREDICT function to get predictions in your process automation formulas. Your automation logic can make decisions based on predicted outcomes from deployed Einstein Discovery predictions. For example, in an approval process, a formula can determine whether a predicted outcome meets a threshold required for automatic approval.
Get Predictions in CRM Analytics Recipes: Use the Discovery Predict transformation in Data Prep recipes to populate CRM Analytics datasets with predictions, top predictors, and improvements.
Get Predictions and Improvements with Einstein Prediction Service: The Einstein Prediction Service REST API enables you to programmatically run predictions, manage prediction definitions, and manage models. After deploying models with Einstein Discovery, use the Einstein Prediction Service REST API to embed your predictions into any website or application.
Prepare Data for Analysis: Effective data preparation is key to getting great results with your insights, predictions, and improvements. Engage the data integration power of CRM Analytics to load and transform data from one or more data sources into a dataset for Einstein Discovery to analyze. You can pull data from Salesforce and external sources. You can also use third-party tools and utilities to further expedite data cleansing and wrangling tasks.
Collect, Cleanse, and Optimize Your Data: All data prep efforts are oriented toward providing the best data you can for the business outcome you want to examine or predict. CRM Analytics provides the tools you need to consolidate, clean, and prepare your data for analysis. This topic describes approaches to consider when preparing data for your solution.
Address Common Data Issues: Handle common issues that you encounter during data prep for Einstein Discovery.
Adding Calculated Columns into Your Dataset: Create calculated columns in your dataset to extract more useful information, such as a ratio or aggregation. A calculated column uses a formula to derive its value from other data (such as fields, expressions, and values).
Get Started with Data Prep: Excellent data preparation is fundamental to the success of solution implementations powered by Einstein Discovery.
Determine Data Requirements: Data preparation is a process of iterative refinement. As you dig deeper into your data, new clues emerge. Discoveries can cause you to reassess previous assumptions and adjust your data prep implementation accordingly.
Data Prep Terminology for Einstein Discovery: Einstein Discovery applies machine learning, statistical analysis, and AI to your data. These disciplines (and Einstein Discovery) use terminology that differs from what we use to describe CRM Analytics datasets and Salesforce objects.
Handle Quality Alerts: Einstein Discovery displays quality alerts when it detects issues in your training data, your model’s quality metrics, or during cross-validation.
R2 Quality Alert: Indicates that this model's R2 metric is so high or low that it warrants further examination.
Assign Einstein Discovery Permission Sets to Users: After you have defined permission sets for Einstein Discovery users, you can assign the appropriate permission sets to individual users or groups of users. You can assign multiple permission sets to an individual user account.
Permission Sets and Permissions for Einstein Discovery: To provide user access to Einstein Discovery functionality, the CRM Analytics Plus license and Einstein Predictions license provide standard permission sets and permissions that you can assign to user accounts.
Smart Sampling: Model training is faster with smart sampling. Smart sampling downsizes large datasets to representative samples. Einstein evaluates the data and then selectively reduces the number of rows used to train the model.
Create and Manage Models: Models tell Einstein Discovery what to analyze. Creating a model involves defining the dataset fields, outcome variable, explanatory variables, and other settings to include in the analysis. Einstein Discovery uses this information to generate insights into your data.
Detect and Remove Bias from a Model: Einstein Discovery helps you practice ethical use of AI by detecting bias in your data so that you can remove its distorting effects on your analysis and predictions. Bias indicates that variables are being treated unequally in your model.
Cancel Model Creation: You can cancel model creation before Einstein finishes creating it. Canceling a model interrupts Einstein data analysis and abandons any model elements created after you clicked Create Model. This feature allows you to change your mind and not wait until Einstein after completes analyzing your data and generating insights.
Delete a Model: Delete a model you no longer need. Once you delete a model, it cannot be recovered.
Explore Data Insights: You can explore insights for any model to which you have access. An insight is a statistically significant finding in your data. When you create a model version, Einstein Discovery analyzes the data in your dataset and generates insights based on its analysis. Insights provide a starting point for you to investigate the relationships among your model’s explanatory variables and its goal.
Apply Fuzzy Matching for Smarter Categorizations: Fuzzy matching adds uniformity to spelling variations in variables, resulting in smarter categorizations and better predictions. Einstein Discovery transforms the data at prediction time, enabling you to skip cleansing during data prep.
Edit General Settings for a Model: You can edit general settings for a model. View the CRM Analytics dataset and select the algorithm and validation type.
Replace Missing Numerical Values: Einstein Discovery allows you to replace missing numerical values in your model. Use the Replace Missing Variables transformation to enable statistical imputation on a numeric variable. Einstein Discovery automatically replaces missing values with data derived from another subset of your data.
Impute Missing Text Values: Einstein Discovery allows you to impute missing text values in your dataset. Enable imputation on a text variable, and Einstein Discovery automatically replaces missing values with data derived from another subset of your data.
Bookmark an Insight in a Model: Bookmarking an insight allows you to export it to a Quip document. It also makes it easier to quickly find it in the Insights List when the Bookmark filter on the toolbar is disabled.
Model Interface: The model interface consists of a navigation pane, tools and actions, and a variable filter panel.
Edit Model Settings: Working with models is often an iterative process of further refinement. As you investigate insights in your model, you can decide to improve it by revising your settings and creating a newer version. For example, you can include or exclude a column and rerun the analysis. By interacting with the model, you overlay your intuition and domain knowledge to make the model more insightful and its recommendations more pertinent.
Configure Projected Predictions: Einstein Discovery allows you to provide trending data for numeric variables that factor into your predictions to make them more accurate. For example, you predict opportunity win/loss based on expected price fluctuations for raw materials. To factor price into your predicted outcome, use the Projected Predictions transformation and provide a dataset that projects the expected price for raw materials on the close date.
Open a Model: Open a model to explore relationships in your data.
Analyze Data: Einstein Discovery prepares your model, analyzes your data, and displays its progress along the way.
Select Data Options: Manually select and configure the variables in your dataset for Einstein to analyze and use to build a predictive model.
Select a Dataset: Select the CRM Analytics dataset that contains the data you want to analyze.
Select Model Options: Choose how you want to select the fields in your dataset to analyze. Einstein can select the fields for you automatically, or you can select and configure them manually.
Configure Model: Select the goal, title, and location for your model.
Track Model Versions: Each time you create a model, whether it’s brand new or an update of an existing model, Einstein Discovery creates a new version and keeps previous versions so that you can track and manage your progress.
Compare Model Versions: Updated data or changed model settings can motivate the creation of a new model version. Compare model versions to better understand what's changed between them.
Get Predictions in Tableau: Einstein Discovery in Tableau provides predictions and improvements for Tableau data in worksheets, calculated fields, and flows.
Get Predictions in Tableau Dashboards: Integrate on-demand, interpretable predictions from Einstein Discovery natively in Tableau dashboards. With the Einstein Discovery dashboard extension, you can embed dynamic predictions in worksheets and explore “what if” prediction scenarios with parameters.
Introduction to Einstein Discovery in Tableau: Einstein Discovery in Tableau brings trusted, real-time predictions and improvements to your Tableau data in a no-code required, rapid iteration environment.
Prerequisites for Einstein Discovery in Tableau: Einstein Discovery in Tableau is supported in Tableau version 21.1 or later. Integration requires the appropriate license and user account setup.
Embed Einstein Predictions in Tableau Flows: Use the Prediction step in Tableau Prep to include Einstein Discovery predictions directly into your flows. Enrich your flow output with predictions and, optionally, improvements and top factors.
Get Predictions in Tableau Calculated Fields: It’s easy to get Einstein Discovery predictions in calculated fields. In Model Manager, simply select a prediction definition, generate a Tableau script, and then copy and paste the script into a Tableau calculated field.
Jumpstart Solutions with Model Templates: Model templates provide out-of-the box solution starter kits that you can customize to your solution requirements. Model templates provide end-to-end implementations of common business use cases. Designed to integrate seamlessly with your Salesforce data, model templates are built with industry best practices in mind. Einstein Discovery handles the heavy lifting of building the initial training dataset, creating a data pipeline with feature engineering, generating the first Einstein Discovery model, deploying a model into Salesforce, and getting predictions on your data and writing them back to the dataset.
Maximize Customer Revenue Model Template: Analyze which types of accounts are leading to higher sales based on the last 365 days. Use a linear regression model to predict which accounts could bring additional revenue.
Allow Trusted Sites for Embedded Dashboards: Embed CRM Analytics in any web page or web-based app. By specifying your trusted sites in the CRM Analytics allowlist, you can include embedded CRM Analytics dashboards in websites and apps outside of Salesforce servers.
Take Action in Salesforce from Experience Cloud Sites: From CRM Analytics in Experience Cloud sites, you can open Salesforce records and perform record-level actions and bulk actions. You set up an action menu on a dataset or a bulk action on a table widget. Then your site users can open records and take action in Salesforce directly from the CRM Analytics dashboard, without leaving your site.
Collaborate on CRM Analytics Dashboards in Experience Cloud Sites with Annotations: Foster an environment of open communication sparked by CRM Analytics dashboards embedded in Experience Cloud sites. With annotations, partners and customers can converse in Chatter feeds on individual dashboard widgets. Annotations are available whether dashboards are embedded via Visualforce or the Experience Builder.
Control How Filtering Works in Embedded Dashboards: Use query faceting settings to control whether filters get applied to embedded dashboards. For example, to apply filters to a dashboard when it’s opened from a link widget, enable faceting in the underlying query.
Filter and Selection Syntax for Embedded Dashboards: To specify filters or dashboard selections that require JSON strings, use the Filter attribute of the LWC CRM Analytics Dashboard component or the Filter String attribute of the embedded CRM Analytics Dashboard Aura component.
Embed Dashboards in Lightning Pages: Add LWC or Aura CRM Analytics dashboards to Lightning home pages, record pages, and app home pages to provide interactive visualizations of your data. Users can drill down and explore the dashboard within a Lightning page.
Add a Analytics Dashboard to a Visualforce Page: By embedding a Analytics dashboard into a Visualforce page, you can provide an interactive presentation of your data. Users can drill in and explore the dashboard within the frame on the Visualforce page or in a new window in Analytics.
Manage Tableau View Filters: You can edit parameters to make minor adjustments, change for a different use, or delete them when they’re no longer needed.
Converse with Your Data: Ask data questions using non-technical language, and view answers in automatically configured charts. Start with a common word or two, and CRM Analytics provides relevant suggestions that you can accept or edit.
Explore and Visualize Your Data in CRM Analytics: When sifting through an overwhelming amount of business data, sometimes you know exactly what you’re looking for. Other times you don’t, but you recognize it when you see it. Salesforce CRM Analytics gives you a fast, fluid way to discover the compelling stories within your data and to create the right visuals to tell your story.
Apply the Action to the Object’s Page Layout: Add the action to the page layout for the object. Users whose profiles are assigned to that layout can see the action in Salesforce. Page layouts control the layout and organization of content, including custom actions, on record detail pages. Each record detail page can have multiple page layouts to give different users a unique experience.
Change the Chart Scale: With some data, a chart displays values that range widely on an axis and make the visualization more difficult to understand. You can constrict the range's scale by applying a logarithmic scale. Scaling is available for certain charts only.
Change the Chart Type: Switch between chart types to see which visualization options tell the most compelling story for your data.
Change Measures: Your choice of measure defines your entire visualization and determines the scope of your exploration. For example, change the measure and the aggregation method from “Count of Opportunities” to “Sum of Amount.” Then add a second measure to see 2 visualizations side by side.
Clip a Lens to a Dashboard: To include the lens in a dashboard, clip it. When you clip the lens, CRM Analytics adds a query to all the open dashboards. If a dashboard isn’t open, CRM Analytics adds the query to a new dashboard in dashboard designer. After you add the query to the designer, you can apply it to a widget in the dashboard. The widget overrides the visualization settings of the lens—the widget determines how to display the results of the query.
Clone a Lens: Build upon a visualization by cloning its containing lens to a new tab where you can continue exploring. You can save the original in its own tab.
Column Properties: The Column Properties panel lets you jazz up the columns you want in compare, values, and pivot tables. To highlight data that meets specific criteria, set conditional formatting rules on a column. Or, highlight data based on conditions set on a reference column. To make a column stand out without applying any conditions, you can use text alignment and text styles. In addition to displaying data in the text format, you can set a measure column’s data type as a bar.
Window Functions Available in the Compare Table: The compare table's function menu provides UI-based access to useful window functions, saving you the time of having to write them in SAQL. Once you choose a function, you can edit the generated SAQL in the formula editor.
Add Custom Formulas to Columns: Use the column editor to define custom formulas in compare tables or charts. View measures side by side, and perform math across the table’s columns and rows. Use string values to create labels, concatenate dimension values, provide simple buckets, or add image URLs.
Take Control of Your Experience with CRM Analytics Custom Actions Menus: CRM Analytics custom actions menus in charts and tables let you take advantage of Salesforce actions. With action menus, you can post to Chatter, create an event or opportunity, update a record, or open a Salesforce record directly.
Explore Multiple Datasets with a Single Query: Answers to business questions sometimes require context beyond the current dataset. Or, your dataset structure may not match the way you want to explore your data. To surface insights that require data from disparate datasets, blend the data from multiple datasets in a lens. Then query the blended data just as you would with a single dataset.
Collections of Data in CRM Analytics: CRM Analytics collects and organizes your data in datasets, lenses, dashboard, and apps. These collections, also known as “CRM Analytics assets,” represent levels of data refinement—from raw data that’s uploaded from your source systems to highly curated, packaged views of your data.
Optimize Data Operations with Semi and Anti Joins: Semi- and anti-joins offer a straightforward way to compare datasets in a lens. They're particularly useful for identifying the presence or absence of related rows without needing complex queries. For example, you can easily find accounts that have at least one opportunity or those that have no opportunities.
Limitations and Considerations for Semi and Anti Joins: Semi and anti joins are powerful tools for filtering data based on the existence of related records. To ensure accurate results and optimal performance, consider these specific behaviors when getting started.
View and Configure Dataset Columns: You can view, hide, and rename dataset columns. Users with view-only access to the dataset can view the names and API names of columns. You can also change the display format of measure columns. You can also configure column properties for fields from Snowflake live data.
Explore Data Directly in Snowflake: After you complete setup, CRM Analytics Direct Data for Snowflake lets you run queries on the dataset associated with the connection in real time. You can also build dashboard charts and tables based on the queries you run on Snowflake data.
Drill Deeper Into Data: You can drill deeper into your data to explore the underlying information and surface hidden insights.
Add the Input Widget to Formula Queries: Retrieving values from the input widget to use in a formula can be done through the UI. You can add the input widget as a column in a query or with SAQL. The input widget works as a data source by collecting user inputs for use in other parts of the dashboard.
Data Exploration Concepts: As you learn how to explore and visualize your data, it’s helpful to review key concepts such as visualization, measure, and dimension.
Learn to Explore Data: See what unexpected insights you can surface by interactively exploring and visualizing your data, using CRM Analytics tools.
Filter Your Data: Apply filters in a lens to unclutter your chart and focus on the subset of data that’s most relevant for your business. You can create partial filters on measures and dimensions. You can also create global lens filters on measures, dimensions, or dates and apply them to the compare table in a single step. You can even apply filter logic to a lens or a dashboard step.
Focus on Selected Data: You can focus your exploration by changing the view to include only specific data from an existing table or chart.
Format Numbers: Choose either preset or custom number formats. Specify custom formats for positive, negative, and zero values.
Use Raw Field Names in Query Formulas: When writing compare-table formulas, you can reference measure and group (dimension) columns by name. Referencing measure and group columns by name lets you write more intuitive and easily understandable formulas. Even better, the formula editor includes nifty function and formula pickers that save you time as you enter your SAQL formulas.
Group Your Data into Categories: Group data to organize it so that the data is displayed in useful categories. You can group your data by up to ten dimensions or date fields in charts and tables, except in blended queries.
Limit the Size of Your Query Results: Interested in reviewing just the top 10 accounts in your product pipeline—without excessive scrolling? To fast-track to meaningful insights and for improved performance on your dashboards, you can limit the size of query results in a lens.
Add the Action to the CRM Analytics Dataset Field: When you add the Update Opportunity action to the Opportunity Name field in the Opportunities dataset, you specify which dimension shows the action menu. You also specify the dataset field that contains the Salesforce record ID. The ID identifies both the Salesforce object and the record.
Unify, Find, and Manage Your Analytics: Your analytics are now together in an intuitive and personalized home page that you can experience in Analytics Studio and the Analytics Tab. On this centralized home page, you can see the latest changes for your CRM Analytics assets, and dashboards and reports. You can also customize the home page with items you build for quick access, along with better organization, and a powerful search experience.
Organize and Summarize Data in a Pivot Table: To identify patterns in large amounts of data quickly, arrange the data by a key dimension in a pivot table. For deeper comparative analysis, you can group the pivoted data further and then add row and column totals, subtotals, and grand totals to the pivot table. To evaluate data in a pivot table based on ranking, sort a measure for a pivoted dimension in ascending or descending order (beta).
Share a Visualization: Share a visualization with your colleagues by posting to Chatter, getting its unique URL, downloading a screenshot of it, or downloading its filtered data. A Chatter post provides an image and a link to the asset—lens, dashboard, or app—in CRM Analytics. Colleagues with the link and access to the asset can drill down and explore the information that’s presented. To share without giving access to the asset, use the download options.
Change the Sort Order: Choose whether data in your chart visualization is sorted ascending, sorted descending, or unsorted.
Table Properties: Use table properties to control a table's appearance.
Manage the View of Data in a Values Table: The values table displays all the columns in your dataset. You can customize the view of columns in a values table, and you can sort the row values using any column except for the index column.
Return to a Previous View by Using History: Exploring your data can lead you down many paths. Not all of them are fruitful, and that’s OK. CRM Analytics keeps a full history of your activity in a lens. If you’ve changed your lens in undesired ways, or just want to see those nifty animations again, use history to backtrack quickly to a previous state in your visualization.
Create a Lens: When you look at data in an exploratory mode, you do so in a lens. A lens has several aspects: it’s how you view data in a dataset, it’s the place where you explore the data graphically, and it’s the basis for building any dashboard.
View the Query Behind Your Lens: CRM Analytics uses Salesforce Analytics Query Language (SAQL) or Salesforce Object Query Language (SOQL) behind the scenes in lenses and dashboards to gather data for visualizations. You can view and edit the underlying code that’s written as you explore, or copy it for use elsewhere.
Configure Incremental Sync for Salesforce Data: Before you run or schedule data sync, specify whether the sync extracts incremental changes or all records from each Salesforce object. By default, CRM Analytics performs an incremental sync. An incremental sync runs faster because it extracts only the latest changes to the Salesforce object.
Analytics Glossary: Familiarize yourself with common Analytics terminology.
Explore Data and Take Action with CRM Analytics: CRM Analytics is a cloud-based platform for connecting data from multiple sources, creating interactive views of that data, and sharing those views in apps. CRM Analytics is a better way to distribute insight to business users so they can understand and act on changing information.
Control Who Sees What in an App with Asset Visibility: App Managers and Editors can use the Asset Visibility feature to control the visibility of select assets. Asset visibility is limited for users that are assigned ‘Viewer’ sharing access. Assets remain visible to app Managers and Editors regardless of the asset’s status.
Learn about Internal Analytics Users: Analytics uses internal users, Integration User and Security User, to access Salesforce data, preview data in Data Prep recipes, and enforce row-level security on datasets.
Set Up the CRM Analytics Platform: Set up your organization to use CRM Analytics and assign basic permission sets or create and assign your own custom permission sets.
Advanced CRM Analytics Platform Setup: Follow these steps to learn the details of CRM Analytics permissions and to create and assign your own custom permission sets.
Basic CRM Analytics Platform Setup: Follow these few steps to give users in your organization access to CRM Analytics platform, templates, and apps quickly. Basic setup uses permission sets included with the CRM Analytics Plus or CRM Analytics Growth licenses, which cover most organizations’ analytics needs.
Complete Setting Up the CRM Analytics Platform: After assigning permission sets with either basic or advanced setup, follow these optional procedures to enhance the CRM Analytics experience and fine-tune CRM Analytics access to Salesforce data.
Browse and Find Analytics Assets: The analytics home page provides easy access to all of your apps, dashboards, lenses, reports, and more. With a variety of filter and search options, you can find those assets fast.
Add Items to a Collection: Curate analytics items for your collection. Items can exist simultaneously in multiple collections.
Create a Collection: Create collections of important items to personalize the home page and have quick access to them.
Edit Collections: You can edit your collection properties, such as name and description, and also delete the collection altogether. Shared users with the Manager role can also edit and delete the collection.
Add Collections to Lightning Pages: Insert and display collections to your Lightning pages using the CRM Analytics Collection component.
Pin and Hide a Collection: End users can pin and hide their collections giving them more options with customizing their homepage. Admins and managers have the option to pin collections to end-user homepages and promote trusted content among groups and teams.
Curate and Share Insights with Collections: Collections enable you to curate analytics to create a more personalized experience that makes it easier to find and use the information that matters most to you. Your analytics can be drawn from different apps and folders into a single collection that brings together all items in one place. In addition, collections can be used to personalize the home page and save you time and effort in finding the information you need.
Monitor Important Metrics with the CRM Analytics Watchlist: The CRM Analytics Watchlist gives users the advantage of capturing and tracking up to 20 KPIs across different dashboards, plus historical trending. No more switching between dashboards to see what has changed, and track your metrics all in one place.
Embed and Customize CRM Analytics: Extend CRM Analytics everywhere throughout your business. The CRM Analytics visualizations you’ve built are more powerful when you share them across your Salesforce experience by integrating them into custom pages, Visualforce pages, Experience Cloud sites, and more. In addition, custom menus in lenses and dashboards allow you to perform common Salesforce actions directly from CRM Analytics.
Share Dashboards, Widgets, and Lenses: Share a dashboard, widget, or lens with your colleagues by posting it to Chatter or copying its unique URL. A Chatter post provides an image and a link to the asset in CRM Analytics. Colleagues with the link and access to the asset can drill in and explore the information that’s presented. You can also export snapshot images of CRM Analytics assets to Quip. An exported image is published to a new Quip document.
append Parameters: When you define an append transformation in the dataflow JSON, you set the action attribute to append and specify the parameters.
append Transformation: The append transformation combines rows from multiple datasets into a single dataset.
Special Cases for Matching Records with the augment Transformation: For each record in the left dataset, the augment transformation performs a lookup to find a matching record in the right dataset. However, it’s critical that you understand how the augment transformation handles special cases when matching records.
augment Parameters: When you define an augment transformation, you set the action attribute to augment and specify the parameters.
augment Transformation: The augment transformation adds columns to a dataset from another related dataset. The resulting, augmented dataset enables queries across both related input dataset. For example, you can augment the Account dataset with the User dataset to enable a query to return account records and the full names of the account owners.
computeRelative Transformation: You can use the computeRelative transformation to analyze trends in your data by adding derived fields to a dataset based on values in other rows. For example, to analyze sales pipeline trends, create derived fields that calculate the number of days an opportunity remains in each stage. You can also calculate the changes to the opportunity amount throughout the stages of the opportunity.
computeRelative Parameters: When you define a computeRelative transformation, you set the action attribute to computeRelative. You also specify the parameters for the input source, partition-by field, sort field, and derived field definitions.
Connect and Sync Your Data to CRM Analytics: Use the CRM Analytics connectors to connect data inside and outside of Salesforce. You can access data in your local org and external Salesforce orgs, apps, data warehouses, and database services.
Amazon RDS Connection: Create a remote connection using one of the Amazon RDS connectors to bring data managed on the Amazon Relational Database Service into CRM Analytics.
Application Connectors: Use these connectors to sync data from your external applications to CRM Analytics.
Customer 360 Global Profile Data Connection: Use the Customer 360 Global Profile Data connector to access global profile data from connected orgs, along with other Salesforce and remote data, for integration into Salesforce Data Pipelines.
Database Connectors: Use these connectors to sync data from your external database services to CRM Analytics.
Databricks Connection: Sync Databricks to CRM Analytics by creating a remote connection..
Google Analytics 4 Connection: Create a remote connection using the Google Analytics 4 connector to sync Google Analytics data to CRM Analytics.
Google BigQuery Standard SQL Connection: Bring your large volumes of Google BigQuery data with standard SQL support into CRM Analytics using the Google BigQuery Standard SQL connector.
Google Cloud Spanner Connection: Create a remote connection using the Google Spanner connector to sync data from Google Cloud Spanner to CRM Analytics.
Heroku Postgres Connection: Create a remote connection using the Heroku Postgres connector to sync Heroku Postgres data to CRM Analytics.
HubSpot Connection (Beta): Pull your marketing, sales, and customer service data from HubSpot into Data Manager using the HubSpot connector for CRM Analytics.
Marketo Connection (Beta): Create a remote connection using the Marketo connector to sync data from Marketo to CRM Analytics.
Microsoft Azure SQL Database Connection: Create a remote connection using the Microsoft Azure SQL Database connector to sync data from Microsoft Azure SQL Database to CRM Analytics. To connect to Microsoft Azure Synapse Analytics or a dedicated SQL pool (formerly SQL DW), add a Microsoft Azure Synapse Analytics connection instead of this one.
Microsoft Azure Synapse Analytics Connection: Create a remote connection using the Microsoft Azure Synapse Analytics connector to sync data from Azure Synapse Analytics or dedicated SQL pool (formerly SQL DW) to CRM Analytics. To connect to SQL databases on Azure, add an Azure SQL Database Connection instead of this one.
Microsoft Dynamics 365 Sales Connection: Create a remote connection using the Microsoft Dynamics 365 Sales connector to sync sales data from your Microsoft Dynamics 365 account to CRM Analytics.
NetSuite Connection: Create a remote connection using the NetSuite connector to sync data from NetSuite to CRM Analytics.
New Connector Version Upgrades and Considerations: The remote connectors that you use to sync data to CRM Analytics have new versions. Customers new to CRM Analytics after August 1, 2021 use only the new connector versions, while customers before August 1 can choose when to upgrade. If you choose to be an early adopter, you can switch over to the new connector versions and try them out now. During the Spring ’22 release, we plan to auto-switch customers to the new connector version when it reaches or exceeds the level of functionality and performance of the legacy connector version. We plan to finish the auto-switching during the Summer ’22 release.
Oracle Eloqua Connection: Create a remote connection using the Oracle Eloqua connector to sync data from Oracle Eloqua to CRM Analytics.
Amazon S3 Output Connection: Create a remote connection using the Amazon S3 output connector to write .csv data from CRM Analytics to an Amazon S3 bucket when using Data Prep.
Azure Data Lake Output Connection: Create a remote connection using the Azure Data Lake output connector to write .csv data from CRM Analytics to Microsoft Azure Data Lake Storage Gen2 when using Data Prep.
Salesforce Output Connection: Push your prepared dataset data into any Salesforce org from CRM Analytics Salesforce Output connector and Data Prep. With your prepared data back in Salesforce, you can integrate external data, apply the suite of Salesforce automation tools to act on the data, and allow non-CRM Analytics users access to the data through reports and dashboards. External connectors require OAuth 2.0 for server to server communication.
Snowflake Output Connection: Create a remote connection using the Snowflake output connector to write data from CRM Analytics to a Snowflake table. Then, use Sync Out to push raw data from CRM Analytics to Snowflake, or a Data Prep recipe output node to push transformed data.
Snowflake Private Output Connection: Create a remote connection using the Snowflake private output connector to write data from CRM Analytics to a Snowflake table. The private output connector uses a virtual private connection (VPC) for additional security. To use the Snowflake Private Output connector, you configure a network connection, external credential, and named credential. For authentication, use private key or OAuth.
Tableau Online Output Connection: Push your prepared data from CRM Analytics into Tableau Online with the Tableau Online Output connector and Data Prep recipes. Use the connector to push your transformed, merged, and cleaned data to Tableau Online as a .hyper file for further analysis. For example, prepare historical Opportunity data for struggling products in CRM Analytics, and then use Tableau Online’s analytics tools to identify helpful trends to optimize deals.
Pilot and Beta Connectors: You can use these connectors as a pilot or beta feature to sync external data with CRM Analytics. Before choosing a connector, review its pilot or beta instructions.
Setup OAuth and Provider for Snowflake Private Connection: To use the CRM Analytics Snowflake private connector with an OAuth connection, you must configure Snowflake, Salesforce, CRM Analytics, an external OAuth authorization server, and an external auth identity provider.
Amazon Redshift Connection: Create a remote connection using the Amazon Redshift connector to sync data from Amazon Redshift to CRM Analytics.
Amazon Redshift Private Connection: Pull your data securely from Amazon Redshift into Data Manager using the virtual private connection (VPC) for Redshift input connector for CRM Analytics. To use the Amazon Redshift Private connector, you configure an outbound network connection, external credential, and named credential.
Connect to Data Outside of Salesforce: Create a remote connection to sync external data with CRM Analytics. For each connector, we provide information on how to create the connection, configure its properties, and track important considerations.
Add and Remove Remote Objects and Fields from Data Sync: Update data sync for connected external data sources to keep up with your changing business by adding objects, removing objects, and managing which fields are synced. Synced data is stored in CRM Analytics as objects that can be used in recipes.
Amazon S3 Connection: Create a remote connection using the Amazon S3 connector to sync CSV data from an S3 bucket to CRM Analytics.
Amazon S3 Private Connection: Pull your data securely from S3 running on AWS into Data Manager using the Virtual Private Connection (VPC) for S3 input connector for CRM Analytics. To use the S3 Private connector, you configure an outbound network connection, external credential, and named credential.
Salesforce External Connections: Use a Salesforce external connector to sync data from another Salesforce org to CRM Analytics. External connectors require OAuth 2.0 for server to server communication.
Salesforce Connectors: Use these connectors to sync your data in systems that are part of the Salesforce ecosystem, including external Salesforce orgs, to CRM Analytics.
Connect to Local Salesforce Data: Use the Salesforce Connector to manage the data synced between your local Salesforce org and CRM Analytics. Filter data synced to CRM Analytics, create more connections using the Salesforce Local connector, manage objects between connections, and add or remove objects and fields from sync. Use the Salesforce External connector to sync data from a remote Salesforce org.
Add and Remove Local Salesforce Objects and Fields from Data Sync: Update data sync to keep up with your changing business by adding local Salesforce objects, removing objects, and managing which fields are synced. Synced data is stored in CRM Analytics as objects that you can use in recipes.
Create Another Salesforce Local Data Connection: Create connections using the Salesforce Local connector to strategically manage when your Salesforce data syncs to CRM Analytics using individually scheduled local connections. A local connection, SFDC_LOCAL, is included when CRM Analytics is activated.
Filter Local Data Synced to CRM Analytics: Exclude unnecessary or sensitive data from syncing to CRM Analytics with data sync filters. Filters run on the source object and speed up data sync by pulling only the data you need into CRM Analytics. If you want to use excluded data in the future, use a dataflow or recipe filter to limit the data shown in a dataset instead of a data sync filter.
Optimize Recipes That Use the Local Salesforce Connection: Improve the performance of recipes run after a local Salesforce object sync by choosing to optimize the synced data. Optimization is available for full, periodic full, and incremental syncs.
SAP HANA Cloud Connection: Create a remote connection using the SAP HANA Cloud connector to sync data from cloud-based SAP HANA to CRM Analytics.
Snowflake Connection: Create a remote connection using the Snowflake connector to sync data from Snowflake to CRM Analytics. This connector supports private key or OAuth 2.0 for authentication. Using OAuth 2.0 requires additional setup steps in Snowflake and the Salesforce credentials.
Snowflake Private Connection: Pull your data securely from Snowflake into Data Manager using the Virtual Private Connection (VPC) for Snowflake input connector for CRM Analytics. To use the Snowflake VPC connector, you configure an outbound network connection, external credential, and named credential. This connector supports private key or OAuth 2.0 for authentication. Using OAuth 2.0 requires additional setup steps in Snowflake and the Salesforce credentials.
Sync Out for Snowflake: Sync Out for Snowflake exports your raw local Salesforce data via CRM Analytics to Snowflake using the CRM Analytics output connector for Snowflake. With Sync Out for Snowflake, keep your Salesforce data in Snowflake up to date using scheduled Data Sync without the need for a third-party ETL tool. Fresh Salesforce data is vital if you maintain a central Snowflake data lake for processing, analysis, business automation, or storage. For example, give your shipping logistics team the freshest data by merging your account data from your system of record, Salesforce, with your ERP’s shipping data in your data lake.
Zendesk Connection: Create a remote connection using the Zendesk connector to sync data from Zendesk to CRM Analytics.
How CRM Analytics and Salesforce Data Pipelines Work Together: CRM Analytics and Salesforce Data Pipelines are unique products, but settings, limits, and assets sometimes are shared or interrelated when you use both products. These features are shared when you use CRM Analytics.
Set a Source Time Zone for .CSV Data: When you upload .csv data to create or update a dataset, you can select the time zone of the data if it’s not GMT.
Inherit Custom Fiscal Years from Salesforce: If your company uses a custom fiscal year, and has defined the custom fiscal year in Salesforce, then CRM Analytics can inherit the custom fiscal year definition from Salesforce. Once inherited, the custom fiscal year is available for use throughout CRM Analytics. For example, a dashboard filter can return opportunities from this fiscal quarter.
Integrate and Prepare Data for Analysis: You can use CRM Analytics lenses and dashboards to analyze your data. To prepare the data for analysis and increase performance when processing large amounts of data, first load, clean, and transform the data into datasets.
Data Manager for CRM Analytics: The data manager is where you monitor your data jobs, prepare datasets with recipes and dataflows, and connect to data.
Monitor: The monitor provides options for you to manage jobs.
Data Integration Best Practices: Review these tips to improve the performance of your recipes, speed up the editing of large recipes, and streamline data sync.
Clean, Transform, and Load Data with Data Prep Classic: Use a recipe to clean and combine data from multiple datasets or connected objects. Add bucket and formula fields, filter rows, transform field values, convert field types, and standardize date formats. You can then output the results to a new target dataset.
Set Up the Preview in a Recipe: A recipe displays a preview of your data on the Preview tab as you work. You can set the columns and number of rows that appear in this preview. If you are working with a large set of data, reducing the size of the preview speeds up the data refresh as you add steps to your recipe.
Data Prep Access Based on Your User Type: Not all Data Prep users in CRM Analytics need access to all data prep features. For example, a business analyst doesn’t need to change dataset security to grant themselves and others access to more data. To support advanced and limited Data Prep user types, CRM Analytics comes with two user permissions: Edit Analytics Dataflows and Edit Dataset Recipes.
Standardize Date Formats: If a dimension field contains dates in different formats, use the Format Dates recipe transformation to standardize the format for all values in the field. A consistent format enables you to correctly filter and group records by date, including filtering by date component, such as month. It also ensures that you can successfully convert the field type from dimension to date.
Clean, Transform, and Load Data with Data Prep: Data Prep provides an intuitive, visual interface in CRM Analytics that allows you to easily point-and-click your way to build recipes that prepare data and load it into a target. Use the graph of a recipe to see at a glance where the data comes from and how it flows through the recipe to the target. To validate the recipe as you build, preview how raw data is transformed at every step of the way.
Add More Data in a Recipe: You can add columns of data from related objects to existing data in a recipe. Depending on how you want to combine the new data, use one of the following methods: lookup, left join, right join, inner join, or full outer join.
Aggregate and Group Data to a Different Grain: Large datasets can be hard to digest due to the amount of information and low-grain details. Aggregation allows these datasets to be rolled up to a higher granularity, thus allowing users to create recurring summary statistics and join datasets with different granularities. You can also aggregate data to perform calculations on grouped records without aggregating the measures. For instance, group by website session IDs and then calculate the average time on each page and total number of clicks.
Add Rows in a Recipe with Append: Use append to add rows to the data in your recipe from another dataset or connected object. Fields are mapped automatically, or you can map them manually.
Convert a Dimension Field Type to Measure: When you load data into a dataset, CRM Analytics tags a measure field as a dimension if it contains unexpected strings or characters. To use measure functionality, such as calculating the average, use the To Measure transformation to change the dimension field type to measure.
Convert Field Types in a Recipe: The type assigned to a dataset field determines how you can query that field’s data. For example, you can filter and group by a dimension or date field, or perform math calculations on a measure field. When you load data into a dataset, CRM Analytics sometimes tags a dataset field with the wrong type. If needed, convert fields to the correct types.
Convert a Measure Field Type to Dimension: When you load data into a dataset, CRM Analytics tags a dimension field as a measure if it contains only numbers. For example, CRM Analytics tags a numerical Room Number field as a measure, even though it’s actually a dimension that identifies rooms. To use dimension functionality, such as grouping and filtering by dimension fields, use the To Dimension transformation to change the measure field type to dimension.
Add a Filter in a Recipe: Filter data in a recipe to remove rows that you don’t need in your target dataset.
Numeric Functions for Formula Fields: Use numeric functions to calculate new values from measures in your recipe. For example, you can round, truncate, and determine the absolute value of a number. The arguments in each function can be numbers or measure columns.
abs Function: Calculates the absolute value of a number. The absolute value of a number is the number without its positive or negative sign.
ceil Function: Rounds a number up to the nearest integer.
exp Function: Returns a value for e raised to the power of a number you specify.
floor Function: Returns a number rounded down to the nearest integer.
log Function: Returns the logarithm of a number in the base you specify.
power Function: Raises a number to the power of another number.
round Function: Returns the nearest number to a number you specify, constraining the new number by a specified number of digits.
sqrt Function: Returns the positive square root of a given number.
String Functions for Formula Fields: Use string functions to create values in a formula field based on one or more dimension columns in your recipe or strings. For example, you can change the casing of text strings, concatenate values from multiple columns, and replace values.
concat Function: Returns a string by concatenating the values of the specified columns and input strings. For example, to display the close date as MM-DD-YYYY, concatenate the Close_Date_Month column, Close_Date_Day column, and Close_Date_Year column, and add a dash between each of them.
lower Function: Returns a string with all characters from the input string in lowercase. If the input string is null, then the result is null.
ltrim Function: Removes the specified substring from the beginning of a string. To remove leading spaces, do not specify a substring.
replace Function: Replaces a substring with the specified characters. If any of the arguments are null, then the function returns null. This function is case-sensitive.
rtrim Function: Removes the specified substring from the end of a string. To remove trailing spaces, do not specify a substring.
substr Function: Returns characters from the string, starting at the specified position and of the specified length.
title Function: Returns the string with the initial character of every word in uppercase and the remaining characters in lowercase. For example, “united states” becomes “United States.”
trim Function: Removes the specified substring from the beginning and end of a string. To remove leading and trailing spaces, do not specify a substring.
upper Function: Returns the string with all characters in uppercase. If string is null, then the result is null.
Manage Recipes: After you create a recipe, use the Recipes tab to edit and delete them.
Navigate Columns in a Recipe: As you add data and transform fields, the number of columns in your recipe preview increases, which makes it harder to find the columns that you want to work with. Use the column view to quickly find the columns that you need, and hide the ones you don't.
Navigate and Edit Recipe Steps: As you prepare your data, each change you make appears as a recipe step on the left. Think of these steps as your recipe history. You can move back and forward through this history to see how the data looks at different stages of the recipe. If you don’t like what you see, you can edit or remove any step.
Predict Missing Values in Dimension Columns: When a dataset or connected object has missing values in a dimension column, CRM Analytics can fill in missing values to complete your data. CRM Analytics intelligently predicts values based on values in other strongly correlated columns in your data.
Run a Recipe: Run a CRM Analytics recipe for the first time to create the dataset it defines. Run a recipe again to update the dataset with the latest synced data. You can run a recipe manually or on a schedule.
Save a Recipe: If you’re not ready to create the target dataset, save the recipe and come back to it later. That saves your steps without you having to create the dataset.
Convert a Dimension Field Type to Date: When you load data into a dataset, CRM Analytics tags a date field as a dimension if it contains unexpected string values or dates in different formats. To use date functionality, such as grouping by month, use the To Date transformation to change the dimension field type to date.
Transform Fields in a Recipe: Data is not always consistent, especially when you combine data from different sources. In a recipe, you can transform fields to ensure that values are consistent in your target dataset. Change case, split values to get just the parts you need, and replace incorrect values.
Cross Join: In CRM Analytics, a cross join combines unrelated records and includes all rows from the left (recipe data) and the right. Unlike other joins that use keys to find matches, the cross join pairs every row from one dataset with every row of another dataset (known as the Cartesian Product).
Full Outer Join: In CRM Analytics, a full outer join includes all rows from the left (recipe data) and right, regardless of whether they have matches. The join includes all matched rows in the target when multiple rows match.
Inner Join: In CRM Analytics, an inner join includes only matching rows from the left (recipe data) and right. The join includes all matched rows in the target when multiple rows match.
Considerations When Using Joins: In CRM Analytics, unlike a lookup, a join creates a separate record for each match in the target dataset when multiple rows match. Before using a join, ensure that you understand the implications of duplicate rows.
Left Join: Like a lookup, a left join in CRM Analytics includes all rows from the left (recipe data) and only matching rows from the right. Unlike a lookup, a join includes all matched rows in the target when multiple rows match.
Right Join: A right join in CRM Analytics includes all rows from the right and only matching rows from the left (recipe data). The join includes all matched rows in the target when multiple rows match.
Create a Recipe with Data Prep Classic: Use a Data Prep Classic recipe to transform data before loading it into a dataset. Only Government Cloud users can create recipes with Data Prep Classic. All other users, including those using Government Cloud Plus, must use Data Prep to create recipes.
Convert a Dataflow to a Recipe: To use joins, aggregates, and sentiment detection in Data Prep, convert your dataflows to recipes. However, if your dataflow achieves your desired outcome, there’s no need to convert it, and you can continue using it. But consider using recipes for new Data Prep jobs because they offer more features.
Dataflow Conversion Mapping: During conversion, dataflow functionality is mapped to similar recipe functionality. Consider the mapping specifications when evaluating the conversion of your dataflows to recipes.
Design Complex Datasets with Dataflow Editor: Use Dataflow Editor, a point-and-click interface, to build your dataflow logic from scratch. Add transformations to determine what source data to use, how to transform that data, and which datasets to load the results into.
Plan the Dataflow: Before you start creating the dataflow, think about the dataflow design. Consider what data to make available for queries, where to extract the data from, and whether you must transform the extracted data to get the data you want.
Edit Nodes in the Dataflow: When you edit a dataflow node, for example to change its name, or add or remove fields, consider that these changes can impact downstream nodes. If you're manually editing nodes in the JSON dataflow definition file, make sure that your changes are reflected in downstream nodes. If you're working in the dataflow editor, CRM Analytics propagates many of your changes to downstream nodes for you.
Run a Dataflow Manually: Run a dataflow for the first time to create the datasets it defines. Run it again to update the dataset with the latest synced data.
Monitor a Dataflow Job: In Data Manager, the Monitor's Dataflows tab shows status, start time, and duration of the last 10 dataflow jobs. It retains the last 7 days of job history. You can view error messages about each job, view the run-time details about every transformation that is processed, monitor the number of jobs remaining from your org’s 24-hour limit, and download error logs.
Set Dataflow Notifications: Set dataflow notifications to receive an email notification when a dataflow job finishes. You can be notified only when there are warnings, only when the dataflow fails, or every time the dataflow finishes. You can also set an elapsed time notification to notify you when a dataflow is still running after a specified length of time.
Schedule a Dataflow to Run Automatically: You can schedule a dataflow to run after an event, such as when a sync job completes. Or you can schedule it to run hourly, weekly, monthly, or on specific days of the week or dates.
Restore a Previous Version of a Dataflow: CRM Analytics uses version history to create copies of dataflows when you edit them so you can restore a previous version.
Data Prep Date Configuration: Use Data Prep’s date configuration to analyze your CRM Analytics data according to a custom year. Date configurations are automatically applied to Data Prep recipe data. Date configurations are ideal for orgs that want to analyze by a custom week start, or by a fiscal year without manually configuring the fiscal month offset or applying a fiscal year to the entire org.
Column Types in Datasets: Datasets support the following column types: dates, dimensions, and measures.
Columns and Rows in Datasets: A dataset is analogous to a table in a database. It organizes data by columns and rows.
Denormalized Data: The data in Salesforce objects is normalized, meaning that it’s stored efficiently in a collection of related tables to minimize redundancy, enhance consistency, and ensure data integrity. In contrast, data in datasets is denormalized, where it’s stored in an optimal format for querying. All the work required to combine the data from multiple sources is done ahead of time. The data is centralized, compressed, and ready to be analyzed.
Reserved Dataset Field Names: CRM Analytics data prep doesn’t support using some reserved keywords as field names in lenses and dashboards.
Dataset Capacity and Limits: Before you create any datasets, review the limits. For example, each Salesforce org has a maximum number of rows for all datasets in the org. There are also limits on the number of columns in a dataset and characters in a column.
Considerations for Numeric Values: Confirm that numeric values in a column are uniform in granularity and units. Inconsistencies can occur within a data source and after merging data from multiple data sources.
Range Limits for Numeric Values: The maximum numeric value that can be stored in a dataset is 576,460,752,303,423,488 and the minimum numeric value is -576,460,752,303,423,488.
Restore a Dataset Version: Restoring a dataset rolls back its data to a previous version. Restore a dataset to reverse a recent update or to undo the results of a recipe or dataflow change.
Dataset Security: For each dataset that you create, you can apply security to restrict access to sensitive rows in the dataset.
Apply Extended Metadata to Change the Formatting of a Dataset: A dataset’s extended metadata (XMD) file allows you to customize the formatting of dashboards associated with the dataset. You can update the XMD file through the dataset’s edit page.
About Datasets: A CRM Analytics dataset is a collection of related data that can be viewed in a tabular format. The data can come from many sources, including Salesforce objects, external data sources, and even other datasets.
Set Data Sync Notifications: Set Data Sync notifications to receive an email notification when a CRM Analytics sync job has warnings, when sync fails, or every time the sync finishes.
Considerations for Date Columns: Confirm that date values in a column are uniform in format and time zone. Inconsistencies can occur within a data source and after merging data from multiple data sources.
Date Formats and Fiscal Dates for Source Data: You can set metadata attributes to control how dates are loaded into CRM Analytics and to enable CRM Analytics to generate fiscal date columns.
Handle Date Values: When CRM Analytics loads dates into a dataset, it breaks up each date into multiple columns, such as day, week, month, quarter, and year, based on the calendar year. For example, if you extract dates from a CreateDate column, CRM Analytics generates columns such as CreateDate_Day and CreateDate_Week. If your fiscal year differs from the calendar year, you can enable CRM Analytics to generate fiscal date columns as well.
delta Transformation: The delta transformation calculates changes in the value of a measure (number) column in a dataset over time. The delta transformation generates an output column in the dataset to store the delta for each record. Create deltas to make it easier for business analysts to include them in queries.
delta Parameters: When you define a delta transformation, you set the action attribute to delta and specify the parameters.
Design Datasets with Dataflows and the Dataset Builder: Use a dataflow to create one or more datasets based on source data from existing datasets or synced data. A dataflow is a set of instructions that specifies what input data to include, how to transform that data, and which datasets to load the transformed data into.
digest Transformation: The digest transformation extracts synced connected data in a dataflow. Use it to extract data synced from an external Salesforce org, or data synced through an external connection. Use the sfdcDigest transformation to extract from your local Salesforce org.
digest Parameters: When you define a digest transformation, you set the action attribute to digest and specify the parameters.
dim2mea Transformation: The dim2mea Transformation creates a new measure based on a dimension. The transformation adds the new measure column to the dataset. The transformation also preserves the dimension to ensure that lenses and dashboards don’t break if they use the dimension.
dim2mea Parameters: When you define a dim2mea transformation, you set the action attribute to dim2mea and specify the parameters.
edgemart Transformation: The edgemart Transformation gives the dataflow access to an existing, registered dataset, which can contain Salesforce data, external data, or a combination of the two. Use this transformation to reference a dataset so that its data can be used in subsequent transformations in the dataflow. You can use this transformation and the augment transformation together to join an existing dataset with a new dataset.
edgemart Parameters: When you define an edgemart transformation, you set the action attribute to edgemart and specify the parameters.
Enable Null Measure Handling in Orgs Created Before Spring ’17: Use null measure handling to specify null as the default value for numeric columns in your recipes, dataflows, and CSV uploads. When another default value isn’t specified for a numeric column and null measure handling is enabled, CRM Analytics replaces a blank with a null value. If CRM Analytics was initially set up in your org after the Spring ’17 release, null measure handling is enabled and you can’t disable it.
Enable Data Sync and Connections: Data Sync is enabled by default if you turned on CRM Analytics after the Winter ’20 release. If you turned on CRM Analytics before the Winter ’20 release, manually enable Data Sync and Connections to optimize your dataflows and connect to external data.
export Transformation: The export transformation creates a data file and a schema file from data in a specified source node in your dataflow. After the dataflow runs, Einstein Discovery users can access these files through the public API.
export Parameters: Add an export transformation to a dataflow either in the dataflow editor, or directly in the JSON dataflow definition file. The parameters you specify are the same for both methods.
filter Transformation: The filter transformation removes records from an existing dataset. You define a filter condition that specifies which records to retain in the dataset.
filter Expression Syntax: You create a filter expression in the filter transformation based on one or more dimensions in a dataset. You can use a standard filter expression or a SAQL filter expression.
filter Transformation Parameters: When you define a filter transformation, you set the action attribute to filter Transformation and specify the parameters.
flatten Transformation: The flatten transformation flattens hierarchical data. For example, you can flatten the Salesforce role hierarchy to implement row-level security on a dataset based on the role hierarchy.
flatten Transformation Parameters: When you define a flatten transformation, you set the action attribute to flatten Transformation and specify the parameters.
Ways to Get Data from Data Sources Into Datasets: To populate datasets with data from data sources, you can import the data directly from the source. Or, you can extract the data from the source and prepare it before loading it into a dataset.
Get Started with Data Integration: In CRM Analytics, data integration involves gathering and preparing the Salesforce and external data you want to analyze. External data is data that resides outside of the Salesforce org that you use for Analytics, such as data from another Salesforce org, outside applications, spreadsheets, and databases. After integrating the data, you prepare it into datasets. Data preparation is the process of transforming your data into a form that’s meaningful and valuable to the people consuming it. For example, you can define data preparation logic that combines data from two data sources and cleans up inconsistencies, such as differently formatted dates and codes. Users then explore and visualize datasets through CRM Analytics lenses and dashboards.
Run a Dataflow: Like recipes, you can run dataflows manually or schedule them to run automatically at regular intervals to create your datasets and keep the data up to date. You can also stop a dataflow while it’s running.
Use the Recipe Inspector: Use the CRM Analytics recipe inspector to troubleshoot issues and improve performance. Deep-dive into your recipe jobs to view the detailed information at each stage of the flow. Monitor individual node performance and status. You can quickly identify a slow join or a transformation node that failed to make fixes immediately.
Recipe Inspector Scenarios: The CRM Analytics recipe inspector details provide information about the complexity of runtime processing. The following are common scenarios seen in the recipe inspector and what they mean.
Monitor a Recipe Job: The Jobs subtab on the Monitor tab shows the status of data sync and recipe CRM Analytics jobs. You can view error messages about a job, view the run-time details about every transformation that is processed, and monitor the number of jobs remaining from your org’s 24-hour limit.
Run a Recipe Manually: Run a CRM Analytics recipe for the first time to create the datasets it defines. Run it again to update the dataset with the latest synced data.
Schedule a Recipe to Run Automatically: You can schedule a CRM Analytics recipe to run at a specific time or after an event. Use time-based scheduling to ensure that fresh data is available by a particular time or to run the job during non-business hours. You can schedule a recipe to run hourly, weekly, monthly, or on specific days of the week or dates. Use event-based scheduling to run a recipe after the sync runs to ensure that datasets include the latest data. If you split data prep across multiple recipes, use event-based scheduling to chain these recipes together to obtain the desired outcome automatically.
Export a Recipe Schedule: Export your CRM Analytics recipe schedules as calendar format (ICS) for quick reference and visibility in your calendar. For instance, share a recipe's run schedule with stakeholders in other teams to coordinate data readiness and reporting deadlines.
Run Data Sync to Synchronize Source Data in CRM Analytics: Make sure that the data available to CRM Analytics matches the data in your source, whether its Salesforce or an external data source. You can manually run or schedule each sync.
Manage Datasets: Edit a dataset to update its data, change its extended metadata, apply row-level security, or restore the dataset to a previous version.
Rules for Automatic Generation of a Metadata File: When you upload a CSV file from the user interface, Analytics automatically generates the metadata file as long as the CSV file meets certain requirements.
Handle Missing Values: Gaps in your data can throw off analysis. Missing values are best fixed in the source application by, for example, making it a required field. However, if you can’t do that, you can use CRM Analytics to fill in missing data.
Handle Null Numeric Values: When you create or update a dataset through your dataflow, recipe, or a CSV upload, for example, CRM Analytics replaces blank numeric values with the specified default value. When no default value is specified, CRM Analytics replaces blanks in numeric columns with 0 or null based on whether you enable null measure handling.
Other Ways to Integrate Data: Dataflows and recipes can integrate most data from internal to external data sources. However, CRM Analytics provides additional ways to integrate data that are more effective for specific use cases. For example, if you don’t have to transform data before loading it into a dataset, you can import data directly from CSV or Excel files. If you need to transform it, you can upload the file to create an intermediate dataset, and then use a recipe or dataflow to change that data before loading the results into another dataset. Or, to analyze Salesforce report data over time, you can create a dataset and dashboard that trends the report with just a few clicks. With trending, CRM Analytics creates a dataset based on snapshots of your Salesforce report data.
Output Connectors: Use output connectors to push data from CRM Analytics to the remote system. Output connectors can push as part of a data prep output node, and some can also push out unprepared data using sync.
Plan for Your Data Integration Project: Before you build your data integration solution, think about the use case you want to analyze and the data you need to get you there.
prediction Transformation: The prediction transformation produces an Einstein Discovery prediction for a dataset. Einstein Discovery uses predictive analytics, which analyzes historical data (based on data mining, machine learning, and predictive statistical modeling) to identify patterns and predict future outcomes.
Why Should I Use Recipes Instead of Dataflows?: Recipes and dataflows both prepare data. Compared to dataflows, recipes are newer and are recommended for functionality and ease of use. Recipes allow you to preview the data as you transform it, while dataflows only show your node schema. For example, recipes have more join types and transformations with built-in machine learning - such as Predict Missing Values and Detect Sentiment - that aren’t available in dataflows. Recipes can also aggregate data to a higher level.
Connect to a Data Source or Target: Connect to data in your local Salesforce org, external orgs, and other sources, such as apps, data warehouses, and database services. Use an input connector to extract data from a data source. Use an output connector to load the recipe results into a target.
Schedule Smarter with Priority Scheduling: Priority scheduling for CRM Analytics recipes automatically manages your run queue. It prioritizes smaller and faster runs while ensuring that larger and longer runs are completed on time. Priority is automatically calculated based on factors such as historic runtime, dataset input size, and CSV file size. Priority scheduling is most helpful to smooth out occasional queue-time spikes. If you never or frequently see long queue times, then priority scheduling isn’t as helpful. Activate the feature in advance to manage your queue, not during a problem when your queue is already overloaded. This feature doesn’t increase your maximum number of concurrent runs.
Private Connectors: Use private connectors to push and pull data between CRM Analytics and external services AWS using a Virtual Private Connection (VPC). Supported services include Snowflake and Amazon S3. To use private input and output connectors you must configure a network connection, external credential, and named credential. These credentials provide the security and privacy required for the private connection.
Input Node: Add Data to the Recipe: Although you choose input data when you create a recipe, if needed, you can add more data to an existing recipe. In CRM Analytics, Data Prep shows a separate Input node for each set of data that you add to the recipe.
Add or Subtract Days or Months: In CRM Analytics, return the column date with the specified days or months added or removed.
Additional Functions for Formulas: In CRM Analytics, these versatile functions can be used with the data types text, number, date_only, and datetime to build logic into your formulas.
Aggregate Node: Use an Aggregate node in CRM Analytics to roll up data. You can aggregate to a higher level of granularity or roll up hierarchical data.
Aggregate Node: Hierarchical Aggregation: Use Hierarchical Aggregation in CRM Analytics to sum measures across multilevel data. For example, you can roll up revenues, assets, or liabilities for companies and their subsidiaries. Or, you can roll salesperson data up the management chain to see aggregates by team, region, area, and beyond. Hierarchical Aggregation requires rows with an ID and a parent ID. Many rows can have the same parent ID, or the parent ID can be empty. Some rows can even share an ID, such as a record with multiple parents. You can also use the optional roll up percentage to apply percentage splits to allocate portions to different parents, such as commission among members of the sales team.
Aggregate Node: Roll Up Data to a Higher Level: Use an Aggregate node to roll up data to a higher granularity in CRM Analytics. You can aggregate using: unique, sum, average, count, maximum, and minimum. You can group by any dimension or date column. You can also pivot dimensions as columns. For example, you can group opportunities by account to get account-level details, like total opportunity amount, average number of cases, and the average days to close an opportunity for each account. You can then pivot on account type to analyze each combination of account and account type.
Append Node: Stack Rows from Different Sets of Data: Use an Append node in a Data Prep recipe in CRM Analytics to stack rows from multiple sets of input data into one dataset. For example, use this node to combine sales records from two different Salesforce orgs, each containing sales transactions from a specific region.
Clean and Prepare Data Intelligently with Column Profiles and Smart Suggestions: The column profile gives you key insights into the quality of your data and suggests additional transformations to help you clean and prepare it. This profile is especially useful when you are combining data from different sources, where inconsistencies are often introduced.
Connect and Reorganize Nodes or Branches in a Recipe: In CRM Analytics, connect disconnected nodes or branches to join or append data from any node to any other node in a Data Prep recipe. You can also create a branch at an intermediate node, and join or append it to another path in the recipe.
Data Type Conversion Transformations: Convert Column Types: In CRM Analytics, the data type of a dataset column determines how you can query that column’s data. For example, you can filter and group by a text or date column, or perform calculations on a number column. When you load data into a dataset, CRM Analytics sometimes tags a dataset field with the wrong type. If needed, use the column-type conversion transformations in a Data Prep recipe to convert columns to the correct types.
Copy and Paste a Recipe: In CRM Analytics, use the toolbar or keyboard shortcuts to copy and paste nodes within a single recipe or across multiple ones. To use this feature, enable clipboard access permissions in your browser.
Create a Recipe with Data Prep: Use a Data Prep recipe in CRM Analytics to clean, transform, and enrich data before loading it into one or more targets. Preview your results as you build the recipe to confirm that the logic is correct.
Data Preview Sampling: In CRM Analytics, use data preview sampling to see a more accurate picture of your data at each node in Preview. By widening the sample size and selecting the mode to arrange your data, data sampling helps prevent working without preview data. Select Filtered Rows to isolate specific values. Select All Column Values to populate a sample when you need to see all column values, such as when creating a bucket.
Date Functions for Formulas: In CRM Analytics, use date functions to adjust or calculate values from dates in your recipe. For example, you can add time, find time until, and extract a day of the week from date fields. The arguments for date functions are date columns.
Delete a Recipe: Delete recipes that you no longer need. Deleting a recipe doesn’t delete its target dataset.
Pull Data Directly into Recipes: Direct data for recipes in CRM Analytics lets your recipes access data in Salesforce objects that you don’t sync. When you use direct data, the latest data is pulled directly from the Salesforce object with each recipe run, bypassing the data sync step. Direct data is designed to access a small number of objects instead of optimizing for performance as is the case with data sync connected objects. After you decide to use direct data, you can use it in recipes, add filtering, and switch the data to be connected instead.
Preserve Original Currency Values for Salesforce Direct Data: By default, all currency fields are converted to the default org currency on all data syncs. With Salesforce direct data objects, use the preserveCurrencyFields in your recipe JSON to specify which fields you want to preserve the original currency value for on data sync. This process allows you to use unconverted currencies in some workflows and converted currencies in others, even within the same Salesforce direct data object.
Date Difference: In CRM Analytics, calculate the duration between two selected date columns as days, months, or years.
View or Edit a Recipe: In CRM Analytics, view or edit a recipe by clicking its name. By default, all recipes open with Data Prep. When you edit a Data Prep Classic recipe, the recipe upgrades to and opens with Data Prep.
Filter Node: Filter Rows: In CRM Analytics, use a Filter node to remove rows that you don’t need in your target. For example, you can filter case records to focus on escalated cases only. Filter values are case-sensitive. Filter nodes next to a direct data input node speed up recipe runs by reducing the number of records pulled into the recipe.
Use a Join Node in the Recipe Canvas: In CRM Analytics, create joins between Salesforce objects, external objects, and CRM Analytics datasets in a recipe.
Join Node: Add Related Columns of Data to the Recipe: Use a join node in CRM Analytics to add columns of data from related objects to existing data in a recipe. When working with CRM Analytics datasets and external objects, add a join node on the recipe canvas. When working with Salesforce objects, you can either create a join on the recipe canvas or join the data when you first create your dataset on the Add Input Data screen.
Lookup: In CRM Analytics, a lookup returns all rows from the left data stream (recipe data) and only matching rows from the right data stream (lookup source). If multiple matches are found in the right data stream, you can set the lookup to return either a single row or all matching rows. To ensure that the grain of the results doesn’t change, the lookup outputs one row for each row in the left data stream.
Use a Join Node on the Add Input Data Screen (Salesforce Objects Only): When working with Salesforce objects in CRM Analytics, you can create your dataset and join data source in one step on the Input Data screen. Add Input Data also recommends columns that match each of your data sources and can serve as join keys.
Multivalue Functions for Formulas: In CRM Analytics, use multivalue functions with your multivalue data. For example, look for a specific value, count how many values there are, create a new multivalue by combining other text, convert a multivalue to a string, or split a string and return a multivalue. The arguments for multivalue functions must be text strings or fields.
Nodes for Data Prep Recipes: In CRM Analytics, Data Prep provides nodes that allow you to bring in data, transform it, and write the results to a target. For example, you can use a Join node to add columns from a related object to a recipe. You add nodes in the Graph area of Data Prep. You can string together multiple nodes to sequentially change the flow of the data.
Now: In CRM Analytics, create a column in a Data Prep recipe that displays the current date and time in a specified format.
Numeric Operators and Functions for Formulas: In CRM Analytics, use numeric functions to calculate values from numbers in your recipe. For example, you can round, truncate, and determine the absolute value of a number. The arguments for numeric functions can be number values or columns.
Numeric Operators and Functions for Unconverted Formulas: Use numeric functions to calculate values from measures in your recipe. For example, you can round, truncate, and determine the absolute value of a number. The arguments for numeric functions can be numbers or measure columns. If your recipe was upgraded to Data Prep, but your recipe doesn’t convert, this topic is for your unconverted recipe.
Output Node: Write Recipe Results to a Dataset or External System: In CRM Analytics, use Output nodes with Data Prep recipes to write recipe results to a target, such as a dataset, staged data, .csv, a Data 360 data lake object, or remote location like Amazon S3. A recipe can have more than one Output node. If the recipe writes to a dataset, you can change the API name and label of the dataset in the Output node. If an existing dataset uses the specified API name, the recipe overwrites the existing dataset when it runs.
Recipe Output to CSV: Use the Data Prep output node in CRM Analytics to save your prepared data as a data and schema file in a Salesforce BPO (storage database). You can then download the data locally using the public API as a CSV file. Use the CSV file to transfer prepared, cleaned, and improved data into your system for analysis, storage, or to inform business processes.
Write Recipe Output to Data 360: Use the Data Prep output node in CRM Analytics to save your prepared data as a Data 360 data lake object (DLO). After your data is in Data 360, you can create calculated insights, semantic models, and Data 360 reports and dashboards.
Run Sequential Recipes with Staged Data: When your data strategy involves multiple recipes, you can reduce processing time by using staged data instead of datasets. Place the steps you would otherwise repeat into one recipe that outputs results as staged data. Subsequent recipes can then use the staged data as input nodes to do other work. For example, rather than merge account and opportunity data in several region-specific forecasting recipe, merge in an initial recipe that outputs the results as staged data, and use this data in the other recipes.
Preview Results in a Data Prep Recipe: As a best practice in CRM Analytics, catch mistakes early by previewing the results of each node and transformation that you add to your Data Prep recipe.
Profile Columns to Understand Data in a Data Prep Recipe: In CRM Analytics, run column profiling on sample data to estimate key stats about columns, such as the frequency of values and percent of columns with missing values. Column profiling is especially useful when you are combining data from different sources, where inconsistencies are often introduced. Column profiles are available only for columns shown in preview.
Revert a Data Prep Recipe to Data Prep Classic: By default, when you open a Data Prep Classic recipe, it upgrades to and opens in Data Prep. If the upgraded version has any issues, you can revert it to the Data Prep Classic version. The recipe reverts to the last saved Data Prep Classic version. CRM Analytics also creates a copy of the Data Prep recipe so you don’t lose changes made in Data Prep.
Reserved Keywords in Formulas: In CRM Analytics, Data Prep has reserved keywords that need special handling when manually writing formulas. Use double quotes " when referencing dataset columns that share a name with reserved keywords. To avoid cross-referencing your column names to this list, the best practice is to use double quotes around all column names.
Notation for String Formulas: In CRM Analytics, use formulas to create custom calculations. If you manually type your formulas, use single quotes ' for string literals and double quotes " for joined fields with dot notation or reserved keywords. If you use the recipe builder tool instead of typing, the correct single or double quote is automatically used.
String Functions for Formulas: In CRM Analytics, use string functions to create values based on other strings. For example, you can change the casing of text strings, concatenate values from multiple columns, and replace values. The arguments for string functions must be text strings or fields.
String Functions for Unconverted Formulas: Use string functions to create values based on other strings. For example, you can change the casing of text strings, concatenate values from multiple columns, and replace values. The arguments for string functions must be text strings or dimension columns. If your recipe was upgraded to Data Prep, but your recipe doesn’t convert, this topic is for your unconverted recipe.
Transform Node: Transform the Data Before Loading It Into a Target: Use a Transform node in CRM Analytics to manipulate data based on the transformations added to the node. You can add one or more transformations to each Transform node. For example, you can add transformations to concatenate two columns, standardize the formats in a date column, and detect the sentiments of comments in a Feedback column. Each transformation modifies the data in a unique way. To manipulate data at multiple stages of a recipe, add a separate Transform node at each stage.
Categorize Date Column Values into Buckets: In CRM Analytics, Data Prep provides transformations that allow you to prepare, clean, and transform your data. For example, you can use a transformation to create a calculated column based on a formula. You add transformations inside a Transform node. You can string together multiple transformations to manipulate data sequentially.
Categorize Dimension Column Values into Buckets: In CRM Analytics, use a bucket column in a Data Prep recipe to categorize and organize text values. For example, you can create buckets West, Central, and East to group regions.
Categorize Measure Column Values into Buckets: In CRM Analytics, use a bucket column in a Data Prep recipe to categorize and organize numeric values. For example, you can create buckets High, Middle, Low to group numbers of cases per account.
Cluster Transformation: Segment Your Data: In CRM Analytics, use the Cluster transformation in a Data Prep recipe to segment rows of data into distinct clusters based on common characteristics. For example, you can cluster accounts based on number of employees, numerical rating, and annual revenue. Using the clusters, you can identify products and services to upsell to each account based on other accounts in the same cluster, apply different service handling or marketing campaigns based on cluster, or define different metrics and KPIs for analysis.
Date and Time Transformations: Calculate on Date Fields: In CRM Analytics, your date fields are key to understanding trends over time or keeping teams aware of upcoming milestones and deadlines. Use the Date and Time transformations to calculate based on dates or make them more helpful to your analysis. With the Now option, you can insert a column with the current date and time in a specified format. Use Date Difference to calculate the duration between two selected date columns as days, months, or years. And use the Add or Subtract Days or Months function to add or subtract days or months from a date column.
Extract Transformation: Get a Date Component: In CRM Analytics, use the Extract transformation in a Data Prep recipe to pull a selected component from a date field into a new field. For example, extract the hour component from the Case Created Date column to analyze case creation by hour of the day.
Detect Sentiment Transformation: Determine the Sentiment of Text: In CRM Analytics, text fields show valuable information such as product reviews and social media posts. Use the Detect Sentiment transformation in a Data Prep recipe to quickly categorize text into sentiments. Choose whether you want sentiment results as a decimal value on a 5-point scale, with 5 as the most positive, or as labels of Positive, Negative, and Neutral. For example, detect the sentiment of survey responses to evaluate how customers feel about your product support. If more than a certain percentage—say 30%—of the comments are negative, escalate the feedback to support management.
Text to Date Transformation: Convert the Column Type: When you load data into a dataset, CRM Analytics tags a date column as a text (dimension) if it contains unexpected string values or dates in different formats. To use date functionality, such as grouping by month, use the Text To Date transformation to change the Text column type to Date.
Text to Number Transformation: Convert the Column Type: When you load data into a dataset, CRM Analytics tags a number (measure) column as text (a dimension) if it contains unexpected strings or characters. To perform a calculation on the column, such as calculating the average, use the Text To Number transformation to change the Text column type to Number. You can perform calculations on number columns only.
Drop Columns Transformation: Drop Columns from the Recipe: In CRM Analytics, drop unwanted columns from a Data Prep recipe. For example, you can add a Drop Columns transformation after a Formula transformation to drop input columns used for a calculated column.
Edit Attributes Transformation: Change the Column Names and Value Formats: In CRM Analytics, to make column names more descriptive and apply consistent formats to column values, use the Edit Attributes transformation in a Data Prep recipe. You can set the labels and API names of all columns. You can also set the precision and scale for number columns, character length for text columns, and date formats for date columns.
Discovery Predict Transformation: Get Einstein Discovery Predictions: In CRM Analytics, use the Discovery Predict transformation to populate your datasets with predictive and prescriptive intelligence. When you run a Data Prep recipe with a Discovery Predict node, Einstein calculates and saves predicted outcomes on a row-by-row basis. You can optionally store descriptions of top predictors and improvements. With the Discovery Predict node, you can quickly evaluate predictions across a large set of data, assess multiple models before deploying them into production, and aggregate this information in a dashboard.
Flatten Transformation: Flatten Hierarchies: In CRM Analytics, the Flatten transformation flattens hierarchical data. For example, you can flatten the Salesforce role hierarchy to implement row-level security on a dataset based on the role hierarchy.
Format Dates Transformation: Standardize the Date Format in a Column: In CRM Analytics, if a text column contains dates in different formats, use the Format Dates recipe transformation in a Data Prep recipe to standardize the format for all dates in the column. A consistent format enables you to correctly filter and group records by date, including filtering by date component, such as month. It also ensures you can successfully convert the column type from text to date. Optionally, you can convert the column type from Text to Date.
Formula Transformation: Create a Calculated Column Based on an Expression: In CRM Analytics, create a column in a Data Prep recipe that displays values based on a formula calculation. The calculation can include input from other fields in the same row or across rows. For example, you can create a Profit column based on input from Revenue and Cost columns. Enter formulas in EA-SQL format. EA-SQL is a collection of standard and custom functions for numeric, string, and date data.
Number to Text Transformation: Convert the Column Type: When you load data into a dataset, CRM Analytics tags a text (dimension) column as a number (measure) column if it contains only numbers. For example, CRM Analytics tags a numerical Room Number column as the number type, even though it’s text that identifies rooms. To group and filter by a number column, use the Number To Text transformation to change a Number column type to Text. You can group and filter by text columns only.
Predict Missing Values Transformation: Fill In Missing Values: In CRM Analytic, use the Predict Missing Values transformation in a Data Prep recipe to complete your data by filling in missing values in a text column. CRM Analytics Intelligently predicts values based on values in other strongly correlated columns in your data.
Upgrade or Edit Formulas for Converted Recipes: You see a different formula editor if your recipe was converted from Data Prep Classic to Data Prep and your formulas couldn’t automatically upgrade. You’re still able to edit and use these formulas, but to get the most formula options through EA-SQL and best experience, recreate the formula in the Data Prep recipe editor.
Split Transformation: Break Up Column Values: In CRM Analytic, you can split the strings in a text column into two values by specifying a delimiter. To split column values into more than two parts, add multiple instances of the Split transformation. For instance, you can use 3 Split transformations in a Data Prep recipe to split the full address into the following components: street address, city, state, and zip code.
Time Series Forecasting Transformation: Forecast Measures: In CRM Analytic, make decisions today based on forecasts about tomorrow with time series forecasting. A time series forecast takes an ordered series of points and intelligently forecasts the next values. For example, estimate units sold for the next 4 quarters based on the last 5 years of sales. Use the Time Series Forecasting transformation in a Data Prep recipe to run forecasts based on historical data and seasonality.
Transformations for Data Prep Recipes: In CRM Analytics, Data Prep provides transformations that allow you to prepare, clean, and transform your data. For example, you can use a transformation to create a calculated column based on a formula. You add transformations inside a Transform node. You can string together multiple transformations to manipulate data sequentially.
Update Node: Swap Column Values: In CRM Analytics, you can swap column values with data from another data source when key pairs match with the Update node. For example, to update selected account names after a series of recent acquisitions and mergers, replace the name from an uploaded spreadsheet based on matching account IDs. Processing time is minimized by only changing column values with matching key pairs, instead of every value in the column.
Restore a Previous Version of a Recipe: Test new ideas on recipes in CRM Analytics. If you break a recipe, you can revert to a previous version. For recipes with multiple collaborators, each version displays the name of the contributor who made an update.
Window Functions for Formulas: In CRM Analytics, use window functions in a Data Prep recipe to perform calculations across rows. For each row of input data, perform a calculation on a selection—or window—of related rows. Unlike aggregation that groups rows, window functions return a result for each row. For example, you can calculate the changes to the opportunity amount throughout the stages of each opportunity to see if you can hit your sales target.
sfdcRegister Transformation: The sfdcRegister transformation registers a dataset to make it available for queries. Users can’t view or run queries against an unregistered dataset.
sfdcRegister Parameters: When you define an sfdcRegister transformation, you set the action attribute to sfdcRegister and specify the parameters.
Pre-Pull Data for Recipes with Data Sync: Use data sync to pull data to CRM Analytics on a regular schedule. By scheduling sync from Salesforce and remote systems ahead of time, your recipes have less to do and run faster. To lighten the load even more, CRM Analytics can sync supported local Salesforce data incrementally by default, meaning that only data that’s changed gets synced.
Considerations When Using CRM Analytics Templated Apps with Data Sync: If you use template-based CRM Analytics apps, such as Sales Analytics or Service Analytics, here are some things to consider before and after you enable Data Sync. Data Sync is enabled by default if you turned on CRM Analytics after the Winter ’20 release.
Run Data Sync Manually: Run data sync manually the first time to make the data available in CRM Analytics to build recipes. Schedule subsequent syncs to regularly update the data.
Run Data Sync and Recipes to Create and Refresh Datasets: Whether you use local Salesforce data or pull data from an external source, you must set up CRM Analytics to load the data, make it available to CRM Analytics, and keep it up to date.
sfdcDigest Transformation: phThe sfdcDigest transformation generates a dataset based on data that it extracts from a Salesforce object. You specify the Salesforce object and fields from which to extract data. You can choose to exclude particular fields that contain sensitive information or that aren’t relevant for analysis.
Salesforce Big Object Support in CRM Analytics: The sfdcDigest transformation can extract data from standard and custom big objects in Salesforce, with or without data sync enabled. Consider these limitations for big objects before you configure extraction.
Advanced Filter in sfdcDigest Transformation: You define an advanced filter using a Salesforce Object Query Language (SOQL) WHERE clause expression. Use an advanced filter only if you are familiar with SOQL.
Filtering Records Extracted from a Salesforce Object: Add a filter to the sfdcDigest Transformation transformation to extract a subset of all records from a Salesforce object. You can filter records to reduce the number of extracted and processed records, exclude records that contain irrelevant or sensitive data, and increase dataflow performance.
sfdcDigest Parameters: When you define an sfdcDigest transformation, you set the action attribute to sfdcDigest and specify the parameters for the object and fields that you want to extract. Optionally, you can also specify parameters to filter the records extracted from the Salesforce object.
Overriding Salesforce Field Metadata: You can override the field metadata that the sfdcDigest transformation extracts from a Salesforce object to make the data appear differently in a dataset. For example, CRM Analytics can add a default value to records that have missing values for a field.
Unsupported Salesforce Objects and Fields in CRM Analytics: The sfdcDigest transformation and data sync can’t extract data from all Salesforce objects and fields. Before configuring extraction and sync, review the unsupported objects and fields.
computeExpression Transformation: The computeExpression transformation enables you to add derived fields to a dataset. The values for derived fields aren’t extracted from the input data source. Instead, CRM Analytics generates the values using a SAQL expression, which can be based on one or more fields from the input data or other derived fields. For example, you can use an expression to assign a value to a field, concatenate text fields, or perform mathematical calculations on numeric fields.
computeExpression Parameters: When you define a computeExpression transformation, you set the action attribute to computeExpression. You also specify the parameters for the input source and the expression used to generate the values.
Schedule Data Sync to Run Automatically: To ensure that the latest source data is loaded into datasets, schedule data syncs to pull data into CRM Analytics before the corresponding recipes. You schedule data sync for each connection, where all objects under the connection sync at the specified time, and not individual objects. To sync objects from the same data source on different intervals, create multiple connections to the data source, and set a unique schedule for each connection.
Schedule, Run, and Monitor Data Sync: In CRM Analytics, you can schedule sync to run automatically, manually run a sync, and monitor a sync's progress, all in the data manager.
sliceDataset Transformation: The sliceDataset transformation removes fields from a dataset in your dataflow, leaving you with a subset of fields for use in a new dataset or in other transformations. This allows you to create multiple datasets, each with different sets of fields from a single dataset.
sliceDataset Parameters: Add a sliceDataset transformation to a dataflow either in the dataflow editor, or directly in the JSON dataflow definition file. The parameters you specify are the same for both methods.
System Columns in Datasets: System columns are dataset columns that don’t appear in the CRM Analytics user interface, such as lenses or Dashboard Designer. For example, to enable dataset security based on the Salesforce role hierarchy, the Flatten transformation generates columns that store the role hierarchy details. To prevent a CRM Analytics user from seeing and querying these columns, flag them as system columns.
Monitor an External Data Upload: When you upload an external data file, CRM Analytics kicks off a job that uploads the data into the specified dataset. You can use the Monitor tab of the data manager to monitor and troubleshoot the upload job.
Handle Text Values: Confirm that text values in a column are uniform in formatting, spelling, and language. Inconsistencies can occur within data sources and after merging data from multiple data sources.
Handle Custom Time Zone Values: Time zone support lets you view time-specific data on dashboards in a time zone that you specify for your org. By default, CRM Analytics datasets aren’t time-zone aware, so CRM Analytics treats all date-time values as being in GMT. The data you see on your dashboards is in GMT, regardless of your local time zone. When you enable time zone support, CRM Analytics converts date-time values in your datasets to the time zone selected for CRM Analytics. You can then create time zone enabled dashboards to display these converted date-time values. Users see dashboard data in the single custom time zone you set, not their personal timezone specified in Salesforce. Recipe formulas and filters with date-time fields operate in GMT.
Transformations for CRM Analytics Dataflows: A transformation refers to the manipulation of data. You can add transformations to a dataflow to extract data from Salesforce objects or datasets, transform datasets that contain Salesforce or external data, and register datasets.
Detect Sentiment Model Card: In CRM Analytics, the Detect Sentiment transformation uses the model to determine whether the sentiment of text is positive, negative, or neutral. Use this model card to better understand the model, how it’s trained, its capabilities, its intended use, and its limitations.
Overriding Metadata Generated by a Transformation: Optionally, you can override the metadata that is generated by a transformation. You can override object and field attributes. For example, you can change a field name that is extracted from a Salesforce object so that it appears differently in the dataset. To override the metadata, add the overrides to the Schema section of the transformation in the dataflow definition file.
Why are my data sync and recipe runs getting canceled?: Tightly scheduled recipe or data sync jobs can result in canceled data sync and recipe runs because too many runs are queued. You can usually avoid this situation by extending the time between a recipe’s or data sync’s scheduled runs.
Why does my dataflow run after a data sync failure?: When a data sync job fails for an object that is used by a downstream dataflow, the system is designed so that the dataflow execution is deferred until the failed object is successfully reprocessed. This behavior ensures that dataflows work with up-to-date and complete data, and is consistent in the vast majority of cases.
Why is my scheduled recipe not running as expected?: When recipes use scheduling, users can see unexpected results or sometimes no results at all. Here are some common reasons why your recipe might be having issues.
Troubleshoot an Unsuccessful Salesforce Output Connector Run: The CRM Analytics Salesforce Output Connector pushes data from CRM Analytics into a Salesforce org as a Bulk API job. If a portion of the data push fails, use Salesforce Setup and Postman or cURL to troubleshoot.
Why was a recipe unscheduled?: When a user is deactivated, the recipes in which they made the most recent modification are unscheduled to prevent unnecessary runs.
Understand What Happens When You Enable Data Sync and Connections: Data Sync is enabled by default if you turned on CRM Analytics after the Winter ’20 release. If you turned on CRM Analytics before the Winter ’20 release, you can manually enable Data Sync. When you enable the Data Sync and CRM Analytics Connections setting, CRM Analytics looks at the sfdcDigest nodes in your existing scheduled dataflows to see which Salesforce objects and fields you’re currently extracting. Using this information, CRM Analytics enables sync for each object to extract the data separately. Understanding exactly what happens when you enable Data Sync helps you identify other actions to consider to ensure that your sync runs successfully.
update Parameters: When you define an update transformation, you set the action attribute to update and specify the parameters.
update Transformation: The update Transformation updates the specified field values in an existing dataset based on data from another dataset, which we call the lookup dataset. The transformation looks up the new values from corresponding fields in the lookup dataset. The transformation stores the results in a new dataset.
Create Datasets Programmatically from CSV Files Using the External Data API: You can use the External Data API to create a single dataset based on external data in a .csv file. You can also use the API to edit the dataset by uploading a new .csv file. When you edit the dataset, you can choose to overwrite all records, append records, update records, or delete records.
Create Datasets from Uploaded CSV Files: Use the upload user interface to create a single dataset based on external .csv data. To refresh the data, you can overwrite the data in the dataset by uploading a new .csv file.
Should I Use Data Prep Classic or Data Prep?: CRM Analytics provides two versions of Data Prep: Data Prep Classic and Data Prep. Data Prep is the latest version that replaces the previous version, Data Prep Classic. Recipes built in Data Prep can’t be opened in Data Prep Classic. When you open a Data Prep Classic recipe, it is upgraded to and opens in Data Prep. As of the Winter ’21 release, you can no longer create recipes in Data Prep Classic.
Learning Resources: In addition to these Help pages, CRM Analytics has a variety of in-app assistance, and there’s more educational content on Trailhead and other websites.
Learn CRM Analytics with In-App Examples: The CRM Analytics Learning Adventure app walks you through best practice examples for designing just the right visualizations and for building powerful dynamic apps. Hands-on examples are presented in dashboards that show you how to build dashboards. Because it's an app, you can look under the hood and see the JSON used to build each dashboard.
Learn Best Practices from the Experts: Apply best practices throughout your CRM Analytics journey. Learn from Salesforce experts as they tackle topics from project planning to performance to dashboard design best practices.
Get Started, Educate Yourself, and Stay Informed in the Learning Center: The Learning Center is the home of assistance resources in the CRM Analytics Analytics Studio. With getting started experiences and continuing educational content, there’s learning for all users, anywhere along their analytics journey.
Take Tips from Trailblazers: The community of CRM Analytics power users grows daily, and they love to share their knowledge. Check out this treasure trove of blogs and channels by Trailblazers across the community. Thank you, Trailblazers!
Learn CRM Analytics Through Video: Sometimes it’s easier to understand how things work when you see them in action. Check out these video playlists for all areas of CRM Analytics. Video playlists are available in Help Menus, and most videos are also available from in-app video icons in context throughout CRM Analytics.
Watch CRM Analytics Learning Days Webinars: Learn directly from expert trainers. The Learning Days webinars cover a range of topics on CRM Analytics and Einstein Discovery, with deep dives into use cases, common questions, and best practices to inspire and guide you to create great analytics implementations.
CRM Analytics MCP Server (Beta): Connect analytics apps, datasets, and dashboards to MCP-compliant AI clients so you can query governed CRM Analytics data in natural language.
Enable CRM Analytics MCP Server (Beta): Create an external client app (ECA), enable CRM Analytics MCP server in Setup, and connect your MCP client to query CRM Analytics data through AI assistants.
CRM Analytics MCP Server Tool Reference: Read-only tools that let MCP-compliant AI clients discover apps, inspect schemas, run SAQL queries, and read dashboard and lens metadata.
Migrate CRM Analytics Queries to Data 360 SQL (Beta): Data 360 SQL provides improved data handling, larger result sizes, and better scalability. New compact form queries and custom SQL queries now run on Data 360 SQL by default. Existing custom SQL queries continue to use legacy SQL, and can be switched manually to Data 360 SQL for improved performance.
CRM Analytics Mobile Beta Testing: Join the CRM Analytics for iOS and Android beta program to help make the CRM Analytics app better than ever.
Notifications: Create and manage notifications right from your device. Receive push notifications to stay informed when your business reaches a milestone or experiences critical changes.
Access Dashboards on a Lightning Page: Add your dashboard to a Lightning page, along with other relevant components, so your users can access it from outside the Salesforce record context.
Access Dashboards on Record Layouts: Embed a dashboard on a record layout when you want to show an important insight every time your users open a record.
Add Your Data: Import your raw data files, such as CSV files, onto your local device. Each imported data file becomes a dataset.
Download CRM Analytics for Mobile: Click the link to download CRM Analytics in the Salesforce app, or CRM Analytics for iOS and Android.
Which Analytics Mobile Experience Do I Use?: CRM Analytics gives you multiple ways to get insights on the go: CRM Analytics in the Salesforce app and CRM Analytics for iOS and Android. Find out which mobile experience is right for you.
Explore Your Data: See what unexpected insights you can surface by interactively exploring and visualizing your data.
Filter Your Data: Apply global filters in a dashboard to focus on the subset of data that’s most relevant for your business unit, project, or management team.
Change the Chart Type: Switch between chart types to see which visualization options tell the most compelling story for your data.
Change Measures: Your choice of measure defines your entire visualization and determines the overall scope of your exploration.
Change the Sort Order: Choose whether data in your chart visualization is sorted ascending, sorted descending, or unsorted.
Return to the Initial View: Exploring your data can lead you down many paths. Not all of them are fruitful, and that’s OK. To go back to the dashboard’s original state, or just see those nifty animations again, you can use the reset option to quickly return to the initial view.
View Your Data in a Lens: When you look at data in an exploratory mode, you do so in a lens. A lens has several aspects: it’s how you view data in a dataset, it’s the place where you explore the data graphically, and it’s the basis for building any dashboard.
CRM Analytics Mobile Features: CRM Analytics is available as a downloadable app on iOS and Android devices. This table details supported CRM Analytics features in the mobile apps.
Learn Your Way Around CRM Analytics for Android: Explore your data in Android using the toolbar and a few simple gestures to create meaningful visualizations, and and analyze your data in new ways.
Access Insights from the Analytics Mobile App: Analytics on mobile gives you a fast, fluid way to discover the compelling insights within your data and to share the right visuals wherever you are. This page has resources for Analytics in the Salesforce app and Analytics for iOS and Android.
Set Notifications: Create and set notifications right from your device. Receive push notifications to stay informed when your business reaches a milestone or experiences critical changes.
CRM Analytics Settings: CRM Analytics settings include a preference setting for event logs, Developer Mode, and links to app information.
Switch between Apps: Change apps to explore a different collection of lenses and dashboard.
Track and Manage Notifications: Track and manage notifications right from your device. Stay informed when your business reaches a milestone or experiences critical changes.
Understanding Data Exploration and Best Practices: Before diving into your data, familiarize yourself with useful tips, learn about different kinds of data collections, and review important data exploration concepts.
View Data in a Dashboard: A dashboard tells you a specific, focused story about data by using a curated set of lenses and widgets. For example, a dashboard can effectively summarize your company’s support cases.
Monitor and Troubleshoot Templated App Installations: Click an app name to see the entire install history for the app, cancel running installations, and view app logs and state. The app logs and state provide details for each installation and each task. Use this information for troubleshooting why an installation or task wasn’t successful or when an installation was canceled.
Set and Send Smart Notifications: Set up CRM Analytics to notify you about changes to your important business metrics, when and where you choose. Attach notification criteria to widgets in dashboards, and select when to run the queries. You can see notifications in CRM Analytics, Lightning Experience, CRM Analytics for iOS, and email. Add recipients from your org to the email’s distribution list to notify colleagues at the same time as yourself.
Handle Numeric Values: CRM Analytics internally stores numeric values in datasets as long values. For example, CRM Analytics stores the number 3,200.99 with a scale of 2 as 320099. The user interface converts the stored value back to decimal notation to display the number as 3200.99.
Customize Onboarding with In-Dashboard Instructional Content: Drive adoption and engagement with specialized educational resources right where users work. With the widget-specific Learn option, you can provide videos and webpages that help users get the most out of each dashboard and its charts.
CRM Analytics Packaging Considerations: Keep these considerations in mind when using packages to distribute CRM Analytics to other Salesforce users and organizations.
Distribute CRM Analytics Assets Through AppExchange: Once your managed package is certified through our security review, you can make it available to your customers by uploading it to AppExchange. You can specify your package release type, control major and minor version numbers, and specify licensing options through the Licensing Management Application.
Distribute Analytics Assets: Use change sets, bundle assets together in managed packages, and distribute and track packages through App Exchanges and the License Management App. Also, use the Metadata API to manage customizations for your org. For Analytics apps, templates are the preferred way to distribute your work.
Migrate CRM Analytics Assets with Change Sets: Use change sets to move customized CRM Analytics assets between orgs that have a deployment connection. For example, create a CRM Analytics app containing dashboards, lenses, datasets, dataflows in your Sandbox org, then migrate the app assets to your production org once testing is complete.
Package CRM Analytics Assets in Managed Packages: You can create managed packages of CRM Analytics assets, including CRM Analytics apps, dashboards, lenses, datasets, recipes, dataflows, and user XMD. Use packages to distribute those assets to other users within or outside your company.
Post CRM Analytics Dashboards and Lenses to Slack: Facilitate data-driven collaboration by sharing directly from CRM Analytics to your channels in Slack. Slack recipients get a snapshot showing the latest data. Or post a link with quick action buttons.
Publish Tableau Content to CRM Analytics: Easily publish Tableau Cloud and Tableau Server dashboards, sheets, and stories to a CRM Analytics app or Lightning page without first embedding Tableau views or installing additional software.
Answer Ad Hoc Questions with CRM Analytics Lenses: Dashboards are great for answering common questions, but sometimes you don’t have a question in mind. You want to poke around your data and ask questions as you notice things. To explore your data, open a dataset and create ad hoc queries. To revisit the query later, save it as a lens. The lens includes the query and the visualization—how the results appear. Like dashboards, lenses can be shared.
Query Your Data to Know Your Business: It’s great to have a gut feeling about how well your business is doing. However, before you take action, test the feeling against your data. Ask your data questions.
Monitor Your Business Regularly with CRM Analytics Dashboards: CRM Analytics dashboards are populated with your data based on queries that you define. To continually get answers to common questions, save and organize your queries in interactive CRM Analytics dashboards. Each time you open the dashboard, the queries get the latest results based on the available data.
Extend Analytics: REST APIs help you speed up the development of user functionality and create your own solutions outside of Salesforce. Construct API calls to automate updating assets, schedule data syncs, run predictions, and interpret natural language queries into SAQL.
Reuse Results for Data 360 Live Queries: Balance dashboard performance and data freshness by configuring how long to reuse query results. CRM Analytics reuses results when you run the same query with the same data access. This reuse improves dashboard responsiveness and reduces repeated queries. Set how long results remain valid before retrieving fresh data.
Customize Data, Queries, and Dashboards: Access Analytics data and construct advanced queries using either Salesforce Analytics Query Language (SAQL) or SQL. Customize Analytics dashboards by creating and using your own Lightning Web Components. Add interactions to bind dashboard widgets and tailor the extended metadata (XMD) for your datasets.
Embed Analytics in Salesforce: Use the SDK, the Analytics Dashboard Component, and Lightning Web Component (LWC) wire adapters to facilitate access to insights created inside Analytics Studio. Your users can view and interact with data and insights from wherever they normally do their work without having to open Studio.
Character Set Support: CRM Analytics supports UTF-8 characters in dataset column names and values in a predicate expression.
Add Row-Level Security with a Security Predicate: Applying a predicate to a dataset is more than just defining the predicate expression. You must also consider how the predicate depends on the information in the dataset and where to define the predicate expression.
Values in a Predicate Expression: The value in the predicate expression can be a string literal or number literal. It can also be a field value from the User object in Salesforce.
Set Up Dataset Security to Control Access to Rows: If a CRM Analytics user has access to a dataset, the user has access to all records in the dataset by default. To restrict access to records, you can implement row-level security on a dataset when you use sharing inheritance and security predicates. Sharing inheritance automatically applies a Salesforce object’s sharing logic to the dataset’s rows. A security predicate is a manually assigned filter condition that defines dataset row access.
Add Row-Level Security by Inheriting Sharing Rules: Use sharing inheritance to let CRM Analytics apply the same sharing setup for your datasets as Salesforce uses for your objects. Sharing inheritance increases access accuracy and reduces the need for complicated security predicates for most objects and situations. The tradeoff for applying sharing inheritance is an increase in the time to complete data syncs, dataflow and recipe jobs, and queries. The more complicated the sharing settings, the more impact there is.
Determine If Sharing Inheritance Will Work for You: To understand how well sharing inheritance can work for your dataset row-level sharing needs, review these supported objects, maximum number of sharing descriptors, and considerations. Use the Sharing Inheritance Coverage Assessment tool to evaluate and understand your object and user eligibility.
Security Metadata Drift: The data you use in CRM Analytics can come from Salesforce objects and fields. A dataflow job runs, and then you can analyze the resulting dataset. In an ideal world, each object in your dataset would stay in perfect sync with its source object. In the real world, the correctness of an object is only as good as the last update. The longer the time between updates, the greater the likelihood of drift. The security metadata (predicates and descriptors) of a Salesforce object is subject to the same risk of drift.
Enable Sharing Inheritance: Turn on sharing inheritance and select the objects to use as a sharing source. Sharing inheritance is on by default in new Salesforce orgs.
CRM Analytics Security Implementation Guide: CRM Analytics has different levels of security that your organization can implement to ensure that the right user has access to the right data.
Row-Level Security Example based on Record Ownership: Let’s look at an example where you create a dataset based on a CSV file and then implement row-level security based on record ownership. In this example, you will create a dataset that contains sales targets for account owners. To restrict access on each record in the dataset, you will create a security policy where each user can view only sales targets for accounts that they own. This process requires multiple steps that are described in the sections that follow.
Add the Predicate to the Metadata File: For a dataset created from a CSV file, you can specify the predicate in the metadata file associated with the CSV file or when you edit the dataset.
Create the Dataset: Now that you updated the metadata file with the predicate, you can create the dataset.
Determine Which Data to Include in the Dataset: First, determine what data you want to include in the dataset. For this example, you will create a Targets dataset that contains all sales targets.
Row-Level Security Example based on Role Hierarchy and Record Ownership: Let’s look at an example where you create a dataset based on Salesforce data and then implement row-level security based on the Salesforce role hierarchy and record ownership. In this example, you will create a dataset that contains all opportunities. To restrict access on each record in the dataset, you will create a security policy where each user can view only opportunities that they own or that are owned by their subordinates based on the Salesforce role hierarchy. This process requires multiple steps that are described in the sections that follow.
Create the Dataset: Now that you have the final dataflow definition file, you can create the dataset.
Design the Dataflow to Load the Data: Now it’s time to figure out how the dataflow will extract the data and load it into a dataset. You start by creating this high-level design for the dataflow.
Determine Which Data to Include in the Dataset: First, determine what data you want to include in the dataset. For this example, you will create the OppRoles dataset that contains all opportunities as well as user details about each opportunity owner, such as their full name, division, and title.
Row-Level Security Example based on Opportunity Teams: Let’s look at an example where you create a dataset based on Salesforce data and then implement row-level security based on an opportunity team. In this example, you will create a dataset that contains only opportunities associated with an opportunity team. To restrict access on each record in the dataset, you will create a security policy where only opportunity members can view their opportunity. This process requires multiple steps that are described in the sections that follow.
Create the Dataset: Now that you have the final dataflow definition file, you can create the dataset.
Design the Dataflow to Load the Data: Now it’s time to figure out how the dataflow will extract the Salesforce data and load it into a dataset. You start by creating this high-level design for the dataflow.
Determine Which Data to Include in the Dataset: First, determine what data you want to include in the dataset. For this example, you will create an OppTeamMember dataset that contains only opportunities associated with an opportunity team.
Row-Level Security Example based on Territory Hierarchy and Record Ownership: If your territory hierarchy is specified with Territory Hierarchy 2.0, you can create a dataset to implement row-level security based on the territory hierarchy and record ownership. This example shows how you do this with a dataset for opportunities.
Salesforce Data Access in CRM Analytics: CRM Analytics requires access to Salesforce data when extracting the data and also when the data is used as part of row-level security. CRM Analytics gains access to Salesforce data based on permissions of two internal CRM Analytics users: Integration User and Security User.
Control Access to Salesforce Objects and Fields: CRM Analytics requires access to Salesforce data when extracting the data and also when the data is used as part of row-level security. Configure the permissions of the Integration User on Salesforce objects and fields to control the dataflow’s access to Salesforce data. Configure the permissions of the Security User to enable row-level security based on custom fields of the User object.
Assign CRM Analytics Permission Sets to Users: Assign custom permission sets to one or more users—either one at a time, or in bulk—to give them access to CRM Analytics functionality.
Connected App for CRM Analytics on Mobile: Install the CRM Analytics App Package to allow your mobile clients to easily connect to your org. This package contains the Connected App component that gives you control over who’s logging in and how your mobile clients share images and links.
Connected App for Wave Web: Use the Wave Web App Package when you want to manage how web clients like mobile browsers and apps display CRM Analytics content. This package contains the Wave Web Connected App component, which gives you access to and control of the CRM Analytics REST API endpoints that the app uses.
Control Which Analytics Tab Users Access the Analytics Studio: Specify who can create apps and edit and delete datasets and dashboards by making Analytics Studio invisible to Analytics Tab users on a per profile basis. When you remove Analytics Studio visibility from a profile, Analytics tab users with the profile don’t see Analytics Studio in the App Launcher. They also don’t have the Open in Analytics Studio action in asset menus and dashboard headers.
Mark Downloaded Images and Exported Data as Confidential: Meet data compliance and regulatory requirements with the new confidentiality notice. Remind coworkers of data confidentiality with a notice that appears in the Download window and on all downloaded images and exported data.
Enable Downloading Data from CRM Analytics: Enable users to download the results from lens explorations and dashboard widgets as Microsoft® Excel® (.xls) or comma-separated values (.csv) files. Enable users to subscribe to tables and receive email with the table data in attached .csv files.
Enable Color Contrast: Add a clear distinction between adjacent chart segments or between chart segments and the dashboard background.
Enable Dashboard Views: Enable this feature to allow each CRM Analytics user to create their own unique views of a dashboard. By default, this feature is enabled for your org. If the feature is disabled and you re-enable it, all previously created views are available.
Enable Unified Analytics: Enable this feature to unify your reports and dashboards and CRM Analytics items on the Analytics tab in Salesforce and Analytics Studio.
Enable CRM Analytics Watchlist: Enable this feature to capture and track up to 20 KPIs across different dashboards, plus historical trending. No more switching between dashboards to see what has changed, and track your metrics all in one place.
Identify CRM Analytics Platform User Types: Identifying types of CRM Analytics platform users helps assure that custom permission sets meet your team’s analytics needs.
Enable CRM Analytics App Push Notifications: Push notifications let your users know when their data conditions have been met. For example, push notifications let users know when a number goal was reached or when it dipped below a specified amount.
Open Salesforce Records from CRM Analytics dashboards in New Browser Tabs: By default, selecting a Salesforce record action in a CRM Analytics dashboard opens the record in a new Lightning Experience tab. To change the default behavior, so that selecting an action opens the record in a new browser tab, enable this setting. The setting applies to dashboards in the CRM Analytics tab and dashboards embedded in Salesforce pages.
Set Up the Analytics Platform with Licenses Purchased Before October 20, 2015: If you purchased an Analytics Cloud Builder or Analytics Cloud Explorer license before October 20, 2015, you can continue to use your licenses to set up the Analytics platform for users in your organization or you can migrate to the new single-user Analytics Growth license.
Enable Previews for All Assets: Make it easier for users to browse analytics assets in collections. Replace generic icons with preview thumbnails of your CRM Analytics contents and Lightning reports and dashboards, including those that have row-level security restrictions.
Disable Input Caching for Recipes: When you first run a recipe, Data Prep caches the input data, storing a copy of the source data in cached files, by default. To save time during subsequent recipe runs, Data Prep uses the cached data instead of extracting the same data again. To use input caching, the recipe must extract data from all fields in the source.
Enable the CRM Analytics REST API: You can access CRM Analytics datasets and lenses programmatically using the CRM Analytics REST API. Selecting this option overrides the "APIEnabled" permission for individual users and gives all CRM Analytics users (including Experience Cloud sites users) access to the API.
Secure Image Sharing and Downloading: Control who shares dashboard and lens images to prevent users or profiles from sharing sensitive images. By default, all users can share images of lenses, dashboard widgets, and whole dashboards that contain your org’s data.
Enable Custom Time Zones: Time zone support lets you view time-specific data on dashboards in a time zone that you specify for your org. By default, Analytics datasets are in GMT. When you enable time zone support, Analytics converts date-time values in your datasets to the time zone selected. You can then create time zone enabled dashboards to display these converted date-time values.
What Happens When You Enable Time Zone Support: This table summarizes what happens when you enable time zone support, the actions you must take to update assets or stop using the feature.
Learn about CRM Analytics Permission Set Licenses and User Permissions: When you create your own CRM Analytics permission sets, you start by selecting the CRM Analytics Growth permission set license instead of using the prebuilt permission sets. The permission set license includes the user permissions required to explore data with and manage the CRM Analytics platform.
Considerations When Converting Query Types: CRM Analytics treats query types differently. Before you convert an aggregate or aggregateflex query type to saql, review the following implications.
Get Scheduled Updates with Email Subscriptions: Subscribe to lenses and dashboard widgets and receive a daily or weekly email with updates on your most important charts and metrics, and table data in .csv attachments.
Subscribe to Multiple Shared Collections: Subscribe to collections of lenses and receive a daily or weekly email with updates on your most important charts and metrics, and table data in .csv attachments. Subscribe to as many as four collections that you created or that were shared with you. Each collection subscription has its own schedule for when you receive CRM Analytics email.
Subscribe to CRM Analytics Dashboard Widgets in Experience Cloud Sites (Beta): Subscribe to dashboard widgets embedded in your Experience Cloud site to receive regular updates via email. Each subscription captures the state of the asset with any filters or selections that you applied, ensuring that you see the metrics most relevant to you.
Tableau Views in CRM Analytics: Gain insights on Tableau Cloud and Tableau Server data from the comfort of CRM Analytics Home. Easily publish Tableau Cloud and Tableau Server dashboards, sheets, and stories to a CRM Analytics app or Lightning page without first embedding Tableau views or installing additional software. You can seamlessly combine Tableau Cloud and Tableau Server views with CRM Analytics content in a single dashboard, apply Tableau filters to it, and share it with colleagues who need to stay on top of the same data. To access the published Tableau content more easily, favorite the dashboard in Analytics Home.
Prerequisites for Tableau Views in CRM Analytics: Publishing Tableau Cloud and Tableau Server content to CRM Analytics and viewing it requires the appropriate licenses, org and user account setup, and permissions assigned to user accounts and apps.
Explore Data with Tables: You can use tables to get a view of the data that is close to the underlying dataset, and you can use tables to manipulate and extend the data to expose fresh insights. With values, compare, and pivot tables, CRM Analytics gives you options for both of those goals.
Before You Begin: Before you create an app from the Analytics for Communications - Business template note these requirements
The Einstein Accuracy Analytics Template: The Einstein Accuracy Analytics app helps you monitor how well Einstein Discovery models predict actual outcomes over time.
Adoption Analytics Template: Get insight into how your team uses CRM Analytics apps, dashboards, lenses, and datasets. Use the Adoption Analytics template to create an app with datasets and dashboards to track the use of your CRM Analytics assets.
Set Up Your Org for the Adoption Analytics App: Before creating an app from the Adoption Analytics template, configure your org to collect the necessary data. The app collects logs that track how often users open CRM Analytics apps, dashboards, and lenses. If you have the CRM Analytics Plus or CRM Analytics Growth license, your app includes Analytics log data. Your app includes Event Monitoring bundle data if you also have the Event Monitoring license.
Schedule the Data Refresh for the Adoption Analytics App: The Adoption Analytics template creates a dataset called WaveChange, which collects the logs that track how often users open CRM Analytics apps, dashboards, and lenses. To keep the data in your app up to date, schedule the Adoption Analytics app to refresh daily.
Advanced Account Forecasting Analytics for Manufacturing: The Advanced Account Forecasting Analytics for Manufacturing app provides insights into your forecast data and develop accurate forecasts to optimize your production and operations.
Customer Lifecycle Analytics: Customer Lifecycle Analytics app provides actionable insights into your customers and answer key questions about your business.
Employee Analytics: Employee Analytics app provides insights to analyze workforce data and identify opportunities for improving employee policies.
Education Analytics: The Education Analytics app provides insights into recruitment, admissions, learner engagement, and student success that you can use to improve processes and analyze learner metrics.
Analytics for Emergency Response Management: Analytics for Emergency Response Management app provides insights to identify the hotspots in which requests for emergency intakes are increasing and track intake persons needing attention
Enhanced Analytics for Healthcare: The Enhanced Analytics for Healthcare app provides a complete solution for streamlining the prior authorization review workflow.
Insurance Claims and Policy Analytics: Insurance Claims and Policy Analytics app provides actionable insights to effectively manage your claims, quotes, and policies.
FSC Insurance Analytics Fast Start: The FSC Insurance Analytics Fast Start app provides insights to effectively monitor the growth of your policies over time and to identify cross-selling opportunities within the existing customer base to improve business revenue.
CRM Analytics Prebuilt Apps: CRM Analytics prebuilt apps are ready-to-use applications that offer advanced analytics and reporting capabilities specifically tailored for sales and customer relationship management. The apps provide preconfigured dashboards, reports, and data visualizations to analyze business data.
Requirements for Analytics for Subscriber Lifecycle Management Data: Before you create Analytics for the Subscriber Lifecycle Management app, ensure that your data meets these specific requirements. Otherwise, the data fails the CRM Analytics check and you see an error message.
Analytics for Subscriber Lifecycle Management: The Analytics for Subscriber Lifecycle Management app provides a detailed view of various subscriber trends that affect revenue, churn, and customer lifetime value. By using the data-driven dashboards, Marketing Managers can quickly track overall subscriber trends, analyze the trends' effects on revenue, and respond with better products and marketing strategies.
Deploy Analytics for Subscriber Lifecycle Management: Before you create an app from the Analytics for Subscriber Lifecycle Management template, deploy Analytics for Subscriber Lifecycle Management in Salesforce.
Use Analytics for Subscriber Lifecycle Management Dashboards: By using the Subscriber Lifecycle Management Analytics dashboards, Marketing Managers can track overall subscriber trends, analyze the trends' effects on revenue, and respond with better products and marketing strategies. Service Agents can view the resolution status of their cases and monitor the improvement of case-resolution KPIs and Customer Lifetime Value (CLTV) over time.
Trade Promotion Effectiveness: Trade Promotion Effectiveness app provides data-driven dashboards to analyze trade promotion data, evaluate the effectiveness of a promotion, and make decisions for future promotions.
Evaluate Your Scheduling Efficiency with the Appointment Analytics App: Learn about your team’s appointment volume and your reps’ performance to see how well your service team is applying their resources to meet customer needs. The Appointment Analytics template guides you through creating the app.
Increase Approval Process Visibility with the Approval Analytics App: Use the Approval Analytics app to view approval history, understand trends, identify bottlenecks, and act to streamline the process. See the number of approvals and rejections, pending approvals, who’s approved what, and how long approvals take. The Approval Analytics template guides you through creating the app.
Analytics for Automotive: The Analytics for Automotive app provides the dealers and Original Equipment Manufacturers (OEM) insights to track and monitor their dealership-based automotive business.
Get Ecommerce Data Insights with the B2B Commerce Analytics App: Help your team track orders, see who’s buying what products, discover year-to-date and year-over-year sales revenue, and more. The B2B Commerce Analytics template guides you through creating the app.
Evaluate Your Marketing with the Campaign Analytics App: See how your marketing campaigns translate to revenue and impact the bottom line. Compare performance over time, drill into regional top performers, and highlight successful campaigns. Also review channels to see which are most effective. The Campaign Analytics template guides you through creating the app.
Monitor Object Usage with the Change Analytics App: Bring your historical data to life and gain insights into how your team uses an object by visualizing data from any Salesforce object that uses field history. The Change Analytics template guides you through creating the app.
Analytics for Corporate and Investment Banking: The Analytics for Corporate and Investment Banking app is designed to help you understand your Corporate and Investment Banking deals. This app includes details about deal participants, deal products, and deal parties (such as legal teams or other external partners).
Account Insights: The Account Insights dashboard answers questions such as how many deals are being managed in a given account space? What are your customer interactions and profitability? And what meetings are scheduled with deal parties?
Deal Insights: Deal insights help you answer questions such as how is the deal progressing? What milestones have been met? What interactions are planned for the future?
Home Embedded Dashboard: The Home embedded dashboard includes pipeline metrics, call reports (including any interactions between internal or external deal parties), and account insights.
Visualize Sales Data and Opportunities with the Cluster Analysis Account Segmentation App: Use no-code machine learning to visualize sales data, KPIs, and opportunities by cluster. Get insights into your accounts and products, and change the number of clusters to see different account segments. The Cluster Analysis Account Segmentation template guides you through creating the app.
Analytics for Communications-Business: The Analytics for Communications-Business app brings insights to sales processes with three embedded dashboards highlighting opportunities, quotes, and orders. This app allows sales reps to reduce time to close, prioritize accounts and quotes to follow up with and understand the order completion and fulfillment behavior.
Account Insights Embedded Dashboard: The Account Insights embedded dashboard allows you to embed your account information anywhere so that it’s accessible at-a-glance. This dashboard provides information about opportunities, quotes, and orders for an account so you can optimize selling.
Home Embedded Dashboard: The Home Embedded dashboard helps you stay on top of the details of your communications sales processes. The app brings insights to these processes with a comprehensive dashboard that shows details about opportunities, quotes, and orders.
Upload Dataset: Upload the plain_quota Dataset to view relevant metrics in the Home dashboard.
Quote Insights Embedded Dashboard: The Quote Insights dashboard is designed to be embedded wherever you track your quotes. Use it to quickly and easily understand where to put your efforts.
Self-Service Embedded Dashboard: The Self-Service dashboard offers a comprehensive view of account-specific details, such as contracts, quotes, orders, bills, and asset distribution. Integrate the dashboard into the community portal to provide Procurement Managers with a comprehensive view of the entire enterprise. Small and Medium Businesses (SMB) executives can access self-service analytics from the SMB Console for a 360° view of the complete account.
Analytics for Communications - Consumer: The Analytics for Communications - Consumer app gives customer service reps instant insights into accounts to help them gauge customer sentiment, anticipate customer needs, and provide timely solutions.
Analytics for Communications - Consumer Dashboards: Use the Analytics for Communications - Consumer dashboards to get quick insights into key customer account or subscription metrics, provide better service to your customers, and enhance their experience.
Deploy Analytics for Communications - Consumer: Before you create an app from the Analytics for Communications - Consumer template and use the dashboards, deploy Analytics for Communications - Consumer in your Salesforce org.
Enable CRM Analytics: Before you create an app from the Analytics for Communications - Consumer template, enable CRM Analytics in your Salesforce org.
Analytics for Communications - Order Tracking: The Analytics for Communications - Order Tracking app brings insights to track commercial and service orders through various business milestones. Managers can use the Analytics for Communications - Order Tracking dashboards to monitor the order journey, identify orders and tasks that require immediate attention, minimize order fallout risks, and improve SLA adherence.
Use Analytics for Communications - Order Tracking Dashboards: Use the Analytics for Communications - Order Tracking dashboards to monitor the order journey and to identify orders and tasks that require immediate attention. By using these insights, you can develop strategies to minimize order fallout risks and improve SLA adherence.
Requirements for Analytics for Communications - Order Tracking Data: Before you create the Analytics for Communications - Order Tracking app, ensure that your data meets these specific requirements. Otherwise, the data fails the CRM Analytics check and you see an error message.
Quote to Order Process Visualization Embedded Dashboard: The Quote to Order Process Visualization dashboard provides B2B Sales Agents with a comprehensive and clear view of their Quote to Order progress. At each stage of the Quote to Order journey, gain insights into any missing details that are required for task completion, and identify focus areas to streamline the process effectively.
Report Download Dashboard: Use the Report Download dashboard to view and download customer-specific reports for every stage of the Quote to Order process. These reports offer a comprehensive view of the stage's status and highlight any missing or partially valid data, enabling users to quickly correct crucial details, such as addresses.
Complaint Management Dashboard: The Complaints Management dashboard analyzes customer complaints and provides quick insights into complaint volume, trends, and complaints resolution progress. You can analyze complaints based on channels, subtypes, customers, and representatives, to make informed decisions on improving complaint resolution processes.
Complaints Intelligence Dashboard: Use the Complaints Intelligence dashboard to quickly monitor and manage complaints, reduce complaint volume, identify areas for improvement, and make data-driven decisions to enhance complaint management processes.
CRM Analytics for Complaint Management: The CRM Analytics app for Complaint Management gives your customer experience managers and complaint team managers quick insights on customer complaints.
Consumer Goods - Advanced Data Model: The Consumer Goods - Advanced Data Model app provides actionable account-level insights to increase sales, improve account compliance, identify accounts at risk, and maximize sales rep effectiveness.
Analytics for Consumer Goods: Analytics for Consumer Goods gives your sales team actionable insights into your retail business performance. Sales managers can use the app to increase sales, improve store compliance, identify stores at risk, and maximize sales rep effectiveness.
Create a Template: Create a template to package your app for customization and distribution to other organizations. The app you choose to create a template for is called the source app.
Customer Insights: Get a combined analysis of Sales and Service clouds in an easy-to-customize app. Use the component building blocks to create a dashboard for your environment.
Create the Customer Insights App: Guided setup walks you through creating an app from the Customer Insights template. You then schedule the app’s data sync and recipes.
How to Answer CRM Analytics Template Custom Wizard Questions: Some CRM Analytics templates include a custom configuration wizard, which asks you a series of questions about your data. Follow these general guidelines to get the best result. Help for each template with a custom wizard provides details for that template.
Use Revenue Management for Financial Services Component Library: Revenue Management for Financial Services Cloud provides components to build customized dashboards. Components are container elements that contain groups of charts, tables, filters, text, and more in multiple dashboards, making it easy to add a group of objects to your dashboard.
Deploy Analytics for Communications-Business: Before you create an app from the Analytics for Communications - Business template and use the dashboards, deploy Analytics for Communications - Business in your Salesforce org.
Deploy Revenue Management for Financial Services Cloud: Before you create an app from the Revenue Management for Financial Services Cloud template, deploy Revenue Management for Financial Services Cloud in Salesforce.
Analytics Design Toolkit: Use the Analytics Design Toolkit to create an app with ready-made dashboard, charts, and widget templates that you can use to get started fast with your own apps.
Analytics for Energy and Utilities: With the CRM Analytics for Energy and Utilities App, it’s easy to know which opportunities are most likely to generate a sale. You can track how many quotes you’ve created and know whether customers have responded to them. The three embedded dashboards (Home, Quotes, and Account) provide details about opportunities, quotes, and accounts.
Get Sales Predictions with the Einstein Discovery for Sales Analytics App: Identify your most promising accounts—the largest potential opportunities or the ones that you have the greatest chances of winning or closing quickly. The Einstein Discovery for Sales Analytics template guides you through creating the app.
Predict Client Churn Risk for Wealth Management Analytics: Predict Client Churn Risk for Wealth Management Analytics allows financial advisors to intelligently predict customer churn. Using the power of Einstein Discovery, the app prescribes corrective actions to help minimize occurrences of churn.
Predict Likelihood to Add Assets for Wealth Management Analytics: Predict Likelihood to Add Assets for Wealth Management Analytics allows financial advisors to intelligently forecast the likelihood of adding assets to accounts. Using the power of Einstein Discovery, the app prescribes actions on how to increase the chances to grow your account assets.
Wealth Starter Analytics Template: Creating an app from the Wealth Starter Analytics template gives Financial Services Cloud customers a quick way to apply the power of CRM Analytics to data about their book of business.
Explore Fundraising Performance with the Fundraising Analytics App: Analyze the success of your campaigns with visualizations of your donation data from Account, Contact, Opportunity, and other objects of your choice. The app works with the Salesforce Nonprofit Success Pack. The Fundraising Analytics template guides you through creating the app.
Fundraising Performance Analytics App: Measure the impact of fundraising work with a precise picture of donor retention and attrition. Get insights into donation gains and losses and other key performance indicators.
Patient Risk Stratification Template: The Patient Risk Stratification template creates an app that lets healthcare coordinators identify high-risk patients to make sure they get the extra care they need.
The Analytics for Healthcare Template: Use actionable insights from CRM Analytics for Health Cloud apps to drive intelligent patient engagement, improve care effectiveness, and manage patient risk.
Analytics for Insurance: A comprehensive set of CRM Analytics dashboards offers insurance executives and managers insights into their book of business and identifies opportunities to improve sales.
Lead Trending Analytics Template: Create an app from the Lead Trending Analytics Template to increase your visibility into your team’s lead conversion process. Sales operations can get instant insight into how quickly the team converts leads and can identify bottlenecks in the conversion process.
Analytics for Loyalty: By using Analytics for Loyalty app, get actionable insights into loyalty programs and devise strategies to increase membership, retain customers, improve processes, and grow your business.
Managing Your Templates: Existing templates that you or other CRM Analytics admins created are manageable from CRM Analytics Studio. You can update, decouple from source apps, and delete templates. Before they can be deleted, templates must be decoupled from their source app.
The CRM Analytics for Manufacturing Template: The CRM Analytics for Manufacturing lets account managers visualize all aspects of their business to keep them on top of sales agreements, orders, and contracts.
Analytics for Mortgage Template: Analytics for Mortgage enables loan officers and managers to drive increased mortgage sales by helping prioritize the customer leads and mortgage applications to focus on. Process loans faster by highlighting the mortgage applications that have the highest value, applications that have been open the longest, and applications that are missing documents.
Multi Org Sales Analytics Template: Use the Multi Org Sales Analytics template to create a scaled-down version of the Sales Analytics that pulls in data from all your connected orgs.
Net Zero Analytics: Net Zero Analytics enables you to bring your environmental accounting in line with your financial accounting.
Deploy Analytics for Communications - Order Tracking: Deploy Analytics for Communications - Order Tracking by assigning permissions, enabling CRM Analytics, and creating and sharing the analytics app for your users.
Enable CRM Analytics: Enable CRM Analytics in Salesforce before you create an app from the Analytics for Communications - Order Tracking template.
Set Field-Level Security to Analytics Integration User: Before you create the Analytics for Communications - Order Tracking app, make sure that the Analytics Cloud Integration User profile has access to all of the fields that the app uses.
Understand Your Pharmaceutical Sales with the Salesforce Analytics for Veeva App: Salesforce Analytics for Veeva assembles key metrics to give you insight into pharmaceutical reps’ processes and effectiveness. See how reps manage their accounts using key messages, products, and samples. And use Einstein Discovery to understand the impact of sales call activities on account orders. The Salesforce Analytics for Veeva template guides you through creating the app.
Pipeline Analytics Template: Use Pipeline Analytics to plug your pipeline snapshot data into a ready-made waterfall dashboard. You get instant insight into how the pipeline changes between two snapshots.
Get Insights into Deal Pricing with the Pricing Analytics App: Create an app to get instant insights into how your organization prices deals. Connect with Einstein Discovery to get discount recommendations for every quote. The Pricing Analytics template guides you through creating the app.
Public Sector Case Analytics Template: Public Sector Case Analytics lets public sector service organizations measure their effectiveness and identify immediate actions to improve case work performance.
Use Public Sector Case Analytics Prebuilt Dashboard: After you create Public Sector Case Analytics, navigate to the app and use its dashboards to measure your team’s effectiveness and improve services delivery.
Public Sector Case Analytics Wizard Data and Feature Check: At the start of app creation, Public Sector Case Analytics scans your org. It checks to make sure you can create the app and looks for features and data to add to your app.
Monitor Your Configure-Price-Quote Process with the Quoting Analytics App: Create an app to get instant insights into your configure-price-quote process and its effectiveness across teams, regions, and products. View quotes across all your accounts, uncover bottlenecks, and assess reps’ discount practices in ready-made dashboards. The Quoting Analytics template guides you through creating the app.
Consumer Banking Starter Analytics Template: Consumer Banking Starter Analytics is part of a suite of CRM Analytics apps for Financial Services Cloud customers. It gives you a quick way of creating an analytics solution personal bankers can use to move clients up the value chain.
Revenue Operations Analytics App: Help sales teams gain performance insights to build stronger pipelines, improve forecast accuracy, and generate more revenue.
Configure an Embedded Forecast Overview Dashboard: You can embed the Forecast Overview dashboard in Lightning Experience and Aloha pages. Edit the embedded dashboard to set target lines and the upper bounds for gauges.
Upload a Quota Dataset to the Revenue Operations Analytics App: If you don’t have Salesforce Forecasting enabled, you must add quota data to your app. Without quota data, the Team Performance charts can’t provide an accurate visualization of performance metrics.
Revenue Management for Financial Services Cloud: The Revenue Management for Financial Services Cloud app is a one-stop solution to track, monitor, and manage revenue for multiple sales processes. It’s a purpose-built app that adapts to nuances of opportunity management within the financial service industry. It comes prepackaged with the component library to make it easier to customize the app to your liking.
Book of Business Components: Use the Book of Business components to understand your business performance. They include a summary of your metrics, customer and advisor leaderboards, client segmentation charts and whitespace analysis charts.
Use Revenue Management Embedded Dashboard: The Revenue Management dashboard provides insights to build better pipelines, improve forecast accuracy, and generate more revenue. The dashboard includes the Revenue Optimizer page and Client 360 page.
Lead Conversion Components: Use the Leads Conversion components to get a comprehensive view of your lead pipeline. They include a Lead Pipeline chart, Leads by Time chart, Lead Conversion Rate by Lead Source chart, Open Lead Distribution by Lead Source chart, Lead Projections by Lead Source chart, and Summary Metrics.
Revenue Management Components: Use the Revenue Management components to get performance insights to build better pipelines, improve forecast accuracy, and generate more revenue. They include Revenue Tracker, Top Open Opportunities chart, Opportunity Conversion chart, Pipeline Forecast Changes chart, and Opportunities by Product and Opportunity Stage chart.
Sentiment Analysis Template: Use the Sentiment Analysis data template to categorize selected text into sentiments quickly.
Schedule the Sentiment Analysis Recipe: The Sentiment Analysis template creates one recipe to perform the data analysis on the specified source object. To update the analysis data, you must schedule the recipe for your app.
The Sentiment Analysis Recipe and Dataset: The Sentiment Analysis template creates a recipe that performs sentiment analysis on the source object field that you selected in the configuration wizard. This recipe outputs a dataset with the analysis results and any additional selected fields.
Trend Data Using the Snapshot Analytics App: Monitor trends by capturing a snapshot of a CRM Analytics dataset or Salesforce object. If you’re using a Salesforce object, CRM Analytics imports its data into a dataset. The Snapshot Analytics app then creates a dashboard with a snapshot of your data. You can record one snapshot per day.
Review Customer Service Performance on Social Channels with the Social Case Analytics App: Give managers a fast way to analyze customer service data. Explore how agents use social channels to deliver customer service. Monitor the number of posts to social channels and agents’ response times. The Social Case Analytics template guides you through creating the app.
Assign Permissions to Admins and Users: Assign relevant permissions to admins and users to manage the Analytics for Subscriber Lifecycle Management app.
Enable CRM Analytics: Enable CRM Analytics in Salesforce before you create an app from the Analytics for Subscriber Lifecycle Management template.
Track Your Renewal Business with the Subscription Analytics App: Create an app to track your subscription renewals and the value of your subscription-based products in a dashboard. The Subscription Analytics template guides you through creating the app.
Use Analytics for Communications-Business Dashboards: The Analytics for Communications-Business app provides a comprehensive range of B2B business analytics, starting from opportunity, quote, order completion, and fulfillment insights to enhance sales and operational efficiency. It simplifies the complex enterprise quoting process through visualizations and provides SMB executives with a complete account 360 view. Procurement Managers can access self-service analytics on community portals to get a 360-degree view across the whole enterprise.
Analytics for Wealth Management Template: Analytics for Wealth Management a comprehensive analytics solution that’s part of a suite of CRM Analytics apps for Financial Services Cloud customers. Its extensive sets of dashboards apply the power of CRM Analytics to all significant data and KPIs from the Financial Services Cloud. .
Schedule Data Refresh for a CRM Analytics Template: The app creation process includes data assets that import the latest data to CRM Analytics. Schedule the app to be refreshed every day to ensure that your app uses up-to-date data. An app refresh runs all the data assets associated with the app in the required order, including recipes, dataflows, connectors, Einstein Discovery models, and prediction definitions.
Create Apps and App Templates: Create an Analytics app and all its assets in your scratch org, then turn it into a template to migrate it to other orgs. Templates let you distribute your app to customers, partners, and other teams in your company. They can use the template to create a custom version of the app using their own data. Use VS Code with Analytics plugins to create, customize, and test your templates. VS Code enables the creation of Analytics projects, making team development and merging into GitHub a breeze.
Creating Datasets and Trending Dashboards from Salesforce Reports: By creating trending CRM Analytics dashboards that track key metrics over time, you can make sense of your growing, ever-changing datasets. Trending data in CRM Analytics helps you discover hidden insights and allows you to share them with colleagues. When you create a trending dashboard based on a Salesforce report, CRM Analytics generates a dataset and a dashboard based on the underlying Salesforce data.
Limits and Limitations for Trending Data in Analytics: Trended datasets count toward the overall CRM Analytics platform limits, including total number of rows. To prevent the performance of your org from degrading, certain limitations are placed on snapshot size and the number of trended datasets each user can create.
Share Trending Dashboards: You can share a trended dashboard just as you can with any CRM Analytics dashboard.
Stop Trending Salesforce Reports: The number of reports you can trend is limited. To remain under the limit so you can trend other reports, stop trending reports that are no longer needed.
Learn CRM Analytics with Trailhead: Trailhead is the fun and free way to learn Salesforce. Get acquainted with CRM Analytics through Trailhead using a special CRM Analytics-enabled Developer Edition org.
Visualize Data With Charts: If your goal is to understand vast amounts of business data, and to communicate that understanding with coworkers, partners, and customers, being able to visualize your data is critical. CRM Analytics provides a chart for every need, each a means for illustrating key aspects of your business in just the right way.
Open CRM Analytics: Access all your analytics from the Analytics tab within Salesforce. Alternatively, go to your home page by opening the app menu or App Launcher in the Salesforce header and then finding and opening CRM Analytics Studio.
View, Collaborate, and Take Action from Einstein CRM Analytics: View conversational explorations of your data, and personalize your CRM Analytics experience with saved dashboard views. Collaborate with features such as notifications, annotations, presentation mode, and downloading filtered data from CRM Analytics. Use custom menus in lenses and dashboards to take action in Salesforce directly from CRM Analytics.
Add a Summary Formula Column to a Report: Before adding a summary formula to your report, be sure to group report data. Summary formula columns require at least 1 group.
Accessibility Support in Tableau Semantics: Use keyboard shortcuts in the semantic model editor to improve productivity. Navigate between different panels, perform actions on the canvas, and manage objects using only your keyboard.
Add a Predictive Insight to a Semantic Model (Beta): Add a Predictive Insight to surface an AI model's prediction as a field in your semantic model. For each record your model returns, Tableau Semantics passes the mapped field values to the AI model and returns the predicted value in real time.
Add and Delete Data Objects: Add Data Model Objects (DMOs), Data Lake Objects (DLOs), and Calculated Insight Objects (CIOs) from your org to a local model.
Analytics Agent Readiness: Analytics Agent Readiness is a property that marks a semantic model as mature and ready for use by the Analytics Agent. Enabling the property indicates that the semantic model follows best-practice guidelines and can be used by the Analytics Agent.
Enable Analytics Agent Readiness: Perform this task to verify your model's readiness and mark it as mature and ready for use by the Analytics Agent.
Authorize Your Org: The extension uses Salesforce CLI for authentication. You must authorize an org before using extension features.
Create a Numeric Bin in Tableau Semantics: A numeric bin is an equal—sized container that stores data values that correspond to or fit within the bin size. Bins divide a set of data into groups of equal intervals or sizes, resulting in a systematic distribution of data. Data from any object field can be used to create a bin. Generally, numeric bins are created out of measure fields. Bins are useful in data analysis as they provide a systematic data range that allows users to organize information and discover patterns efficiently. Bins are displayed as dimensions in Tableau Semantics. You can also use bin ranges in calculated fields.
Business Preferences for Analytics Agent: Analytics Agent Business Preferences let you define business-specific knowledge within the semantic model. Business Preferences help the agent respond to analytical questions about your business based on its unique context and logic.
Best Practices for Adding Business Preferences: Follow these guidelines to write effective Business Preferences that helps Concierge: Analytics Q&A work better with your business context and logic.
Understand the C360 Semantic Model: The C360 Semantic Model integrates Salesforce cloud standard DMOs into a unified model, providing your business context wherever you use Salesforce. With consistent cross-cloud interpretation of your data, the C360 Semantic Model solves the problem of ambiguous metrics and charts by offering a single model that acts as a “primary map” for your data.
The C360 Semantic Model: Use the C360 Semantic Data Model (SDM) to get unified and trusted insights when you work across multiple Salesforce products to create cross-cloud KPIs.
Install and Manage the C360 Semantic Model: The C360 Semantic Model is automatically added to your default data space when you have Tableau Next. This procedure is for adding the C360 Semantic Model to additional data spaces.
Tune the C360 Semantic Model for AI Readiness: Before rolling out Tableau Next assets to your team, first calibrate Tableau Agent responses with verified questions and business preferences.
Use Calculated Fields: If the underlying data doesn't include all the fields necessary for analysis, you can create fields by using calculations. These fields are called calculated fields. Calculated fields don’t physically exist in the database. They’re based around a formula and can include functions, operators, literals, or other inputs such as existing fields from your dataset.
Add Filters to a Calculated Field: Filters let you choose which data can be in the formula expression. Filters help you increase the accuracy of your measures.
Configure Calculated Field Settings: Like all fields, calculated fields have a field type of dimension or measure. Depending on the field type, calculated fields have additional field settings or metadata to be configured, such as data role, aggregation type, or sentiment.
Create a Calculated Field: Calculated fields are fields that don't exist in the underlying data and are defined with a formula. Calculated dimensions are qualitative, categorical fields used to group, classify, and segment data. Calculated measures are quantitative, numerical fields that are aggregated.
Formulas in Calculated Fields: A formula tells the Tableau Semantics Query Generator how to calculate the fields when running queries. You can add logic on how to calculate raw data within the dimension or measure.
Write a Formula in a Calculated Field: Formulas define how a calculated field is computed. How a formula is written is called its syntax. A formula may contain functions, fields, operators, literal expressions, parameters, or comments.
Cardinality in Tableau Semantics: Define how objects in your Semantic Layer relate to each other to ensure accurate results and faster queries. By setting cardinality, you tell Tableau Semantics how records from one object connect to records in another, helping the system optimize joins, reduce duplicate data, and improve the reliability of your metrics.
Create a Relationship with Suggest Relationships: You can use Artificial Intelligence (AI) to get relationship suggestions and create relationships in Tableau Semantics. Suggestions are based on semantic objects and sample data added to your model.
Create or Retrieve a Semantic Model: Get a semantic model into your workspace by retrieving an existing model from your org or by creating, extending, or cloning one.
Currency in Tableau Semantics: Use currency fields in Tableau Semantics to convert values into the corporate currency based on the settings in the semantic model. A currency field is a specific field type that is automatically associated with a Currency ISO Code field. You can convert data from one currency to the corporate currency when analyzing dashboards, building semantic models, or creating calculated fields. You can also create calculated fields based on currency measures, sort by currency fields, and ensure values are displayed in the corporate currency for global audiences.
Change the Data Type of a Field to Currency: Change a numeric field, such as a measure, to a currency field. For example, you need to report account revenues in multiple currencies to accommodate international operations, but the account revenue is stored in a Number field. To get accurate reporting and enable conversions between currencies, you must use a currency field that’s linked to a Currency ISO Code field.
Create a Semantic Model with a Currency Field: Create a semantic model with a currency field to convert the values into the corporate currency. To preserve the original value of the measure before conversion, clone the Currency field as a Number data type field.
Set Data-Defined Goals: Data-defined goals are based on a measure in the semantic data model. With data-defined goals, anyone who filters or follows a sub-metric inherits the goal, and the goal stays synchronized when the source goal value changes. For example, you can create a data-defined goal based on the win rate measure. If the team has a data-defined win rate goal of 30%, and the West territory has its own goal of 28%, then users who filter the sub-metric to the West territory see the correct goal—28%. This way, users don’t need to create individual goals for each sub-metric, and your team stays in sync working towards the same goal. When a data-defined goal exists, individual goals for sub-metric can’t be created and vice versa.
Data Governance in Tableau Semantics: Data governance in Tableau Semantics ensures that you see and work with only the authorized data. Governance combines dataspace permissions with Data 360 security policies. These policies determine who can view semantic models and what data is available during model design and when running queries in Tableau.
Structure Data within a Semantic Model: With Tableau Semantics, you can use the Semantic Model Builder in Data 360 or Tableau Next to build and make changes to your semantic model.
Create Semantic Models: Semantic Models are first—class Salesforce metadata, integrated throughout Data 360, Tableau Next, and intelligent apps to power analytical and data-driven experiences. The semantic model includes definitions tailored for a specific analytical use case. The model can have one or more data objects. You can create flexible joins and unions across objects and enrich your analysis with calculated dimensions and measures.
Manage Access to Semantic Models in Tableau Semantics: Control who can view or edit your semantic models by sharing them with specific users or your entire org. Semantic models are private by default, but you can manage access through the Share option and assign access roles, such as Viewer or Editor.
Create an Extended Model Based on a Base Model: Use an existing semantic model and extend it to create an extended model. When you create an extended model it stays connected to the base model, and the base model remains the single source of truth. Changes made to the referenced model are automatically added to the composed model, but changes made to the composed model aren’t pushed to the base model.
Create a Semantic Model from a Data Kit: To create consistent semantic models, use a data kit. A data kit includes all the semantic definitions, such as the data objects, their relationships, parameters, calculated fields, and metrics. However, a data kit doesn’t include dependencies like data streams and calculated insights.
Create a Semantic Model in Data 360: Create a semantic model to serve specific business needs directly from Data 360. There are three ways to create a semantic model: start from scratch, extend an existing semantic model, or build it using a Data 360 data kit.
Connect a Published Data Source (PDS) to a New Semantic Model: Once a trusted connection between your Salesforce org and Tableau Cloud site is configured, you can create a model in the Data 360 Semantic Layer by connecting to the appropriate PDS.
Remove Access to Semantic Model in Tableau Semantics: Remove access to a semantic model so that users can no longer view or edit that model in Tableau Semantics. You can remove access for individual users, and you can restrict the general access for the model so that only users who are individually added can access it.
Share Semantic Model in Tableau Semantics: Share and grant other users access to a semantic model in Tableau Semantics. You can share a model with individual users, or you can set general access to make the model available to all users in your org.
Dimension Hierarchies in Tableau Semantics: Create a dimension hierarchy to provide a defined analysis path for data exploration through consumption layers. Group related dimensions and build a hierarchy using fields from single or multiple objects. This structure sequences consumption layers logically for efficient data exploration, eliminating the need to manually add separate fields to a report.
Create a Dimension Hierarchy: Group related dimensions using fields from single or multiple objects to create a single, navigable path for data exploration.
Edit a Semantic Model: Edit your local model by modifying JSON files, adding data objects, or removing them.
Edit Model Files Locally: After retrieving a model, edit the JSON files using VS Code's built-in JSON editing features, including IntelliSense, formatting, and syntax highlighting.
Semantic Model Builder: Smart Canvas: When large semantic models become difficult to navigate, the enhanced canvas in the Semantic Model Builder simplifies authoring and navigation. An infinite canvas, smart auto-layout, non-crossing relationship lines, domain-based grouping, focus mode, and independent extended-model positioning work together to make enterprise-scale Customer 360 models clear and traceable.
Explore the Tableau Semantics Interface: You can create and define semantic models in the Semantic Layer in Data 360. Here you can see all the semantic models that exist in your org. You can create new semantic models, edit them, and even clone them directly from here.
Extension Commands Reference: Use the Command Palette (Cmd+Shift+P or Ctrl+Shift+P) or right-click files or folders to access commands.
Apply a Filter to a Related Metric: Use submetrics to provide additional granularity and track more detailed performance indicators. A submetric breaks down the main metric into more specific, actionable data points.
About Tableau Semantics: With Tableau Semantics, you can create and manage your semantic models. Before you start using Tableau Semantics, review requirements and other introductory information.
Global Filters in Tableau Semantics: Create global filters to ensure that all reports share the same focused, consistent baseline data. By restricting data at the model level, you filter out irrelevant records before they reach any downstream visualization.
Create a Global Filter: Create a global filter in your semantic model to get a focused view of your data. The semantic model applies the global filter before any other filters that are manually added.
Combine Multiple Global Filters: If you want to use more than one global filter, configure the relationship between them to control how they work together.
Submetric Goals in Data 360: Enhance your data with actionable insights by adding business goals to your submetrics. Tableau Semantics goals eliminate the need for external tools and manual processes. You can track your progress in real time, measure your progress consistently, and align data insights with business outcomes.
Create a Goal in Data 360: Help business users monitor their metrics against specific targets by creating one or more goals in Tableau Semantics. Adding goals is an optional final step when creating or editing a submetric.
Create a Group in Tableau Semantics: Combine related objects to segment customers based on demographics, behavior, or purchasing patterns. You can also use a group to correct data errors, such as combining CA, Calif., and California into one data point, or to answer “what if” questions (“What if we combined the East and West regions?”). Reference groups directly in calculated fields to build custom metrics and dimensions using group labels.
Join Data Objects in a Logical View: Creating joins between two objects is crucial for providing a comprehensive view of the data when information is distributed across multiple tables or datasets. You can define the type of join you want to create between two data objects in the logical view including left, right,and inner join.
Logical Views in Tableau Semantics: A logical view is a data object that combines objects using joins or unions rather than relationships. Although a logical view is a separate object type in the semantic model, it functions like any data model object (DMO) or data lake object (DLO). Use logical views in relationships, calculated fields, metrics, and other semantic definitions.
Create a Logical View: A logical view connects data objects. There are a few steps you need to complete to create the logical view. You first have to define the properties, next select the data object for the logical view, and then you can configure the logical view.
Remove an Object from a Logical View: You can remove an object from the logical view if no fields are in a relationship or if the object is not connected via a join. Removing the object removes any joins between this object and other objects in the logical view.
Metrics in Tableau Semantics: A metric is a KPI tracked over time, such as revenue over the past year. The core components of a metric are a measure or calculated field representing the KPI and a time dimension that tracks its progression over time.
Edit or Delete a Metric: Keep your data model current by updating metric configurations or removing them. Deleting a metric removes it from all associated reports and dashboards.
Add Hierarchy Filters to Metrics (Beta): After you add an organizational hierarchy to your semantic model, hierarchy levels become available as filter dimensions when you create or edit metrics, letting you scope a metric to specific branches of your organizational structure.
Add Hierarchy Filters to Sub-Metrics (Beta): Use sub-metrics to provide additional granularity and track more detailed performance indicators scoped to specific teams or reporting lines in your organizational hierarchy.
Add an Organizational Hierarchy to a Semantic Model (Beta): Add an organizational hierarchy to your semantic model to answer hierarchy-related analytics questions across your company's reporting structure such as "What's my team's revenue?" or "How is my region performing?", without requiring separate metric definitions for each level.
Organizational Hierarchy Types and Structures (Beta): Tableau Semantics supports two organizational hierarchy types — Manager Hierarchy and Role Hierarchy — each of which aggregates data differently and determines the scope of 'My Team' filtering for your users.
Organizational Hierarchies in Tableau Semantics (Beta): Organizational hierarchies reflect your company's reporting structure — who reports to whom and how teams are organized — and let analysts and business users analyze metrics at any level of that structure in Tableau Semantics. Without organizational hierarchies, each organizational level requires a separate metric definition.
OSI v0.1 Overview: Tableau Semantics currently supports the initial release of the OSI open-source workflow (OSI v0.1).
OSI Workflow: The OSI workflow uses open-source translators and customer-managed infrastructure.
Override a Calculated Field or Metric in an Extended Model: Learn how to override a calculated field or metric in an extended model to preserve custom logic across base model updates. When you override a calculated field's formula or properties, or a metric's definition, that field or metric disconnects from the base model and stops receiving automatic updates.
Override Semantic Definitions in an Extended Semantic Model: When you extend a model, the extended model inherits all semantic definitions and their properties from the base model. If you change any property, the updated property is defined as an override. An overridden property is not connected to the base model. As such, your overridden properties are not changed when the base model is updated. Properties without overrides on the other hand, remain connected to the base model data source and receive updates from it, keeping it as the source of truth. If you want to return to the original values, you can always revert the override and resume automatic updates.
Parameters in Tableau Semantics: A parameter is a variable, such as a number, date, or string, that can replace a constant value in a calculated field and can be used in other functions, such as filtering.
Create a Parameter: You can create a new parameter directly from your semantic model.
Delete a Parameter: You can delete any parameter from your semantic model.
Edit a Parameter: You can edit any parameter in your semantic model.
Use Parameters in Calculated Fields: You can use parameters within the formula field when creating and editing calculated fields. You can either insert the parameter or when creating the formula use auto-suggestion. The formula automatically includes parameters when suggesting fields.
Performance Recommendations for Semantic Models: A scan suggests cardinality and Match Case Insensitivity changes that speed semantic model queries. Model Health shows which recommendations remain open.
Predictive Insights in Tableau Semantics (Beta): Predictive Insights adds real-time machine learning predictions to your semantic model as a field you can chart, filter, and reuse. Connect any AI model from Data 360, map your semantic model fields to its inputs, and get a predicted value for every record—no data exports, separate tools, or code needed.
Prerequisites: Before you begin, ensure that you have the required software and org configuration.
Primary Name Field: The Primary Name Field represents the table record. A meaningful Primary Name Field is essential for the Analytics Agent to provide clear and accurate answers, improving data clarity and effective communication.
Relationships in Tableau Semantics: A relationship connects different tables based on common fields. Establish relationships between your data objects to enrich your data model without the need to define a join type. Using a relationship to combine objects makes data preparation and analysis easier and more intuitive. It’s recommended you use relationships to combine your data before trying out other options.
Revert Overrides: Learn how to reconnect semantic definitions to an associated base model. This action reverts the semantic definition back to the current value of the base model. It doesn't delete the extended model.
Row Count in Tableau Semantics: Use the auto-generated row count measure to track the number of records in a data object. Include row counts in calculated fields for further analysis, reporting, or metrics. The row count automatically adjusts when you add filters or change the dimensions.
Semantic Definitions in Tableau Semantics: In Tableau Semantics, you can create semantic definitions that are composable and reusable across semantic models, such as calculated fields, parameters, groups, bins, and more.
Set Cardinality for Relationships: Set the correct cardinality type to define how two objects are related and ensure Tableau Semantics interprets joins accurately. This helps prevent unexpected duplicates or missing data and improves overall query performance.
Set Up the Extension: Get ready to build semantic models by installing the extension and connecting it to your org.
Create a New Shared Table: Use shared tables to link multiple fact tables through a common dimension, to compare records across different tables. This keeps your data model clean and unambiguous and ensures that filters behave correctly across different datasets.
Shared Dimensions in Tableau Semantics: Analyze and compare data across multiple fact tables by connecting them through the same dimension tables. Build complex, business-relevant analysis in a clean and trustworthy way, avoiding duplicate relationships and eliminating cycles in your semantic model.
Copy a Generated Semantic Query: Use the copied semantic query to access data from your semantic model in Business Intelligence (BI) tools that don't automatically display your semantic model data as a standard table. Before you can copy the semantic query, you must first preview the semantic model and test a query.
Test a Semantic Model in Data 360: You can test the semantic model at any point to make sure that it works as expected. Tableau Semantics has a test system that checks that the model works. It runs your model through the Tableau Semantics Query Generator and gives you test results.
Test a Semantic Model: You can run a test at any point from your semantic model. Make sure you don’t have any errors in your model before running tests.
Track Model History with Git: If your model folder is inside a Git repository, use version history features to track changes over time.
Unions in Tableau Semantics: In a logical view, use a union to combine and unify multiple data model objects. You can merge datasets that share similar structures, providing a more holistic analysis of data. Appending values from one object to another results in a comprehensive dataset that integrates information across sources.
Create a Union: Merge datasets that share similar structures in a logical view. You can create one union per logical view. You cannot combine unions and joins.
Delete an Object from a Union: You can remove an object from the unified table if it is not connected to another object or if it is not being used by another semantic definition. For example, if the object is being used in a calculated field, you cannot remove it from a union. You can also delete the entire union. Removing an object affects the selected matching fields. If only two matching fields exist, removing the DMO eliminates the match entirely, because a single field cannot remain matched.
Work with Multiple Orgs: If you work with multiple orgs, set the default target org that the extension uses for all operations.
Add Fields to an Extended Model: In an extended model, you can add standard or custom fields that weren’t originally selected in the base model. You can edit these fields only in the extended model. They aren’t included in the base model.
Override a Field, Data Object, or Calculated Field Property: Learn how to override a semantic definition property in an extended model. The process disconnects one or more definitions from a base model allowing you to keep the custom definition free from updates to the base model.
Bar Charts: A bar chart shows values as horizontal lengths, so this format can be good for comparing distance or time. Use a bar chart when you have a summary report with a single grouping, or you only want to display one grouping.
Grouped Bar Charts: Use a grouped bar chart when you have multiple groupings, and you want to compare values within a secondary grouping, but not the totals.
Stacked Bar Charts: Use a stacked bar chart when you have multiple groupings and are interested in the proportions between values in each grouping, as well as each grouping's total.
Bar Charts Stacked to 100%: Use a bar chart stacked to 100 percent when you have multiple groupings and are interested in the proportions between values in each grouping, as well as each grouping's total.
Column Charts: A column chart is very much like a bar chart, but it can be a better format for showing relative counts of things, such as leads or dollars. Use a column chart when you have a summary report with a single grouping, or you only want to display one grouping.
Grouped Column Charts: Use a grouped column chart when you have multiple groupings, and you want to compare values within a secondary grouping, but not the totals.
Stacked Column Charts: Use a stacked column chart when you have multiple groupings and you’re interested in the proportions between values in each grouping, as well as each grouping's total.
Column Chart Stacked to 100 Percent: Use a column chart stacked to 100 percent when you have multiple groupings and are interested in the proportions between values in each grouping, as well as each grouping's total.
Data Settings for Dashboard Chart Components: Choose the data you want to show on your report or dashboard chart. The chart automatically selects groupings and summary values from your report. You can override some of those choices to focus on the data you need to share.
Donut Charts: Use a donut chart when you have multiple groupings and want to show not only the proportion of a single value for each grouping against the total, but also the total amount itself.
Funnel Charts: Use a funnel chart when you have multiple groupings in an ordered set and want to show the proportions among them.
Line Charts: Line charts are good for showing changes in the value of an item over a series of points in time, such as week to week or quarter to quarter. Use a line chart when you have one important grouping representing an ordered set of data and one value to show.
Cumulative Line Charts: Use a cumulative line chart when you have one important grouping representing an ordered set of data and one value to show, summed over time.
Grouped Line Charts: Use a grouped line chart when you have multiple groupings—each with one important secondary grouping representing an ordered set of data—and one value to show.
Grouped Cumulative Line Charts: Use a cumulative line chart when you have one important grouping representing an ordered set of data and one value to show, summed over time. Use a grouped line chart when you have multiple groupings—each with one important secondary grouping representing an ordered set of data—and one value to show.
Pie Charts: Use a pie chart when you have multiple groupings and want to show the proportion of a single value for each grouping against the total.
Scatter Charts: Use scatter charts to show meaningful information using one or two groups of report data plus summaries.
Chart Types: You can show data in reports and dashboards in the form of bars, columns, lines, shapes, or other elements. Which is right depends on what the data is about and what you want to show with it.
Visual Settings for Dashboard Chart Components: Choose the type of chart that fits the data you are sharing, then apply the visual settings that will communicate the data most effectively.
Filters Type Reference: Several different types of filters help you scope your report data: standard filters, field filters, cross filters, and row limit filters. Each filter type filters your report in different ways. This list of filter types helps you choose the right filter types for your report.
CSI Score (Beta): CSI Score synthesizes and translates customer signals into a trusted score that reveals drivers, business impact, and risk across the customer lifecycle. With this unified score, leaders can align teams, mitigate risks early, and drive measurable business outcomes. By unifying operational, sentiment, and unstructured signals across departments, it surfaces not just customer health, but emerging risks, growth opportunities, and operational priorities. Change becomes explainable and impact measurable, enabling leaders to act early, align teams, and make confident, outcome-driven decisions.
About CSI Score: Transform fragmented data into executive clarity with CSI Score, a unified intelligence standard that consolidates cross-cloud signals into a single, trusted index for proactive decision-making.
Customize CSI Score: Customize and extend CSI Score based on the company's priority metrics and KPIs, scoring logic, and visualization preferences. Admins or analysts can add new scores, update the data source for scores, enhance the scoring logic, calculations, and weights, and customize visualizations. Modify metric weights to change the influence of specific scores, such as increasing the weight of the Sentiment Score from 0.5 to 0.7. Add new KPIs to integrate proprietary metrics such as custom churn probability, or customize dashboards to enhance the visual layout for specific business needs.
Update CSI Score App with Custom Dashboards: The CSI Score app shows the Tableau Next dashboards provided with CSI Score. If admins or analysts clone installed dashboards or add company-specific dashboards, they must also add the customized dashboards to the CSI Score app.
Customization Considerations in CSI Score: Keep these considerations and limitations in mind when customizing the assets of CSI Score across Data 360 and Tableau Next.
Enhance Scoring Logic in Custom Semantic Model: Adapt the semantic model that you created by extending or cloning the CSI Score semantic model based on your company's requirements. Data 360 provides all the customization options to add data sources, adjust score weights, or create scores. The customization options available to you differ based on whether you cloned or extended the installed CSI Score semantic model.
Configure a Semantic Model for Customizations: The CSI Score semantic model that you install is where all the metrics and calculations of composite scores and the all-up CSI score reside. The installed model is locked. You can't customize either the resources of the locked model or the CSI Score assets in Tableau Next that reference the locked model. You can either clone or extend the CSI Score semantic model to make your customizations.
Example: Add a New Score with a Custom Data Source: The business executives at Northern Trail Outfitters (NTO) want to identify customers who are at risk due to frequent missed service deadlines. They need a new SLA Risk Score that reduces the all-up CSI score when NTO fails to meet contractual obligations. The admin must add SLA violations as a data source, create the scoring logic, and create the visual representation of the new score.
Example: Change Logic for Composite Score and Update Visualization: The Service VP at Northern Trail Outfitters (NTO) believes the standard Sentiment (SENT) score is too optimistic. They want to adjust the scoring logic to penalize negative feedback, flagging at-risk customers earlier. The admin must create a new calculated field with custom weights and update the dashboard widget to use the new logic.
Example: Add a KPI to a Composite Score: Northern Trail Outfitters (NTO) tracks webinar attendance as a key indicator of engagement. They want to add this KPI to the Voice of Customer (VoC) composite score. This makes sure that customers who actively participate in enablement sessions are reflected as having higher health.
Example: Create an Industry-Specific Voice of Customer View: Marketing executives at Northern Trail Outfitters (NTO) want to compare customer sentiment across different market sectors. They want to see if retail customers are happier than manufacturing customers. The admin creates a simple bar chart by using the existing Voice of Customer (VoC) score and the Account Industry field and adds it to the CSI Score Overview dashboard.
Example: Change Weightage of Composite Scores in All-Up CSI Score: The executive strategy at Northern Trail Outfitters (NTO) has shifted to prioritize revenue stability. They want the Revenue Health Score (RHS) to account for 25% of the overall all-up CSI score. This increases the weight from the default 15% without changing any underlying data sources. The admin must update the aggregation formula in the semantic model and point the dashboard to the custom logic.
Customize Tableau Next Assets of CSI Score: After the CSI Score is installed, a set of Tableau Next assets are added. The assets include visualizations and dashboards. Like the installed CSI Score semantic model, the installed visualizations and dashboards are locked. All locked visualizations use resources from the installed CSI Score semantic model that's also locked. You can't change resources used in locked visualizations to resources from your custom semantic model. The locked dashboards can be cloned but the new dashboards inherit locked visualization from the parent dashboard. You can either add widgets that use metrics from your custom semantic model. Or, you can replace the model and metric of the inherited widgets.
Learn About CSI Score: Understand the key concepts, workflows, and terms related to CSI Score. Map your company's business requirements with the scores, KPIs, metrics, and insights offered by CSI Score.
Prerequisites for CSI Score: Learn about the Salesforce editions, products, and licenses required for CSI Score.
Composite and All-Up Scores Available with CSI Score: CSI Score is built from nine composite scores that roll up into a single, trusted all-up CSI Score for the company. Grounded in established best-practices, each composite score is calculated from a set of customer signals normalized to deliver consistent behavioral, perception, and operational insights. Together, these scores act as critical decision signals across the customer lifecycle. The all-up CSI Score provides a clear measure of overall business performance that serves as a strategic anchor for prioritizing action and guiding key decisions.
Key Terms in CSI Score: Familiarize yourself with the core terminology used in Customer Signals Intelligence (CSI) Score.
Why CSI Score?: Companies generate vast amounts of customer data across functions, but those signals often remain fragmented and isolated. When teams measure the same KPIs differently, it becomes difficult to understand what is truly changing or where to act. CSI Score resolves this by bringing critical customer signals together into one trusted score. It explains drivers, impact, and risk across the full customer lifecycle. This enables leaders to align decisions, prioritize action, and manage the business with confidence.
How CSI Score Works: Explore the technical architecture and the tiered calculation logic of CSI Score. Discover how Data 360, semantic models, and Tableau Next turn raw signals into weighted composite scores and a definitive executive metric.
Understand the Scoring Logic of CSI Score: Gain a deep understanding of the mathematical models, calculation logic, and resources that power the CSI Score scoring framework. Learn about how these elements transform raw customer signals and data into the composite scores and the unified all-up CSI score. Understanding the framework is key because it enables admins and analysts to customize the scores and extend the framework based on the company's requirements.
All-Up CSI Score Logic and Resources: Understand the calculation logic, underlying formulas, and resources used to calculate the all-up CSI score.
Composite Score Logic and Resources: Understand the calculation logic, underlying formulas, and resources used to compute the composite scores. You can find and review all the building blocks for the composite scores in the CSI Score semantic model.
Manage Predictive Models for CSI Score: CSI Score uses Einstein Studio's Gradient Boosting Machine (GBM) to identify the top-drivers for the composite scores. Each composite score gets its unique predictive model. You can retrain the models with your company's data and modify the models to include KPIs unique to your company.
Train Predictive Model with Customized CSI Score Semantic Model: During CSI Score installation, the predictive models are trained automatically with synthetic data. This training doesn't consider any company-specific nuances, such as additional KPIs or updates to composite score calculation logic. After your company's custom CSI Score semantic model is ready with company-specific scores, weights, and formulas, retrain the predictive models with actual data. And set a training schedule for the updated models.
Example: Include New KPI in Predictive Analysis: Northern Trail Outfitters (NTO) recently added webinar attendance as a new KPI to their Voice of Customer (VoC) composite score. Executives want to know if webinar attendance is statistically driving customer satisfaction. The admin must update the predictive model dataset and retrain the VoC model to include this new variable.
Example: Retrain Model After Logic Change: The Service VP at Northern Trail Outfitters (NTO) wants to adjust the Sentiment Score (SENT) logic to penalize negative feedback. Because the logic has changed, the existing predictive model is no longer accurate. The admin must retrain the sentiment predictive model to make sure that the top drivers accurately explain why sentiment scores are dropping.
Understand How Predictive Models Work in CSI Score: Before you start working with predictive models for CSI Score, learn about the predictive modeling assets of CSI Score installed in your org. Know the predictive modeling tasks completed during CSI Score installation and review AI Models resources to learn about predictive modeling in Data 360.
Set Up CSI Score: Review the prerequisites for CSI Score and turn on other Salesforce products that CSI Score uses. Map the data model objects in Data 360 that the composite scores use as data source and install the CSI Score assets.
Install CSI Score: CSI Score provides a set of prebuilt assets that harmonize data sources, drive scoring logic, and visualize the all-up CSI Score and the composite scores. All assets work together to give admins a jumpstart on company-specific customizations and reduces the time-to-value.
Permission Sets to Work with CSI Score: Assign business users Tableau Next permission sets that they require to view the CSI Score dashboards and other assets on the CSI Score app.
Data Requirements for CSI Score: CSI Score sources data from data model objects (DMOs) in Data 360 to calculate composite scores. Map the specific DMOs you want the CSI Score Semantic Model and the C360 Semantic Model to use for calculating scores. The Account DMO is mandatory. All other DMOs are optional. You can map them based on whether your Salesforce implementation uses those objects and whether you want to include that data in your score calculations.
Work with CSI Score App: Business executive and line-of-business leaders use the CSI Score app to review their company health and locate emerging risks, growth opportunities, and operational priorities. Executives can view the company's all-up CSI score, composite scores, and key indicators such as KPIs, KPI relationship trajectory, and top drivers.
KPI Relationships: Visualizes the correlation between the composite scores and their KPIs on a unified timeline. See the global top drivers for the composite scores that are derived from predictive models. Overlay KPI trends and use SHAP values to reveal cause-and-effect connections to validate hypotheses and pinpoint the exact levers driving overall business health.
CSI Metrics: Business executives and line-of-business leaders use the CSI Metrics tab to uncover the drivers of business health with the composite scores. Detailed breakdowns of the composite scores spanning sentiment, operations, and revenue quantify performance across the customer lifecycle. Comprehensive KPIs and targeted account watchlists for each composite score empower leaders to diagnose root causes, predict churn risks, and unlock growth opportunities.
Customer Health Score (CHS): Integrates usage, adoption, and support signals to reflect the overall vitality of the customer relationship. It aggregates adoption, support, and relationship signals to predict renewal likelihood, blending behavioral and operational data to quantify customer dependence and value.
Customer Lifetime Score (CLS): Estimates the long-term financial value of a customer based on spend, tenure, and retention probability. It estimates total lifetime value by blending revenue continuity, expansion potential, loyalty engagement, and cost-to-serve to connect emotional loyalty to the financial potential of accounts.
Churn Risk Index (CRI): Tracks behavioral and emotional warning signals from customers who are disengaging, downgrading, or expressing negativity. It measures attrition and risk indicators across all stages from engagement decay to payment failure, highlighting exactly when customers withdraw to enable proactive intervention.
Engagement Effectiveness Score (EES): Quantifies the depth and frequency of customer interactions with products, services, and channels. It measures interaction depth across touchpoints such as digital, human, and transactional to drive adoption, stickiness, and upsell by signaling the true level of customer commitment.
Journey Experience Score (JXS): Quantifies the quality of customer experience during journeys, such as brand introduction, quote-to-cash, service, and renewals. It tracks the customer lifecycle from awareness to renewal, quantifying cross-departmental fluidity to drive funnel efficiency and highlight where customers stall.
Operations Score (OPS): Evaluates the efficiency and effectiveness of internal processes that drive customer delivery. It evaluates process efficiency and quality of delivery across the value chain from marketing execution to quoting, servicing, and billing to drive cost optimization and service quality.
Revenue Health Score (RHS): Tracks contribution to top-line performance by combining revenue growth, lead-to-cash conversion, expansion, and retention outcomes. It combines growth, conversion, and retention indicators across all clouds to measure total top-line performance and predict revenue risk, serving as a key driver of pipeline health by quantifying the financial impact of accounts.
Sentiment (SENT) Score: Measures emotional tone across customer conversations, text, and feedback to reveal underlying attitudes and opinions. It unifies textual and vocal sentiment across all interactions across marketing, sales calls, field notes, and billing to surface emotional drivers of loyalty or risk for proactive resolution and customer experience improvement.
Voice of Customer (VOC) Score: Assesses how customers feel about their experiences and serves as a key driver of brand reputation and customer advocacy. It provides an end-to-end view of perceived experience quality across marketing, sales, service, and billing to capture how customers feel from quote to cash to support.
Other Resources of CSI Score App: Explore other resources within the CSI Score app. Use My Metrics for personalized tracking of your most critical data, and the KPI Catalog as a comprehensive reference guide for all available performance indicators.
CSI Overview: Business executives and line-of-business leaders use the CSI Overview tab to review the overall company health and performance. An AI-generated summary provides leaders insights about the top composite scores that are driving the all-up CSI score. Each of department's perception and the behavioral CSI score highlights the performance gaps and departmental risks. Leaders can also view a target list of accounts that require executive intervention to protect revenue or drive growth.
Salesforce CRM Call Center Reports: Call Center reports help you analyze the Salesforce CRM Call Center phone calls that were handled by you and your team.
Pre-Designed Custom Report Types: Some Salesforce features come with custom report types that are designed for you in advance, so you don't have to create a new report.
Modify a Dashboard Component in Salesforce Classic: A dashboard component is a visual representation of the data in a report. You can change where the component’s data comes from, what the data looks like in the component, and what kind of component it is.
Salesforce Classic Dashboards: A dashboard shows data from source reports as visual components, which can be charts, gauges, tables, metrics, or Visualforce pages. The components provide a snapshot of key metrics and performance indicators for your organization.
Add a Dashboard Filter: To create a dashboard filter, select a field that contains the information that you want to filter, then specify the values that you want your filter to match.
Equivalent Fields in Dashboard Filters: Fields that you select for a dashboard filter often have equivalent fields, which are derived from the same field on the underlying Salesforce object.
Dashboard Filter Examples: You can view dashboard data from different perspectives by applying filters. A single dashboard with well-chosen filters can do the work of several dashboards. When you apply a dashboard filter, the view is preserved. The next time you view the dashboard, the data is filtered in the same way. To provide the filtered dashboard view to others, you can create a URL to post or email.
Filter a Dashboard: Dashboard filters make it easy for users to apply different data perspectives to a single dashboard. Filtering rules apply to fields from the dashboard’s source reports. After filters are defined, a user viewing the dashboard can select a filter to visualize the data of most interest. The filtered view is remembered - the next time the user visits the dashboard, the same filtered view is shown.
Apply a Dashboard Filter: Filter a dashboard to analyze the information interactively. After you filter a dashboard, the filtered view is preserved so that the next time you see the dashboard, data is filtered in the same view.
Add a Dashboard Component in Salesforce Classic: Add components by dragging a component type onto the dashboard, then dropping a data source (report, s-control, or Visualforce page) onto it.
Change Dashboard Owner: Transfer dashboard ownership if responsibilities change or the current dashboard owner leaves your organization. The new owner has full control over the dashboard contents. You can change ownership of one or more dashboards at a time. You must have edit access to the folder that contains the dashboard. The new owner is granted edit access to the dashboard folder.
Color Dashboard Data with Themes and Palettes: Customize the colors in your dashboard by choosing a light or dark theme and one of 14 color palettes. Themes and color palettes help you organize and curate data in your dashboard. For example, highlight a set of summary metrics by giving them a dark theme and setting them against a light-themed dashboard.
Data Settings for Dashboard Bar and Column Chart Components: Horizontal bar and vertical column charts are useful for comparing the values of one or more report groupings. Use the Component Data tab to select the groupings and summaries you want your bar or column chart to display.
Formatting Settings for Dashboard Bar Chart Components: Horizontal bar and vertical column charts are useful for comparing the values of one or more report groupings. Set the scale and sorting to help you make sense of the data you are displaying.
Data Settings for Dashboard Pie and Donut Chart Components: A pie chart or a donut chart is good for showing the relative shares of different quantities. Use the component data tab to select the values your pie or donut chart will compare.
Data Settings for Dashboard Scatter Chart Components: Scatter charts are useful to show one or two groups of report data plus summaries. These data settings for the scatter chart are available in the dashboard component editor under Component Data.
Formatting Settings for Dashboard Scatter Chart Components: Scatter charts are useful to show one or two groups of report data plus summaries. These settings for the scatter chart are available in the dashboard component editor under Formatting.
Data Settings for Dashboard Table Components: A table component shows columns of data from a custom report in a dashboard. The settings on the Component Data tab control how a table component gets and manages the data it displays.
Formatting Settings for Dashboard Table Components: A table component shows columns of data from a custom report in a dashboard. You can use color and scale to help users interpret the report data the table displays.
Dashboard Component Types: Dashboard components can be charts, tables, gauges, metrics, or other components that you can create with VisualForce.
Align Report and Dashboard Charts with Your Corporate Branding: Apply a consistent, professional look to all your charts without the need to manually select colors for each element. Configure a brand palette once in your org’s theme settings and apply them to report and dashboard charts.
Delete a Dashboard: It’s a good idea to delete dashboards that you no longer need.
Download a Dashboard to a PNG File: Download your dashboard as a PNG file that you can embed in presentations or refer to wherever you are.
Dynamic Dashboards: To provide individualized views of data without configuring separate dashboards and folders, create a dynamic dashboard. A dynamic dashboard shows each user only the data that they have access to.
Add Custom Lightning Web Components to Lightning Dashboards: Use custom Lightning Web Components (LWCs) in Lightning dashboards to add meaningful visual context and support new use cases. Custom LWCs provide interactive, real-time data views that go beyond standard dashboard capabilities. For example, you can add an LWC to display a waterfall chart, which isn’t available in standard dashboard components.
Add a Lightning Dashboard to an LWR Experience Cloud Site (Beta): Give your partner users direct access to a Lightning dashboard by embedding it on your Lightning Web Runtime (LWR)-based Experience Cloud site. In Experience Builder, add the Lightning Dashboard component to a page, select the dashboard, and publish the site. Lightning dashboards added to an LWR Experience Cloud site are available in view mode only.
Filter Dashboards with URL Parameters in Lightning Experience: There’s no need to set up the same filters each time you view your dashboard in Lightning Experience. Create a custom URL that includes the filter values. Then when you access the URL, your dashboard opens with the filters already set. For example, you can customize your Opportunity Stages dashboard with a different URL for each account owner. Or you can create a URL to zero in on the closed deals for a particular account.
Install the Preconfigured Sales Cloud Dashboards from AppExchange: The preconfigured Sales Cloud dashboards and underlying reports are based on Sales Cloud objects and fields. Use these dashboards to track key performance metrics for your executives, sales managers, sales reps, and other team members.
Dynamic Gauge Charts in Lightning Experience Dashboards: A gauge chart shows how close you are to reaching a goal or target. The chart target is the maximum value in the chart. The chart measure is the current status or performance, as indicated by the pointer. The segment ranges green, yellow, and red correspond to excellent, fair, and poor performance.
Link from Dashboard Widgets in Lightning Experience: A sales overview dashboard is a great way for teams to collaborate on opportunities because it provides a complete picture of your pipeline. Dashboard widgets already link to source reports, so you can get more details. Link to other destinations to take action on the data.
Visit a Link from a Dashboard Widget: Dashboard builders can link dashboard widgets to Salesforce records or other websites. Visit a link to act on dashboard insights!
Improve Dashboard Performance: Dashboard refresh times can vary based on available resources. Since Salesforce is built on a multi-tenant structure, dashboard refreshes are processed in a queue. Refreshes tend to complete faster off peak hours, when more resources are available. Other factors like caching can also affect dashboard refresh times.
Post Lightning Dashboards to Slack: Share analytics with colleagues across your Slack workspaces and channels. Recipients can view dashboard details, share, subscribe, view a snapshot of the dashboard with the latest data, and quickly open the dashboard in Salesforce.
Print a Dashboard: Print dashboards using your browser’s print option.
Refresh Dashboard Data: To get the most current data, refresh a dashboard at any time. The refresh updates the results for every user who has access to the dashboard. Changing filters on a dashboard also refreshes the dashboard, unless there’s existing cached data. You can view when the dashboard was last refreshed under Last Refreshed.
Subscribe to Dashboards in Lightning Experience: Rather than manually refreshing a dashboard, you can subscribe to it. Subscribe yourself and other users, groups, or roles to receive refreshed dashboard results by email on a schedule that you set.
Subscribe to or Schedule Dashboards: Set up automatic dashboard refreshes and receive refreshed dashboard results by email on a schedule that you set. If you start your week by reviewing your Sales Overview dashboard, expedite your morning by subscribing to the dashboard. After subscribing, you can have it automatically refreshed and in your email inbox each Monday morning at 8:00 AM.
Show Tabular Information with a Lightning Table: Show up to 200 records and 10 columns and from any field in a source report’s report type. After grouping data in a Lightning table, you can add measures (sum, average, minimum, and maximum) to the grouped data. If a Lightning table's source report has a summary formula column, then you can show it on the Lightning table.
Show Subtotals on First-Level Groups in Lightning Tables: Lightning tables are a powerful tool for summarizing data on a dashboard. Second-level groups automatically show subtotals for each measure column. Show subtotals to add subtotals for each measure column to first-level groups.
Sort Data in Dashboard Widgets: Sort dashboard widgets by groups and measures. Sort ascending or descending. Dashboard widgets support two-level sorting. So you can sort by Stage and then by Sum of Amount, or by Average Age and then Record Count.
Dashboard Data Visibility Considerations: A dashboard viewer sees data based on the assigned privileges and access of the dashboard’s running user. A static dashboard displays data using the configured user’s privileges. A dynamic dashboard displays data based on the logged-in user.
Control Dashboard Data Visibility: A dashboard displays data from each widget’s source report. You can control which data is visible based on the running user.
View Filtered Source Reports in Lightning Experience: Want to know more about the data shown in a dashboard chart? Click a chart segment, axis label, or legend to view a filtered version of the source report.
Report on Historical Data with Reporting Snapshots: A reporting snapshot lets you report on historical data. Authorized users can save tabular or summary report results to fields on a custom object, then map those fields to corresponding fields on a target object. They can then schedule when to run the report to load the custom object's fields with the report's data. Reporting snapshots enable you to work with report data similarly to how you work with other records in Salesforce.
Data 360 Reports and Dashboards: Create standard reports on single or multiple related data model objects (DMOs) or Calculated Insights, or build a custom report type on DMO fields and records from Data 360. Visualize KPIs and trends from Data 360 reports in your dashboards.
Define a Reporting Snapshot: After you create a source report, target object, and target object fields, you can define your reporting snapshot. You define a reporting snapshot by naming it and choosing the source report that will load report results into the target object you specify when the reporting snapshot runs.
Manage Reporting Snapshots: After you set up a reporting snapshot, you can view details about it and edit and delete it. From Setup, enter Reporting Snapshots in the Quick Find box, then select Reporting Snapshots to display the Reporting Snapshots page, which shows the list of reporting snapshots defined for your organization.
Map Reporting Snapshot Fields: After you create a source report, target object, target object fields, and define your reporting snapshot, you can map the fields on your source report to the fields on your target object. You map source report fields to target object fields so that when the report runs, it automatically loads specific target object fields with data from specific source report fields.
Schedule and Run a Reporting Snapshot: After you create a source report, target object, target object fields, define your reporting snapshot, and map its fields, you can schedule when it runs. You can schedule a reporting snapshot to run daily, weekly, or monthly so that data from the source report is loaded into the target object when you need it.
Prepare Reporting Snapshots: To set up a reporting snapshot, you need a source report and a target object with fields to contain the data in the source report.
Troubleshooting Tips for Reporting Snapshots: The Run History section of a reporting snapshot detail page displays if a reporting snapshot ran successfully or not. When a reporting snapshot fails during a scheduled run, the failure is noted in the Result column. To view the details of a run, click the date and time of the run in the Run Start Time column.
Custom Four-Column Table: Table visuals are useful to list values of several items against some criteria. The fields you add to the source report chart are the ones available for the table columns in dashboards.
Why Doesn't My Dashboard Display the Data I Expect?: If you’re not seeing the data that you expect, refresh for the latest data, check that you have the right running user, and verify your dashboard data sources.
Why do my chart labels overlap?: Sometimes the labels for your charts can overlap and be difficult to read. There can be too much data in the same space, or the segments or wedges of the chart can be too small.
Why doesn’t my report return the data I expect?: Check your filters, groupings, fields, report type, and role or user hierarchy to make sure that you are asking the right questions of your data.
Why doesn’t my report return any data?: Check with your administrator to make sure that you have access to the records you’re trying to report on. If you’re still not seeing any results in your report, try casting a wider net.
Why can't I see formula options in report builder?: If you can't see the Add Formula or Add Summary Formula option in the Fields pane of the report builder, add a row grouping to the report. (In Salesforce Classic, change your report format to summary, matrix, or joined.) Formulas require at least one row grouping.
Relative Date Filter Reference: Relative date filters let you filter on date fields using easy-to-understand, human-speech-inspired syntax.
Filter Operators Reference: The operator in a filter is like the verb in a sentence. Operators specify how filter criteria relate to each other. Refer to this list of filter operators when setting filters on list views, reports, dashboards, and some custom fields.
Filter Report Data by Role Hierarchy: You can view records based on org structure or job function? Get records owned by everyone in a job role (like sales manager) and their subordinate roles (such as sales person) by filtering your report on a role.
Preparing Sales Leaders to Report on Forecasts: Unlock details about forecasts with and without adjustments, overall planned revenue, and quota attainment when you create custom report types for forecasting When you create a custom report type, choose the primary and related objects carefully, because they determine the forecast types you can report on.
Create a Custom Report Type for Forecasting: To make a forecasting report available to users, administrators must create a custom report type. A report type defines the set of records and fields available to a report based on the relationships between a primary object and its related objects. Reports display only records that meet the criteria defined in the report type.
Idea Reports: Create custom report types so users can create reports about ideas, idea comments, and votes.
Improve Dashboard Performance: Best Practices: If a dashboard is running slowly, it likely because of inefficient source reports or because many people are refreshing it at once. By optimizing source reports and planning dashboard refreshes, a slow dashboard can be sped up. Follow the tips in this guide to speed up sluggish dashboards!
Improve Report Performance: If your report is running slowly, it’s generally because it’s processing a lot of data, which can be reduced with filters and other methods. Review these best practices and recommendations to optimize your report and help it run faster.
Report on Knowledge Views and AI Grounding: Track how customers and service agents interact with Knowledge content so you can discover trends and measure article effectiveness. See how Knowledge is used for AI-generated content across all service channels and Einstein products.
Manage Your Intelligent Apps: Use the App Management tab to manage Intelligent Apps, such as Revenue Intelligence, Commerce Intelligence, or Service Intelligence. You can monitor the status of Intelligent App installs, view logs to troubleshoot install issues, update and delete apps, and schedule app refreshes.
Site Case Deflection from Discussions and Articles: Get insight into how well the Contact Support Form and Case Deflection components actually deflect cases from being created. Using the Case Deflection Dashboard, get real-time metrics on potential and confirmed case deflections, the most helpful articles and discussions, and the least helpful articles and discussions.
Related Articles and Questions Reports: Create custom report types so users can report on click-through rates on related articles and questions. Salesforce Einstein suggests related articles and questions in communities. Use the click-through rates reports to create content around the most accessed articles and questions, empowering your customers to find the information they need.
Generate the Extended Semantic Model (Beta): Review the data model objects that the Reports Metadata Bridge adds to the base semantic model, and then generate the extended semantic model.
Review Objects in the Base Semantic Model (Beta): During field mapping, the Reports Metadata Bridge shows source objects that are already in the base semantic model so that you focus on the objects that the extend flow adds.
Start the Reports Metadata Bridge and Select Your Data: Start the Reports Metadata Bridge to select your Lightning Experience report types from the populated list to find the source data objects to use for semantic model generation.
Reports Metadata Bridge Prerequisites: Using the Reports Metadata Bridge requires the appropriate licenses, org and user account setup, and permissions assigned to user accounts.
Build a Dashboard: When you’re ready to share Salesforce data with colleagues, build a dashboard. Dashboards let you curate data from reports using charts, tables, and metrics. If your colleagues need more information, they can view your dashboard’s data-supplying reports.
Organize Dashboards: Keep your dashboards at your fingertips by embedding them around Salesforce, printing them, or deleting dashboards that you don’t need anymore.
Dashboards: Dashboards help you visually understand changing business conditions so you can make decisions based on the real-time data you’ve gathered with reports. Use dashboards to help users identify trends, sort out quantities, and measure the impact of their activities. Before building, reading, and sharing dashboards, review these dashboard basics.
Work with Dashboards: Click a dashboard’s name to run it. Dashboard charts are interactive, so hover and click them to get more information. From refreshing dashboard data to sharing dashboard widgets, there are lots of ways to use dashboards to keep yourself and your team informed and collaborating.
Adding Calculated Columns with Einstein Report Formula Generation: Einstein Report Formula Generation can help you with the technical work of creating row-level and summary formulas. Describe a calculation in simple terms, and Einstein generative AI discovers the relevant data and suggests a formula.
Einstein Report Formula Generation Error Messages: If Einstein’s response doesn’t meet your expectations, you can generate another response by entering a new request. On the other hand, if your request doesn’t provide the information needed, Einstein lets you know. Either way, just try again, phrasing your request differently.
Guidelines for Requesting Row-Level Formulas: A row-level formula evaluates each record in a report, with the results of the formula shown in each report row. Learn more about row-level formulas to help you request useful formulas from Einstein Report Formula Generation.
Guidelines for Requesting Summary Formulas: A summary formula evaluates a report's group subtotals and grand totals, with the results of the formula shown in summary rows. Learn more about summary formulas to help you request useful formulas from Einstein Report Formula Generation.
Add an AI Sentiment Formula to a Data 360 Report (Beta): Add the AI_SENTIMENT function in a Data 360 report to analyze unstructured text fields, such as support tickets, customer reviews, or survey responses, and return a sentiment classification for each record.
Add Dimensional Hierarchies to Data 360 Reports: Explore data across different levels of granularity by using dimension hierarchy in your Data 360 reports. Drill down or roll up your data structure to uncover the exact insights you need.
Create a Data 360 Report on Calculated Insight Object History (Beta): Analyze historical data trends by building reports on Calculated Insight Object (CIO) history. Compare metric snapshots across different time periods. CIO history reports support only aggregated tables, such as summary or matrix tables. Detail rows aren't supported.
Create a Standard Report on Data 360 Objects: Create standard reports on calculated insights, semantic models, or single data model objects (DMOs) to determine which areas of your business to focus on.
Dimension Hierarchies in Data 360 Reports: Hierarchies in Data 360 Reports improve data exploration and reporting efficiency. By structuring related data fields, hierarchies save you from manually selecting each field and help you find answers faster.
Data 360 Report Examples: Find answers to business questions with Data 360 reports. These examples illustrate how different industries can use Data 360 reports to identify which customers or areas of business to focus on.
Generate Trend Charts for Calculated Insight Object History (Beta): Visualize how your metrics evolve over time by tracking patterns, such as segment growth or account transitions. When you use snapshot dates to compare historical data points directly in Data 360 reports, you can identify trends and improve forecast accuracy.
Compare Data Across Data Model Objects with Joined Reports: Use joined reports to compare data from multiple Data Model Objects (DMOs) in a single report. Create separate report blocks from report types based on different DMOs, and group them across blocks using a shared common field to compare data side by side.
Build a Joined Data 360 Report: Create a joined Data 360 report to compare data from related Data Model Objects (DMOs) in a single report. Joined Data 360 reports use the same report blocks, grouping, filtering, and cross-block summary formulas as the joined reports in Lightning reports.
Use Multiple Currencies in Data 360 Reports: Use a report currency to analyze data that contains currency values from multiple countries or business units. Convert currency fields to a single reporting currency so you can accurately compare, sort, filter, group, and summarize records in the same report.
Currency Support in Data 360 Reports: Data 360 reports support the currency data type. Reporting behavior depends on whether the org uses a single currency or multiple currencies. Currency support works across Data Model Objects, Calculated Insights, and Semantic Data Models.
Use Organizational Hierarchies in Data 360 Reports (Beta): Use organizational hierarchies in Semantic Data Model reports to analyze data based on role or manager relationships defined in your semantic model. This is useful when your users need to view report data scoped to their position in your company. For example, managers can view records for users below them in the hierarchy, or view all records without hierarchy-based filtering.
Filter Report Data by Organizational Hierarchy (Beta): Add an organizational filter to a Semantic Data Model report to scope the data by a role or manager hierarchy defined on the underlying semantic model. Select whether to see your team's records or all records in the hierarchy tree.
Global Search for Folders: You can search for Lightning report or dashboard folders using global search. Global search for folders isn’t supported in the mobile version of Salesforce.
Quick Search for Folders: Quick search allows you to find folders within the Lightning Reports tab, Dashboards tab, or selected folder context.
Share a Lightning Report or Dashboard Folder: Use enhanced folder sharing to share a report or dashboard folder with users, groups, roles, or territories. Enhanced folder sharing is the default option for all orgs created after 2013.
Build a Report: When you have questions about your Salesforce data, like “How much revenue did we earn in the South East last quarter?”, “Which lead source is generating the most closed opportunities?”, or “What is the average age of all open cases?”, build a report to get the answers.
Report and Dashboard Limits, Limitations, Allocations, and Technical Requirements: As you report on your Salesforce data and build dashboards, be aware of these limits, limitations, and allocations. If you experience slow load times or sluggish interactions with reports and dashboards, verify that your network and device meet the minimum recommended technical requirements for Lightning reports and dashboards.
Organize Reports: Keep your reports at your fingertips by sorting them into folders and deleting unused reports. If you have a lot of reports, you can use the search field to find the one you need.
Reports: Reports give you access to your Salesforce data. You can examine your Salesforce data in almost infinite combinations, display it in easy-to-understand formats, and share the resulting insights with others. Before building, reading, and sharing reports, review these reporting basics.
Run and Read a Report: Click on a report’s name to run it. After running a report, there are a series of tools you can use to help you read the information.
Troubleshoot Reports: Use these tips to help solve problems that arise when you’re working with reports.
Sales Engagement Reports: Sales Engagement reports give you information about your inside sales efforts. As your reps work through cadences, you can evaluate how your prospects become qualified leads and which cadence step is most effective. You can also examine phone call statistics for your reps, which reps are converting the most prospects, and more.
Account and Contact Reports: Use account and contact reports to learn about active, neglected, or new accounts, as well as accounts by account owner or partner. The two standard contact reports let you create a mailing list of contacts or track opportunities by contact role.
Activity Reports: Activity reports are useful for gathering information about open activities, completed activities, multi-person events, or pending approval requests for which you're a delegated approver.
Add Custom Disclaimers to Exported Reports: Control the legal and compliance language on your exported reports using custom disclaimers. Add a custom disclaimer after the standard Salesforce disclaimer to ensure compliance with corporate policies, regulatory requirements, and international standards.
Add a Block: Adding blocks to joined reports lets you create multiple views of the data included in a single report.
Administrative Reports: Administrative reports help you analyze your Salesforce users, documents, and pending approval requests. You can report on the active Salesforce users and see who has been logging in.
Run Reports in the Background: Run large reports in the background so you can keep working in Salesforce without waiting for results to display. This is a good way to run data-intensive reports that might otherwise time out due to the large number of report results.
View and Manage Background Reports: After you've set up a report to run in the background, you can view and manage it from the background report exports list.
Show and Hide the Record Count for a Block: You can choose to show or hide the number of records, or record count, for each block in a joined report. By default, record count is displayed for each block in the report builder and on the run reports page.
Edit a Bucket Column: When it’s time to add, remove, or change buckets in a bucket column, edit it.
Enter Values for Text Buckets: A text bucket field needs a descriptive name and two or more buckets containing text strings.
Edit a Numeric Bucket Field: A numeric bucket helps you sort data that can be described in terms of numbers.
Numeric Bucketing Example: Deal Size: To gain insight into your deals, use bucketing to group by deal size instead of looking at individual deals. This lets you concentrate on the large deals that affect your quota the most.
Categorize Data with Bucket Columns: Quickly categorize report records without creating a formula or a custom field by bucketing them. When you create a bucket column, you define multiple categories (buckets) used to group report values. Like any other column in your report, you can sort, filter, and group by bucket columns.
View Your Reports on Mobile with Enhanced Reports: Enhanced Reports enables you to view your Lightning reports in a mobile-friendly interface without reconfiguring report formats. Creating new reports and editing reports are still done on the full desktop site, with those changes automatically rendering on mobile.
Build a Report in Lightning Experience: Lightning Report Builder is a powerful and intuitive tool for analyzing your Salesforce data. Group, filter, and summarize records to answer business questions like “How much revenue did we generate from new business in California last quarter?” For a visual overview of your data, add a report chart. Refresh the preview to see progress as you build. When finished, run your report to see the full results.
Keep Working While Your Report Preview Loads: For most actions, you can continue working on your report while the preview loads. For example, when editing a report you can drag multiple fields into the report, then create a grouping while those columns load.
Present Data Effectively with Charts: Report chart appearance depends on the chart type. For example, donut and funnel charts don't render negative values. Chart appearance can also be affected by very large or small numbers, custom summary formulas, and field-level security. Chart appearance can change when you switch the report format or change groupings and blocks.
Add a Chart to a Report: Add a chart to give users a visual way to understand the data in your report.
Build a Report in Salesforce Classic: Report Builder is a drag-and-drop tool for accessing your data quickly and comprehensively. Use it to set up new reports and edit existing ones.
Report Fields in Salesforce Classic: The Fields pane displays fields from the selected report type, organized by folder. It also lists custom summary formulas, which you can create, edit, and delete.
Group Your Report Data in Salesforce Classic: Group data in columns or rows in summary, matrix, and joined reports to display meaningful information. For example, group opportunities by Close Date to see closed opportunities or group cases by product to see the number of cases for each product. You can have groupings inside groupings.
Summarize Report Data: When you want to know sum, average, highest, or lowest value of a numeric column (like Amount, Age, or Probability), summarize the column. Summaries calculate subtotals for every group in a report, as well as a grand total for all report data.
Filter Reports by Values: Filter a report by value when you want to define the filter criteria yourself. For example, filter for opportunities worth more than $50,000.00, cases that mention word "widgets" in the subject, or Accounts located in California.
Filter Reports Using Field Comparisons with Field-To-Field Filters: Field-to-field filters let you filter a report by comparing the values of two different report fields. For example, see cases modified after closing date by filtering on cases with a last modified date after the closed date.
Filter Report Data: What if your report gives you more data than you need? Use filters to pare down your report until it only shows the data that you want.
Highlight Data Ranges with Conditional Formatting: Highlight field values reports based on ranges and colors you specify. You can apply conditional formatting to reports that are grouped by rows (summary reports) or grouped by rows and columns (matrix reports).
Choose a Report Type in Salesforce Classic: The report type you choose determines which records and fields appear in your report. For example, the Opportunities report type gives you access to Opportunity records and fields like Amount, Stage, and Type.
Customizing Reports in Salesforce Classic: Report builder is a visual editor for reports. The report builder screen lets you work with report fields and filters, and shows you a preview of your report with just some of the data.
Campaign Reports: Use campaign reports to analyze your marketing efforts. You can report on the ROI of your campaigns, track who you targeted with your campaigns and who has responded, or analyze which opportunities resulted from your campaigns.
Report Formats in Salesforce Classic: A report can use the tabular, summary, matrix, or joined format. Choose a format that’s complex enough to capture the data you want to show, but simple enough to communicate it effectively.
Change the Principal Report Type: The principal report type controls how common fields are named. A joined report must have a principal report type. You can change the principal report type at any time.
Chart Properties: You can add a chart to any report that’s grouped by rows (summary report) or rows and columns (matrix report). The chart properties specify the data that appears in the chart, its labels and colors, and any conditional highlighting.
Report on Chatter Top 100 Feed Item Views with Interaction Count Report: Get a report on the top 100 feed item views in your organization or community with the primary object Interaction Count Report. Knowing the posts that people are looking at most is a great way to get insight into current Chatter trends. Actions counted as views include liking, commenting, and viewing a feed item detail. View counts aren’t unique. For example, a user can add three comments to a post, and that is counted as three views. After you set up the report, it’s run weekly.
Combination Chart Examples: Use a combination chart to show multiple values against a single axis range, show two chart types together, or compare two continuous summary values.
Show Multiple Sets of Data in One Chart: A combination chart plots multiple sets of data on a single chart. Each set of data is based on a different field, so values are easy to compare. You can also combine certain chart types to present data in different ways in a single chart.
File and Content Reports: Run File and Content reports to analyze how users are engaging with files and Salesforce CRM Content.
Create a Personalized Report Filter: Create a report that dynamically shows results that are personalized for each user. For example, create an opportunity report for your sales team with a filter that personalizes the results for each opportunity owner when they open the report.
Filter Across Objects with Cross Filters: Use a cross filter to fine-tune your results by including or excluding records from related objects and their fields, without having to write formulas or code. You can apply cross filters by themselves, or in combination with field filters.
Example: Multiple Cross Filters and a Subfilter: Use cross filters to filter a report by an object's child objects using both WITH and WITHOUT conditions. For example, filter a report to show accounts that have cases but don’t have activities.
Create a Cross Filter: Use cross filters to include or exclude records in your report results based on related objects and their fields.
Delete a Cross Filter: When you no longer need a cross filter, or a subfilter in a cross filter, delete it.
Edit a Cross Filter: To change how a cross filter filters the primary or secondary object, edit it.
Example: Use WITH in Cross Filters: Use cross filters to filter a report by an object's child objects using WITH conditions. For example, filter a report to show just accounts with cases.
Example: Use WITHOUT in Cross Filters: Use cross filters to filter a report by an object's child objects using WITHOUT conditions. For example, filter a report to show just contacts without activities.
Create a Custom Report: You can customize standard reports, or you can build custom reports from scratch to suit the exact needs of your organization.
Delete a Report: You can delete a selected report from the Reports tab or from its run page.
Delete a Block: When you no longer need a block, delete it.
Disable Formulas in Exported Reports: Disable formulas in reports exported as a CSV file or an excel file by adding a straight apostrophe (') before field values that start with characters such as equals (=), plus (+), minus (-), and so on. This prevents spreadsheet programs from misinterpreting these cell values as formulas and reduces security risks.
Drill Down into Your Reports to Learn Even More: Drill-down helps you take a closer look at records in a report. For example, as a sales manager, drill-down can help you track the progress of just a few of your reps or review the breakdown of current opportunities based on type.
Filtering Report Charts to Show Data Relevant to the Page: Charts usually filter to show relevant data when the chart’s report type has a matching ID field for the record, such as Account ID for an account record. Occasionally, charts show data unfiltered. But you can set up a chart to be filterable if its report type has a lookup to the matching ID field.
Refreshing Report Chart Data: Normally, charts refresh data once every 24 hours. But you can change a chart to refresh each time someone opens the page it’s on. The refresh option is under the Chart Properties dialog box of the page layout editor. However, we recommend daily refresh over selecting the option, because users will soon reach the refresh limit or will wait for chart data to show until refresh is complete.
Hiding a Report Chart That Shows an Error: A chart can sometimes, for whatever reason, show an error on the detail page instead of data. Since a chart with an error isn’t that useful, you have the option to hide it from viewers.
Example of Report Charts on an Account Page: In this example, we've embedded two report charts on an important account page that show deals in the pipeline and open support cases for the account. From looking at the charts, the account executive can quickly gauge the account’s activity and health.
Add a Lightning Report to an LWR Experience Cloud Site (Beta): Give your partner users direct access to a Lightning report by embedding it on your Lightning Web Runtime (LWR)-based Experience Cloud site. In Experience Builder, add the Lightning Report component to a page, select the report, and publish the site.
Add a Report Chart to a Page Layout: To embed a report chart on object pages, edit the object’s page layout with the enhanced page layout editor, then add the chart.
Embedded Report Charts: Typically, users have had to navigate to the Reports tab to find data. But you can give them valuable information directly on the pages they visit often. To do that, embed report charts in detail pages for objects. When users see charts on pages, they can make decisions based on the data they see in the context of the page without going elsewhere. For example, an opportunity record shows important data directly on its detail page.
Salesforce Reports in Quip: Work with connected report data in a document to collaborate on live Salesforce data in context. @Mention colleagues to discuss data inline or in the document conversations pane. Update filter values from Quip to customize your Salesforce report views without altering the base report in Salesforce.
Calculate Values Relative to a Parent or Peer Group: You can add a formula that calculates values relative to a parent or peer grouping in a Lightning report. PARENTGROUPVAL calculates values relative to a parent grouping. PREVGROUPVAL calculates values relative to a peer grouping. For example, calculate the proportion of total opportunity amounts from each lead source, or compare sales each month to the previous month.
Joined Report Examples: Creating a sales rep performance scorecard, reviewing support cases by status, and predicting your opportunity pipeline are some examples of what you can do with joined reports. Here’s how.
Export a Report: You can export report data to a spreadsheet.
Filter Field History Reports by Old and New Values: Field history reports let you track changes in fields by adding an “Old Value” and a “New Value” column. With a field history report, each report row represents a change to a record. By filtering a field history report, you can answer questions like “Which opportunities have we closed so far this financial quarter?”
Filter Reports with URL Parameters in Lightning Experience: When linking to or bookmarking a report, add filter value parameters to the URL to customize how the report appears when opened. For example, bookmark your opportunities report and add a filter to specify whether you see New Business or Existing Business.
Evaluate Report Data with Formulas: Reports feature built-in basic math functions (sum, average, min, and max) that you can apply to any numerical column in a report. When you're ready to perform advanced logical or mathematical operations, write a formula.
Compare Groups with PARENTGROUPVAL() and PREVGROUPVAL(): Write row level formulas to evaluate each record in a report. For example, see how many days it took each opportunity to close by subtracting the Open Date from the Close Date.
Write a Row-Level Formula: Writing a row-level formula adds a row-level formula column to your report that makes calculations on every report row. Write row-level formulas directly in the Lightning report builder.
Show Report Data Graphically: To help readers understand your data quickly and easily, show the data in chart form. Charts appear just above the report table. They can help users get a feel for the data before they delve into the details. Use line charts to track changes over time, or a bar or pie chart to compare values at a point in time. You can add a chart to any report that’s grouped by rows (summary report) or rows and columns (matrix report). Charts can also appear in dashboard widgets.
Hide Unused Report Types: If your users use only a subset of the available standard report types, you can hide the ones they don’t need.
Track History for Cases: Monitor activity across cases and identify your case status changes in the last three months of a case’s life cycle.
Track Changes Over Time with Historical Trend Reporting: Historical trend reporting uses a special custom report type designed to highlight changes between five snapshot dates, such as five business days or five business weeks. You can visually represent the data changes in charts and on dashboards.
Filter Historical Report Data: When you filter on a historical field in a historical trending report, you specify a past date or date range in addition to an operator and value. Results are returned if the filter criteria are true during the historical date range.
See How Forecast Amounts Have Changed: Let's build a historical report, in matrix format, of the amounts your team members have assigned to each forecast category. Changes in those amounts can reveal how accurately your team is forecasting.
Limitations on Historical Trend Reporting: Historical trending in Salesforce is subject to certain limits on the time during which data is tracked, the number of rows of data, and the fields and objects you can track. These limits are designed to restrict the data set so that reports return data quickly.
Track Changes in Your Sales Pipeline: A historical report can help you monitor your company’s sales pipeline to make sure it contains enough activity to meet current and future sales goals. For example, focus on deals whose value grew or shrank in the last three months, or deals that moved into or out of a given target period.
Report on Historical Changes: On top of the standard up-to-the-minute reporting on the current state of your business, you can analyze day-to-day and week-to-week changes in opportunities, cases, forecasts, and custom objects.
Analyze Reports with Einstein Discovery for Reports: Einstein Discovery for Reports (previously known as Einstein Data Insights) scans your report data - quickly and thoroughly - using artificial intelligence and comprehensive statistical analysis. Einstein Discovery for Reports goes deep into the report data, explores underlying patterns, identifies insights, and surfaces those insights with charts and explanations that are easy to understand. Einstein Discovery for Reports works with Tabular and Summary reports.
Create a Joined Report: With up to five blocks, joined reports let you view different types of information in a single report. Each block shows data from one standard or custom report type.
Add a Summary Formula to a Joined Report: Summary formulas in joined reports are just like summary formulas in standard reports, except they can apply to multiple blocks or just one. When you add a summary formula to a block, you specify whether it applies across blocks or only to that block.
How Joined Reports Work: A joined report can contain data from multiple standard or custom report types. You can add report types to a joined report if they have relationships with the same object or objects. For example, if you have a joined report that contains the Opportunities report type, you can add the Cases report type as well because both have a relationship with the Accounts object.
Lead Reports: Use lead reports to show information about the source and status of leads, how long it takes to respond to leads, neglected leads, and the history of lead fields.
Limit Report Results: Set limits to the scope of your report to avoid processing too many records. The built-in choices for limiting your results vary according to the object you’re reporting on.
Chat Session Reports: Use Chat Session reports to consolidate data about agents’ activities while they chat with customers–for example, how long agents are online or how many chat requests are assigned to them.
Subscribe to Get Report Notifications in Salesforce Classic: In Salesforce Classic, subscribe to a report to receive report notifications periodically when the metrics you care about most meet certain conditions. Set the conditions that trigger notification, and specify if you want to be notified via Salesforce app notifications, Chatter, or email.
Opportunity Field History Report: View information about the change history of key opportunity fields, including old and new values and the dates edits were made.
Opportunity History Report: View information about the history of your opportunities, including stages and close date.
Opportunity Trends Report: View information about trends shared by the opportunities in your pipeline.
Opportunity Reports: Opportunity reports provide information about your opportunities, including owners, accounts, stages, amounts, and more. The default settings show you the most commonly used information from each object, but you can customize a report to view other information, such as primary campaign source, forecast category, and synced quote.
Opportunity Report Types: Report types provide a report template that you can customize to capture the unique data you’re after without creating a report from scratch. Many of the opportunities custom report types include information from associated objects, such as products, partners, and quotes.
Standard Opportunity Reports: Standard opportunity reports help you report on your opportunity pipeline and history, opportunity sources, opportunity types, and more.
Opportunities Reports: View standard information about your opportunities, including owners, accounts, stages, amounts, and more. The default settings show you the most commonly used information from each object, but you can customize the report to view other information, such as primary campaign source, forecast category, and synced quote.
Opportunities with Historical Trending Report: The Opportunities with Historical Trending report is a custom report type designed to help you analyze historical trends in your sales pipeline.
Opportunities with Competitors Report: View information about your company's competitors for opportunities, including their strengths and weaknesses.
Opportunities with Contact Roles and Products Report: View information about the contacts and opportunities associated with a selected product. You must select a product to filter results by when you run the report. This report isn’t supported in Lightning Experience. In Lightning Experience, select a different report type.
Opportunities with Partners Report: View information about the partners you team with on your opportunities, including opportunity name, amount, and partner role.
Opportunities with Products Report: View information about the products associated with your opportunities, including product name and opportunity stage.
Opportunities with Quotes and Quote PDFs Report: View details about the quote PDFs created for each quote associated to an opportunity. The default settings show you the most commonly used information from each object, but you can customize the report to view other information, such as who created or last modified each listed quote PDF.
Opportunities with Quotes and Quote Line Items Report: View details about the quotes associated with opportunities, and the line items for each quote. The default settings provide the most commonly used information from each object, but you can customize the report to see any opportunity, quote, or quote line item field.
Opportunities with Opportunity Teams and Products Report: View information about your opportunity team members and their products, organized by opportunity. You must specify either a product or an opportunity team member to filter results by when you run the report.
Keep Favorite Report Folders in View: Pin your most-used report and dashboard folders to the top of the folder list so you don’t have to scroll down every time you need them.
Turn Automatic Updates to the Report Preview On or Off: Edit reports faster by turning off automatic preview updates. When off, you can make multiple edits without waiting for the preview to refresh after each edit. When you’re ready to preview data, manually refresh the report preview. Or, see sample records returned after each edit by keeping automatic previews on.
Print a Report: Print a report from the run page of a report using your browser’s print function.
Product and Asset Reports: Use product and asset reports to view information about the products your users currently have installed. Find out what assets your customers have, list the cases filed for a particular asset, or identify assets that aren’t associated with a product.
Describe a Report: Give your report a description to help people (and yourself) distinguish it.
Rename a Report: What’s in a name? A report by any other name still answers your business questions. When it’s time to change the name of a report, do so from report properties.
Preview a Record from a Report (Beta): Review and work with records directly from a report instead of opening each record on a separate page. Turn on preview mode to open records in a side panel while keeping the report visible. Record preview is available only in the report view mode.
Rename a Block: You can rename blocks to provide more user-friendly descriptions of the information they contain.
Reorder Blocks: You can reorder blocks in joined reports. Reordering blocks affects the report’s appearance but doesn’t affect the data in the blocks.
Report Type Reference: The report type you choose determines which records and fields appear in your report. For example, the Opportunities report type gives you access to Opportunity records and fields like Amount, Stage, and Opportunity Owner.
Save Your Report in Salesforce Classic: Click Save to update an existing report with recent changes, or Save As to clone the original report without changing it. In Lightning Experience, click Clone to clone the report.
Schedule a Report for Refresh: Schedule a report to run daily, weekly, or monthly. An HTML version of the report can be sent by email to users in your organization.
Manage a Report’s Schedule: Create, change, view, or delete a scheduled report from the Schedule Report page.
Tips on Scheduling Reports: Some tips to keep in mind about timings, limits, and email notifications when scheduling a report.
Schedule Reports in Salesforce Classic: You can set up a report to run itself daily, weekly, or monthly and send the results automatically to the people who need them. That way, you don’t have to remember to log in and do it yourself.
View a Report's Schedule: View a report's schedule on the Schedule Report page or from the Reports tab. View all report schedules for the organization under Setup.
Self-Service Reports: Self-Service reports help you analyze the effectiveness of your Self-Service portal. Find out how many cases are being viewed, how many customers are logging in, or what customers think of the solutions you’re offering.
Deliver Your Report: To get the information in your report to the people who need it, you can share the report’s URL, make the report available for Chatter feeds, or export the data to another tool, such as Excel. You can also set the report to run on a schedule so that viewers always have the latest information.
Show and Hide Report Details: You can show or hide report details from either the run reports page or the report builder. When you hide details, individual records don’t display in the report. Groupings, summary formulas, and record counts remain visible.
Smart Totaling in Reports: Smart totaling means that duplicate data is counted only once in a subtotal or total. Salesforce uses smart totaling when you run reports that include duplicate data in any column chosen for summing or averaging. Smart totaling is also available in dashboard widgets.
Attach Files to Report Subscriptions: When subscribing to a report, choose to receive report results as a formatted spreadsheet (.XLSX) or a comma-separated (.CSV) file attached to the subscription email.
Subscribe to Reports in Lightning Experience: Instead of manually running and emailing a report to your team, you can subscribe to a report. Subscribe yourself and other users, groups, or roles to receive refreshed report results by email on a schedule that you set.
Schedule and Subscribe to Reports: Receive notifications that keep you informed about metrics you care most about without having to manually run reports. In Salesforce Classic, you can specify criteria that trigger report notifications.
Report on Subscriptions and Notifications: Combine subscriptions, actions, and conditions in a single joined report that shows who set up each subscription, the schedule and conditions, and the recipients and actions. Learn about the objects used in subscription reporting and the cron expressions for subscription schedules.
Combine Subscriptions, Actions, and Conditions in One Report: To view the details of analytic subscriptions, create a joined report that shows the subscription owners, schedules, recipients, and triggering conditions. The joined report is based on two custom report types. You can filter the report by subscription and notification source.
Subtotal Report Results in Salesforce Classic: Subtotaling your reports gives you a tool to analyze trends in the data. You can group sets of information, sort the groupings, and compare subtotals for each set against the overall total. In summary and joined reports, you can also subtotal by multiple fields to give you cascading sets of information.
PARENTGROUPVAL and PREVGROUPVAL: Use PARENTGROUPVAL to calculate values relative to a parent grouping. Use PREVGROUPVAL to calculate values relative to a peer grouping.
Use a Summary Function in a Custom Summary Formula: Summary functions let you use grouping values in custom summary formulas for summary, matrix, and joined reports. There are two summary functions: PARENTGROUPVAL and PREVGROUPVAL.
Reporting on Support Activity: Use support reports to track the number of cases created, case comments, case emails, case owners, case contact roles, cases with solutions, the length of time since the case last changed status or owner, and the history of cases.
Fields Available for Case Reports: You can report on a number of key case fields in addition to the ones that are included in the standard and custom report types.
Show Report Data in Tables: To help readers scan for data easily, hide details and ranges, limit the number of results shown, and highlight with color. You can also show your table in a dashboard widget.
Use a Tabular Report in a Dashboard: You can use a tabular report as the source report for a dashboard table or chart widget if you limit the number of rows it returns.
Identify Historical Deals Over a Given Value: Suppose you want to focus only on deals in your pipeline worth more than $5,000, but you’re not concerned about today’s fluctuations. Use a historical trending report that filters out any deal with a value that was below $5,000 yesterday.
Identify Shrinking Deals: Historical trending analysis can help you prioritize by quickly identifying deals that are at risk. For example, target the deals in your pipeline that have decreased in value since yesterday. Use a historical trending report that gives you the deals whose value yesterday was greater than their value today.
Find Deals that Have Been Pushed Out: To focus on deals that are taking more time to close than expected, create a historical trending report that finds deals in your pipeline that have had their close dates moved to a later date.
Watch Your Pipeline Change Over Time: Let’s say you want to know how the total value of your pipeline today compares with its value yesterday. Create a simple report to compare the two dates.
Joined Report Limits, Limitations, and Allocations: Most of the things you can do with reports you can also do with joined reports, such as find, add, and remove fields; summarize fields; and run and save reports. However, there are some things you can’t do. When working with Lightning Experience joined reports, there are some Lightning Experience-specific limits and limitations to consider.
Work with Blocks: Blocks let you create different views of the information contained in a joined report.
Combine Different Types of Information in a Joined Report: The joined report format lets you view different types of information in a single report. A joined report can contain data from multiple standard or custom report types. You can turn any existing report into a joined report using the report builder.
Set Up Einstein Discovery for Reports: Einstein Discovery for Reports is available after you enable CRM Analytics and assign the Can Run Einstein Discovery for Reports permission to users. Can Run Einstein Discovery for Reports is included with several standard permission sets, which come with various Salesforce licenses. Or you can assign the permission to users individually.
Analyze Report Data: Let Einstein Discovery for Reports augment your report with AI and comprehensive statistical analysis to produce Einstein Discovery-powered insights into your data. Einstein does the analytic heavy lifting for you. Your efforts aren’t required to group report data, or summarize it, or chart it (unless you really want to).
Report on External Documents Attached to Cases: Create a report to see how many and which specific external documents are being attached to cases. Use this information to see which external sources have the most impact on closing cases. This report is helpful when you have set up Salesforce Federated Search, which gives users access to external search results when using the Knowledge One Widget. Users can attach external documents to cases only if Chatter is enabled.
Report on File Search Activity: Use a report to determine the top 300 search term queries for file searches and to identify which content is missing or needs to be renamed based on users’ search terms and the click-rank.
Tableau Semantics: Use Tableau Semantics to build semantic models for the Data 360 semantic layer and for Tableau Next analytics assets. Semantic models are where you define, govern, and query all your business metrics.
Standard Report Types: Salesforce provides a rich collection of standard report types that you can tailor to your unique requirements. You rarely need to create a brand-new report.
Tableau: Tableau is a complete, flexible analytics platform with cloud, on-premises, and desktop applications for harmonizing, managing, and exploring data with interactive visualizations. You can combine and manage data from multiple sources, including Salesforce, in the Tableau platform.
Tableau Product Portfolio: Tableau is a complete, flexible analytics platform for harmonizing, managing, and exploring data with interactive visualizations. You can combine and manage data from multiple sources, including Salesforce data, in the Tableau platform.
Tableau Pulse: Tableau Pulse empowers every employee with intelligent, personalized, and contextual insights delivered in the flow of work. Integrate data into your daily jobs to make better, faster decisions. Without having to learn a new tool or build comprehensive visualizations, Tableau Pulse helps you go beyond the how and what and shows you the why behind your data.
Preparing Your Data for Pulse: Once you know the critical KPIs you’d like to build in Pulse, the next step is to locate where the supporting data resides.
Learn Tableau Pulse with Trailhead: Get acquainted with Tableau Pulse through Trailhead, the fun and free way to learn Salesforce products.
Use Case Identification: Before jumping into how to best identify your first Pulse use case, start with Pulse’s value proposition.
Tableau Pulse On-Demand Webinars and Training Videos: Learn directly from expert trainers with deep dives into use cases, common questions, and best practices to inspire and guide you to create great Tableau Pulse implementations.
Connect to Salesforce Data in Tableau: Before you begin your analysis, connect to your data and set up the data source using the resources listed. If you have data outside of Salesforce, you can use one of the supported Tableau connectors or a partner-built connector from Tableau Exchange.
Learn Tableau with Trailhead: Get acquainted with the Tableau platform through Trailhead, the fun and free way to learn Salesforce products. Learn about Tableau with these trails, quick looks, modules, and projects.
Why aren't middle names appearing in full name fields on reports?: If your org has Enable Middle Names for Person Names turned on, which adds a "Middle Name" field to standard objects like users, leads, and contacts, then you may notice that the "Full Name" field on reports excludes middle names.
Tableau Next: Tableau Next is agentic analytics that delivers personalized, contextual, and actionable insights everywhere work happens.
About Unmetered Tableau Next: With the Tableau Next unmetered permission, certain analytics features are available for unmetered usage, including most queries and pre-configured AI agent usage. Unmetered usage doesn’t consume credits.
Add Filters to a Tableau Next Visualization: Use filters to focus on a subsection of your data. For example, zero in on sales orders greater than $10,000 in the Central region. You can apply filters to both measures and dimensions.
Tableau Next Setup: Tableau Next is the composable, AI-analytics platform that turns any type of data into actionable insights. Tableau Next requires Data 360 and the Data 360 semantic layer.
Select a Model Response Type for Tableau Agent: Admins can select an LLM model response setting for each agent using Agent Settings in Tableau Next. Applies to agents based on the Analytics and Visualization template or a custom agent that uses the Data Analysis subagent.
Create an Analytics Group: Analytics groups help manage your users’ access to assets in Tableau Next.
Assign a User a Tableau Next Admin Permission Set: To access the Tableau Next Administration page, and to enable Tableau Next in your org, users require the Tableau Unmetered Admin or Tableau Next Admin permission set.
Assign a User a Tableau Next Permission Set: Assign permissions to users based on their level of interaction with Tableau Next capabilities. Users must already be created in Salesforce. You can’t add users in the Tableau Next app. Each Tableau Next permission set that you assign to a user consumes a license. You can view your license status as you assign permission sets.
About Tableau Next Auditing (Beta): Learn how to view auditing data in Tableau Next, which attributes are collected in the AnalyticsUserActivityEvent object, and considerations for using Tableau Next Auditing.
Create a Tableau Next Exploratory Org for an Analyst: A Exploratory Org is an isolated environment designed to provide analysts with self-service capabilities, reuse assets from other places, perform their work independently, and not affect other environments.
Delete Tableau Next Exploratory Org: To remove a Exploratory Org when a user is offboarded or no longer requires access to Tableau Next, follow these steps. Deleting a Exploratory Org is permanent: it will delete all associated content and cannot be restored after the action is confirmed.
Set Up Agent Scoping for Tableau Agent in Tableau Next: Agent Scoping defines the list of semantic models that Tableau Agent can search when a user asks a question in a agent with no clear context. For example, the agent may lack context when it's used in Slack or when it's embedded in a page without Tableau Next assets such as a metric or dashboard.
Set Up Tableau Agent in Tableau Next: Configure users, turn on organization features, and create and activate an agent for analytics and visualizations in Tableau Next.
Enable Einstein Generative AI for Tableau Next: Einstein Generative AI provides summaries and automated visuals in Tableau Next. Tableau requires Einstein generative AI and the Einstein Trust Layer to access AI features. You also need to turn on Einstein generative AI data collection to save feedback and auditing data.
Enable Tableau Next Auditing (Beta): Track and analyze user interactions with Tableau Next Auditing. Turn on Auditing to collect interaction events in the AnalyticsUserActivityEvent object.
Enable Beta Connectors in Tableau Next: Enabling Tableau Next beta connectors allow your analysts to create connections using beta data connectors. As an admin, use this process to enable all beta connectors for your organization.
Enable Tableau Agent in Slack: When enabled, your users can open Tableau Agent in Slack. They can then ask questions in context about metrics and dashboards that have been shared to Slack from Tableau Next. The agent runs a query allowed based on the user's permissions, and analyzes questions based on the data for the metric and the semantic model enabled for the agent in Tableau Next.
Enable Metric Insight Summaries in Tableau Next: Tableau Metric Insight Summaries give users a personalized overview of the most important insights from the metrics that they care about. The Metric Insight Summaries feature is built natively into Tableau Next and is independent of the Agentforce platform. The summary is generated based on the metrics that a user follows and is displayed on the Following page in Tableau Next. Only users with permission can access the metric summaries.
Enable Tableau Next Exploratory Orgs: Tableau Next Exploratory Orgs allow your analysts to experiment with data and create content in a dedicated environment. As an admin, use this process to activate the Exploratory Org feature for your organization.
Enable Q&A Calibration for Tableau Agent (Beta): When Q&A Calibration is enabled in Tableau Next, analysts can test, calibrate, and improve responses to questions in Tableau Agent with conversational analytics.
Enable Slack for Tableau Next: When enabled, users can share Tableau Next links in Slack with a preview of the metric. After an admin Connects Salesforce and Slack, previews sent in Slack are displayed according to the senders’ permission access that is governed by Tableau Next. And this seamless integration between Slack and Tableau Next requires no additional apps. From Slack or Tableau Next, users can view related conversations that they have access to about Tableau Next analytics that have been shared in Slack.
Enable Tableau Next: Turn on Tableau Next. This also assigns you a Data Cloud Architect and Tableau Unmetered Admin permission set.
Set Up Tableau Next Features: Activate and configure additional Tableau Next functionality. Connect Slack, turn on Metric Insight Summaries, and control other capabilities.
Guided Tableau Next Setup: Get up and running with Tableau Next through the guided setup process. Configure Data 360, choose admin users, activate AI features, and define data security policies for standard objects.
Manage Embedding Authentication with External Client Apps: Create and manage External Client Apps (ECA) directly from the Tableau Next Administration menu. ECAs are packageable frameworks used to enable a third-party app to integrate with Salesforce using APIs and security protocols.
Tableau Next Permission Sets and Licenses: Each user that interacts with Tableau Next requires a license and a permission set. Each Tableau Next license maps to one or more standard permission sets that allow different levels of interaction with Tableau Next capabilities.
Tableau Next Exploratory Org: Tableau Next includes the Exploratory Org, an isolated environment designed to provide analysts with self-service capabilities.
Set Up Tableau Next: Get Tableau Next ready by activating and configuring platform functionality.
Exploratory Org Capabilities in Shared Workspaces: In Tableau Next, all of an analyst's data and resources reside within their Exploratory Org. This topic details the feature and function capabilities related to Exploratory Orgs when a workspace is shared with other analysts.
Set Up Data 360 for Tableau Next: Set up Data 360 for Tableau Next to enable real-time data access and insights in your visualizations. Tableau Next requires Data 360 for data ingestion and data queries. Before setting up Tableau Next, configure Data 360 and create a Data Model Object for your org.
Grant Salesforce Support System Admin Access to Exploratory Orgs: Grant Salesforce Support temporary system administrator access to a Exploratory Org so that support staff can investigate and resolve issues. Control which Exploratory Orgs a support user can access and set an expiration date for that access.
Assign Unmetered Tableau Next Licenses and Permission Sets: To enable unmetered Tableau Next usage, assign an unmetered Tableau Next license to your users. You can assign permission sets and licenses within Tableau Next or create custom permission sets and assign them within Setup.
Manage Tableau Next Users: Grant users access to Tableau Next and create groups for sharing Tableau Next assets.
Tableau Agent in Tableau Next: Learn about setting up and using Tableau Agent in Tableau Next. Prepare your data in Data Cloud, then enable Agentforce capabilities and create agents using templates with pre-built agentic capabilities that turn data into insights faster.
AI and Data Usage in Tableau Next: As a Tableau Next customer, it’s important for you to know what data is being used in the features where you’ve enabled AI.
Build an AI-Ready Semantic Model: A well-designed semantic model helps analytics agents understand data and confidently answer questions. You'll need to make your semantic model AI-ready to provide real-world context and reduce ambiguity for your agents. Success with using Tableau Agent in Tableau Next requires the agent to be grounded with an AI-ready semantic model.
Plan Your Tableau Agent Implementation for Conversational Analytics: As an analyst implementing Tableau Agent in Tableau Next, you play an integral role in getting the agent to work well for your organization. Successfully enabling conversational analytics in Tableau Next includes five major phases.
Optimize and Test a Semantic Model: Building, enriching, and testing a semantic model is an iterative process rather than a linear one. As you define and refine the semantic model, you can use various feedback mechanisms available in the Semantic Model Builder to make incremental adjustments. As you fine-tune your approach, each improvement can lead to better operational outcomes.
Design Semantic Models for AI Readiness: A well-designed semantic model is the key to getting accurate and relevant insights with agentic analytics in Tableau Next. It helps your analytics agents understand the data and confidently answer questions. When designing your semantic model, build on a foundation of AI readiness standards and well-established industry best practices for data modeling and data quality.
Build App Templates from Your Tableau Next Workspace: App templates are collections of related assets that give users a quick way to install customized, reusable functionality. For Tableau Next, create an app template from a workspace. The workspace's data and insights resources become a template app that users can install in the same org or in other orgs. The app template maintains the relationships between the resources for each installation, so users get the same workspace functionality wherever they install an app from the template.
Download Your App Template: Download your Tableau Next app template to add customizations with variables, rules, and chains for installation flow. The downloaded template is a Salesforce package that you can deploy and install in other orgs.
Create an App Template: App templates bundle assets from one or more Tableau Next workspaces so you can share related insights and data sources.
Edit or Delete an App Template: Edit a Tableau Next template's details or resources, remove template resources, or delete a template.
Use App Templates: The Templates page in Tableau Next shows app templates that you and other users have created. Create an app from a template to install customized workspace functionality in your org.
Template Chains and Rules in Template Builder: Visualize how a Tableau Next app template uses chains and rules to make runtime decisions based on configuration options and the subscriber org.
Build Dashboard Templates for Your Marketplace: In the Tableau Next Marketplace, analysts can share, customize, and deploy dashboard templates across your organization to improve efficiency and standardize your work. For example, after you build a dashboard highlighting your sales pipeline data, save it as a template to share with your team and other sales teams.
Create a Tableau Next Dashboard Template: Use dashboard templates to share your data knowledge with your team, allowing others to install and customize shared insights.
List Your Dashboard Template in the Tableau Next Marketplace: Share your dashboard template with other users by listing it in the Tableau Next Marketplace. Sharing empowers your team and other users to collaborate and share knowledge. It also reduces duplication in work and resources.
View Custom Templates: View the list of all custom templates created by yourself and other analysts in Tableau Next. This list tells you which templates are listed in the Tableau Next Marketplace, who created them, and what the template type is.
Work with Tableau Next: Access Tableau Next metrics, visualizations, and dashboards for effective, data-based collaborations with your team. Customize and personalize Tableau Next on your home page by filtering and following metrics. Explore and refine metrics and other analytics assets to show you the insights most relevant for your work.
Work with Tableau Next Dashboards: Filter and facet dashboards to see the most relevant views for your work. Download snapshot images of dashboards for your projects and to collaborate with your team.
Work with Tableau Next Metrics: Metrics are business KPIs used to track, compare, and assess performance or business processes over time. In Tableau Next, you can explore metrics, create goals, download, copy, and follow metrics to get insights in your flow of your work. You can also access and share metrics in Tableau Next for Slack.
Work with Tableau Next Visualizations: Take action on records directly from visualizations. Download snapshot images of visualizations for your projects and to collaborate with your team.
Creating Visualizations and Dashboards in Tableau Next: Explore data in a semantic model and surface insights that you can act on by building visualizations on related data in your workspace to draw conclusions and make business decisions. Take data visualization to the next level with dashboards, where you can add interactivity, enhance data exploration, and provide a clear overview of the critical information that you need in one place.
Configure the Account Growth Readiness Score Composite Metric: The Tableau Next Account Growth Readiness Score composite metric template integrates sales conversion, service risk, and marketing email engagement data to assess full-funnel readiness of your accounts. This template installs the pre-configured metric and 3 calculated fields used to determine your account growth readiness scores. Designed to allow analysts to visualize their data instantly and provide stakeholders with actionable insights, bypassing the time-consuming process of manual chart configuration and complex data modeling.
Configure the C360 Semantic Model: The Tableau Next C360 semantic model template offers a unified perspective of your data by consolidating metrics from various Customer 360 features, such as Sales, Service, Marketing, and Commerce. The template installs the semantic model and more than 40 pre-configured metrics, including open pipeline, open and closed cases, email delivery and open rates. These detailed insights are designed to support program managers and stakeholders. By providing a clear breakdown of performance across multiple dimensions, the template allows effective progress monitoring, the identification of areas needing improvement, and the execution of data-driven decision-making.
Create Effective Dashboards with Tableau Next: The Tableau Next dashboard is designed to streamline your workflow by providing a clear, intuitive overview of all the critical information you need. Whether you’re tracking performance metrics, managing tasks, or monitoring key data, dashboards bring everything together in one place.
Analyze Critical Data with Dashboards: Tableau Next dashboards help you view and act on your data to make smart decisions. Whether you're monitoring or analyzing business data, Tableau Next custom and templated dashboards help you organize and understand the information that matters most to you.
Add Single-Click Actions to Your Data: Create a data journey for your users by turning your static Tableau Next dashboard into an interactive experience with single-click actions that lead to detailed pages or initiate Salesforce Flows. For example, use actions to add dynamic navigation to guide sales managers from a summary dashboard to pipeline details, progressively revealing more detailed information.
Organize Content with Dashboard Pages: Make the information on a Tableau Next dashboard easier to digest by chunking the content into multiple pages. And, with fewer queries per page, you can expect an increase in dashboard performance.
Format Multiple Dashboard Widgets at Once: Apply the same formatting to several dashboard widgets in one action. Select widgets together and edit their shared styles, or copy formatting from one widget and paste it onto others.
Configure a C360 Metrics Overview Dashboard: The C360 Metrics Overview dashboard template provides a comprehensive overview of your key performance indicators (KPIs) relevant to C360 features. The dashboard is designed for program managers and stakeholders, offering a detailed breakdown of performance across various dimensions. It enables progress monitoring, identifying areas for improvement, and making data-driven decisions.
Configure a Case Insights Dashboard: The Tableau Next Case Insights dashboard template provides insights to track and optimize the total case volume across different origins and times. It highlights case escalations and resolutions. The dashboard is designed for service teams, delivering real-time insights.
Apply Conditional Formatting to Visualize Dashboard Data Performance: Improve your data visualization by adding conditional formatting to Tableau Next dashboards. Create custom rules to automatically highlight important data, making it easier to analyze and interpret key performance metrics. By applying custom text formatting, you can quickly identify areas of success or those needing attention, which helps you make more informed decisions.
Optimize Dashboard Loading with Query Cache: Accelerate page load times for Tableau Next dashboards by switching your system interactions to cached query results. Storing and reusing data for up to thirty minutes eliminates system overhead from expensive live queries during frequent visits.
Configure an Executive Overview Template: The Executive Overview dashboard template provides a high-level snapshot of critical business KPIs, including revenue, profit margins, growth rates, risk management, and trend analysis. It is designed for executives and senior leaders, highlighting monthly or quarterly trends, enabling data-driven strategic planning, and performance reviews
Refine Results with the Filter Widget: Create interactive dashboards by using versatile filter to help you explore data. You can use the properties panel to select how widgets appear, such as a list or toggle.
Interact with Dashboard Filters: In Tableau Next, dimensions are categorical or qualitative fields, such as names, dates, or geographical data. When you have a dashboard made of metrics and visualizations (vizzes), you can select one or more marks to filter the dashboard to show only the selected data. And when you select marks to filter data on the dasfhboard, the metrics and vizzes update to reflect the filtered data. For example, if your dashboard has multiple vizzes that show sales by category and region, you can click region to filter the dashboard to show data for only that region. Or you can select multiple categories and a region to filter the dashboard by using different dimensions at the same time.
Add a Salesforce Flow Action to a Dashboard: Add a Salesforce Flow action to a widget to execute flows using the Tableau Next dashboard context. Flows empower your users to seamlessly create follow-up tasks, dispatch reminder emails, or refresh records directly from their dashboard insights. For example, add a flow action that lets sales reps create a follow-up task directly from a dashboard widget showing at-risk opportunities.
Configure a F/Z Layout Dashboard: The F/Z Layout dashboard template is designed to show your data insights using proven eye-tracking principles. The layout guides your users naturally through the dashboard content for maximized comprehension. You can select the z-pattern layout for high-impact visuals or the f-pattern layout for text-heavy content.
Personalize Dashboard Visuals with the Image Widget: Upgrade the visual impact of your Tableau Next dashboards by adding custom graphics, such as logos and icons, to your workspace. You can upload image files directly from your local drive or select assets from your existing library to foster brand recognition.
Set Initial Values for Your Dashboard: Select values to filter as your dashboard loads, ensuring that users see the most relevant information right away. For example, you can set your dashboard to automatically display sales by quarter in EMEA.
Optimize Dashboard Visibility for Mobile Devices: Show embedded Tableau Next dashboards directly within your mobile Lightning pages. Widgets automatically adjust to compact screen sizes to deliver optimal readability and layouts on phones and tablets. This bridges the gap for mobile users, ensuring consistent data access and informed decision-making anywhere.
Simplify Dashboard Management: Duplicate a dashboard for testing purposes, remove an outdated version, or download a copy.
Use Dashboard Templates from the Marketplace: When designing dashboards, you can start with a blank dashboard or you can use an out-of-the-box dashboard template to decrease your design lift. Tableau Next dashboard templates provide layouts tailored to your data and how you want to present it.
Add and Configure a Metric Widget: Integrate real-time metrics into your Tableau Next dashboard for enhanced, data-backed decision making. Displaying KPIs visually enables users to quickly assess trends and track progress.
Restructure and Format Tableau Next Dashboard Layouts with Multi-Widget Selection: Drag a selection box around multiple dashboard widgets to format, move, duplicate, or delete them simultaneously. This multi-select capability eliminates the need to modify elements individually, accelerating layout adjustments across your dashboard.
Add a Navigation Action to a Dashboard: Add a Navigate To action to a widget to guide your users to relevant URLs or other Tableau Next dashboards and dashboard pages with detailed information on the selected data.
Build Automated Navigation for Multi-Page Tableau Next Dashboards: Use the Navigation widget to transform dashboard pages into a horizontal navigation bar. The Navigation widget automatically scales with new dashboard pages. You can also hide or rearrange pages as you want them to appear in the bar.
Create Interactive Dashboards with Parameters: Create interactive Tableau Next dashboards by using versatile parameter widget to help you model your data. Build more interactive dashboards that help you explore data and model different scenarios.
Navigate Dashboards with Parameter Actions: Parameter actions enable you to map visualization fields to parameters, transforming static charts into interactive navigation tools. This setup enables users to trigger dashboard updates by clicking data points, streamlining data exploration within a single interface.
Model Hypothetical Scenarios by Using Parameter Inputs: Enter text, numbers, or dates into parameter widgets for lookup and modeling hypothetical scenarios. Dashboard calculations update instantly by using your custom values rather than just existing data.
Customize Dashboard Widgets by Using Parameters: Use parameters to transform static widgets into versatile tools that adapt to various project requirements. By using parameters to drive widget behavior, you can repurpose a single visualization for different projects, regions, or time frames without rebuilding the underlying logic.
List Parameter Example: You’ve been asked to create sales visualizations with several groupings, such as Region and Segment. You're going to reuse the existing visualization that you can update as grouping requirements change.
Configure a Performance Overview Dashboard: The Performance Overview dashboard template provides a comprehensive view of your key business metrics, helping your teams track progress, identify opportunities, and mitigate risks. It emphasizes a central hero metric, supplemented by supporting data and related metrics. The dashboard is designed for sales, finance, service, and operations teams, delivering real-time insights.
Preview and Save Dashboards: Previewing your dashboard is a critical step in the design and development process. It allows you to thoroughly check the dashboard for data accuracy, visual clarity, and performance.
Remove Dashboard Widgets: Remove widgets when you need to declutter your dashboard and remove outdated elements.
Reuse Widgets Across Dashboards and Workspaces: Maintain design consistency and speed up your workflow by copying widgets across pages and workspaces in Tableau Next. Use standard keyboard shortcuts to duplicate components while preserving all custom formatting, colors, and styles.
Configure a Sales Leaderboard Dashboard: The Tableau Next Sales Leaderboard dashboard template provides a comprehensive overview of your sales pipeline with key metrics and visualizations. It highlights sales wins and emphasizes which opportunities sales reps want to priortize. The dashboard is designed for sales teams, delivering real-time insights.
Configure a Service Agent Analytics Dashboard: The Tableau Next Service Agent Analytics dashboard template provides data about your service agent performance, conversation metrics tracking, and how to optimize your agents. It includes 12 visualizations focusing on agent performance, usage, and quality. The dashboard is designed for users who refine and improve AI agents, delivering real-time insights.
Consolidate Multi-Metric Overviews on Tableau Next Dashboards: The Summary widget enables you to merge Tableau Next metrics into a cohesive narrative on your dashboard. Instead of scanning individual metrics, the summary widget helps you pinpoint essential performance drivers quickly.
Add Live Metrics to Tableau Next Text Widgets: Enhance your dashboards by embedding live metric attributes directly into text widgets. This capability provides a highly adaptable formatting space that merges live performance metrics with strategic observations.
Create and Share Custom Dashboard Views: Share precise dashboard information with users by creating custom views. These saved views allow quick access to specific data configurations without needing to reset filters and settings repeatedly.
Add and Configure a Visualization Widget: Add visualizations to Tableau Next dashboards to enhance communication and collaboration by presenting data in a clear and intuitive way. Use visualizations to help you effectively act on key issues, such as drivers for sales growth.
Using the Visualization Widget Context Menu: In Tableau Next, use a dashboard visualization’s context menu to continue analyzing, capture the current view or its data, or share the visualization with others.
Integrate Workspace Asset into Tableau Next Dashboards: Access metrics and visualizations directly through a convenient authoring panel to speed up your dashboard creation. The integrated search bar helps you locate specific workspace assets, enabling you to add high-value data into your dashboard layout.
Navigate Levels of Detail Efficiently with Dimension Hierarchies: Define dimension hierarchies in Tableau Next to move between high-level summaries and detailed breakdowns in your analysis. Create hierarchies using related dimensions or date fields, and use them in visualizations or tables to roll up or drill down data.
Show Part-to-Whole Relationships with Donut Charts: Use donut charts in Tableau Next to compare how categories contribute to an overall total. Include additional dimensions to analyze contributions at a finer level of detail while preserving the main categories. Optionally show a KPI in the center of the chart to highlight the total value. You can also add the visualization to a dashboard to provide business context alongside other key metrics. For example, create a donut chart to compare sales by product category and highlight total sales with a KPI in the center.
Analyze Comparable Measures with Dual and Synchronized Axes: Use dual axes in Tableau Next to compare two measures on separate axes in the same chart across a shared dimension. Because each measure retains its own scale, you can highlight patterns and trends side by side while keeping the chart easy to interpret. To surface differences between measure values more clearly, synchronize the axes to align them to a common scale. For example, in a chart, compare sales and profit by product sub-category using dual axes. Then synchronize the axes to evaluate how closely profitability aligns with sales.
Create a Funnel Chart to Show Value Drop-Offs Across Stages: In Tableau Next, you can quickly surface patterns as your data moves through a process or workflow with funnel charts. Compare conversion from one stage to the next and spot bottlenecks in business operations to prioritize areas for improvement and efficiency gains. For example, analyze how opportunities move through your sales stages in a funnel chart to pinpoint the stages where opportunities need attention first.
Visualize Data Intensity and Patterns in Heat Maps: In Tableau Next, use heat maps to compare measure values across related categories and quickly locate where your data shows high or low concentrations. With color coding applied to a heat map, identify key data correlations, such as the impact of web traffic on sales. Or highlight outliers, like a low-engagement period on a top-selling product, so that you can address anomalies quickly. For example, compare product subcategories across each region in a heat map to see which subcategories drive the most profit in each region.
Map Layout in Tableau Next: Use the Map layout to understand how your data varies by location. Choose point maps to show specific locations or choropleth maps to compare values across geographic areas.
Analyze Seasonal Patterns with Nightingale Rose Charts: Compare values across equal, cyclical time periods to highlight differences in magnitude with a Tableau Next Nightingale rose chart. Include additional dimensions to compare patterns across groups while preserving the cyclical sequence. For example, visualize monthly sales by region to identify seasonal peaks and compare sales patterns across regions throughout the year.
Use Parameters to Explore Data Dynamically in Tableau Next: Define parameters in Tableau Next to explore what-if scenarios in dashboards and see how different inputs affect your business. Create a parameter on any field type and use it with a calculated field, filter, or reference line to update the visualization interactively. Use a parameter list to switch between fields or field values, such as countries, to compare each value’s impact separately. You can also create parameters for values outside the dataset, such as discount percentages, to see how they affect results.
Compare Contributions to a Total with Pie Charts: Compare how individual values contribute to a total in a Tableau Next pie chart. Use pie charts when you want to analyze a small number of values that together make up an aggregate. For example, visualize website visits by traffic source to understand how each source contributes to total website traffic.
Analyze Performance Across Multiple Measures with Radar Charts: Compare entities across multiple measures with radar charts in Tableau Next. Evaluate results side by side to quickly identify strengths, weaknesses, and overall performance patterns using their distinctive shapes. For example, compare sales representatives across measures such as quota attainment, win rate, and customer satisfaction scores.
Compare Progress Toward Goals with Radial Bar Charts: Visualize how close each entity is to reaching its goal in a compact circular layout with a Tableau Next radial bar chart. The chart's common scale makes it easy to compare relative performance and quickly identify leaders and areas that need attention. For example, review quota attainment by sales representatives to identify the top performers against their annual sales goals.
Analyzing Data with Radial Charts in Tableau Next: Radial charts use a circular layout to organize and show data around a central point. Different radial chart types are designed to support different analytical needs. Use them to compare parts to a whole, identify cyclical patterns, evaluate progress toward goals, and visualize recurring trends.
Identify Hotspots Across Cyclical Time Periods with Radial Heatmaps: Surface trends, gaps, and intensity across equal, cyclical time periods with a Tableau Next radial heatmap. Use color to reveal variations in a measure across categories while preserving the cyclical sequence. For example, visualize weekly case volume by support channel to identify seasonal peaks, quieter periods, and recurring patterns throughout the year.
Reveal Recurring Trends in Radial Line Charts: Use a Tableau Next radial line chart to visualize a measure over a repeating sequence and identify cyclical or seasonal patterns. Compare multiple groups across the same cycle to see where trends align or differ. Because the beginning and end of the sequence appear next to each other, you can see whether trends continue or change across the cycle boundary.
Evaluate Cyclical Patterns with Spoke Bar Charts: Quickly identify recurring patterns and changes across cyclical or periodic data with a Tableau Next spoke bar chart. Bars extend outward from a central hub, with each spoke typically representing a regular interval in a recurring cycle. For example, visualize monthly order counts to identify seasonal peaks and slower periods during a fiscal year.
Configure the Sales Insights Semantic Model and Metrics: The Tableau Next Sales Insights Model and Metrics template offers comprehensive visibility into your sales data. It includes key metrics for analyzing sales, team performance, leaderboards, stage progression, conversion rates, and sales lead analysis. This template installs the semantic model along with 15 pre-configured metrics and over 70 calculated fields. Designed for sales managers and key stakeholders, these insights provide a detailed breakdown of performance and forecasting across multiple dimensions, empowering them to monitor sales progress, pinpoint areas for improvement, and make data-driven decisions.
Configure the Service Agent Analytics Semantic Model and Metrics: The Tableau Next Service Agent Model and Metrics template is designed to enhance service agent performance and conversation metric tracking. It helps you optimize agents by providing a tailored set of data. The template installs the semantic model, 14 pre-configured metrics, and over 30 calculated fields. These insights offer a detailed analysis of agent sessions across various dimensions, enabling data-driven decisions, improving agent quality, and identifying specific areas for agent improvement.
Configure the Service Insights Semantic Model and Metrics: The Tableau Next Service Insights Model and Metrics template offers a comprehensive look at your service data with metrics for cases, customer satisfaction, Omni-Channel, service agents, and knowledge. Designed for service managers and stakeholders, this template delivers detailed breakdowns of CSAT and case escalation across various dimensions. By installing the semantic model, you immediately gain access to over 25 pre-configured metrics and more than 100 calculated fields. These insights are essential for continuous service progress monitoring, identifying areas that need improvement, and driving data-informed decisions.
Configure the Service-Risk Adjusted Win Score Composite Metric: The Tableau Next Service-Risk Adjusted Win Score composite metric template combines sales conversion performance with service-risk. It signals which accounts have strong win rates and low service escalations, ideal targets for upsell and expansion. This template installs the pre-configured metric and 1 calculated field used to determine your service-risk adjusted win scores. Designed to allow analysts to visualize their data instantly and provide stakeholders with actionable insights, bypassing the time-consuming process of manual chart configuration and complex data modeling.
Build Insightful Visualizations in Tableau Next: Visualizations are visual representations of your data as a chart or graph. A visualization is a powerful way to explore and analyze data. In Tableau Next, you can build visualizations with rich features, filters, calculations, and actions.
Enhancing Your Analysis with Advanced Features: Analyze your data for different scenarios and compare outcomes using parameters. Use calculated fields to define new values and answer targeted questions. Explore your data more efficiently across multiple levels of detail with dimension hierarchies.
Evaluating Performance with the Analytics Pane: Interpret your data with advanced analytics tools in Tableau Next to support informed business decisions. Apply forecasting models to historical data to project future outcomes and identify trends. Use reference lines to compare metrics against benchmarks and evaluate performance in your visualizations.
Comparing Values and Categories with Area Charts: Spot changes in overall volume and how categories contribute to those changes with Tableau Next area charts. Show a measure as a single filled area, or divide it into stacked, colored bands to compare category contributions.
Working in Tableau Next Visualization Builder: Uncover meaningful insights from your data with the Tableau Next’s primary authoring interface: the Visualization Builder. Create visualizations or tables in your workspace that connect related data and explore trends, and analyze performance. Use the results to draw conclusions about your business and make informed decisions.
Configure a Case Volume Visualization: The Tableau Next Case Volume visualization template provides instant insights to track and optimize the total case volume across different origins and timelines. This visualization uses the data from the Service Insights semantic model. Designed to allow analysts to visualize their data instantly and provide stakeholders with actionable insights, bypassing the time-consuming process of manual configuration and complex data modeling.
Work with Data Tables in Tableau Next: Use Data Table view in Tableau Next to review and compare exact values across records in a structured, row-and-column layout. This view makes it easy to scan, sort, and validate data across multiple fields without opening individual records. Data Table view also helps you identify duplicates and anomalies at a glance.
Forecasting with Tableau Next: Apply forecasting models on historical data to predict future outcomes that guide business decisions. You can choose the forecasting model to use, set how far ahead to forecast, and define the time range to base forecasts on. You can add forecasting to up to three measures in a single visualization. All selected measures use the same forecast settings, such as model type, duration, and prediction interval.
Create a Visualization with Semantic Model Data: Build a Tableau Next visualization on your semantic model data to discover valuable insights about your business. For example, create a visualization that shows sales patterns by product category and sub-category.
Use Visualization Templates from the Marketplace: When designing Tableau Next visualizations, you can start with a new or existing visualization or you can use an out-of-the-box visualization template to decrease your design lift. Tableau Next visualization templates provide insights tailored to your data and how you want to present it.
Customizing Tableau Next Data Views with Marks Properties: Customize how your data appears and control the level of detail shown in your data views using Marks properties. Apply colors that align with your data and preferences. Add labels to show exact values and provide context. Or encode data by size for faster comparison of values. The marks available to you depend on the data view you select.
Applying Reference Lines to Tableau Next Visualizations: Use reference lines in Tableau Next to evaluate performance against benchmarks, such as customer satisfaction (CSAT) scores or monthly sales targets. You can base the reference line on the same measure, calculated field, or date field as the axis it’s on. You can also choose a different source field to compare against another business metric. Define the reference line as a constant for fixed targets or a computed measure value, such as average monthly revenue, to compare performance within context. Personalize it with labels and colors to interpret data easily.
Analyzing Flow Across Stages with Sankey Charts in Tableau Next: Review how data values move across stages in a process to understand how they split, combine, or drop off. Define the stages using levels, and show the flow between them with links, where the width reflects the relative value being transferred. Use Sankey charts to evaluate how inputs lead to outcomes and identify the most and least effective paths. The order of levels determines the direction of the flow and what you focus on in the analysis.
Size Encoding in Tableau Next Visualizations: Use size encoding to highlight differences in values in bar, square, or circle mark types. You can base mark size on the values of the continuous measure field represented by the marks. Or, base the mark size on values of a different continuous measure field of your choice. Choose relative sizing to highlight contrast between values or absolute sizing to to apply a uniform scale based on a fixed pixel range. Combine size with color to interpret data easily
Tooltips in Tableau Next Visualizations: Gain deeper insights into your data by reviewing additional context for a data mark in a Tableau Next visualization with custom tooltips. By default, a tooltip includes information based on the fields in the visualization. Customize the tooltip to control its content and appearance.
Configure a Top Open Opportunities Visualization: The Tableau Next Top Open Opportunities visualization template provides instant insights for sales managers to review and track top open opportunities. This visualization uses the data from the Sales Insights semantic model. Designed to allow analysts to visualize their data instantly and provide stakeholders with actionable insights, bypassing the time-consuming process of manual configuration and complex data modeling.
Highlight Metric Value Changes in a Waterfall Chart: Visualize how a key value evolves over time or across categories by showing the cumulative effect of both positive and negative changes in a waterfall chart. This chart type, based on a bar chart, is ideal for highlighting the net effect each individual value has on the final value. For example, compare pipeline value across opportunity stages to see how each stage contributes to total potential revenue.
Highlight Data Patterns in Tables with Color Encoding: Apply color or a range of colors to measure values or their cell backgrounds in a Tableau Next table to highlight key values, such as profit or sales. Use calculated fields with color encoding to highlight values that meet specific criteria.
Customize Colors in Visualizations: Apply custom colors to a Tableau Next visualization to make them more engaging and easier to interpret. Choose colors that align with your branding, provide visual cues about data patterns, or highlight business priorities. You can set up to 10 custom colors in a chart. If a chart has more than 10 values, it reuses the colors in sequential order.
Download a Snapshot of a Dashboard: You can download a snapshot image of a dashboard to a .png file. The snapshot is taken while running the item with your filters and the latest data.
Connect and Prepare Data in Tableau Next: You can access data for creating visualizations and dashboards by connecting to data directly (Data 360 DMOs and CIOs), by connecting to a semantic model that already has a data connection, or by uploading your own data from CSV files. Use Tableau Semantics within Tableau Next to create a semantic model and semantic calculated dimensions and measures.
Adding Data to a Tableau Next Workspace: You can analyze any data that's available in your workspace. You can access data in Data 360 from data assets such as an existing data model object (DMO), Data Lake Object (DLO) and Connections, or upload your own data from files or cloud-based resources. Changes you make in Tableau Next don't affect the source data in Data 360 or in your original files.
Adding Assets to a Data Space: A data space acts as a logical partition for organizing your data. The app places your assets in the Default data space. However, if other data spaces are available in Data 360, you can add assets to one of them.
Connect to AWS Aurora PostgreSQL: Bring your AWS Aurora PostgreSQL data into Tableau Next with the AWS Aurora PostgreSQL connector.
Upload Data to a Tableau Next Workspace Using a CSV File: Add your own data to a new or existing workspace using a CSV file. The data is parsed, verified, and added to a new data object. You can then use it in a semantic model, visualization, or dashboard.
Prepare Excel Files for Ingestion by Using Data Interpreter: Clean your Excel files to make sure that your data has the correct structure for ingestion. The Data Interpreter cleans and structures Excel data during upload. It resolves formatting issues, such as merged headers or blank fields, and ignores non-essential images and hidden formatting.
Connect to Databricks: Bring your Databricks data into Tableau Next with the Databricks connector.
Data Source Connectors: Connect Tableau Next to external data sources, including Amazon Redshift, Databricks, Google BigQuery, Snowflake, and PostgreSQL databases.
View and Use Google Drive Data in Your Workspace: Work with Google Drive data in Tableau Next using an admin-managed connection. Analysts can browse and select CSV, XLSX, and Google Sheets files from authorized Shared Drives without configuring connections or managing separate login credentials.
Run a Batch Data Transform: Run a batch data transform for the first time to populate the data in the output objects. Run it again to update it with the latest data.
Analyzing Data with Tableau Agent: Analytics Authoring (Beta): When you analyze data with a Tableau Agent, the agent guides you from questions about your data to insights with greater efficiency. Ask business questions in natural language, and the agent discovers the relevant data, determines what assets are required to answer your questions, and creates them as needed.
Connect to Data Sources with the Connector Catalog: Centralize your data management by building a curated library of data connectors within the new Connector Catalog. This unified interface shows all supported platforms alongside their specific connection features and requirements.
Turn on Acceleration for Data Connections: Data acceleration improves query performance by temporarily caching data in Data 360. Acceleration is off by default when you add an external database connection. You can turn it on by selecting Full Refresh or Incremental Refresh.
Keep File-Based Connections Current: You can now set up the file-based connectors to run regular updates that ensure your Tableau Next dashboards and metrics show the most up-to-date information.
Search File-Based Connections with Enhanced Search: You can now search for files and folders by name in shared drives instead of navigating complex folder structures. This enhanced search extends beyond the local cache, enabling you to identify the correct files at a glance.
Re-Upload Excel Files Using Data Interpreter: Use the Data Interpreter during Excel (.xlsx) file re-uploads to refresh Data Lake Objects built from non-standard layouts. A built-in sheet selector enables you to manually identify specific sub-tables rather than relying on automatic matching logic.
Tableau Next MCP Beta Server (Beta): The Tableau Next Model Context Protocol (MCP) Beta Server is a companion to the generally available Tableau Next MCP Server. It gives you early access to tools that create, update, and delete dashboards, semantic models, visualizations, and alerts before they reach general availability.
Use Metric Templates from Marketplace: Creating effective metric insights is essential for monitoring performance and driving business decisions. You have two primary options for building these insights, developing custom key performance indicators (KPIs) from scratch or by using a pre-configured metrics template. Tableau Next metric templates offer a significant advantage by decreasing the time required to construct your KPIs.
Monitor Your Data in Tableau Next: Monitor your data objects, data connections, and data transforms created or referenced in workspaces directly in Tableau Next without switching contexts. Use the Tableau Next Data Monitor to track data assets, including status, refresh history, source workspace, and other details. To quickly find assets, use the filter or search on any tab.
Update Data Lake Objects with Excel and CSV File Refreshes: Keep your analytics current by replacing existing data sources with updated Excel or CSV files directly within the Data Lake Object. This streamlined upload process maintains your established data structures while ensuring users always work with the latest records.
Rename Data Source Columns in Your Workspace: Optimize data clarity by renaming data source columns to align with your organization's business terminology. Modifying these field names in your workspace ensures that you interact with familiar labels during analysis.
Replace the Source File for a Google Drive Data Lake Object (Beta): Replace the source file for an existing Google Drive data lake object (DLO) when a file becomes outdated or the owner account is inactive. Select a file from the original Google Drive connection or a different connection without recreating the DLO or its refresh schedule.
Review Data Uploaded to Tableau Next: After you upload an existing data asset or a file to a Tableau workspace, you can review the fields and data types, along with up to 100 rows of data in each field. After reviewing, you can use newly uploaded data immediately for models or visualizations.
Create Semantic Models and Define Metrics: Tableau Semantics within Tableau Next gives you the power to create semantic models and then build metrics upon them. You can also use semantic models to create calculated fields in your visualizations.
About Metrics: A metric is a business KPI that you can track over time and use to assess performance or business processes. A metric is derived from one or more measures. You can apply a sentiment to a metric (for example, up is good or bad), aggregation (for example, sum, non-cumulative, average, minimum, maximum), and other business contexts.
Semantic Models in Tableau Next: With semantic models, your data becomes unified into a single source of truth, accessible throughout Salesforce. Semantic models provide a way for your data to be interpreted consistently across Salesforce by mapping it to standardized business terms and logic. Semantic models are first-class Salesforce metadata, so they’re available throughout Data 360 to power analytics and data-driven experiences.
Create a Semantic Model in Tableau Next: Tableau Semantics within Tableau Next gives you the power to create semantic models and then build metrics upon them. You can also use semantic models to create calculated fields in your visualizations.
AI-Generated Descriptions in Semantic Models (Beta): Quickly draft comprehensive descriptions for semantic data objects by using Einstein. This input is crucial for conversational analytics, giving AI agents the context they need for accurate discovery and insights.
Draft Data Object Descriptions with AI Semantic Description Generator (Beta): Quickly draft and apply accurate descriptions for semantic model entities by using AI Semantic Description Generator (Beta). Review and save the suggested descriptions to ensure your agents use precise metadata for optimal accuracy.
Tableau Next Model Context Protocol (MCP) Server: Tableau Next Model Context Protocol (MCP) connects your Tableau Next governed analytics, metadata, and semantic models to external MCP-compliant AI agents such as ChatGPT or Claude. This integration lets you use your Tableau Next data and user experience directly within your AI workflows without moving data or breaking trust boundaries.
Metrics and Goals in Tableau Next: A metric is a business KPI that you can track over time and use to assess performance or business processes. Sub-metrics are detailed performance indicators that provide additional granularity to a main metric. They allow you to break down primary metrics into more specific, actionable data points, offering deeper insights and more precise analysis.
Semantic Model AI Optimization (Beta): Semantic Model AI Optimization (Beta) helps data teams prepare their Semantic Models for use with AI agents such as Concierge: Analytics Q&A. It evaluates a semantic model's quality, identifies inefficiencies, and offers guided resolutions to improve model reliability for AI-powered analytics.
Using Semantic Model AI Optimization (Beta): After a system administrator enables the feature, you can begin using it to evaluate and improve your semantic models. You interact with the feature primarily through the Optimize Model Panel, which visualizes the Model Health and AI-readiness strength of your models.
Use Q&A Calibration to Improve Accuracy in Tableau Agent (Beta): When Q&A Calibration is enabled in Tableau Next, analysts and data experts can test and verify questions and answers for Tableau Agent with the Data Analysis subagent, based on the selected analytics agent and its associated semantic model. Through evaluating questions to determine if questions are accurate (verified) or inaccurate, analysts can provide valuable input that your analytics agents can use to respond consistently and accurately to semantically similar questions.
Create and Refine Semantic Models with Semantic Modeling (Beta): Build and fine-tune semantic models faster by describing your analytic goals directly to Semantic Modeling in your workspace. It translates your natural language requirements into fully mapped semantic models and helps refine existing ones. The tool automatically scans your active workspaces to find relevant data objects, create relationships, and generates calculated field formulas. This accelerates your time to insights and removes traditional friction, helping both experienced modelers and less expert users build and refine semantic models without manual configuration. Plus, you can review and adjust all selected data objects and formulas before generating the semantic model.
Refine an Existing Semantic Model with Semantic Modeling (Beta): To refine an existing semantic model, simply describe the changes you need in natural language to Semantic Modeling within the model builder. Whether you’re adding calculated fields, including new data objects, or updating relationships, Semantic Modeling helps you make these modifications through natural conversation.
Set Up Semantic Modeling (Beta): Enable your users to build and refine semantic models through natural language by deploying Semantic Modeling. This setup extends your existing Semantic Modeling configuration with specialized capabilities for semantic modeling.
Resolve Overlapping Data Definitions with Similar Meanings (Beta): Improve model clarity by proactively resolving similar definitions that confuse AI agents. The Similar Meanings (Beta) feature uses AI to detect synonyms, acronyms, and semantic overlaps to prevent ambiguity. By analyzing field labels, descriptions, and parent object semantics, the tool identifies when a model contains "two names for one thing" or "the same name for different things"—concepts that often lead to inaccurate agent reasoning. This deep scan allows you to distinguish between closely related fields, helping agents correctly interpret your data and provide more reliable answers.
Create Relationships within a Semantic Model in Tableau Next: You can use Artificial Intelligence (AI) to get relationship suggestions and create relationships in Tableau Semantics. Suggestions are based on semantic objects and sample data added to your model.
Test a Verified Query for Regression in Q&A Calibration: Open a verified query in Q&A Calibration. Tableau Agent runs a regression test that compares the new answer with the query you saved. Retest after a semantic model change.
Accelerate Semantic Model Creation with Marketplace Templates: When developing semantic models to unify and make sense of your enterprise data, you can build a model entirely from scratch or use a pre-configured template. Tableau Next Marketplace semantic model templates are designed to accelerate your development process.
Search for Data Tables and Schemas: Efficiently navigate Tableau Next data structures by searching for schemas and tables across all active connections in Tableau Next from the search bar. Scan your data sources and see matches that preserve their original folder hierarchy and context.
Tableau Next MCP Beta Server Tool Reference (Beta): This reference contains beta tools for Tableau Next Model Context Protocol (MCP) Server. Each tool is organized by its category in alphabetical order
Delete a Batch Data Transform: Delete any batch data transforms that you no longer need. Deleting a batch data transform doesn't delete its target data.
Add Input Data to a Batch Data Transform: Create an input node for each dataset that you want to add to the batch data transform. The batch data transform creates an input node when you create a transform by using a data object, but to add more data, you add another node.
Batch Data Transforms in Tableau Next: A batch data transform is a repeatable series of operations that you can run when data updates. The first time that you run a batch data transform, it pulls in data and transforms it according to your definition. You can also run a transform manually or set it up to run at scheduled intervals.
Schedule a Batch Data Transform: To make sure that the most up-to-date data is available by a particular time or to run a job during non-business hours, schedule a batch data transform.
Set a Currency for a Batch Data Transform: To use currency values in a data transform, set a currency. This setting converts data with multiple currencies to a single value. For example, if your data includes GBP and USD and you set the currency to USD, the GBP values convert to USD. If you don't set a currency, the data remains unchanged.
Add a Batch Data Transform: Create a batch data transform with data objects by using the in-app builder.
File Upload Processing Modes and Limits: Upload processing modes in Tableau Next are based on file size and column count thresholds. Files exceeding standard limits are automatically queued for asynchronous processing, optimizing resource allocation without impacting platform stability.
Embed, Share, and Deploy Tableau Next: Share your workspaces and assets with other users. Embed Tableau Next visualizations, metrics, followed metrics, and dashboards into Lightning pages. Copy Tableau Next component code to embed Tableau Next assets into third-party web apps. Transform your dashboards and workspace assets into standardized templates to list on Marketplace, empowering collaboration, promoting standardization, and reducing duplicate efforts across teams. Deploy your Tableau Next workspaces, visualizations, and dashboards from your sandbox org into your production org using change sets and data kits.
Deploy Tableau Next Assets with Change Sets: Deploy your Tableau Next workspaces, visualizations, and dashboards using an outbound change set to your target home and companion orgs.
Deploy Tableau Next Assets in a Data Kit: Deploy your Tableau Next workspaces, visualizations, and dashboards to your target home org with a data kit. Data 360 semantic models, the primary data source for Tableau Next assets, must be deployed to target home orgs using data kits.
Deploy Tableau Next Assets: Deploy Tableau Next workspaces, visualizations, and dashboards from your sandbox org to other sandbox orgs and your production Data 360 home and companion orgs using change sets. You can also deploy Tableau Next and Data 360 assets from sandbox to production home orgs using data kits. Using sandboxes allows you to develop, test, and train without compromising the data and apps in your production org. Using change sets and data kits for deployments moves the customizations you created and tested in your sandbox directly to your production org.
Share and Embed: Share your workspaces and assets with other users. Use Lightning App Builder to embed Tableau Next metrics, visualizations, and dashboards into Lightning pages. Copy Tableau Next component code to embed Tableau Next assets into third-party web apps.
Build Templates for Your Tableau Next Marketplace: Transform your Tableau Next dashboards and workspace assets into standardized templates, then list them on the Marketplace for others to use. Templates empower collaboration, promoting standardization, and reducing duplicate efforts across teams and customers.
Tableau Next Editions: Learn about the user interface and editions that support Tableau Next.
Bring Insights into the Flow of Work: Extend Tableau Next everywhere throughout your org by integrating Tableau Next metrics, dashboards, visualizations, and followed metrics into Lightning pages and custom pages using Lightning web components. Specifiy component properties and filter to customize how each insight renders and interacts with the page.
Add a Tableau Next Dashboard to a Lightning Page: Integrate your Tableau Next dashboards into Lightning pages to surface key insights into user workflows. To embed a dashboard component in the page, follow these steps.
Embed Tableau Next Assets in Your External Apps: Tableau Next embedded analytics brings Salesforce’s interactive and analytical capabilities to your apps quickly. Tableau Next integrates with your existing technologies by using Salesforce authentication and platform security, bringing insights into your flow of work and reducing context switching for your users. For example, embed daily service metrics directly into your custom service web app to give your technicians instant access to key information.
Copy Tableau Next Component Code for Embedding: Tableau Next provides a quick start for embedding code directly from your dashboard, visualization, or metric. The embed feature creates JavaScript component code for your selected asset, that you can copy and use in your web app.
Filter Values for Embedded Visualizations, Dashboards, and Metrics: Create filters for embedded Tableau Next visualizations, dashboards, and metrics with the filter builder to show relevant embedded content. You can use the builder to set dynamic or static values in the filter when the visualization, dashboard, or metric renders. On a record page, you can also base filter values on the page context, such as account ID, or specify static values so that the embedded content shows only relevant data.
Add a Personalized View of Metrics That a User Follows with the Tableau Next Following Component: See Tableau Next metrics that you follow directly in Salesforce Lightning pages, and view data insights at a glance without switching context. When an admin adds the Tableau Next following component into a Lightning page, users see a personalized view of the Tableau Next metrics that they follow. Admins can also add suggested Tableau Next metrics that they think users might also want to follow.
Add a Tableau Next Metric to a Lightning Page: Integrate your Tableau Next metrics into Lightning pages to bring specific insights directly to your users. To embed a metric component in the page, follow these steps.
Add a Tableau Next Visualization to a Lightning Page: Integrate Tableau Next visualizations into Lightning pages so users can explore visualizations and identify data trends in their workflows. To embed a visualization component in the page, follow these steps.
Data Governance and Sharing in Tableau Next: Tableau Next has a multi-layered access control structure powered by flexible Salesforce permission sets, Data 360 governance policies, and sharing settings on each Tableau Next asset.
Tableau Next Home and Workspaces: The Tableau Next analytics home page offers a quick way to build analytics assets, organize them within workspaces and access the resources and assets available to you.
Tableau Next Learning Resources: Link from this page to all of the videos, blogs, and other learning resources in Trailhead, the Developer Center, and more.
Tableau Next Considerations, Guidelines, and Limits: Considerations are factors to understand before working with Tableau Next. Guidelines define best-practice recommendations to improve your use of Tableau Next for optimal performance. Limits are boundaries beyond which features are unavailable or usage billing charges are applied.
Manage Templates: The Templates page lists every available template, including templates that Salesforce provides and custom user-created templates. Use this page to review what a template includes, install templated apps and components, or build and maintain your own templates.
Manage Installed Apps from the Apps Tab: In the App Hub, the Apps tab lists every app and component installed from a template, along with its status. From this tab, drill into an app's details, run app actions, or delete an app.
Monitor and Manage Templated Apps: Use the App Hub to manage the apps and components installed from templates in your org, monitor installation and refresh activity, and troubleshoot issues. Templated apps include Tableau Next apps for Salesforce, Salesforce flows, and Data 360 and Tableau Next components such as semantic models, dashboards, metrics, and visualizations.
Tableau Next Marketplace: The Tableau Next Marketplace centralizes all templates for metrics, semantic models, dashboards, and visualizations. Tableau Next templates, including out-of-the-box, custom and AgentExchange packages, are tailored for your data and presentation needs.
Install App Packages from AgentExchange in Tableau Next Marketplace: Install trusted Tableau Next analytics templates from AgentExchange directly in Tableau Next Marketplace. Only packages reviewed and approved by Salesforce are available. After you install the package, use the templated assets as a starting point to build custom solutions in your Tableau Next workspaces.
Ask Questions with Tableau Agent in Tableau Next: When Tableau Agent with Data Analysis is enabled in Tableau Next, you can ask questions to explore your data. When Tableau Agent is enabled in Slack, you can use it to ask questions about shared Tableau Next metric and dashboard data in that context.
Limitations for Conversational Analytics in Tableau Agent: In Tableau Next, agents enabled for conversational analytics have the following limitations related to questions and responses. Conversational analytics are available in agents based on the Analytics and Visualization template with the Data Analysis subagent in Tableau Next.
Supported Questions and Surfaces for Tableau Agent with Data Analysis: You can use Tableau Agent to ask questions about your data across Tableau Next, Salesforce, and Slack. This topic lists the types of questions, surfaces, and contexts where Tableau Agent with Data Analysis is currently supported.
Copy a Tableau Next Metric Image to Your Clipboard: Copy a snapshot image of a metric to your clipboard. From your clipboard, you can then paste the image for quick sharing in presentations, documents, or wherever you need it. The snapshot is taken while running the item with your filters and the latest data.
Receive Proactive Data Alerts in Tableau Next: Get data alerts delivered right where you’re working—in Tableau Next, email, or Slack. Create custom data alerts based on queries about your semantic data model (SDM) by using Agentforce in Tableau Next or with Tableau Next model context protocol (MCP) in Slack. Or create scheduled alerts for specific events from a Tableau Next metric.
Get Custom Data Alerts in Slack with Agentforce in Tableau Next or Tableau Next MCP: Receive alerts about your queries for your data or for custom metric alerts by using Tableau Next Model Context Protocol (MCP) in Slack, or ask Agentforce to alert you when specific conditions are met. To create alerts for queries about your semantic data model (SDM), make sure that you have access to the SDM.
Use Proactive Intelligence in Tableau Next to Monitor Data and Take Action: Turn hindsight into foresight with proactive intelligence features in Tableau Next. With data alerting, you stay on top of changes to your data, whether it’s progress to a goal, month-over-month changes, or any change in your dataset. Data alerts help you monitor what you know and care about while autonomous insights help you anticipate important events you might not have been looking for—all while spending less time asking questions and searching dashboards for answers.
Manage Data Alerts in Tableau Next: When you receive a data alert, click the notification to view more information about the alert or log in to Tableau Next to manage your alerts. If the alert was sent by Agentforce or by Tableau Next model context protocol (MCP) in Slack, you can also ask follow-up questions. Depending on the type of alert, notifications are sent to desktop, mobile, or Slack. You can modify or delete your alerts at any time.
Get Email Data Alerts for Metrics in Tableau Next: Create alerts to monitor a metric’s progress towards a goal or threshold, to look out for unusual changes, or to watch period-over-period changes. When you create a metric alert in Tableau Next, notifications are sent to your email and appear under the notifications icon (bell) in Tableau Next.
Get Tableau Next Data Alerts from Agentforce in Slack: When you create data alerts by using Agentforce, your alerts appear under Notifications in Tableau Next. And with help from an admin, you can also receive data alerts in Slack. When the conditions of the alert are met, Agentforce sends an alert to Tableau Next and Slack with insights about what contributed to the change.
Download a Metric Snapshot: Download a snapshot image of a metric to a .png file. The snapshot is taken while running the item with your filters and the latest data.
Set a Dynamic Time Range for All Followed Tableau Next Metrics: You can change the time range for all Tableau Next metrics that you follow simultaneously. Going forward, all metrics that you follow use this time range everywhere you see metrics that you follow—on your Following page and on mobile. Future metrics that you follow won't reflect this update. You can undo this action by updating metrics individually.
Explore Metrics and Insights: Every metric includes one or more insights that you can explore to learn more about your data. When more insights have been detected for a metric, you can click suggested questions in the Discover Insights panel to open those insights.
Filter a Metric in Tableau Next: By adding filters to a metric in Tableau Next, you’re able to focus on the data that is most important to you.
Follow a Tableau Next Metric: Customize your Tableau Next experience by following the most important metrics you want to see on a regular basis.
About Following Metrics in Tableau Next: After you follow a metric, the metric appears on the Following page in Tableau Next. While analysts can find Following in the left panel, users with consumer permissions see the Following tab as their home page web experience in the Tableau Next app.
View Notifications About Metrics: When a coworker adds or removes you as a follower of a metric, Tableau Next notifies you with the bell icon in the header of the Tableau Next window.
Use Metric Forecasting in Tableau Next: When you follow a metric, you can see the data that’s most important to you at a glance. And when you turn on forecasting, you can also see the metric’s predicted performance. When forecasting is turned on, Tableau Next uses historical data to anticipate future trends. The forecast extends only as far into the future as Tableau Next can maintain 95% confidence. For example, if there isn’t data for the previous year, Tableau Next won’t provide a forecast for the entire period because it doesn’t have enough historical data to forecast with high accuracy. When forecasting is turned on, the metric’s historic value is a solid line, the forecasted value appears in a dashed line, and the confidence range appears as a shaded band around the line.
Set Goals and Thresholds for Tableau Next Metrics: When you set a goal, you see your progress toward that goal on your Tableau Next Following page. And thresholds give more context to your metric's value. For example, you can create thresholds for performance ranges, such as "healthy," "at risk," and "critical." You can set thresholds with or without goals. And you can also set goals without thresholds.
Create Goals and Set Thresholds with Tableau Next: With Tableau Next, you can track important metrics and receive updates right where you’re working in Salesforce or in Slack. When you set a goal in Tableau Next, you can also track a metric’s progress toward that goal. And you can set thresholds to add context to your metrics. For example, if you have a metric that tracks sales over time, you can set a goal and turn on forecasting to monitor your progress toward that goal. And you can add labels for ranges such as “at risk,” “healthy,” for more context. Or set thresholds independently of goals, which is particularly valuable for noncumulative metrics. For example, you could set a threshold that indicates that it’s time to investigate when a metric is less than a specified value.
About Metric Insight Summaries: Tableau Next displays a summary of the insights related to those metrics. Tableau Next creates the insight summary differently, depending on whether Tableau Agent with conversational analytics is enabled or not. When the agent isn't enabled, insight summaries are created based on the metrics you follow (two or more required). When the agent is enabled, Tableau Next creates insight summaries using Agentforce (no followed metrics needed).
About Conversational Analytics in Tableau Agent: Tableau Agent with the Data Analysis subagent is a deeply integrated, intelligent layer for conversational analytics, built to meet business users where they work, across Tableau Next, Salesforce, and Slack. Ask Tableau Agent freeform questions about your data, and get answers in the form of rich text and visualizations, grounded in your organization's semantics and business logic.
Add Tableau Next Metrics, Dashboards, and Visualizations to a Slack Canvas: When you paste a Tableau Next link into a Slack canvas, you can choose to display a preview of the metric, dashboard, or visualiation (viz) as a card. Unlike sharing a Tableau Next link in a direct message or channel, previews in Slack canvases regularly refresh data from each viewer’s perspective. And you can see when the data was last refreshed at the bottom of the card in the canvas.
Explore Tableau Next in the Slack Side Pane: When a Tableau Next metric, dashboard, or viz is shared in Slack, users can click the preview to open a side pane in Slack that shows details and live insights.
Find Related Slack Conversations for Tableau Next: When a Tableau Next metric, dashboard, or viz is shared in Slack, you’re able to browse other related messages that you have access to in Slack. You can also view related conversations for metrics from the Tableau Next app.
Work with Tableau Next and Slack: Streamline sharing metrics, dashboards, and visualizations in the flow of work by previewing Tableau Next metrics in Slack. For metrics, dashboards, and vizzes, select Share to compose a message that is sent directly from Tableau Next to Slack—direct messages (DMs), group DMs, or channels. Or paste Tableau Next links to metrics, dashboards, or visualizations in Slack messages to share a preview with your team right where you’re working in Slack.
Share Previews of Tableau Next Metrics, Dashboards, and Visualizations in Slack: When you send previews of Tableau Next metrics, dashboards, and visualizations (vizzes) in Slack, anyone who can see the message will see the preview, including any filters applied. Before sending, make sure you’re not sharing sensitive data.
Set a Default Time Range for Individual Tableau Next Metrics: You can change the time range analyzed for a Tableau Next metric that you follow by setting a default time range. After setting a default time range, the metric uses that time range everywhere you see metrics that you follow—on your Following page and on mobile.
Metric Insights in Tableau Next: Metrics insights give you a quick snapshot of the data you care about in Tableau Next. Every metric includes insights that Tableau Next automatically detects.
Track Installations and Refreshes from the Monitor Tab: The Monitor tab lists every create, update, and refresh event for the templated apps in your org. Use it to confirm that an installation finished successfully, find out why an event failed, or inspect the runtime state used during installation.
About Tableau Next: Learn about Tableau Next editions, limits, guidelines, billable usage types, and terminology.
Replace a Semantic Model with an Extended Semantic Model: When you extend a semantic model with additional calculations, data objects, or modified relationships, you can update an existing visualization with the latest data from the extended version.Tableau Next automatically maps matching fields based on API name, object name, and data type.
Manage Access to Assets in Tableau Next: Manage access to workspaces, dashboards, visualizations, and semantic models in Tableau Next. Use the share option to add or remove viewers or editors for an asset. Access to data model objects and data lake objects is controlled in Data 360.
Share Assets in Tableau Next: Share and grant other users access to workspaces, dashboards, visualizations, and semantic models in Tableau Next. You can grant users and groups access to an asset, and you can set the general access for an asset for all users in an org.
Copy a Link to a Visualization or Dashboard in Tableau Next: Get a link to a Tableau visualization or dashboard in its current or default view. The current view is the visualization or dashboard as you see it, with any modifications you’ve made. The default view is the visualization or dashboard without filters, selections, or other modifications applied.
Remove Access for Assets in Tableau Next: Remove access to a workspace, dashboard, visualization, or semantic model so that users can no longer view or edit that asset in Tableau Next. You can remove access for individual users, and you can restrict the general access for the asset so that only users who are individually added can access the asset.
Get Trusted Answers in Slack with the Tableau Next Model Context Protocol (MCP): Connect the Tableau Next Model Context Protocol (MCP) directly to Slackbot. Then, ask Slackbot about your data in plain language. Tableau Next MCP is grounded in your context and data, so you can ask follow-up questions and take action with Slackbot across Salesforce records, Slack messages, and Slack canvases, or any other tools connected to Slackbot via MCP.
Sort Data in a Visualization: Choose whether data in your visualization is sorted in ascending or descending order. For example, identify the sub-category with the highest sales in each product category.
Tableau Next Apps for Salesforce: Provide your teams with curated paths through your data, plus powerful tools for spontaneous, deep explorations. With a Tableau Next App for Salesforce, you have a purpose-built set of analyses and insights about a specific area of your business. Answer pressing questions, predict outcomes, and get recommendations on the next best actions with insights infused directly within the flow of work in Salesforce.
Billing Considerations for Tableau Next: Tableau Next is built on Data 360 and generative AI services. Usage of certain Tableau Next features impacts credit consumption. Credit consumption can be either Data Services or Flex Credits. In addition, many Tableau Next features are available on an unmetered basis. Unmetered usage does not consume credits.
Add Actions to Visualizations: Make your Tableau Next visualizations more powerful by adding actions so users can act directly from the data, boosting their productivity. Use actions to add dynamic navigation or create and update records based on their selections.
Add a Calculated Field to Your Data: Sometimes, analyzing your data source requires adding a calculated field based on your existing fields. For example, you want to review the cost for each product that you sell. You can either create a calculated field manually, or you can use Semantics AI and let Einstein draft one for you.
Create Bins from Measure Values: Group continuous measure values into ranges, called bins, to simplify analysis and reveal patterns in your Tableau Next data. Create bins to segment values into intervals without changing the underlying data.
Group Dimension Members to Simplify Analysis: Group related dimension values into meaningful categories to simplify comparisons and highlight patterns in your Tableau Next data. Create groups to compare performance across segments without changing the underlying data.
Download a Snapshot of a Visualization: Download a snapshot image of a visualization to a .png file. The snapshot is taken while running the item with your filters and the latest data.
Add a Navigation Action to a Visualization: Embed navigation actions into your Tableau Next visualization. The navigate action allows users to navigate to external sites with clickable links directly in a visualization data field.
Add a Salesforce Action to a Visualization: Add a Salesforce action to your Tableau Next visualization. Users can invoke these actions to create, update, and interact with Salesforce records. For example, add actions so users can create cases, update accounts, or post to Chatter.
Show or Hide Missing Values in a Visualization: Identify gaps in your Tableau Next visualization by displaying dates or bins with no corresponding values. Or hide them for a cleaner view when the gaps aren’t relevant to your analysis.
Formatting Visualizations in Tableau Next: Make Tableau Next visualizations easier to interpret and draw attention to important insights by customizing their appearance using the Style pane. Create a cohesive visual style that aligns with your organization’s presentation or brand standards. Apply settings for text, lines, backgrounds, and marks across an entire visualization. Or customize specific elements, such as field labels, axes, and headers. Available formatting options vary by visualization layout.
Tableau Next Workspaces: A workspace is a flexible place for analysts to create, add, and reuse analytic assets to answer questions about their data.
Analyze Data in Tableau Next Workspaces: Inside a workspace, assets are organized by type. The list view shows your available workspace assets in real time. The layers of the workspace add context about the building blocks of workspace assets, so an analyst has the information needed to answer questions with data.
Create or Add an Asset in a Workspace: Assets can be created within a workspace, or you can add an existing asset to your workspace or from Data Data 360 if the administrator has made these assets visible.
Create a Workspace in Tableau Next: Everything in Tableau Next begins in a workspace. To create an analytical asset in Tableau Next, you must start in a workspace.
Edit or Delete a Workspace in Tableau Next: Update a workspace name and description, or delete the workspace and its assets altogether. If the workspace reused an asset from somewhere else, deleting the workspace won't affect the original asset.
Promote Exploratory Org Workspace Assets to Production: Promote work to the production org without manual handoffs. Built-in dependency checks and contextual notes help admins approve requests. Real-time Task Manager updates keep promotion workflows transparent and trackable from start to finish.
Exploratory Org Considerations: You have complete control over all assets that you create in your Exploratory org. However, access to reused assets is governed by existing access policies and settings.
Access Your Exploratory Org: You can easily switch from your production environment to your Exploratory org, even though it's a separate environment, without needing to log in again.
Reuse Production Org Assets: Your Exploratory org provides the flexibility to integrate and experiment with data sources (DMOs), visualizations, and semantic models to create comprehensive views for analysis.
Share Assets from an Exploratory Org: Foster a collaborative environment for data exploration and analysis by sharing assets organized in workspaces. Sharing enables colleagues to view, interact with, and contribute to the content you create.
Change Your Intelligent App Settings: You can change settings for your Intelligent App, such as instantly refreshing the app or scheduling an app refresh to be performed later.
View Your Intelligent App Resources: You can see and explore the resources your Intelligent App installs. The resources can include Einstein Studio models, CRM Analytics dashboards, recipes, and datasets, and Data 360 transforms and data model objects.
Troubleshoot Your Intelligent Apps: You can see the logs and state of an Intelligent App, cancel running installations, and troubleshoot problems.
