--- schema_version: "1" id: aa970c09f3e2be793dd4277c0f08e17ade4bfdafece107cfdd2be41db86aadc6 title: Einstein Messaging Insights Model Card collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/mktg/264-0-0/mc_anb_einstein_messaging_insights_model_card.md canonical_url: https://help.salesforce.com/s/articleView?id=mktg.mc_anb_einstein_messaging_insights_model_card.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: 90ccab6206b2dd0b2aafd9f56477145aa14e6bfce864cbcd1e26b240e56514a5 description: The model in this card analyzes message engagement rates and notifies marketers when anomalies in their marketing performance occur. products: - Marketing - Marketing Cloud Intelligence release: "264" --- # Einstein Messaging Insights Model Card The model in this card analyzes message engagement rates and notifies marketers when anomalies in their marketing performance occur. 1. **[Model Details](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_einstein_messaging_insights_model_card.htm&release=264.0.0&type=5)** The Einstein Messaging Insights model in Marketing Cloud Engagement provides insight about ongoing marketing email campaigns with email content using these training algorithms, parameters, fairness constraints, and features. 2. **[Intended Use](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_einstein_messaging_insights_model_card.htm&release=264.0.0&type=5)** The Einstein Messaging Insights model in Marketing Cloud Engagement is intended for these use cases. 3. **[Relevant Factors](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_einstein_messaging_insights_model_card.htm&release=264.0.0&type=5)** These factors are associated with the Einstein Messaging Insights model in Marketing Cloud Engagement. 4. **[Metrics](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_einstein_messaging_insights_model_card.htm&release=264.0.0&type=5)** Einstein evaluates and monitors model performance metrics to ensure and improve the quality of the model. These performance measures are associated with the Einstein Messaging Insights model in Marketing Cloud Engagement. 5. **[Training Data](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_einstein_messaging_insights_model_card.htm&release=264.0.0&type=5)** You have a customized version of the model that’s trained on your data alone. Data from one Salesforce customer doesn’t affect the behavior for another Salesforce customer. While model training happens for each customer on their data, the initial development of the model is validated with a representative set of pilot customers’ data. 6. **[Ethical Considerations](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_einstein_messaging_insights_model_card.htm&release=264.0.0&type=5)** Review the ethical factors associated with the Einstein Messaging Insights model in Marketing Cloud Engagement. To avoid bias and other ethical risks, this model doesn’t include demographic data. Customers are responsible for interpreting Einstein’s insights, and must be aware of any assumptions that they make when acting based on the model’s insights. ## Model Details The Einstein Messaging Insights model in Marketing Cloud Engagement provides insight about ongoing marketing email campaigns with email content using these training algorithms, parameters, fairness constraints, and features. ### Person or Organization Salesforce Einstein in Marketing Cloud Engagement ### Model Date and Version - May 2020 - Minor changes can occur throughout the release - Major changes can occur and are communicated via release notes ### Model Type Regression, Causation analysis, Time-series analysis ### General Information Einstein Messaging Insights is an anomaly detection system that surfaces noteworthy facts about ongoing marketing email campaigns running in Marketing Cloud Engagement. The model can detect and compare campaigns with similar attributes, performance changes over time, and relationship changes between activities for complex orchestration. The model comprises multiple decision tree regression models, time series-based predictions, and graph analytics. Einstein trains each customer’s model only using that customer’s data. ### Constraints - A minimum of 10 email batches is required to model for batch insights. - A minimum of 7 days of history is required to model for journey, journey activity, and frequent automation insights. ### License Einstein Messaging Insights is available to Salesforce Marketing Cloud Engagement customers with any of these editions. - Corporate Edition - Enterprise Edition - Enterprise+ Edition ## Intended Use The Einstein Messaging Insights model in Marketing Cloud Engagement is intended for these use cases. ### Primary Intended Uses The Einstein Messaging Insights model powers Einstein notifications and the Einstein Messaging Insights dashboard hub. These tools are designed for marketing professionals to see anomalously high or low engagement rates of email sends compared to expected values. The feature notifies marketers of anomalies so that they can further investigate their journeys and email sends. ### Out-of-Scope Use Cases Anything other than the primary use case is out of scope and not recommended. For example, when an anomaly is raised, inferring that related metrics like click-to-open rate (CTOR) are also anomalous isn’t an intended use case. ## Relevant Factors These factors are associated with the Einstein Messaging Insights model in Marketing Cloud Engagement. ### Model Input Einstein Messaging Insights analyzes up to 90 days of historical engagement patterns of the Enterprise subscribers. The engagement history includes these factors. - Email sends, bounces, and engagement events including open, click, and unsubscribe, spam complaints, and associated timestamps - Data and metadata about customer sending patterns, including how campaigns are executed. This data includes batch sends, Journey Builder sends, and Automation Studio sends, transactional sends, and triggered sends - Data and metadata about customer sending patterns, including past and future automations running in Automation Studio - Pre-personalization subject lines are analyzed with unresolved AMPscript - Email domain The engagement history that Einstein Messaging Insights analyzes excludes these factors. - Data purchased or collected from third parties - Demographic Data, which is typically stored in SFMC as data extensions or subscriber or contact attributes - Specific content within the email template or rendered email body ### Model Output The Einstein Messaging Insights model outputs these insights for three types of engagement (clicks, opens, and unsubscribes) daily. Each insight is generated based on observed engagement rate and incorporates upper and lower confidence bounds. All insights include an anomaly degree, which measures the severity of the insight’s impact. - Batch insights, which consider anomalous batches sent over the past 7 days. - Batch insights include top contributing signals with corresponding signal strengths. - Journey insights, which consider anomalous journeys whose engagement rate is outside of the expected range. - Journey activity insights, which consider anomalous journeys whose activities’ engagement rate’s overall change is outside of the expected range. - Automation insights, which consider anomalous frequent automations whose engagement rate is outside of the expected range. ### Groups The model doesn’t include any demographic data or other data purchased from third-party data providers. The model uses engagement data such as opens, sends, and patterns. ### Environment The model is trained and deployed in the Salesforce Marketing Cloud Engagement environment. #### See Also - [Infrastructure and Sub-processors for the Salesforce Marketing Cloud Services](https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/legal/misc/salesforce-infrastructure-and-subprocessors.pdf) ## Metrics Einstein evaluates and monitors model performance metrics to ensure and improve the quality of the model. These performance measures are associated with the Einstein Messaging Insights model in Marketing Cloud Engagement. ### Model Performance Measures We evaluate and monitor model performance metrics to ensure and improve the quality of the model. Model performance metrics include metrics like model fitness score (root mean squared error) and confidence-bound tightness. ## Training Data You have a customized version of the model that’s trained on your data alone. Data from one Salesforce customer doesn’t affect the behavior for another Salesforce customer. While model training happens for each customer on their data, the initial development of the model is validated with a representative set of pilot customers’ data. #### See Also - [Einstein Messaging Insights Model Card](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_einstein_messaging_insights_model_card.htm&release=264.0.0&type=5 "The model in this card analyzes message engagement rates and notifies marketers when anomalies in their marketing performance occur.") ## Ethical Considerations Review the ethical factors associated with the Einstein Messaging Insights model in Marketing Cloud Engagement. To avoid bias and other ethical risks, this model doesn’t include demographic data. Customers are responsible for interpreting Einstein’s insights, and must be aware of any assumptions that they make when acting based on the model’s insights.