--- schema_version: "1" id: b5fb4f7e5443a5608202bf984eb7ba1512f0db1e281400a668441868fb0d0dc0 title: Einstein Email and Web Recommendations 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_email_web_recs_model_card.md canonical_url: https://help.salesforce.com/s/articleView?id=mktg.mc_anb_email_web_recs_model_card.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: 48b377775eb7cae9e9229a0cbee05c0a9cb454e5e4f59971096cfbbdde2fc2e2 description: The model in this card analyzes and provides personalized product and content recommendations. products: - Marketing - Marketing Cloud Intelligence release: "264" --- # Einstein Email and Web Recommendations Model Card The model in this card analyzes and provides personalized product and content recommendations. - **[Model Details](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_email_web_recs_model_card.htm&release=264.0.0&type=5)** The Einstein Email and Web Recommendations model in Marketing Cloud Engagement personalizes recommendations with training algorithms, parameters, fairness constraints, features, and other applied approaches. - **[Intended Use](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_email_web_recs_model_card.htm&release=264.0.0&type=5)** The Marketing Cloud Engagement Einstein Email and Web Recommendations model is intended for these use cases. - **[Relevant Factors](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_email_web_recs_model_card.htm&release=264.0.0&type=5)** These factors are associated with the Einstein Email and Web Recommendations model in Marketing Cloud Engagement. - **[Metrics](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_email_web_recs_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 Marketing Cloud Engagement Einstein Email and Web Recommendations model. - **[Training Data](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_email_web_recs_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. - **[Ethical Considerations](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_email_web_recs_model_card.htm&release=264.0.0&type=5)** Review the ethical factors associated with the Marketing Cloud Engagement Einstein Email and Web Recommendations model. To avoid bias and other ethical risks, the Einstein Email and Web Recommendations model doesn’t include demographic data. ## Model Details The Einstein Email and Web Recommendations model in Marketing Cloud Engagement personalizes recommendations with training algorithms, parameters, fairness constraints, features, and other applied approaches. ### Required Editions
Available in: Enterprise Enterprise +, and Corporate Edition, and with Email Recommendations only in Professional Edition.
### Person or Organization Einstein in Salesforce Marketing Cloud Engagement ### Model Date and Version - December 2021 - Minor changes can occur throughout the release - Major changes can occur and are communicated via release notes ### Model Type Clustering ### General Information The Einstein model powering Einstein Email and Web Recommendations uses customer data provided by [Collect Tracking Code](https://help.salesforce.com/s/articleView?id=mktg.mc_ctc_collect_code.htm&release=264.0.0&type=5 "Collect Tracking Code is a JavaScript snippet that captures data about the behaviors of known and unknown visitors to your site. You can use this data to enhance contact profiles and optimize customer journeys across channels through predictive and personalized recommendations. You can also update product and content catalog details in real time or track email-driven revenue.") API and the [Einstein Recommendations Catalog](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_catalog.htm&release=264.0.0&type=5 "A catalog includes all fields that contain attributes used to make Einstein Recommendations. Einstein Recommendations filters assets based on these attributes. For instance, you can define assets in the recommendation pool based on a product’s color or size, or an article’s author and subject matter."). ## Intended Use The Marketing Cloud Engagement Einstein Email and Web Recommendations model is intended for these use cases. ### Primary Intended Uses Einstein Recommendations provides personalized product and content recommendations to help drive revenue on email, web, and other marketing channels including these options. - People Who Bought This Also Bought - People Who Bought This Also Viewed - People Who Viewed This Also Bought - People Who Viewed This Also Viewed - User Affinity - Tag Scenarios See the full list of [Einstein Recommendation Scenarios](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_einstein_recommendation_scenarios.htm&release=264.0.0&type=5 "These scenarios help you produce Einstein Recommendations for email or the web in Marketing Cloud Engagement."). ### Out-of-Scope Use Cases Anything other than the primary use case is out of scope and not recommended. ## Relevant Factors These factors are associated with the Einstein Email and Web Recommendations model in Marketing Cloud Engagement. ### Model Input Einstein Recommendations analyzes up to 180 days of historical behavioral data from Collect.js for each business unit. The engagement history includes these factors. - Pageviews - Category Views - Carts - Conversions - Wishlists - Metadata from the product or content catalog is used to build user affinity and provide attribute context. The affinity profile is created from user-tagged metadata from the catalog and is based on behavioral information. - Output is based on localized behavioral data, catalog content, and user attributes by business unit. - Business-specific rules added using [Rule Manager](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_personalization_rule_manager.htm&release=264.0.0&type=5 "Use Rule Manager to refine Einstein Recommendations for email and web.") impact the model The engagement history that Einstein Metrics Guard analyzes excludes these factors. - Data purchased or collected from third parties - Data from other business units - Demographic Data, which is typically stored in SFMC as data extensions or subscriber or contact attributes ### Model Output - The model output is a list of products or content via email or web recommendation call - Web recommendation output is a JSON block - Email Recommendation is a link & image pair ### Environment The model is trained and deployed in the Salesforce Marketing Cloud Engagement environment. ## Metrics Einstein evaluates and monitors model performance metrics to ensure and improve the quality of the model. These performance measures are associated with the Marketing Cloud Engagement Einstein Email and Web Recommendations model. ### Model Performance Measures Model performance metrics include data like model mean absolute error score. ## 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. ## Ethical Considerations Review the ethical factors associated with the Marketing Cloud Engagement Einstein Email and Web Recommendations model. To avoid bias and other ethical risks, the Einstein Email and Web Recommendations model doesn’t include demographic data. Consider any assumptions made when deciding on actions based on the model-generated scores that could lead to a potentially adverse outcome. ![Example](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-mktg-mc_data_management_and_analytics-264-0-0-production-enus/7988f60d-cd94-4b42-8836-dc11773564b5/images/icon_example.png) Example For example, the model predicts low engagement so you choose not to send a marketing communication to a set of customers. If the prediction was erroneous, there could be a negative impact to excluding some of those customers from access to benefits or opportunities.