--- schema_version: "1" id: dd6dbf77164189241a67b782d722152cddfec6f9a6f5f2f5b79687e344904cc6 title: Einstein Recommendations collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/mktg/264-0-0/mc_pb_personalization_builder.md canonical_url: https://help.salesforce.com/s/articleView?id=mktg.mc_pb_personalization_builder.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: 1f82e1d4f6a6fffa9d5d9e3774ca3f415400aa4b63ceffc731a06decefbed4a1 description: Einstein Recommendations deliver the next-best product, content, or offer to every individual through product and content recommendations for email and web. Every customer interaction is an insight. With every click, download, view, and purchase, customers are giving data about their preferences. Einstein Recommendations combine user behavior with algorithms and your unique business rules to build a user profile of affinities. Use that profile to determine the most-relevant content and products for each customer in real time. Hyperforce doesn’t support using email or web recommendations. products: - Marketing - Marketing Cloud Intelligence release: "264" --- # Einstein Recommendations Einstein Recommendations deliver the next-best product, content, or offer to every individual through product and content recommendations for email and web. Every customer interaction is an insight. With every click, download, view, and purchase, customers are giving data about their preferences. Einstein Recommendations combine user behavior with algorithms and your unique business rules to build a user profile of affinities. Use that profile to determine the most-relevant content and products for each customer in real time. Hyperforce doesn’t support using email or web recommendations. ![Tip](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_tip.png) Tip Your web administrator can help you to configure and install Collect Tracking Code and required data feeds. Before you attempt to use Einstein Recommendations, the setup wizard walks you through the steps to import a catalog and configure Collect Tracking Code. Best practice is to observe website behavior for at least 30 days before launching any predictive content logic. This 30-day monitoring period allows the engine to dynamically build profiles and behavioral intent patterns of your user base. - **[Einstein Recommendations Prerequisites](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_personalization_builder_prerequisites.htm&release=264.0.0&type=5)** Learn about the prerequisites for implementing Einstein Recommendations. - **[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. - **[Einstein Recommendations Customer Profile Dashboard](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_profiles.htm&release=264.0.0&type=5)** Review a customer profile from this dashboard. Expand the view for greater detail or make changes to the customer privacy setting. - **[Einstein Recommendations Integrations](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_personalization_builder_integrations.htm&release=264.0.0&type=5)** Integrate data gathered through Einstein Recommendations with Audience Builder and Contact Builder. - **[Einstein Recommendations Status Console](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_personalization_builder_status_console.htm&release=264.0.0&type=5)** Einstein Recommendations Status Console monitors the Collect Tracking Code and catalog imports for common implementation errors. View where an error occurred and the details to help resolve the issue. This feature helps ensure that the Einstein Recommendations engine receives the data necessary to power optimal Einstein Recommendations and behavioral triggers for campaigns and journeys. - **[Fix an Einstein Recommendations Implementation Error](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_fix_an_implementation_error.htm&release=264.0.0&type=5)** You can fix an implementation error from the Einstein Recommendations status console. You must fix each error individually. - **[Einstein Recommendations 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. - **[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. - **[Waterfall Predictions](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_waterfall_predictions.htm&release=264.0.0&type=5)** Prioritize your recommended scenarios from highest to lowest using waterfall predictions in Einstein Recommendations. In a waterfall prediction, Einstein goes down the list of scenarios and returns all available matching items until it reaches the total number of recommendations that you set. Set up a default site data scenario last in your hierarchy to ensure that Einstein fills the total number of recommendations. - **[Localized Recommendations](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_localized_recommendations.htm&release=264.0.0&type=5)** Configure Einstein Recommendations to deliver localized recommendations in multiple languages and currency values from a single business unit. - **[Einstein Recommendations Reports](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_personalization_builder_reports.htm&release=264.0.0&type=5)** Learn about reports available in Einstein Recommendations. - **[Einstein Recommendations FAQs](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_personalization_builder_faq.htm&release=264.0.0&type=5)** Get the answers to frequently asked questions about Einstein Recommendations. #### See Also - [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.") - [Fix an Einstein Recommendations Implementation Error](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_fix_an_implementation_error.htm&release=264.0.0&type=5 "You can fix an implementation error from the Einstein Recommendations status console. You must fix each error individually.") - [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.") - [Einstein Email Recommendations](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_einstein_email_recommendations.htm&release=264.0.0&type=5 "Use Einstein Email Recommendations to observe customer behavior, build preference profiles, and deliver the next-best content or product. You can also refine recommendations to match your business rules. Recommendation content surfaces in placeholders for recommendations on your website.") - [Einstein Web Recommendations](https://help.salesforce.com/s/articleView?id=mktg.mc_pb_einstein_web_recommendation.htm&release=264.0.0&type=5 "Einstein Web Recommendations use Einstein’s capabilities to observe behavior, build preference profiles, and deliver the next-best personalized content to each website visitor. Use scenarios in the application to refine recommendations to match your specific business rules. Predictive content is surfaced in placeholders for recommendations on your website.")