--- schema_version: "1" id: e7917f2cd9f938984a925d3fdad5da1e298cdaf0a96c223c3793a03f5ab93f99 title: Salesforce Personalization collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/release-notes/262-0-0/rn_personalization.md canonical_url: https://help.salesforce.com/s/articleView?id=release-notes.rn_personalization.htm&release=262.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: 976b7a6ec63b8a1b9cbd003f8409dd4b9561244eb12dc056fc0bc995ab0a1ad5 description: Salesforce Personalization in Marketing Cloud Next enhancements includes updates for both the Salesforce Personalization and Marketing Cloud Personalization products. Learn about the latest features and updates to enhance personalized experiences across Salesforce clouds. products: - Cross Cloud release: "262" --- # Salesforce Personalization Salesforce Personalization in Marketing Cloud Next enhancements includes updates for both the Salesforce Personalization and Marketing Cloud Personalization products. Learn about the latest features and updates to enhance personalized experiences across Salesforce clouds. - **[Personalization Features Released by Month](https://help.salesforce.com/s/articleView?id=release-notes.rn_salesforce_personalization_features_released_by_month.htm&release=262.0.0&type=5)** Salesforce Personalization and Marketing Cloud Personalization features and changes are released as often as monthly, so check back soon for the latest updates. - **[Recommend Offers Across Catalogs Using Indirect Signals](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_recommend_offers_using_indirect_signals.htm&release=262.0.0&type=5)** Create custom objectives that enable your deep learning models to discover meaningful associations between direct and indirect signals across catalogs. For example, when your customer completes an online inquiry form for a loan, machine learning can map that indirect signal to display a relevant promotional offer for low-rate financing. You can also include related DMOs in your content schema to enhance offer recommendations by targeting channel-specific content for new or infrequently used offers. - **[Jump Start Your Personalization Journey with a Preconfigured Recommender Use Case](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_preconfig_use_case_setup.htm&release=262.0.0&type=5)** Intended for new Salesforce Personalization customers, this set up option creates and deploys all assets, including a profile data graph, to support a recommender-based Personalization use case. Creating and deploying a use case requires that no profile data graphs exist in the data space that you plan to deploy the use case to. - **[Streamline Personalized Content Creation and Deployment with Personalization Campaigns](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_create_personalization_campaigns.htm&release=262.0.0&type=5)** Build Personalization Campaigns using a guided configuration workflow. Following a step-by-step creation model, this workflow helps to simplify creating, mapping, testing, and deploying personalized content using a more intuitive, user-friendly configuration process. - **[Guide Personalization Configuration With Default Profile Data Graph to Data Space Assignment](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_default_profile_dg_to_ds_assgmnt.htm&release=262.0.0&type=5)** You can assign default profile data graphs to specific data spaces, providing selection guidance for Salesforce Personalization configurations. After the data graph is assigned, it is automatically populated as the default data graph whenever you select its associated data space in a Salesforce Personalization configuration. You can always override the default value by selecting a different profile data graph in the configuration. You can also edit or delete the assignment at any time. - **[Tailor Recommendations for Accounts](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_tailor_recommendations_for_accounts.htm&release=262.0.0&type=5)** Identify the best opportunities for your B2B customers by using rule-based recommenders for account records. Profile data graphs in rule-based recommenders use account profiles, instead of individual or unified individual profiles, to find the best suited recommendations. This enhancement enables sales teams and portals to serve recommendations based on account-specific insights, such as industry type, past purchases, or support cases. For example, if an account in the healthcare sector frequently purchases specific medical supplies, your rule-based recommenders can suggest related sterilization products. - **[Personalize Decisions with Related Attributes and Calculated Insights](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_personalize_decisions_with_rltd_attrib_and_calc_insgt.htm&release=262.0.0&type=5)** Craft impactful customer experiences by including related attributes and calculated insights as merge fields in personalization decisions. When configuring these multi-value attributes, simply apply sort criteria to precisely specify which value to return. This enhancement expands on your existing capability to embed direct profile attributes and segment memberships into your personalization responses. - **[Build and Customize Personalization Experience Templates](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_build_customize_experience_templates.htm&release=262.0.0&type=5)** Build and customize experience templates directly in the Salesforce Personalization app to determine how personalized content, such as hero banners or product carousels, appears to customers. Use preconfigured global templates to jump start your work, or code a custom template from the ground up. Experience templates control how your personalization campaigns and personalization points render on your websites or mobile applications. - **[Build Your Personalization Strategy with Individual Objects in Data Kits](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_build_strategy_individual_objects_datakits.htm&release=262.0.0&type=5)** Import specific personalization components from a data kit only when you need them. Instead of a full data kit deployment, use a data kit during object creation to populate settings for your engagement signals, personalization points, and response templates. - **[Deliver Consistent Recommendations with Fallbacks](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_deliver_consistent_recomms_with_fallbacks.htm&release=262.0.0&type=5)** Eliminate empty or partially filled recommendation blocks by selecting a relevant
 fallback recommender to fill open slots. When a primary recommender with restrictive filters
 returns insufficient items, the fallback recommender automatically provides additional suggestions
 to complete the set. This safety net protects the integrity of your campaigns and ensures that
 your users always get a full list of recommendations. At the same time, Salesforce Personalization
 avoids duplicate recommendations and ensures that each item is suggested only once per set of
 results. - **[Organize Campaign and Segment Folders with Targeted Permissions](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_perms_to_organize_segment_campaign_folders.htm&release=262.0.0&type=5)** Manage campaign and segment folders based on specific campaign and segment permissions instead of relying on a single, high-risk administrative permission. This permission update is more isolated and eliminates cross-entity folder management to protect your dataset structures from accidental modifications. Review and update your custom roles to make sure that your team retains the required folder access under the updated permission structure. - **[Filter Recommendations Using Date and Numeric Placeholders](https://help.salesforce.com/s/articleView?id=release-notes.rn_persnl_dynamic_context_variables_date_numeric_filters.htm&release=262.0.0&type=5)** Recommender filter conditions for number and date fields now accept Salesforce merge field placeholders, so the filter value is resolved at runtime instead of being set at design time. Use placeholders like `{!MinDate}` or `{!MaxAge}` to build flexible recommenders that respond to the context of each personalization request.