--- schema_version: "1" id: 480c9560fdf07ab39f8b55df2485a9149e87e059d5669b7dac83beecf6560d1a title: B2C Commerce Einstein Recommender Strategies collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/cc/264-0-0/b2c_recommender_strategies.md canonical_url: https://help.salesforce.com/s/articleView?id=cc.b2c_recommender_strategies.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: fef5dea6b35619a6e2273ce74edb6e7134a03de6b5976f0b572b4c6a5e8cf29b description: After determining which recommenders you plan to use, specify recommendation strategies. products: - Commerce - B2C Commerce release: "264" --- # B2C Commerce Einstein Recommender Strategies After determining which recommenders you plan to use, specify recommendation strategies. _Strategies_ represent different approaches―different algorithms―for generating lists of recommendations (product IDs). When generating a list of recommended product IDs, B2C Commerce Einstein employs one or more of the strategies listed in the table. The best practice is to select at least a primary strategy and a secondary (fallback) strategy. But select up to three strategies. Strategies apply in the order that you select them. In the rare case the first strategy doesn't return recommendations or has insufficient data to produce high-quality results, the second strategy backfills to send recommendations to the page. The order of strategies matters. The first strategy listed takes priority over the second strategy, which the system uses only if the first strategy fails to generate enough recommendations. The second strategy takes priority over the third strategy, and so on. ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-cc-b2c_merchandiser_administrator-264-0-0-production-enus/ea76f372-6cfa-42a6-8e7c-05c6df7abed8/images/icon_note.png) Note Configuring too many restrictive rules can constrain the recommender results. This table lists the supported strategies, a brief description, and the recommender type for which the strategy is available.

Strategy	Description	Available for recommender types
Customer recently viewed items	Generates recommendations based on items that the customer recently viewed.	Recently viewed
Customers who viewed also viewed	Generates recommendations by analyzing the viewing behavior of other customers who viewed the same product.	Product to Product
Customers who viewed ultimately bought	Generates recommendations by analyzing the purchasing behavior of other customers who viewed the same product.	Product to Product
Customers who bought also bought	Generates recommendations by analyzing the purchasing behavior of other customers who bought the same product.	Product to Product
Recent top sellers	Generates recommendations by analyzing the revenue for products recently purchased by other customers. This strategy provides a selectable timespan with the following options: Realtime: Identical to the pre-existing Recent Top-Selling Products strategy model, this strategy provides real-time approximation for top revenue-producing products, aggregated by user geographical location and device. It uses a dynamically calculated time window (on average, a rolling 7 days) to ensure sufficient data to produce quality recommendations. 7 Days: This strategy uses a rolling, 7-day time window that updates daily to return recommendations that are similar to top revenue-producing products found in your weekly sales reporting. 30 Days: This strategy uses a rolling, 30-day time window that updates daily to return recommendations that are similar to top revenue-producing products found in your weekly sales reporting. Note For categories with low sales, some situations can result in recommendations not showing when using either the 7-Day or 30-Day options.	Products in a category Products in all categories
Recent most viewed	Generates recommendations by analyzing which products other customers recently viewed. Note The maximum number of recent most-viewed products is 10.	Products in a category Products in all categories
Product affinity algorithm	Generates recommendations by analyzing the product's similarity to other products.	Product to Product
Real-time personalized	Generates recommendations by analyzing the customer's current and past viewing and purchasing behavior.	Products to Product Products in a category Products in all categories

Salesforce recommends that you specify at least two strategies, so that if the first strategy fails to return enough recommendations, the system can use the second strategy. Specifying at least two strategies is best practice. However, the _Recently viewed_ recommender type is an exception, as it applies only one strategy. We recommend using the first strategy based on an individual’s real-time behavior. This table shows strategies that use individual customer experience and history blended with the entire customer base of your site. | Strategy | Anchor Expected | Result | | --- | --- | --- | | Product affinity algorithm | product-id | Einstein uses model-generated affinity recommendations based on the purchase history of the entire customer base. | | Real-time personalized | None | Einstein returns the highest ranked products for a specific user based on the user’s recent browsing history. The most recent four products that the user is most likely to be interested in viewing next appear. | When choosing a second strategy, choose one that applies to your site's entire customer base. Then use other strategies based on an individual's history. Using multiple strategies provides a more personalized experience for both new and existing customers. This table shows strategies that use the entire customer base of your site.

Strategy	Anchor Expected	Result
Customers who viewed also viewed	product-id	View-to-view correlations
Customers who viewed ultimately bought	product-id	View-to-buy correlations
Customers who bought also bought	product-id	Buy-to-buy correlations
Recent top sellers	category-id or none	Selectable timespan for the products-in-all-categories and products-in-a-category recommender types. Choose from the following options: Realtime: Identical to the pre-existing Recent Top-Selling Products strategy model, this strategy provides real-time approximation for top revenue-producing products, aggregated by user geographical location and device. It uses a dynamically calculated time window (on average, a rolling 7 days) to ensure sufficient data to produce quality recommendations. 7 Days: This strategy uses a rolling, 7-day time window that updates daily to return recommendations that are similar to top revenue-producing products found in your weekly sales reporting. 30 Days: This strategy uses a rolling, 30-day time window that updates daily to return recommendations that are similar to top revenue-producing products found in your weekly sales reporting. Note For categories with low sales, some situations can result in recommendations not displaying when using either the 7-Day or 30-Day options.
Recent most viewed	category-id / none	Einstein recommends the most viewed products within a specified category or from all categories when a category isn’t specified. Maximum number of recent most-viewed products is 10.