Product affinity

Product affinity is a predictive model that identifies which customers are likely to purchase by using a combination of historical purchase data and lookalike audiences. The predicted affinity model outputs a ranked list of customers with three recommended audience sizes.

Amperity models product affinity for any product taxonomy that contains between 20 and 2000 unique values, such as brand, category, subcategory, color, size, season, and style. Product affinity modeling analyzes:

  • Historical data to identify customers who have purchased a product in the past and are likely to do so again.

  • Lookalike audiences to identify customers who have not purchased a product, but are likely to buy because they are similar to customers who have purchased.

Use cases

The product affinity model enables support for marketing campaigns that benefit from knowing a customer’s preferences across product categories, including:

  1. Recommended audience sizes

  2. Ranking customers by affinity

Customer ranking

Use customer ranking to define an audience using the top N customers. Use customer ranking as an alternate to recommended audience sizes when an audience is too large (or small) or if a recommended audience size is unavailable for a specific product or category.

Customer ranking identifies the top N customers who are most likely to purchase. Use customer ranking to:

  • Provide an alternative to a recommended audience size, such as when a recommended audience size is unavailable for a specific product or category

  • Serve targeted product messages to defined audiences

  • Identify first-time buyer personas

  • Drive up conversion rates

  • Drive down opt-outs

The Ranking attribute in the Predicted Affinity table ranks customer scores by product. A rank that is less than or equal to X will provide the top N customers with an affinity for this product. Combine this attribute with the Product Attribute attribute to build customer rankings for a specific product category, class, or brand. You can access this attribute directly from the segment editor:

Ranking in the Predicted Affinity table.