Affinity-Based Recommendations

For online retailers with large product catalogs, or publishers with large content feeds, affinity-based recommendation is a powerful method to make compelling product recommendations when and to whom they matter most. Affinity-based personalization algorithms gather behavioral data on consumers which are used to develop rich, multi-dimensional affinity profiles for every individual site user. The site then recommends based on the user’s unique preferences and meaningful actions taken on the site.

How affinity-based recommendations work?

Affinity profiling centers on creating weighted scores for users. These scores are calculated and assigned by measuring the correlation between consumer interactions and the item attributes with which they interact with most meaningfully. Meaningful interactions for eCommerce may include purchases, views or adds-to-cart; For publishers, these may include categories, tags, and keywords. The more interactions a consumer has with products or content that share a particular attribute, the better the recommendations engine can assign a level of interest.

What percentage of eCommerce sales are driven by recommendations?

An estimated 35% of Amazon’s revenue can be attributed to its recommendation engine alone. How can a recommendation engine really gauge what someone is more or less likely to buy? See the full article for more details.