Affinity-Based Recommendations

Affinity-based recommendations are product or content recommendations that are made based on the individual shopper’s Style Profile. These recommendations are usually shown to the shopper on a website, in an email or in a notification. The Style Profiles that determine these recommendations are rich user profiles derived from the shopper’s online behavior, the transactions they make, and their demographic data. All this data is used to map the shopper’s preferences—or affinities—across a wide range of visual and non-visual attributes. All of this is captured at every point in the shopper’s journey on the website.

In apparel retail, visual attributes could include colors, patterns, the length of a sleeve, or neckline and hem length. Non-visual attributes could include occasion, weather, etc. Style Profiles map a shopper’s affinities to these attributes based on their activity and intent on the site. Similarly, for groceries, affinities could include a preference for specific ingredients and brands, dietary restrictions, allergies, etc. The personalization engine understands shoppers’ affinities to these attributes.

Based on these affinities, and the actions a shopper takes on the site, product recommendations are made at every step in the shopper journey. Recommendations that take affinities into consideration are able to provide content that is personalized at an individual level, relevant to the specific point in their shopper journey. They have a greater impact on business outcomes.

These Ai personalized recommendations understand the shopper’s interactions and intent and could be exhibited at the right points to:

• Showcase products that every shopper is most likely to engage with
• Enable every shopper to discover products that they like
• Nudge every shopper to add more products to their cart, at every point in the journey 
• Nurture brand loyalty, ensuring that the shopper keeps coming back for more