The purpose of the Affinity Engine is to enrich a user's profile based on their behavior. However, the utility of this data is limited if we don't take action based on it. The three main ways that Affinity Engine information can be used in Lytics are through segmentation, recommendation, and Lookalike Models.
Lytics surfaces Affinity Engine scores on a user's profile. You can then use those scores inside of our audience builder to segment a user based on their Affinities. This opens the door to all sorts of interesting use cases. Perhaps, you are a shoe company and launching a new type of boots. You can now target an audience of users who are interested in boots without wasting ad spend on users who show very little interest in boots.
By creating an audience of users with an interest in boots, you can also see how your user's Affinities shift over time. Perhaps Affinities for boots peak during the winter. Or does boot interest peak during the fall as people explore their options before the coldest months hit. Affinities can become a useful trend tracking tool as Affinities shift among your users.
Learn more about how build Affinity-based Audiences.
Recommendation and Personalization
Lytics' Recommendation system combines AI and marketing strategy to allow marketers to easily suggest content and products that are relevant to their users. Setting up Recommendations can create more personalized experiences with users, which leads to increased user engagement, and more time on site.
Lytics makes it easy to get up and running with Content Recommendations. Upon configuration, our Recommendation system:
Leverages over 500 behavioral signals to provide robust and relevant recommendations.
Is completely autonomous. Our models get retrained and optimized weekly so that your recommendations stay fresh.
Works well out-of-the-box for new users; little or no data is required to provide users with recommended content.
Can handle large loads and is able to rapidly scale to meet your needs.
Learn more about building Content Recommendations and Product Recommendations.
Affinity Engine scores can be very useful for Lookalike Models. Affinity Engine scores serve as a standardized way of scoring users against each other. The data model consistency that Affinity Engine creates across users is extremely helpful when building machine learning models. Due to this consistency, we frequently find affinity scores showing up as important data points in Lookalike Models. If your team plans on executing use cases with Predictive Audiences, we highly recommend that you leverage Affinities.
Learn more about Lytics Lookalike Models.