Leveraging Affinities

The Affinity Engine enriches user profiles based on behavior, but its true value lies in how you act on that data. In Lytics, Affinity Engine insights can be applied in three key ways: segmentation, recommendation, and Lookalike Models.

Segmentation

Lytics integrates Affinity Engine scores directly into user profiles, allowing you to use these scores in the audience builder to segment users based on their Affinities. This unlocks a variety of strategic use cases. For example, if you're a shoe retailer launching a new line of boots, you can create an audience of users with an interest in boots, ensuring that your ad spend targets the right users instead of those with little to no interest.

Beyond targeting, Affinities can help you track trends over time. You may notice that interest in boots spikes during the winter—or perhaps it begins to rise in the fall, as customers start exploring options before the colder months. Affinities provide valuable insights as user interests shift over time, making them a powerful tool for trend analysis.

Recommendation and Personalization

Lytics' Recommendation system blends AI with marketing strategy to help you easily suggest relevant content and products to your users. By setting up personalized Recommendations, you can deliver more engaging user experiences, which in turn boosts time on-site and overall engagement.

Lytics makes it easy to get up and running with Content Recommendations. Upon configuration, our Recommendation system:

• Leverages over 500 behavioral signals to deliver highly relevant recommendations.

• Operates autonomously, with models that retrain and optimize on a weekly basis to keep recommendations fresh.

• Works effectively right out-of-the-box for new users, requiring little to no prior data to provide valuable content suggestions.

• Scales rapidly to meet the demands of large volumes and high user traffic.

To get started, navigate to the Personalize page under Using Profiles, or consult the Content Recommendation API documentation

Lookalike Models

Affinity Engine scores are also highly valuable when building Lookalike Models. These scores offer a standardized way to compare users, creating data consistency across profiles, which is crucial for machine learning models. This consistency allows affinity scores to often emerge as key factors in Lookalike Models. If your team is working on predictive audiences, leveraging Affinities can significantly enhance the accuracy and effectiveness of your models.