Behavioral Scoring

Predict customer behavior without the need to hire an in-house data scientist.

90% cost per conversion savings

by targeting “highly engaged” users on Facebook Ads

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Why care about behavioral scoring?

We collect and analyze your first-party, behavioral data in real time to score your end users and group them into predictive cohorts.

Behavioral scores are the building blocks of Lytics’ decision engine to automatically move users in and out of audience segments and marketing campaigns as their activity evolves.

The value of behavioral scores

Lytics behavioral scores are powered by machine learning and artificial intelligence. Each of our nine scores rank your users from 0 to 100.

The scores represent both insights about people who interact with your brand and the predictions of their future actions…so you can make the right connections to the right people with a message they want to get.

lytics behavioral scores

Build more accurate audiences than ever

If you’re used to segmenting lists based on demographics, get ready for the new best practice using behavioral insights, AI predictions and automation from Lytics.

Each visitor to your site will be scored based on their behavior. These scores are used to dynamically place visitors into different audience segments, so you can specifically target users who are likely to churn differently than users showing high rates of engagement.

Nine powerful out-of-the-box scores


measures overall brand engagement over the user’s lifetime, relative to all other users


measures how consistently each user interacts with your brand


measures how recently users have interacted with your brand; a higher score means more recently


measures the depth of a user’s typical interaction with your brand


measures the rate at which users interact with your brand


reveals when users start to show signs of attrition, allowing you to re-engage them


measures the stability of a user’s engagement


measures how long a user has interacted with your brand


measures how stable or sporadic a user is relative to other users

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