Lytics Laboratory Introduction
Lytics Laboratory allows you to experiment with and leverage the data science capabilities of Lytics. From the beginning, data science has been a core component of Lytics, with features such as Behavioral Scores and Content Affinity enabling marketers to create engaging, data-driven customer experiences.
Now, you can use Lytics to create Predictive Audiences powered by Lookalike Models to identify and reach more users who are likely to convert and become high-value customers.
Built for marketers
Lytics Lookalike Models are designed to be accessible to marketers with minimal effort. All user profile data is accessible for use in building Lookalike Models, including custom user fields, behavioral scores, and content affinities. You can manually select the features you want to build custom models or you can enable an option called Auto Tune, which uses an intelligent feature engineering process to automatically build the best models from all available fields.
Because Lytics Lookalike Models update user scores in real-time, you can start targeting users not only when the model is built, but also as their behavior changes or new users are added. Rather than using a static list, Predictive Audiences built from Lookalike Models provide a dynamic pool of users that will respond best when they are ready for ads or other marketing messages. You can also adjust your targeting criteria to make the best tradeoffs between reach and accuracy to maximize your marketing budgets.
Some common use cases for Lookalike Models include:
- From “unknown users”, find users who are likely to become “users with email addresses”
- From “users who’ve purchased one item”, find users who are likely to become “users who have made multiple purchases”
- From “known users”, find users who are likely to become “known users that have churned”
In addition to scoring users based on their similarity to a target audience, Lookalike Models can be used to predict values for any field available on a user profile, such as number of purchases, total purchase amount, visit count, or customer lifetime value.