Marketers are surrounded by predictive modeling and machine learning that helps shape the audiences included in their campaigns.

Predictive modeling is informing audience selection just about everywhere. Whether it’s audience recommendations in marketing automation platforms, underlying algorithm(s) powering campaigns in DSPs, or a 1st party data look-alike modeling feature.

While a marketer doesn’t have much control over the stock algorithms or modeling capabilities within their platforms, they do have a say once they start entertaining custom audience solutions. And it wasn’t until recently that anyone outside of analytics-related roles really questioned the specifics of their modeled solutions.

Marketers upping their analytic game will help with solution evaluation, foster more strategic discussions with a broader group of teams, and drive better results.

This guide provides the foundation for a deeper understanding of custom audience modeling.