Building Look-Alike Audiences with First-Party Data

This CMSWire feature explains how marketers can build look-alike audience models using first-party data as third-party cookies and DMPs decline. Travis Cameron of Tealium says audience expansion based on high-value segments becomes harder with fewer identifiers, requiring clean correlated customer data, a clear optimization goal, and a structured testing plan. Alex Holub of Vidora outlines machine learning benefits such as continuous segment adaptation and automatic weighting of user behaviors, and recommends data science platforms like Vidora's Cortex for fast deployment. The article notes a shift from demographic and psychographic inference toward contextual, interest-based, and pathing data, plus the use of hashed PII for identity matching. Privacy regulations including GDPR and CCPA are cited as key drivers of the move away from third-party data overlays.