Automating Seed-Based Audience Creation at Enterprise Scale

Client Snapshot:

A national media and advertising organization sought to improve how marketing audiences were defined and activated across its digital properties. With hundreds of millions of user-level records, the organization had rich first-party data but lacked a scalable way to translate it into actionable campaign audiences.

Challenge:

Audience segmentation was largely manual and campaign-specific.

Marketing teams relied on predefined segments or custom rule-based queries to define targets. Expanding high-value seed groups into broader lookalike audiences required recurring data science support, slowing campaign launch cycles and creating inconsistent targeting across initiatives.

Our Approach:

We developed a machine learning-driven audience creation platform to automate the expansion of seed-based segments into production-ready audiences.

A scalable scoring pipeline evaluated approximately 300 million user records across behavioral features, generating similarity-based propensity scores for any defined seed group.

This enabled marketing teams to:

• Define seed audiences aligned to campaign goals

• Automatically generate expanded lookalike audiences

• Standardize targeting logic across campaigns

• Deploy model-driven segments without manual engineering support

Results:

The platform significantly reduced the time required to create and activate campaign audiences.

Marketing teams were able to generate expanded, model-driven audiences directly from seed inputs without custom analysis, improving campaign agility while reducing manual segmentation workload for data science and engineering teams.

The Takeaway:

This case demonstrates how scalable, model-driven audience expansion can replace manual segmentation workflows and unlock the full value of first-party behavioral data across hundreds of millions of user records.