AI audience intelligence from messaging data
Privacy-first NLP that turns consented messaging data into aggregated interest audiences—improving ad targeting and monetization without sacrificing trust.
10×
Higher response vs standard targeting
Challenge
A rich consented messaging dataset spanning personal, professional, and commercial interactions was underused because it was unstructured and sensitive. Targeting relied on broad signals that limited relevance and monetization.
What we did
FocusKPI built a privacy-first NLP pipeline using text embeddings and classifiers to extract aggregated topics and interest signals. Teams continually annotated messages to refine classifications, and audiences were integrated into advertising platforms for more precise campaign targeting.
Results
Advertisers gained differentiated audiences and saw up to 10× higher response rates versus standard targeting. The company established a privacy-focused foundation to monetize a unique data asset while protecting user trust.
“This helped us responsibly unlock the value of one of our most unique data assets while maintaining the trust of our users and advertisers. This is a game changer.”
Why it worked
Advanced NLP was paired with consent, governance, and operational integration—so sensitive unstructured data became aggregated, usable signals without breaking trust.
Discuss a similar engagement
We typically start with a scoped proof of concept before production deployment.