Turning anonymous visitor behavior into tailored subscription pitches using vector similarity search and deep content metadata.
A multi-channel media company had implemented a third-party identity resolution system to unmask anonymous website visitors. They now had the ability to reach these newly identified visitors via email—but the offers they were sending were generic. A one-size-fits-all subscription pitch to someone who had been deeply engaging with niche content was leaving significant conversion potential on the table.
The publisher knew that relevance drives conversion. The question was how to automatically determine what each visitor cared about and craft a message that spoke directly to those interests—at scale, without manual intervention.
We designed a system that analyzes each visitor's content consumption patterns and automatically generates a subscription offer message tailored to their demonstrated interests. Rather than broad messaging, each visitor receives an offer that speaks directly to the topics they care about most—powered by the same deep content metadata we'd already extracted across the portfolio.
Conversion rates on identity-resolved visitor emails increased by over 20%. By simply making the message relevant to what visitors were already reading, the publisher turned a generic outreach channel into a high-performing conversion engine. The system runs entirely autonomously—no manual segmentation, no campaign management, no ongoing tuning required.
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