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The retail decisioning layer

One system decides what each shopper sees. Most retailers run four or five separate tools.

Search in one tool, recommendations in another, pricing in a third, ads bolted on top — nobody owns the order products appear in, and the margin leaks between them. Particular Audience replaces that patchwork with a single decision: a Unified Decision Engine that scores relevance, inventory, margin and sponsored demand in one pass, on every surface, including the AI assistants now doing the shopping.

Face the Future — Relevance vs Bid retail media case study | Particular Audience

When Face the Future swapped relevance for bid-led auction logic, ad scale rose 9× but CTR collapsed -87.3% and sitewide conversion fell. The relevance-first proof point.

Relevance-first retail media on a beauty retailer

Average 2.2% CTR on promoted-item campaigns. Switching from Relevance to Bid collapsed CTR to 0.28%.

  • 2.2% Avg CTR with Relevance
  • -87.3% CTR when Bid replaced Relevance
  • -82.5% Overall desktop CTR drop

Face the Future enjoyed an average 2.2% CTR on promoted item campaigns (merchandise boost + retail media).

To chase scale they tested Bid strategy instead of Relevance. Ad scale jumped 9× — but retail-media CTR plummeted -87.3%, spend doubled, ROAS volatility spiked, and overall sitewide CTR fell -82.5% on desktop and -59.9% on mobile.

The cautionary tale: bid-based scaling without relevance is a tax on the entire site, not just the ad slots.

Turning off Relevance in favor of Bid increased ad scale 9× but collapsed CTR by -87.3%. — PA RMN Growth ReadME