Particular Audience Particular Audience
Talk to us

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.

Multi-brand fashion retailer (anonymized) — Doubling the head of the range without buying more stock | Particular Audience

Computer vision solved the cold-start problem on new arrivals, spreading demand across the catalog instead of concentrating it in a handful of SKUs.

Doubling the head of the range without buying more stock

Computer vision solved the cold-start problem on new arrivals, spreading demand across the catalog instead of concentrating it in a handful of SKUs.

  • +21.1% More SKUs in head of range
  • +14.7% Average order value
  • +11.9% Revenue per visitor

As ranges grow, a static site means fewer items get discovered and revenue concentrates in a shrinking subset of products — the power law working against the buying team.

New arrivals are invisible to legacy recommendation engines because they have no behavioral history. Computer vision surfaced visually similar items to in-market shoppers from day one, paired with bought-together recommendations for bolt-on purchases.

The result was a wider revenue base: more SKUs earning their place in the top 80% of turnover, and higher revenue per visitor without additional traffic.