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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.