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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.

Hyper-Personalization Case Studies

Measured outcomes from adaptive recommendations, search, bundles and next-best-product decisions across retail categories.

One shopper. One context. A better next decision.

Measured outcomes from adaptive recommendations, search, bundles and next-best-product decisions across retail categories.

Useful personalization is not a segment label. It is a product decision that adapts to intent, context and the retailer's commercial rules.

How it works

  • Understand: Read the current visit alongside product, customer and catalog signals.
  • Decide: Choose the most useful product, set or route for this moment.
  • Learn: Improve from behavior while retaining merchandising control.

Personalization in real stores

Named implementations showing how the next product decision changed baskets, conversion and revenue.

Measured results

  • Personalized recommendations across Life Pharmacy — Life Pharmacy. Controlled uplift: +23.2% Visitor conversion lift; +14.5% Revenue per visitor. +23.2% lift in visitor conversion rate; +14.5% revenue per visitor.
  • Mixed-basket merchandising at PVH — PVH Group (Tommy Hilfiger). Controlled uplift: +403% Mixed-basket lift. +403% increase in mixed baskets (shoes + accessories).
  • Cross-selling higher-margin accessories at digiDirect — digiDirect. Compared with sitewide: +32.2% Units per transaction; +14.9% Average order value. +32.2% lift in units per transaction and +14.9% lift in average order value from more relevant related-product recommendations.
  • Search and recommendations at Whittard — Whittard of Chelsea. Rollout trend: 2.2% → 4.58% Bundle-attributed site revenue over five months; +108% Increase in attribution; 8.7% Click through rate; 19.9% Click conversion rate. Bundle-attributed site revenue rose from 2.2% to 4.58% over five months, with 8.7% CTR and 19.9% click conversion.
  • UX-led personalization at Hotel Chocolat — Hotel Chocolat. Controlled uplift: +57.5% Units per transaction; +12.1% Average order value; 6.1% Click through rate; 17.5% Click conversion rate. +57.5% lift in units per transaction and +12.1% lift in average order value from tested bundle placements.
  • Bundles and recommendations at Petbarn — Petbarn. Observed outcome: Lift Cross-sell revenue. Cross-sell modules drove repeat-visit revenue across pet categories.
  • Cross-product discovery for a UK electricals retailer — Hughes. Observed outcome: Lift Long-tail engagement. Recommendations and bundles deepened category browsing on long-tail SKUs.
  • Limited drops, fanatical fans, bigger baskets — DTC boho fashion label (anonymized). Controlled uplift: +5.7% Average order value uplift; +12.3% Units per transaction uplift; 16.7% Click through rate. Multi-modal recommendations merged similar-item discovery with cross-sell and style buys on a direct-to-consumer own-brand range.
  • Doubling the head of the range without buying more stock — Multi-brand fashion retailer (anonymized). Controlled uplift: +21.1% More SKUs in head of range; +14.7% Average order value; +11.9% Revenue per visitor. Computer vision solved the cold-start problem on new arrivals, spreading demand across the catalog instead of concentrating it in a handful of SKUs.

Explore Personalization · Measurement standard