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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.

Large US furniture retailer — From automatic sets to governed promotional bundles | Particular Audience

Two separately measured phases: bundle-attributed revenue grew from 1.49% to 4.7% over five months; a later PDP and cart read showed +36% AOV and +44.3% units per transaction versus sitewide.

From automatic sets to governed promotional bundles

Two separately measured phases: bundle-attributed revenue grew from 1.49% to 4.7% over five months; a later PDP and cart read showed +36% AOV and +44.3% units per transaction versus sitewide.

  • 1.49% → 4.7% Bundle-attributed revenue over five months
  • +36% AOV vs sitewide in later PDP/cart read
  • +44.3% Units per transaction vs sitewide in later PDP/cart read
  • $1,432 Bundle value per user in later read

A large US furniture retailer used two complementary bundle mechanics. Automatically generated sets paired suites and room products from purchase patterns. A governed promotional bundle communicated savings when an approved spend threshold was active.

The first source records rollout growth over five months: the share of site revenue attributed to bundles increased from 1.49% to 4.7% as more categories were enabled. This is an attribution trend, not a randomized uplift claim.

A later furniture-specific deck reports a separate PDP and cart performance read: average order value was 36% above sitewide and units per transaction were 44.3% above sitewide. The source redacts the qualifying spend threshold, so it remains undisclosed here.