Case StudyApparel retail · 1.3M SKUs
Shopper language
had outpaced
the catalog
A digitally mature apparel retailer with search converting at 2x the site average hit a ceiling — because the catalog had no vocabulary for how shoppers were actually searching.
RESULTS AT A GLANCE
Engagement uplift
Engagement on intent-driven queries
Shoppers found what they were looking for — first time
Revenue impact
Search-driven revenue growth QoQ
Search went from plateauing to compounding
Friction reduced
Fewer query refinements
Shoppers stopped having to rephrase to find results
Better first-result relevance — intent-rich queries matched products immediately
Reduced need for query refinement — search understood what shoppers meant, not just what they typed
Stronger alignment between shopper intent and search results across the full catalog
No schema restructuring, no engine changes — enrichment injected directly into existing pipeline
The problem
Search was strong.
But shoppers had moved on.
This wasn’t a broken retailer. Search was already converting at more than 2x the site average — a genuinely strong baseline. But over time, the way shoppers searched had evolved. Queries became intent-rich: “airport outfit,” “elevated basics,” “quiet luxury workwear.” The catalog had no structured vocabulary to match them.
The search engine could only rank on signals it had been given. And the signals it had were category and color. The gap between how shoppers searched and what the catalog described had become the ceiling on search revenue growth.
01 — Shift in shopper behaviour
Queries became intent-rich
- Shoppers used occasion and aesthetic language: “airport outfit”, “elevated basics”
- Search relied on category + attribute matching — not intent
02 — Catalog signal gap
No vocabulary for how shoppers searched
- No structured fields for occasion, aesthetic, or use-context
- Products lacked intent-aligned descriptors entirely
03 — System limitation
Engine could only rank what existed
- Search could only rank on available signals
- Query relevance plateaued despite strong baseline conversion
04 — Observed symptoms
The ceiling showed up in the data
- Engagement on search flattened
- Users refined queries more frequently — a sign results weren’t landing
- Revenue growth from search slowed
What the data showed
- Top 15% of SKUs captured ~65% of all search impressions
- New products took 6–8 weeks to gain any meaningful visibility
- Marketplace sellers saw inconsistent exposure despite comparable inventory quality
- Merchandising teams relied on manual boosts that reset at every collection launch
Why search-layer fixes failed
- Ranking rules amplified already-strong SKUs, widening the visibility gap furthers
- Sparse SKUs stayed invisible — nothing new for the engine to rank on
- Manual boosts didn’t scale and expired at each seasonal launch
- Marketplace metadata arrived inconsistently from dozens of vendor feeds
The intervention
Same engine.
Expanded signal set.
Perspiq.ai injected contextual signal fields into the catalog before search indexing — adding the intent vocabulary the engine needed to match shopper queries. No schema restructuring. No engine migration. Structured JSON dropped directly into the existing pipeline.
Step 01
Contextual field generation
- Intent signals extracted from product imagery and metadata
- Structured fields generated per SKU: occasion, aesthetic profile, use-context, style clusters
- Fields aligned to real shopper query vocabulary — not internal taxonomy
Step 02
Pre-index integration
- Enrichment applied before search indexing — upstream of the engine
- Structured JSON injected directly into existing pipeline
- No schema restructuring required. No integration rework.
Step 03
Signal expansion in index
- More searchable fields per SKU — beyond material, color, and category
- Occasion and aesthetic context added as indexable dimensions
- Natural language queries could now find the right products
Step 04 — System effect
Broader retrieval
- More products qualified for intent-driven queries — not just exact-match ones
- Matching expanded beyond keyword and category constraints
- What shoppers meant and what the catalog described finally aligned
What this unlocked: search began matching natural-language queries against structured contextual signals — not just category and color. The engine didn't change. What it had to work with did.
"Search stopped failing shoppers who knew what they wanted but didn't know what to type."
Why Perspiq.ai
Generating occasion, aesthetic, and use-context fields at scale requires a model that understands fashion vocabulary at the attribute level — not just image recognition or general language patterns. Perspiq.ai is trained on 900,000+ verified retail taxonomy attributes from real brand data, which is why contextual fields are accurate enough to improve retrieval without introducing noise. 95% accuracy from day one. No training period on your catalog required.
Next step
See what intent signals
your catalog is missing
A 30-minute demo or a catalog audit on your own SKUs. We’ll show you exactly where your shopper vocabulary gap is — and what closing it looks like.
Topics
fashion catalog enrichment
shopper language gap
search revenue growth
contextual signal injection
semantic search fashion
query refinement reduction
upstream catalog enrichment
AI-powered catalog enrichment with expert oversight—delivering shopper-ready data that feeds search and SEO.
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