Case StudyMulti-brand fashion retail · 1.8M SKU
The engine was fine.
The data wasn’t.
How a multi-brand fashion retailer corrected metadata inequality at 1.8M SKUs — without replacing its search engine or disrupting its merchandising team.
– See the results
RESULTS AT A GLANCE
65% in top 15% of SKUs
Search concentration in top SKUs
Results stopped favouring the same narrow band
6–8 weeks
New SKU ramp-up to visibility
New collections gained traction in half the time
–
Vendor feed inconsistencies ended
the exposure lottery for marketplace sellers
–
Attribute-complete launches entered
the index ready to rank – not weeks later
More SKUs qualified for retrieval on occasion and contextual queries — not just exact-match searches
Long-tail SKUs entered search results organically — no manual merchandising required
New collection launches hit the index ready to rank — not spending weeks building traction
Merchandising team freed from the manual boost cycle — catalog data did the work instead
The problem
A working search engine.
A catalog it couldn’t use.
At 1.8 million SKUs across multiple brands and marketplace sellers, this retailer had done everything right at the search layer — best-in-class platform, months of relevance tuning, a dedicated team managing ranking rules. Discovery patterns were still uneven. The instinct was to tune harder. The problem was upstream.
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
Fix the data layer.
Every downstream system improves.
Perspiq.ai was introduced as an upstream product intelligence layer — enriching catalog data before it entered the search index. The search engine, ranking logic, tech stack, and merchandising workflows remained completely intact.
01
Structured enrichment
Occasion tags, aesthetic signals, style vocabulary, and contextual attributes added to every SKU before indexing. Sparse catalog entries became semantically rich records the engine could actually work with.
02
Attribute depth alignment
New launches and long-tail SKUs brought to the same attribute floor as hero products — removing the structural advantage that established SKUs held purely by volume of accumulated data.
03
Confidence-aware validation
Uncertain AI-generated attributes flagged and human-expert validated before entering the index. 95% accuracy from day one. No training period. No catalog degradation risk.
04
Zero stack disruption
No search engine migration. No ranking logic changes. No integration rework. Perspiq.ai operated upstream only. 24-hour turnaround on enrichment across the full catalog.
The multiplier effect: search, recommendations, SEO, and GEO all consume the same catalog data. Fixing it once improved every downstream system simultaneously — without a separate initiative for each channel.
Why Perspiq.ai
Most enrichment tools are trained on images or general web text — they don’t understand fashion vocabulary the way retailers and shoppers use it. Perspiq.ai is trained on 900,000+ verified retail taxonomy attributes from real brand data: occasion structures, aesthetic hierarchies, trend signals, and relational context that general models don’t have. 95% accuracy from day one. No training period. No cleanup cycle before your catalog review passes.
Next step
See where your catalog
stands right now
A 30-minute demo or a catalog audit on your own SKUs. No fluff — just a clear read on where your data gap is and what it’s costing you.
Topics
fashion search discoverability
multi-brand catalog
metadata inequality
product data quality
upstream data enrichment
new SKU visibility
ecommerce catalog intelligence
AI-powered catalog enrichment with expert oversight—delivering shopper-ready data that feeds search and SEO.
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