800,000 product listings, 12 storefronts, no shared schema
Search relevance improved ~25% on cross-market queries; seller metadata compliance from 55% to 88%.
Client Context & Operational Challenge
An e-commerce platform operating across 12 regional storefronts discovered that inconsistent product metadata — varying attribute naming, measurement units, category hierarchies, and description formats — was degrading search relevance, recommendation accuracy, and cross-market inventory visibility.
Execution & Governance Model
Conducted metadata audit across all 12 storefronts, identifying 340+ attribute-level inconsistencies. Designed a canonical product data model with market-specific attribute extensions. Built automated normalization pipelines for unit conversion, category mapping, and attribute standardization. Deployed human review for seller-submitted listings failing automated validation, with feedback loops to improve seller input quality over time.
Scale & Velocity Constraints
- 12 regional storefronts with independently evolved category hierarchies
- 800,000+ active product listings with metadata in 9 languages
- Measurement unit inconsistencies (metric vs. imperial, regional sizing standards)
- Seller-submitted metadata with no standardized input validation
- Search and recommendation algorithms consuming metadata in real-time
What Was Delivered
Asset Outputs & Deliverables
- Standardized metadata across all 12 storefronts within an 8-month phased rollout. Search relevance scores improved by an estimated 25% on cross-market queries. Recommendation accuracy measurably improved for cross-category suggestions. Seller metadata compliance rates increased from 55% to 88% through automated validation and feedback loops.
Operational Footprint
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