Pre-built deduplication strategies for multi-platform stores. From strict UPC matching to AI-powered semantic detection — pick the right level of aggressiveness for your catalog.
Each strategy configures which matching methods to use, confidence thresholds, and whether to auto-merge or queue for manual review.
Only merges on exact identifier matches. Zero false positives but misses products without standard identifiers.
Falls back to Levenshtein distance on titles with 0.85 threshold. Catches most dupes, may need review.
BGE-large embeddings find identical products even with completely different titles. Review queue for < 0.95 confidence.
All matching methods, auto-merges above 0.85 confidence. Fast cleanup but higher false-positive risk.
Stores selling on Shopify, Amazon, and eBay simultaneously often have the same product listed with different titles, descriptions, and even identifiers on each platform. Without deduplication, the recommendation engine treats these as separate products — fragmenting purchase signals and reducing recommendation quality.
SellerZoom's dedup service creates canonical product records that unify listings across platforms. When a customer buys Product A on Shopify and Product A on Amazon (listed under a different title), the co-purchase data is consolidated, making recommendations smarter across all platforms.
Strict identifier matching (UPC, GTIN, ASIN) is perfect for branded consumer electronics where every product has a universal identifier. But handmade products, private-label items, and many fashion products don't have UPCs. The AI Embedding strategy uses the same BGE-large model that powers recommendations to find semantically identical products — catching duplicates that no identifier-based system could detect.
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