Every price-benchmarking project rests on one invisible foundation: matching. Before you can say "we're 8% more expensive than the competitor," you have to be certain you're comparing the same product — across Amazon, a competitor's site, a marketplace and your own catalog, where it carries four different titles and no shared ID. Get matching wrong and every insight downstream is fiction. This guide explains how cross-marketplace product matching actually works, and why match accuracy is the metric that matters most.
The metric that matters: match rate and match precision. A vendor who matches 100% of products but gets 15% wrong is worse than one who confidently matches 90% and flags the rest for review. Always ask for both numbers on a real sample before you trust a benchmarking feed.
| Method | How | Confidence |
|---|---|---|
| Identifier match | EAN / UPC / GTIN / model number where exposed | Highest |
| Structured attribute match | Brand + model + key specs (size, capacity, variant) | High |
| Title/text match | Normalized title + brand tokens, fuzzy logic | Medium — validated, not trusted alone |
| Image match | Visual similarity for products with weak text/IDs | Supporting signal |
| Human review | Ambiguous matches flagged, not guessed | Resolves the hard cases |
Good matching runs this as a waterfall — start with the highest-confidence identifier, fall back through attributes, title and image, and route the genuinely ambiguous to review rather than forcing a guess.
Compare your prices against competitors and marketplaces on truly identical products — the basis of any pricing or MAP decision.
Match your catalog to competing listings (e.g., Amazon ↔ a competitor like Zoro) to price competitively and win the buy box.
Cross-platform comparison apps need every product reconciled across sources — matching is the product.
Match against reference catalogs (e.g., by EAN for a PrestaShop/Shopify store) to enrich and deduplicate a product master.
A seller needed its products matched to competing marketplace listings by EAN to power price benchmarking and repricing; another needed Amazon products matched to a competitor's model numbers. In both, Actowiz ran the identifier-first waterfall, treated variants strictly, and delivered matched pairs with confidence scores — with ambiguous matches flagged rather than guessed. The reported match precision on the validation sample is what gave the clients confidence to price against the data.
"We stopped arguing about whether the comparison was fair. The confidence scores meant we trusted the green rows and reviewed the amber ones. That's all we needed."
— Head of Pricing, marketplace seller (name withheld)
Send us a sample of your products and the marketplaces to match against. We'll return matched pairs with confidence scores — and the match rate and precision numbers — so you can judge quality first.
Run a Free Match TestProduct matching uses publicly displayed catalogue and price information — no accounts, no personal data. Collection follows our responsible-scraping framework.
The waterfall falls back to brand+model+attributes, then normalized title, then image similarity, with ambiguous cases flagged for review — so matching works even without a shared identifier.
We report match rate (how many matched) and precision (how many correct) on your validation sample, so you can trust the feed before committing.
Yes — colour, size, pack count and bundles are treated as distinct; a multi-pack never matches a single unit.
Yes — we match external listings to your product master (by EAN, model or attributes) for enrichment, benchmarking or repricing.
Identifier-first matching with confidence scores across every marketplace.
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