Know exactly which competitor products match your own — automatically
Product matching identifies identical items across different retailers, even when titles, descriptions, and SKU formats vary. Our pipelines combine fuzzy text matching, GTIN cross‑referencing, image hashing, and attribute comparison to build a deduplicated product master — with your internal catalogue IDs attached to every competitor SKU.
{
"match_id": "grp_8a21",
"your_sku": "INT-48219",
"sources": [
{ "retailer": "shoestore", "sku": "NB-574-GRY-9", "gtin": "0195925123456", "confidence": 0.98 },
{ "retailer": "anothermart", "sku": "574GRY9", "gtin": "0195925123456", "confidence": 0.99 }
]
}
45,000 unique products
92% coverage
Product matching pipelines running for
One product, a dozen retailers, one unified record
Product matching is the automated process of linking identical items across multiple e‑commerce sources — even when titles, descriptions, and identifiers differ. We apply fuzzy text matching, GTIN/UPC anchoring, image perceptual hashing, and attribute similarity scoring to create reliable match groups, deduplicating millions of SKUs into a clean, canonical product master.
Where you provide your own product catalogue, we map every matched competitor SKU to your internal identifiers — so your pricing engine, assortment dashboard, or BI tool immediately knows which of your products each competitor listing competes with.
- Fuzzy title and attribute matching across multiple retailers
- GTIN, UPC, EAN anchoring for definitive matches
- Image similarity detection for visually matched products
- Internal catalogue mapping — competitor SKUs linked to your IDs
- Delivered as a deduplicated master with full provenance
Why manual matching collapses under retail scale
Even a small product catalogue balloons into thousands of cross‑retailer comparisons — and human matching can't keep up with daily price and stock changes.
Titles are wildly inconsistent
The same shoe might be “NB 574 Core Grey” on one site and “New Balance 574 Classic Sneakers” on another. Simple keyword search fails; fuzzy logic is essential.
GTINs are a silver bullet — but only when present
Many retailers publish GTINs, but others don't. An effective matching system must use GTINs as an anchor where available, and fall back to other signals when they're absent.
Matching degrades as the catalogue grows
Matching 100 products manually takes an hour. Matching 100,000 across 10 retailers is a data‑science problem requiring pairwise comparison at scale — something only automated pipelines can handle.
A matching engine trained on e‑commerce reality
Every feature is built to handle the messiness of real product data — not idealised test sets.
Fuzzy title & attribute matching
We use token reordering, abbreviation expansion, and ML‑based text similarity to match products even when titles share only partial keywords.
GTIN/UPC cross‑referencing
When barcode identifiers are available, we anchor matches on them definitively. Our pipelines scrape structured data, meta tags, and API payloads to find GTINs even when they're not visible on the page.
Image similarity detection
We compare product images using perceptual hashing — catching matches where the same product photo is reused, even if the text description is completely different.
Internal catalogue mapping
We align matched groups with your product database using your internal identifiers — so every competitor listing is connected to your corresponding SKU.
{
"query": "New Balance 574 Classic Sneakers",
"candidates": [
{ "title": "NB 574 Core Grey", "score": 0.97, "match": true },
{ "title": "NB 576 Classic", "score": 0.62, "match": false }
]
}
From raw product feeds to a unified product master
Data acquisition & catalog mapping
We ingest your internal catalog and any scraped competitor feeds. Initial GTIN matching identifies high‑confidence links.
Multi‑signal matching engine
Our matching engine applies fuzzy title, attribute, and image similarity to cluster remaining products into match groups.
Validation & accuracy review
A sample of match groups is delivered for your review. We measure precision and recall against your expectations and fine‑tune thresholds.
Production master delivery
The deduplicated product master — with your internal IDs attached — is delivered to your data warehouse. Matching refreshes run on your schedule.
Deliverables for every product matching project
Catalog audit & matching strategy
A document reviewing your catalogue and target retailers, identifying GTIN coverage, and defining match rules — signed off before build.
Sample match report & accuracy metrics
A representative set of match groups with confidence scores, precision/recall figures, and a review interface for your team to validate.
Full product master + matching runbook
Complete deduplicated catalogue with your IDs mapped. A runbook details matching logic, refresh cadence, and threshold tuning guidelines.
Weekly match health digest
Summary of new products matched, ambiguous cases flagged for review, and any changes in GTIN coverage — proactively shared with your team.
Flexible plans for every matching scope
One‑Time Match Run
Best for a baseline match of your catalog against a set of retailers — ideal for a one‑off competitive snapshot.
- Fixed scope & price
- Single delivery of matched master
- Accuracy report included
Managed Matching Feed
Ongoing matching with scheduled refreshes, new‑product detection, and full maintenance.
- Weekly or daily match refreshes
- New product auto‑matching
- 97% accuracy guarantee
Enterprise Matching Program
For large, multi‑retailer catalogues with custom matching rules, internal ID mapping, and dedicated support.
- Unlimited retailers & SKUs
- Custom matching logic & thresholds
- SSO, audit logs, quarterly reviews
Teams that can't afford to compare prices on the wrong products
Competitive Pricing
Match competitor SKUs to your own before feeding data into your repricer — never reprice against an unrelated product.
Assortment Analysis
Build a unified view of the market by deduplicating products across retailers — understand true category size and share.
Brand Protection
Identify unauthorised sellers of your products by matching your catalogue against marketplace listings — flag MAP violations instantly.
Marketplace Catalog Unification
Merge product data from Amazon, eBay, and regional marketplaces into a single deduplicated master for your own marketplace store.
Product matching vs. DIY approaches
| Capability | Managed Product Matching | Manual Spreadsheet Matching | In‑House Matching Scripts |
|---|---|---|---|
| Fuzzy title & attribute matching | |||
| GTIN/UPC cross‑referencing | |||
| Image similarity detection | |||
| Internal catalog ID mapping | |||
| Scalability (millions of SKUs) | |||
| Ongoing refresh & maintenance | Included | Your team | Your team |
“We stopped repricing against the wrong competitor products”
"Before product matching, our repricer was comparing our SKUs against competitor products based on keyword search alone — and getting it wrong 15% of the time. ScraperScoop's matching engine brought that error rate down to 2%, and our revenue followed."
"Matching our 120,000‑SKU catalogue against 8 retailers used to take two analysts two weeks every month. Now it's a fully automated pipeline that runs weekly — and the data lands in Snowflake with our internal IDs already attached."
"The image matching caught products our keyword matching missed — identical items with completely different titles. That extra 5% coverage made our competitive intelligence actually reliable."
Matched product masters delivered to your existing infrastructure
Schema‑aligned with your internal product IDs — ready to load into any pricing, merchandising, or BI tool.
Get a scoped quote for your product matching project
Share a sample of your catalogue and the retailers you want to match against — we'll return a match sample and accuracy benchmark within a week.
Common questions about product matching
Your catalogue, matched against every competitor — automatically
Send us a sample of your product data and a list of target retailers. We'll return a matched sample with accuracy metrics within one week — no commitment, no sales pitch.
Most matching pipelines deliver validated results within 2–4 weeks of kickoff.
