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Scalable web, app & AI-powered extraction. 99.9% accuracy.
All Services → Core Scraping Services
Top Global Platforms
Pricing & Promotions
Brand & Intelligence
Digital Shelf & Search
Assortment For RetailersWhich solution fits?
Talk to Expert Marketplace Scrapers
Data APIs Universal APIs Delivery & SDKsReady to integrate?
Start Free Trial Knowledge Center
Guides & Playbooks
Downloads & Tools
Trust & Company
Every competitive pricing comparison, every cross-marketplace analysis, and every MAP monitoring program depends on one critical capability: knowing that the product on Amazon is the same product on Walmart, eBay, Target, and your own website. Without accurate product matching, your competitive intelligence is fundamentally flawed.
This sounds simple. It is not. The same physical product appears with different titles, different images, different descriptions, and different identifiers across different marketplaces. A Samsung Galaxy S24 on Amazon might be listed as “Samsung Galaxy S24 128GB Phantom Black Unlocked” while on Walmart it appears as “Samsung S24 5G Phone 128GB Black GSM Unlocked.” Different words, same product. Matching them accurately at scale requires sophisticated AI.
The same product can have radically different titles across marketplaces. Retailers add their own keywords for SEO, abbreviate brand names differently, include or exclude model numbers, and order attributes differently. A traditional string-matching approach fails because the overlap in exact words may be less than 50%.
A 12-pack of a product is not the same as a 24-pack, but they share the same brand, product name, and most attributes. Matching must be sensitive to quantity, size, and packaging variations that change the effective product.
Products sold in different markets may have different names, formulations, or packaging. A product sold as one brand name in the US may have a different brand name in the UK. Voltage-specific electronics (110V vs 220V) are functionally different products despite identical appearance.
Retailer own-label products that are manufactured by the same company as a branded product are functionally identical but carry different names, barcodes, and branding. Matching these requires understanding of manufacturing relationships that goes beyond surface-level product data.
The first layer matches products using unique identifiers: UPC/EAN barcodes, GTIN numbers, manufacturer part numbers (MPN), and ASINs. When identifiers match, confidence is near 100%. However, identifier coverage is incomplete — not all listings include barcodes, and different marketplaces may use different identifier systems.
When identifiers are unavailable, AI matches products based on a weighted combination of attributes: brand name, product name tokens, model number, key specifications (size, color, material, capacity), and pack size. Machine learning models trained on millions of verified product pairs learn which attribute combinations reliably indicate a match versus a near-match.
Product image comparison provides a supplementary matching signal. Computer vision models compare product images across listings to confirm or refute matches suggested by text-based analysis. This is particularly valuable for fashion, home goods, and other categories where visual similarity is a strong matching indicator.
Automated matching handles 95% of products confidently. The remaining 5% — ambiguous cases, unusual products, and edge cases — are routed to human reviewers who make the final match determination. This human-in-the-loop approach ensures that overall matching accuracy exceeds 98%.
| Category | Identifier Match Rate | AI Match Rate | Overall Accuracy |
|---|---|---|---|
| Electronics | 78% | 97% | 98.5% |
| Health & Beauty | 65% | 94% | 97.2% |
| Grocery & FMCG | 72% | 95% | 97.8% |
| Fashion & Apparel | 35% | 91% | 94.5% |
| Home & Garden | 58% | 93% | 96.8% |
| Toys & Games | 70% | 96% | 98.1% |
| Automotive Parts | 82% | 97% | 99.0% |
Electronics and automotive parts achieve the highest accuracy due to standardized model numbers and widespread barcode usage. Fashion is the most challenging category due to style variations, seasonal changes, and limited identifier usage.
A global consumer electronics brand needed to monitor 25,000 SKUs across 12 marketplaces in 8 countries:
Four-layer matching: identifier matching, AI attribute matching, visual matching, and human verification for edge cases. Machine learning models trained on millions of verified product pairs continuously improve accuracy.
Yes, though with lower confidence. We use ingredient matching, specification comparison, and manufacturer relationship data to identify own-label products that are functionally equivalent to branded products. Accuracy for own-label matching is typically 85-90%.
For a catalog of 10,000 SKUs across 5 marketplaces, initial matching typically completes within 3-5 business days. Ongoing matching of new products is processed within 24 hours.
Absolutely. Our system accepts client-provided matching rules, manual overrides, and exclusion lists. Client corrections are fed back into the AI model to improve future matching accuracy.
Our web scraping expertise is relied on by 4,000+ global enterprises including Zomato, Tata Consumer, Subway, and Expedia — helping them turn web data into growth.
Watch how businesses like yours are using Actowiz data to drive growth.
From Zomato to Expedia — see why global leaders trust us with their data.
Backed by automation, data volume, and enterprise-grade scale — we help businesses from startups to Fortune 500s extract competitive insights across the USA, UK, UAE, and beyond.
We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.
How AI-powered product matching maps identical products across Amazon, Walmart, eBay, and 100+ marketplaces. Achieve 98%+ matching accuracy at scale for pricing and competitive intelligence.
How a $50M+ consumer electronics brand used Actowiz MAP monitoring to detect 800+ violations in 30 days, achieving 92% resolution rate and improving retailer satisfaction by 40%.
Track UK Grocery Products Daily Using Automated Data Scraping across Morrisons, Asda, Tesco, Sainsbury’s, Iceland, Co-op, Waitrose, and Ocado for insights.
Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.
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