Client success story · CS20
Product mapping accuracy for a brand protection platform
Clean, structured datasets behind price tracking and MAP enforcement
- Client typeSaaS brand protection platform
- WorkstreamProduct-to-URL mapping
- ScopeThousands of e-commerce sites
Client background
The client is a SaaS brand protection and e-commerce intelligence platform that helps global brands monitor their online presence, track pricing and enforce pricing policy across marketplaces and independent websites.
Their market intelligence depends on precise product mapping across thousands of e-commerce sites. Without reliable mapping, price monitoring and MAP enforcement become inconsistent.
Problem
Operational challenges
Accurate mapping at this volume ran into large quantities of products requiring manual verification, inconsistent product titles across different websites, SKU mismatches and missing identifiers, difficulty linking the correct product URL to internal catalogue data, and a heavy dependency on clean, structured datasets for the price tracking to mean anything.
Solution
Custom solution executed
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01
Multi-parameter matching
Product titles, SKUs and contextual data points used together, with listings cross-verified to eliminate incorrect matches.
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02
Manual validation layer
Human verification for ambiguous or duplicate listings, maintaining precision in product-to-URL mapping where automated matching is unreliable.
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03
Data structuring and standardisation
Outputs delivered clean, sorted and system-compatible, so ingestion into the client's platform needs no intermediate processing.
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04
Quality control
Multiple validation checks before submission, keeping consistency across large datasets.
Impact
Business value delivered
- Significant improvement in product mapping accuracy
- Faster and more reliable price tracking across marketplaces
- Reduced internal effort for data cleaning and validation
- Enhanced platform efficiency for MAP compliance and market visibility
- Scalable support for large product catalogues
What it changed for the client
- Manual intelligence combined with structured process, rather than volume alone
- A focus on data quality that the client's core product depends on
- Ready-to-use datasets that remove operational friction
In short
Price intelligence is only as good as the mapping underneath it. Where automated matching hits ambiguity, a human validation layer is not a fallback — it is the part of the process that makes the dataset trustworthy.
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