A baby products brand onboarded 8 new suppliers in 6 months. The result: 25,000 product attributes with no consistent naming, no value standardization, and broken storefront filters. Here is how they fixed it.
A sporting goods wholesaler preparing for a platform migration discovered their catalog had a 14% error rate. They cleaned, validated, and verified their data to 99.7% accuracy before the switch.
A hardware retailer expanding into power tools needed 60 technical attributes — voltage, RPM, chuck size, battery type — that their catalog schema did not support. They built the entire attribute set in one afternoon.
An office supplies distributor was processing 400+ returns per month — most caused by incorrect product specifications. After implementing systematic data accuracy controls, returns dropped 22% in 90 days.
A beauty brand had incomplete product specifications that were losing them search visibility. They used web scraping to collect competitor data as a benchmark, then rebuilt their own product records.
After onboarding 6 suppliers, an electronics reseller had 15,000 product attributes with duplicate names, inconsistent values, and broken faceted navigation. Here is the 72-hour cleanup operation.
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