Case Study
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Our client, a prominent player in the wine retail analytics industry, sought to centralize and streamline the collection, classification, and validation of wine product data aggregated from multiple retailers and suppliers across the U.S. With a focus on digital transformation in the wine retail supply chain, they envisioned a scalable data matching platform to ensure consistency and accuracy in their centralized wine catalog.
The primary goal was to develop an advanced internal data management and matching platform that could:
This initiative was designed to improve wine product discoverability, reduce duplication, and provide reliable datasets for end-users and internal stakeholders.
We developed a custom web-based Wine Data Matching and Validation Platform that empowered the client’s internal data team to efficiently manage large-scale wine data processing. The key modules of the platform include:
Improved match rate accuracy across millions of SKUs using machine-learning-based keyword mapping logic.
Reduced manual data processing effort by over 60% through automation of validation and rollup.
Seamless onboarding of new data sources with plug-and-play configuration templates per retailer.
Live logs and dashboards allow internal teams to monitor job success, anomalies, and data quality.
Built to support expanding datasets and retailer additions without affecting platform performance.
| Metric | Before Platform | After Platform |
|---|---|---|
| SKU Match Accuracy | ~72% | 98.4% |
| Manual Review Time | ~12 hrs/week | < 4 hrs/week |
| Data Onboarding Time (new retailer) | ~3 days | < 1 day |
| Validation Error Rate | ~15% | < 2 % |
| Number of Retailers Supported | 5 | 30+ |
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