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  Industry Retail / Grocery / CPG Geography United States — 25 metropolitan zip-code clusters Data Coverage SKU-level grocery pricing, multi-pack, promo tags, store-level availability Client Overview The client is a Fortune-500 US consumer packaged goods company with leading brands in cereals, snacks, frozen foods, and beverages. With Walmart, Target, and Kroger collectively accounting for 60%+ of US grocery sales, the client needed continuous visibility into how its products and competitor products were priced across retail channels and zip codes. Modern retail pricing in the US varies dramatically by zip code — same SKU may be priced differently between Manhattan, Brooklyn, and Bronx Walmart stores. Business Challenges Zip-Code Pricing Variation Same SKU priced differently by zip code within the same retailer. Manual store-by-store tracking was infeasible. Promo Tag Complexity 'Buy 2 Save $1', 'Mix & Match 5 for $5', 'Member-only pricing' — each retailer r...

  Industry E-commerce / Marketplace Intelligence Geography United States — pan-US coverage Data Coverage SKU-level pricing, Buy Box ownership, FBA stock, seller-level reviews, ASIN tracking Client Overview The client is a leading US e-commerce aggregator that acquires and scales Amazon FBA brands across categories like home & kitchen, beauty, pet care, and outdoor recreation. With a portfolio of 80+ brands and over 50,000 active ASINs, the client needed continuous marketplace visibility to defend Buy Box ownership, monitor competitor moves, and detect listing-hijacking attempts in real-time. Amazon's algorithmic Buy Box ownership shifts every few hours based on price, seller rating, FBA stock, and shipping speed — making manual monitoring impossible at this scale. Business Challenges Buy Box Loss Detection Latency Buy Box ownership changes hourly, but the client's existing tools reported losses with 24–48 hour delays — too late to react. Hijacker & Counterfeit Listings ...