How a Sporting Goods Retailer Reduced Price Volatility and Improved Match Accuracy by 40% with Competitor AI

Background

A national sporting goods retailer with both online and in-store operations faced growing competition from digitally native brands and discount-heavy marketplaces. Categories like running shoes, fitness equipment, and performance apparel experienced constant price volatility.

Legacy pricing tools relied heavily on manual tracking and SKU-based matching, which failed to accurately align products—especially for private label, seasonal bundles, and multi-variant apparel.

• Manual competitor tracking was time-consuming and difficult to scale
• Product mismatches led to inaccurate comparisons, hurting price positioning
• Lack of visibility into competitor promotions and MAP violations exposed the brand to pricing inconsistencies
• Pricing decisions were frequently reactive, resulting in over-discounting and eroded margins

The retailer implemented Hypersonix Competitor AI, gaining:

• Daily competitive data collection across DTC brands, marketplaces, and regional players
• AI-driven product matching using CommerceLLM and computer vision for apparel, bundles, and size variants
• A customizable rules engine to automate pricing triggers and flag MAP violations
• Dashboards and alerts that empowered pricing teams with clean, contextualized insights

• Improved product matching accuracy by 40%, particularly across size/color variants and bundles
• Saved 22 hours/week previously spent on manual benchmarking
• Increased margin by 1.9% through optimized pricing on high-demand SKUs
• Reduced unnecessary pricing changes by 20%, enabling more stable and confident price execution
• Improved MAP compliance by leveraging automated violation detection and downstream retailer notifications

The Hypersonix Impact

• Improved match precision for multi-variant and bundled products
• Daily data updates created always-current visibility into the competitive landscape
• Strategic pricing actions replaced reactive markdowns
• Manual tasks were automated, freeing up resources for high-value analysis
• Confidence and alignment increased across pricing, merchandising, and leadership teams