Powering Precision with Data-Driven Assortment Optimization

Solution Retail Assortment Planning
Solution Retail Assortment Planning
Industry Retail
Region US
Technology Databricks
Context
Retail assortment optimization has become a critical lever for growth as consumer preferences shift rapidly, product lifecycles shorten, and shoppers expect every store to feel tailored to their needs. Retailers need more than intuition to decide what to stock and where — yet traditional, centralized planning approaches often fall short of capturing the local nuances that drive real demand. Store-level assortments built on assumption rather than data lead to inconsistent product availability, missed sales, and diluted brand experiences across the network. MathCo's data-driven assortment planning framework equips retailers with a store cluster optimization approach that aligns inventory with actual demand signals, not averages. By combining retail merchandising analytics with granular, cluster-level insight, retailers can respond faster to changing trends, localize assortments at scale, and deliver personalized in-store experiences that convert visits into revenue across thousands of locations.
Problem Statement

A leading US-based apparel retailer sought retail assortment optimization to better reflect evolving consumer preferences. Store managers often relied on assumptions rather than data, leading to inconsistent product availability and missed opportunities. With over 5,000 stores and multiple in-house brands, the retailer needed a scalable, data-driven assortment planning approach to localize assortments and boost customer satisfaction.

Impact

  • ~$5M incremental revenue from improved assortment planning. 
  • 7% sales growth across optimized store clusters. 
  • 75% of total revenue captured by top 10 optimized clusters.

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