Assortment Optimization

Assortment Optimization is the data-driven process of determining which products to carry, in which quantities, locations, and configurations, to maximize sales performance, margin, and customer satisfaction. It involves analyzing demand signals, inventory data, customer preferences, competitive positioning, and store or channel constraints to identify the ideal product mix. Modern assortment optimization leverages machine learning to process far more variables than traditional manual planning allows.

In retail and e-commerce, assortment decisions directly affect revenue and operational efficiency. Carrying too many low-performing SKUs drives up holding costs and complexity; too narrow an assortment risks losing customers to competitors. AI-powered assortment optimization enables retailers to localize product selection by store cluster, personalize online catalogs by customer segment, and rapidly respond to shifting demand patterns. It is particularly valuable for large-format retailers, grocery chains, and omnichannel operators managing thousands of SKUs across multiple fulfillment channels.