An Algorithm and Demand Estimation Procedure for Retail Assortment Optimization

We consider the problem of choosing, from a set of N potential SKUs in a retail category, K SKUs to be carried at each store so as to maximize sales or a defined profit function. Assortments can vary by store, subject to a maximum number of different assortments. We describe an approach in which we view a SKU as a set of attribute values, use sales history of the SKUs currently carried by the retailer to estimate the demand for attribute values and from this, the demand for any potential SKU, including those not currently carried by the retailer. We also introduce a model of substitution behavior, estimate the parameters of this model and consider the impact of substitution in choosing assortments. We use maximum likelihood estimation to fit the parameters of our model and describe several alternative heuristics for choosing SKUs. We describe application of this approach to optimize assortments for two real examples, snack foods and tires, which produced sales lifts of 20% and 32% respectively.