A Modeling Framework for Category Assortment Planning

The complexity of managing a category assortment has grown tremendously in recent years due to the increased product turn-over and proliferation rates in most categories. It is an increasingly di±cult task for managers to ̄nd an e®ective assortment due to uncertain consumer preferences and the exponential number of possible assortments. This paper presents an empirically-based modeling framework for managers to assess the revenue and lost sales implication of alternative category assortments. Coupled with a local-improvement heuristic, the modeling framework generates alternative category assortment with higher revenue. This framework, which consists of a category purchase incidence model and a brand share model, is calibrated and validated using sixty thousand shopping trips and purchase records. Speci ̄cally, the purchase incidence model predicts the probability of an individual customer who purchases (and who does not purchase) from a given product category during a shopping trip. The no-purchase probability enables us to estimate lost sales due to assortment changes in the category. The brand share model predicts which brand the customer chooses if a purchase incidence occurs in the category. Our brand share model extends the classical Guadagni and Little's model by utilizing three new brand width measures that quantify the similarities among products of di®erent brands within the same category. We illustrate how our modeling framework is used to recon ̄gure the category assortment in eight food categories for ̄ve stores in our data set. This recon ̄guration exercise shows that a recon ̄gured category assortment can have a pro ̄t improvement of up to 25.1% with 32 products replaced. We also demonstrate how our modeling framework can be used to gauge lost sales due to assortment changes. We ̄nd the level of lost sales could range from 0.9 % to 10.2 % for a period of 26 weeks.