Clustering Retail Products by Customer Behavior

Clustering Retail Products by Customer Behavior

This paper proposes a new method to categorize retail products based solely on customer purchase data. It formulates product clustering as an optimization problem, using a genetic algorithm on market basket data to assign products to a given number of clusters. Key assumptions are that customers generally purchase one product per category, and products frequently purchased together should not be clustered together. Tests on simulated and real drugstore data from Czechia demonstrate the method can identify categories similar to those defined by experts, as well as revealing potential subcategories. The technique could help optimize shelf layouts, promotions, and product substitutions when stock runs out.