Market basket analysis using Bayesian Networks
This paper addresses the question of how promotions work across categories. Promotions in one product category can affect sales of products in another category either directly or indirectly. Given a set of product categories and market basket data, we analyze the presence of cross category impacts using Bayesian Networks. We model the occurrence of a product category, and not the number of units (of a product category) in a basket. The data set we employ is an IRI market basket data set that contains transactions including 22 categories over 2 years for 500 panelists. Bayesian networks are learned from this data and are used to identify the underlying dependencies across product categories. Specifically, we study how the associations across categories vary based on marketing mix activities, and also based on demographics. The results from such an analysis can help in 1) identifying clusters of categories wherein associations exist primarily between categories within a cluster and not across clusters, and 2) in making predictions on basket choices given a set of specific marketing mix activities. The ability of Bayesian networks to learn based on new evidence also makes such an approach possible in an online context when customers’ choices can be observed, and marketing activities can be dynamically customized.