A case study on customer segmentation of a supermarket chain

Abstract

In order to obtain commercial advantages over competitors, companies in all segments are improving their relationship with customers. The supermarket segment is no different and investments in customer relationship management (CRM) are increasing over the last years. The first step towards a successful CRM strategy is to know customers better, for which customer segmentation plays an important role. In this work, we segment customers from Nordest˜ao, the third largest supermarket chain in the Northeast of Brazil. To do so, we adapt the recency-frequency-monetary model, enrich it with new features, and use Gaussian mixture models to clusterize the data. Furthermore, we employ a well-established a priori segmentation from the Brazilian supermarket literature. For each a priori segment, customer groups were obtained for each retail store, with each group representing a different customer profile. Among the most interesting are prime and opportunity customers, who respectively focus on high-end and on-sale products. Importantly, most of the behaviours are consistent across the different stores, varying only as to store-specific parameters. We conclude our work with a further algorithmic validation and interpretability analysis of our findings.