Supermarket customer segmentation: a case study in a large Brazilian retail chain

Abstract

In order to obtain commercial advantages over competitors, companies in all segments are improving their customer relationship management (CRM). The supermarket segment is no different, and investments in 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 Nordestão, 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 cluster the data. Furthermore, we employ a well-established a priori segmentation from the Brazilian supermarket literature. Our segmentation considers stores individually, and for each store we further refine its a priori segments into customer groups, 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.