Segmenting the retail customers: a multi-model approach of clustering in machine learning

The goal of "serving all" is similar to "serving none." Marketers are constantly looking for ways to refine the way they segment markets. Segmentation involves diving markets into smaller portions (segments) of consumers with similar needs for a given good or service. This chapter explores the application of various algorithms and analytical techniques that are used to segment markets. These techniques include regression, cross-tabulation, hierarchical clustering, and k-means clustering performed through analytical tools such as R-Studio and MS Excel. The analyses drew upon the "customer data" dataset, which contained eight variables: age, income, marital status, ownership status, household size, family total sales, and family total visit. The findings demonstrate how such statistics could help the businesses understand the customers and target the specific customer with unique campaigns and offerings.