Applying RFM model and K-means method in customer value analysis of an outfitter

Abstract

This case study applies RFM model and K-means method in the value analysis of the customer database of an outfitter in Taipei, Taiwan. By considering gender, birth date, shopping frequency, and the total spending, six clusters have been found among 675 member customers from the company's database. In addition to the clustering analysis, different promotion strategies for the members of different clusters are provided. The analyses show that Clusters 5 and 6 are the two most important groups that the company has to devote resources into. Moreover, the company might ration resources for the customers in Clusters 1 and 2 because they do not contribute enough values to the outfitter. © Springer-Verlag London Limited 2009.