Combining RFM model and clustering techniques for customer value analysis of a company selling online

Abstract

A case study of applying RFM (recency, frequency, and monetary) model and clustering techniques in the sector of electronic commerce with a view to evaluating customers' values is presented. Self-organizing maps method (SOM) is first used to determine the best number of clusters and then K-means method is applied to classify 730 customers into eight clusters when R, F, and M are the segmenting variables, and then developing effective marketing strategies for each cluster. The average values of RFM are computed for each cluster and the overall customers. The values of RFM variables for each cluster greater than those of the overall average are identified. The results show that the cluster 7 is the most important cluster because the average values of R F and M are higher than the overall average value. In summary, the purpose of this case study is customer segmentation using RFM model and clustering algorithms (SOM and K-means) to specify loyal and profitable customers for achieving maximum benefit and a win-win situation.