The application of data mining and RFM model in market segmentation of a veterinary hospital

Abstract

This study adopts a two-stage clustering technique, the combination of self-organizing maps and K-means method, and RFM (recency, frequency, and monetary) model to categorize customers in a veterinary hospital in Taichung City, Taiwan in order to make effective marketing strategies in this competitive market. Based on 1,784 customers with the focus solely on cats in 2014, twelve clusters are formed. Specifically, six out of twelve clusters are classified into the best and loyal customers. Three clusters are uncertain and lost customers. One cluster is viewed as the best but lost customers. Finally, two clusters that have relatively higher recency values than the average value belong to uncertain but new customers based on RFM model and based on RFM model.