Customer Segmentation in Clothing Exports Based on Clustering Algorithm

For the success of CRM, it is important to target the most profitable customers of a company. Many CRM researches have been performed to segment customers. The goal of this paper is to segment the countries based on the value of clothing export from Iran during 14 years ending at 2005. To measure the dissimilarity among export baskets of different countries, we define Dissimilarity Export Basket (DEB) function and use this distance function in K-means algorithm. The DEB function is defined based on the concepts of the association rules and the value of export groupcommodities. In this paper, clustering quality function and clusters interclass inertia are defined to, respectively, calculate the optimum number of clusters and to compare the functionality of DEB versus Euclidean distance. We also study the effects of importance weight in DEB function to improve clustering quality. Lastly, when segmentation is completed, a designated RFM model is used to analyze the relative profitability of each cluster.