Customer Segmentation in the Banking Industry by Extended Model of RFMC

When a private banks and financial institutions seriously started working in the banking industry, the competition between enterprises and banks to customer identification, attraction and retention is most important. Many companies, especially banks which deal with a large number of customers, use the application of data mining techniques in the CRM. Knowing customers and their behavior with some techniques, like segmentation, is the key to success in today’s competitive market. The RFM model is used in the most costumer segmentation research. In this paper, we developed the RFM model by adding continuity variable (C) and entitled RFMC model. In one of the private bank, the costumers clustered by proposed models based on Two-Step algorithm and GRISP-DM methodology. The results demonstrate the accuracy of developed model in costumer segmentation is 5.5% higher than the RFM model. Moreover analysis of each cluster customer behavior, the model of feed-forward neural network is predicted the cluster number of customers based on their demographic and behavioral characteristics.