Clustering of bank customers based on lifetime value using data mining methods

Abstract

In the current competition environment, organizations have realized that to gain profit, in addition to attract customers, they should have a good relationship with them Understanding the needs of customers and providing services for them are important factors in the success or failure of any organization. Therefore, we need to a standard measure to assess the value of customers and, as a result, establish a profitable and long-term relationship with them. Customer lifetime value is a standard measure used to predict the value and segmentation of customers in future. In this paper, we collected all English articles in this field from 2001 to 2019, that most of them had only examined the Recency, Frequency, and monetary features. But in this research, we have explored new features of customers and their accounts that have identified profitable customers and, consequently, clustered them with more accurate customer information. Two clustering methods (K-mean and CPSOII) have been used to examine customers. The advantage of CPSOII compared with the K-means is that CPSOII is able to determine the number of clusters automatically. By using algorithms assessment criteria such as SSE, VRC and DBI, we have reached to this result that CPSOII with DBI = 0.44 is the most suitable clustering algorithm. By using the result of CPSOII, we calculated the customers’ longevity, and we found that customers with the highest values of RFM indexes, have the longest lifetime and the bank should plan for their maintenance.