Customer segmentation of bank based on data warehouse and data mining

The problems can not be solved by the traditional method of customer segmentation when banks face with the massive information. Data mining techniques can extract useful information and knowledge that are implicit and unknown in advance but is potential in the practical application. Data mining techniques are broadly used in customer relationship management but there is no unified framework model for customer segmentation by now. Customer segmentation model of bank is built based on data mining which is to define the corresponding mapping relationships between customer attribute and concept attribute in this paper. We apply self-organizing mapping neural network and K-means algorithm to bank customer segmentation and analyze the sample data which we selected from some bank. The outcomes show that we can use a dynamic model of the data mining to describe customer behavior and provide useful information for the managers of banks to decision making.