Bank Customer Segmentation Using Data Mining

One of the important challenges in customer-based organizations is customer cognition, understanding difference between them and ranking them. Customer need-based segmentation was common in past years, but recently customer value as a quantifiable parameter could be used for customer segmentation. The main goal of this research is present a framework for customer segmentation based on customer value. In this regard, the information of thirty thousand customers of Saderat Bank from April 2010 to April 2011 was received. The data was used for segmenting customers based on Weighted RFM which was adapted with retail banking scope using two step algorithm. Then hidden patterns between the data of retail banking product ownership and customer value-based segmentation was discovered using C5.0 algorithm.In the conclusion, customers were divided to four groups and then features of each segment were analyzed. The result of this study could be used as a guideline for marketing strategies and developing of services and products for each group.

Keywords:

Customer segmentation , Banking , Data mining , Customer lifetime value , RFM analysis