The customer churn is a common measure of lost customers. By minimizing customer churn, a company can maximize its profits. Companies have recognized that existing customers are the most valuable assets. Customer retention is critical for a good marketing and a customer relationship management strategy. The prevention of customer churn through customer retention is a core issue of Customer Relationship Management (CRM). The paper presents churn prediction based on data mining tools in banking. In this paper, a study on modeling purchasing behavior of bank customers in Indian scenario has been attempted. A detailed scheme is worked out to convert raw customer data into meaningful and useful data that suits modeling buying behavior and in turn, converts this meaningful data into knowledge for which predictive data mining techniques are adopted. In this analysis, the researchers have experimented with 2 classification techniques namely CART, and C 5.0. The prediction success rate of Churn class by CART is quite high but C 5.0 had shown poor results in predicting churn customers. However, the prediction success rate of Active class by C 5.0 is more effective than the other technique. However, for reaping significant benefits, the models have predicted the churn behavior.

1. Athanassopoulos, A.D. (2000). “Customer Satisfaction Cues To Support Market Segmentation And Explain Switching Behavior.” Journal of Business Research, Volume 47, Issue 3, pp. 191- 207.
2. Berry M.J.A. & Linoff G. (1999). “Mastering Data Mining: The Art and Science of Customer Relationship Management.” John Wiley and Sons, Inc. New York, pp. 335-341.
3. Berson, A.,et al. (19991). “Building Data Mining Applications For CRM.” McGraw-Hill, Rockefeller Center, New York, pp. 825-844.
4. Dass Rajanish (2006). “Data Mining In Banking and Finance: A Note For Bankers.” Indian Institute of Management, Ahmedabad, Prentice-Hall of India, pp. 3-9.
5. Fayyad U., Grinstein G.E. & Wierse A. (2002). “Information Visualization In Data Mining and Knowledge Discovery.” Morgan Kaufmann Publishers. California, Academic Press.
6. Gehrke J., Ganti V., Ramakrishnan R., and Loh W. Y. (1999). “BOAT - Optimistic Decision Tree Construction.” in Proc. ACM SIGMOD International Conference On Management of Data, Philadelphia, PA, pp. 169 - 180.
7. Han Jiewei, Kamber Michelin (2006). “Data Mining: Concepts & Techniques.” 2nd Edition, The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers, Massachusetts, pp. 224-256.
8. Indiastudychannel.com, "Fathimathabasum: Data Mining In Banks.", <a href=http://www.indiastudychannel.com/projects/1804-Data-Mining-anks.aspx target="_blank">http://www.indiastudychannel.com/projects/1804-Data-Mining-anks.aspx</a>. accessed on February 22, 2011.
9. J. Gehrke, Ramakrishnan R., and Ganti V. (1998). “Rain Forest A Framework For Fast Decision Tree Construction Of Large Datasets.” in Proc. 24th International Conference On Very Large Data Bases, New York, pp. 416 - 427.
10. Kotler, P. , Keller, K.L (2006). “Marketing Management.” 12th Edition, Pearson Prentice Hall, Upper Saddle River, N.J., Northwestern University, pp.177-192.
11. Poel, Van Den, D. & Lariviere, B. (2003). “Customer Attrition Analysis For Financial Services Using Proportional Hazard Models.” European Journal of Operational Research, Vol. 157, Issue 1, pp. 196-217.
12. Pressler Margaret (2003). “Signs of Fraud Go Beyond Signature.” Washington Post, Washington, p. H05.
13. Quinlan J. R. (1987). “Decision Trees As Probabilistic Classifiers.” in Proc., 4th Int. Workshop Machine Learning, Irvine, CA, pp. 31 - 37.
14. Rastogi R. and Shim K. (1998). “Public: A decision Tree Classifier That Integrates Building And Pruning.” in Proc. 24th Int. Conf. Very Large Data Bases, NewYork, 1998, pp. 404 - 415.
15. Reichheld, F.F. and Sasser, W.E. (1990). “Zero Defections: Quality Comes To Service.” Harvard Business Review, Vol. 69, September-October, pp. 105- 111.
16. Shafer J., Agrawal R., and Mehta M. (1996). “SPRINT: A Scalable Parallel Classifier For Data Mining.” in Proc. 22nd International Conference On Very Large Data Bases, Mumbai , pp. 544 - 555.
17. Smith A. (2006). “CRM And Customer Service: Strategic Asset Or Corporate Overhead?” Handbook of Business Strategy, Vol .7, pp. 87 - 93.
18. Turban, E., Mclean, E., Wetherbe, J., Bolloju, N., Davison, R. (2002). “Information Technology For Management: Transforming Business In The Digital Economy.” John Wiley and Sons, INC: New York, pp. 441-458.
19. Weiss, S.M. (1997). “Predictive Data Mining: A Practical Guide.” Morgan Kaufmann Publishers, Massachusetts.