Data Mining as a Tool to Predict Churn Behavior of Customers

International Journal of Computer & Organization Trends (IJCOT)          
© 2012 by IJCOT Journal
Volume-2 Issue-3                          
Year of Publication : 2012
Authors :  Vivek Bhambri


Vivek Bhambri Data Mining as a Tool to Predict Churn Behavior of Customers . International Journal of Computer & organization Trends  (IJCOT), V2(3):29-33 May - Jun 2012, ISSN 2249-2593, Published by Seventh Sense Research Group.


: Customer is the heart and soul of any organization. The era of globalization and cut throat competition has changed the basic concept of marketing, now marketing is not confined to selling the products to the customers, but the objective is to reach to the hearts of the customers so that they feel belongingness towards the organizations and hence should remain the loyal customers. In the dynamic market scenarios where companies are coming up with varied options every now and then, customer retention is a critical area to ponder upon, as customers usually churn from one company to another quite often and this too is happening at an alarming rate and is becoming the most important issue in customer relationship management. So prediction of the customer behaviour and hence taking remedial actions before hand is the need of the hour. But the ever growing data bases make it difficult to analyze the data and to forecast the future trends. The solution lies in the use of Data Mining tools for predicting the churn behavior of the customers. This paper throws light on the underlying technology and the perspective applications of data mining in predicting the churn behavior of the customers and hence paving path for better customer relationship management.


[1] Bharati M. Ramageri [2010]-Data Mining Techniques and Applications- Indian Journal of Computer Science and Engineering- Vol. 1 No. 4 301-305.
[2] Berry, M.J.A. and Linoff, G., Mastering Data Mining: The Art and Science of Customer Relationship Management, Wiley Computer Publishing, New York, NY, 2000.
[3] David Hand, Heikki Mannila, Padhraic Smyth. (2001). Principles of Data Mining.
[4] Devendra Kumar Tiwary- Application of Data Mining In Customer Relationship Management (CRM)-, Advances in Computational Sciences and Technology Volume 3 Number 4 (2010) pp. 527–540.
[5] M. Purna Chandar, Arijit Laha and P. Radha Krishna -Modeling churn behavior of bank customers using predictive data mining techniques", National Conference on Soft Computing Techniques for Engineering Applications,
[6] Naveeta Mehta, Ruchi Mittal [2011]- Segmenting and Profiling Customers of A Retail Store Using Data Mining Approach-International Journal of Research in IT & Management-Volume 1, Issue 6.
[7] Rajanish Dass [2006], Data Mining In Banking And Finance: A Note For Bankers- Technical note, Note No.: CISG88., April 2006
[8] R.K. Mittal, Rajeev Kumra[2001], E-CRM In Indian Banks-An Overview, - Delhi Business Review Vol. 2, No. 1. [9] S. P. Deshpande and V. M. Thakare[2010]-Data Mining System And Applications: A Review-International Journal of Distributed and Parallel systems (IJDPS) Vol.1, No.1, September 2010.
[10] Suresh Chandra Bihari[2011]- Technology In The Banking Sector In India- How Profitable it is for The Customer- Asian Journal of Business and Management Sciences, Vol. 1 No.2 [56-76] 2011 .
[11] U. Devi Prasad,S. Madhavi Prediction Of Churn Behavior Of Bank Customers Using Data Mining Tools- Business Intelligence Journal - January, 2012 Vol.5 No.1.
[12] Hanjiewei, (2001) Michelin Kamber, Data mining concepts & Techniques: San Diego.
[13] Yogita Narang, Atul Narang, Shalini Nigam[2011]- Gaining the Competitive Edge through CRM – A Study on Private Sector Banks- IJRFM Volume 1, Issue 3 (July, 2011)
[14] Madan Lal Bhasin (2006), "Data Mining: A Competitive Tool in the Banking and Retail Industries”, The Chartered Accountant October ,2006.


: Churning, Customer Relationship Management, Data Mining, Globalization.