A Study on Customer Rentention using Predictive Data mining Techniques

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2014 by IJCOT Journal
Volume - 4 Issue - 5
Year of Publication : 2014
Authors :  Dr.V.P.Eswaramurthy , S.Induja
DOI :  10.14445/22492593/IJCOT-V12P302

Citation

Dr.V.P.Eswaramurthy , S.Induja. "A Study on Customer Rentention using Predictive Data mining Techniques", International Journal of Computer & organization Trends (IJCOT), V4(5):6-10 Sep - oct 2014, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract

The customer is the soul or even essence of the organizational abilities. The age of globalization together with the competitors has totally changed the fundamental idea of merchandising, at the present merchandising is not tied to providing the services to the customer, and however the goal is to actively meet the hearts of the consumers. In the powerful industry circumstances where exactly companies are setting varying techniques now and then, customer retention is a crucial area to ponder upon, as customers usually churn from one organization to another. So prediction of the customer behaviour or even hence taking corrective actions before hand is the need of the hour. Data mining will help service sector like banking, insurance protection, and telecommunication to make critical business decisions. This paper throws light on the underlying technologies and the viewpoint applications of data mining in predicting the churn behaviour of the customers and hence paving way for better Customer Relationship Management.The customer is the soul or even essence of the organizational abilities. The age of globalization together with the competitors has totally changed the fundamental idea of merchandising, at the present merchandising is not tied to providing the services to the customer, and however the goal is to actively meet the hearts of the consumers. In the powerful industry circumstances where exactly companies are setting varying techniques now and then, customer retention is a crucial area to ponder upon, as customers usually churn from one organization to another. So prediction of the customer behaviour or even hence taking corrective actions before hand is the need of the hour. Data mining will help service sector like banking, insurance protection, and telecommunication to make critical business decisions. This paper throws light on the underlying technologies and the viewpoint applications of data mining in predicting the churn behaviour of the customers and hence paving way for better Customer Relationship Management.

References

[1] S.Janakiraman,K.Umamaheswari, “A Survey of Data mining techniques for Customer Relationship Management”,International journal of Engineering, Business and Enterprise application.
[2] E.W.T. Ngai, Li Xiu, D.C.K. Chau, “Application of Data mining technique in customer relationship management: Al literature review and classification”, Expert Systems with Applications, Vol.36, 2009, pp. 2592-2602.
[3] Kazi Imran, Dr. Qazi Baseer Ahmed, “ Use of Data Mining in Banking”, International Journal of Engineering Research and Applications,Vol.2, No.2, April 2012, pp. 738-742.
[4] Rajni Arora, “Customer Relationship Management”, International Journal of Research in IT & Management, Vol. 3, Issue 8, August 2013, pp. 48-57.
[5] Nastaran Mohammadhossein, Dr. Nor. Hidayati Zakaria, ,” CRM Benefits for Customers: Literature Review (2005 – 2012) “ , International Journal of Engineering Research and Applications ,Vol. 2, Issue 6, December 2012, pp.1578-1586
[6] 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,.
[7] Dr. U. Devi Prasad, S. Madhavi, “Prediction of churn behavior of Bank Customers”, Business Intelligence journal, Vol 5, No. 1, January 2012
[8] Indranil Bose, Xi Chen, “Hybrid models using Unsupervised Clustering for Prediction of Customer Churn”, Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol. 1, March 18-20, 2009, Hong Kong.
[9] S. Balaji, S.K. Srinivasta, “Naïve Bayes Classification approach for Mining Life insurance Databases for Effective Prediction of Customer Preferences over Life Insurance Products”, International Journal of Computer Applications, Vol.51, No. 3,2012.
[10] ] Narander Kumar, Vishal Verma, Vipin Saxena, “ Cluster Analysis in Data Mining using K-Means Method”, International Journal of Computer Applications , Vol. 76, No. 12, August 2013, pp. 11-14.
[11] K.Chitra,B.Subhashini, “Customer Rentention in Banking Sector using Predictive Data mining Techniques” ICIT 2011 The 5th International Conference on Information Technology.
[12] P. Issakki Alias Devi, S.P. Rajagopalan, “Analysis of Customer Behavior using Clustering and Association Rules”, International Journal of Computer Applications, Vol. 43, No.23, April 2012, pp.19-27 .
[13] Vivek Bhambri, “Data Mining as a Tool to Predict Churn Behavior of Customers”, International journal of Computer & Organizatio Trends , Vol. 2, Issue. 3, 2012, pp. 85-88

Keywords
Data mining , Churning , CRM , Data Mining techniques.