CFP last date
22 April 2024
Reseach Article

A Survey on Customer Churn Prediction using Machine Learning Techniques

by Saran Kumar A., Chandrakala D.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 10
Year of Publication: 2016
Authors: Saran Kumar A., Chandrakala D.
10.5120/ijca2016912237

Saran Kumar A., Chandrakala D. . A Survey on Customer Churn Prediction using Machine Learning Techniques. International Journal of Computer Applications. 154, 10 ( Nov 2016), 13-16. DOI=10.5120/ijca2016912237

@article{ 10.5120/ijca2016912237,
author = { Saran Kumar A., Chandrakala D. },
title = { A Survey on Customer Churn Prediction using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 10 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number10/26526-2016912237/ },
doi = { 10.5120/ijca2016912237 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:53.009145+05:30
%A Saran Kumar A.
%A Chandrakala D.
%T A Survey on Customer Churn Prediction using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 10
%P 13-16
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The fast expansion of the market in every sector is leading to superior subscriber base for service providers. Added competitors, novel and innovative business models and enhanced services are increasing the cost of customer acquisition. In such a fast set up, service providers have realized the importance of retaining the on-hand customers. It is therefore essential for the service providers to prevent churn- a phenomenon which states that customer wishes to quit the service of the company. This paper reviews the most popular machine learning algorithms used by researchers for churn predicting, not only in banking sector but also other sectors which highly depends on customer participation.

References
  1. T.Vafeiadis, K.I. Diamantaras, G.Sarigiannidis, K.Chatzisavvas “Customer churn prediction in telecommunications”, Simulation Modelling: Practice and Theory 55 (2015) 1-9.
  2. Burez J., & Van den Poel, D “Crm at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services”, Expert Systems with Applications 32, 277–288.
  3. S.Parvathavardhini and Dr. S.Manju “Analysis on Machine Learning Techniques” International Journal of Computer Sciences and Engineering (IJCSE), Vol-4(8), pp 59-77 Aug 2016, E-ISSN: 2347-2693.
  4. M.A.H. Farquad, Vadlamani Ravi, S. Bapi Raju “Churn prediction using comprehensible support vector machine: An analytical CRM application”, Applied Soft Computing 19 (2014) 31–40.
  5. Chih-Fong Tsai, Yu-Hsin Lu “Customer churn prediction by hybrid neural networks”, Expert Systems with Applications 36 (2009) 12547–12553.
  6. Wouter Verbeke, David Martens, Christophe Mues, Bart Baesens “Building comprehensible customer churn prediction models with advanced rule induction techniques”, Expert Systems with Applications 38 (2011) 2354–2364.
  7. Ning Lu, Hua Lin, Jie Lu, Guangquan Zhang “A Customer Churn Prediction Model in Telecom Industry Using Boosting”, IEEE Transactions on Industrial Informatics, vol. 10, no. 2, may 2014.
  8. Benlan He, Yong Shi, Qian Wan, Xi Zhao “Prediction of customer attrition of commercial banks based on SVM model”, Proceedings of 2nd International Conference on Information Technology and Quantitative Management (ITQM), Procedia Computer Science 31 ( 2014 ) 423 – 430.
  9. Ssu-Han Chen, “The gamma CUSUM chart method for online customer churn prediction”, Electronic Commerce Research and Applications, 17 (2016) 99–111.
  10. Koen W. De Bock, Dirk Van den Poel, “An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction”, Expert Systems with Applications 38 (2011) 12293–12301.
  11. H. Lee, Y. Lee, H. Cho, K. Im, Y.S. Kim, “Mining churning behaviors and developing retention strategies based on a partial least squares (PLS) model”, Decision Support System 52 (2011) 207–216.
  12. Koen W. De Bock, Dirk Van den Poel, “Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models”, Expert Systems with Applications 39 (2012) 6816–6826.
  13. L. Ning, L. Hua, L. Jie, Z. Guangquan, “A customer churn prediction model in telecom industry using boosting”, IEEE Trans. Ind. Inform. 10 (2014) 1659–1665.
  14. T. Verbraken, W. Verbeke, B. Baesens, “A novel profit maximizing metric for measuring classification performance of customer churn prediction models”, IEEE Transaction on Knowledge and Data Engineering 25 (2013) 961–973.
  15. P.C. Pendharkar, “Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services”, Expert System Application 36 (2009) 6714–6720.
  16. Yaya Xie, Xiu Li, E.W.T. Ngai, Weiyun Ying, “Customer churn prediction using improved balanced random forests”, Expert Systems with Applications 36 (2009) 5445–5449.
Index Terms

Computer Science
Information Sciences

Keywords

Customer retention neural networks Ensemble classifier Boosting Genetic Algorithm