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Reseach Article

A Survey on Churn Prediction Techniques in Communication Sector

by N. Kamalraj, A. Malathi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 5
Year of Publication: 2013
Authors: N. Kamalraj, A. Malathi
10.5120/10633-5373

N. Kamalraj, A. Malathi . A Survey on Churn Prediction Techniques in Communication Sector. International Journal of Computer Applications. 64, 5 ( February 2013), 39-42. DOI=10.5120/10633-5373

@article{ 10.5120/10633-5373,
author = { N. Kamalraj, A. Malathi },
title = { A Survey on Churn Prediction Techniques in Communication Sector },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 5 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 39-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number5/10633-5373/ },
doi = { 10.5120/10633-5373 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:15:38.345353+05:30
%A N. Kamalraj
%A A. Malathi
%T A Survey on Churn Prediction Techniques in Communication Sector
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 5
%P 39-42
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The speedy augmentation 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 tedious set up service providers have realized the importance of retaining the on hand customers. It is therefore mandatory for the service providers to inhibit churn- a phenomenon which states that customer wishes to quit the service of the company. To prevent the churn many approaches are used by the researchers. This paper reviews the different approaches used by researchers not only in communication sector but also other sectors which highly depends on customer participation.

References
  1. Bingquan Huang, Mohand Tahar Kechadi, Brian Buckley "Customer churn prediction in telecommunications", Expert Systems with Applications 39 (2012) 1414–1425.
  2. Abbas Keramati, Seyed M. S. Ardabili, "Churn analysis for an Iranian mobile operator", Telecommunications Policy 35 (2011) 344–356.
  3. Marcin Owczarczuk, "Churn models for prepaid customers in the cellular telecommunication industry using large data marts", Expert Systems with Applications 37 (2010) 4710–4712.
  4. Guangli Nie, Wei Rowe, Lingling Zhang, Yingjie Tian, Yong Shi, "Credit card churn forecasting by logistic regression and decision tree", Expert Systems with Applications 38 (2011) 15273–15285.
  5. Adem Karahoca, Dilek Karahoca,"GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system", Expert Systems with Applications 38 (2011) 1814–1822.
  6. Wouter Verbeke, Karel Dejaeger, David Martens, Joon Hur, art Baesens, "New insights into churn prediction in the telecommunication sector: A pro?t driven data mining approach", European Journal of Operation Research, 218 (2012) 211-229.
  7. Beomsoo Shim, Keunho Choi, Yongmoo Suh, "CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns", Expert Systems with Applications 39 (2012) 7736–7742.
  8. Pýnar Kisioglu, Y. Ilker Topcu, "Applying Bayesian Belief Network approach to customer churn analysis: A case study on the telecom industry of Turkey", Expert Systems with Applications 38 (2011) 7151–7157.
  9. J. Hadden, A. Tiwari, R. Roy, D. Ruta, "Computer assisted customer churn management: State-of-the-art and future trends", Computers and Operations Research, vol. 34, no. 10, 2007, pp. 2902-2917.
  10. Dirk Van den Poel, Bart Larivière, "Customer Attrition Analysis For Financial Services Using Proportional Hazard Models".
  11. 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.
  12. Parag C. Pendharkar , "Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services", Expert Systems with Applications 36 (2009) 6714–6720.
  13. Chih-Fong Tsai, Mao-Yuan Chen, "Variable selection by association rules for customer churn prediction of multimedia on demand", Expert Systems with Applications 37 (2010) 2006–2015.
  14. Bingquan Huang, B. Buckley, T. -M. Kechadi, "Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications", Expert Systems with Applications 37 (2010) 3638–3646.
  15. Roberts,J. H, "Developing new rules for new markets", Journal of the Academy of Marketing Science, 28(1), 31–44 (2000).
  16. Antreas D. Athanassopoulos, "Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior", Journal of Business Research Volume 47, Issue 3, March 2000, Pages 191–207.
  17. Donath, Bob, "Show Me the Money", ISBM Insights 8 (2) (1998).
  18. Floyd,T. "Creating a new customer experience", Bank Systems and Technology, 37(1), R8–R13 (2000).
  19. Slater, S. F. , & Narver, J. C. (2000). Intelligence generation and superior customer value. Journal of the Academy of Marketing Science, 28(1), 120–127.
  20. Y. Huang, B. Q. Huang, M. T. Kechadi, "A New Filter Feature Selection Approach for Customer Churn Prediction in Telecommunications", Proceedings of the IEEM, IEEE (2010) 338-342.
  21. Javad Basiri, Fattaneh Taghiyareh, Behzad Moshiri, "A Hybrid Approach to Predict Churn", Proceedings of Asia-Pacific Services Computing Conference IEEE (2010) pp. 485-491.
  22. Yongbin Zhang, Ronghua Liang, Yeli Li, Yanying Zheng, Michael Berry "Behavior-Based Telecommunication Churn Prediction with Neural Network Approach", Proceedings of International Symposium on Computer Science and Society IEEE (2011), 307 – 310.
  23. Afaq Alam Khan, Sanjay Jamwal, M. M. Sepehri, "Applying Data Mining to Customer Churn Prediction in an Internet Service Provider", International Journal of Computer Applications (0975 – 8887) Volume 9– No. 7, November 2010.
  24. Anuj Sharma, Dr. Prabin Kumar Panigrahi, "A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services", International Journal of Computer Applications (0975 – 8887) Volume 27– No. 11, August 2011.
  25. Michael C. Mozer, Richard Wolniewicz, David B. Grimes, Eric Johnson, Howard Kaushansky "Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry", IEEE Transactions on Neural Networks, Vol 11, No. 3, May 2000.
  26. Wai-Ho Au, Keith C. C. Chan, Xin Yao, "A Novel Evolutionary Data Mining Algorithm With Applications to Churn Prediction", IEEE Transactions on Evolutionary Computation, Vol. 7, No. 6, December 2003.
  27. Gang Cui, "A Methodologic Application of Customer Retention Based on Back Propagation Neural Network Prediction", Proceedings of Computer Engineering and Technology (ICCET) IEEE (2010), Volume: 3, Page(s): V3-418 - V3-422.
  28. Yen-Hsien Lee, Chih-Ping Wei, Tsang-Hsiang Cheng, Ching-Ting Yang, "Nearest-neighbor-based approach to time-series classification", Decision Support Systems 53 (2012) 207–217.
  29. Tomasz S Zabkowski, Wies?aw Szczesny, "Insolvency modeling in the cellular telecommunication industry", Expert Systems with Applications 39 (2012) 6879–6886.
  30. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Churn Prediction Customer retention