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Reseach Article

A New Approach for Customer Churn Prediction in Telecom Industry

by Saumya Saraswat, Akhilesh Tiwari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 11
Year of Publication: 2018
Authors: Saumya Saraswat, Akhilesh Tiwari
10.5120/ijca2018917698

Saumya Saraswat, Akhilesh Tiwari . A New Approach for Customer Churn Prediction in Telecom Industry. International Journal of Computer Applications. 181, 11 ( Aug 2018), 40-46. DOI=10.5120/ijca2018917698

@article{ 10.5120/ijca2018917698,
author = { Saumya Saraswat, Akhilesh Tiwari },
title = { A New Approach for Customer Churn Prediction in Telecom Industry },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 181 },
number = { 11 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 40-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number11/29819-2018917698/ },
doi = { 10.5120/ijca2018917698 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:05:44.632549+05:30
%A Saumya Saraswat
%A Akhilesh Tiwari
%T A New Approach for Customer Churn Prediction in Telecom Industry
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 11
%P 40-46
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Since its inception, the field of Data Mining and Knowledge Discovery from Databases has been driven by the need to solve many practical problems. With the rapid development of telecommunication industry, the service providers are inclined more towards expansion of the subscriber base. To meet the need of surviving in the competitive environment, the retention of existing customers has become a huge challenge. In the survey done in the Telecom industry, it is stated that the cost of acquiring a new customer is far more that retaining the existing one. Therefore, by collecting knowledge from the telecom industries can help in predicting the association of the customers as whether or not they will leave the company. The required action needs to be undertaken by the telecom industries in order to initiate the acquisition of their associated customers for making their market value stagnant. This paper describe a framework that was proposed to conduct for the churn prediction model using Naïve Bayes algorithm for classification task and then apply Elephant Herding Optimization algorithm for solving optimization task. Elephant Herding Optimization is a metaheuristic algorithm. The proposed methodology thereby generates optimal customers who will leave the service provider which is beneficial for any enterprise in the current scenario for effective decision making and perform appropriate steps to retain those customers.

References
  1. H. Newton, Newton’s Telecom Dictionary, CMP Books,
  2. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth (1996), Knowledge discovery and data mining: Towards a unifying framework. In: Proceedings of the 2nd ACM international conference on knowledge discovery and data mining (KDD), Portland, OR, pp 82–88
  3. Sheth, Jagdish, and Rajendra Sisodia. - The 4 A's of Marketing: Creating Value for Customer,Company and Society. Routledge, 2012.
  4. L.J.S.M. Alberts, 2006. Churn prediction in the mobile telecommunications industry
  5. Essam Shaaban, Yehia Helmy, Ayman Khedr, Mona Nasr, "A proposed model of prediction of abandonment", International Journal of Engineering and Applications Research (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012.
  6. S. KhakAbi, M. Namvar, Mohd R. Gholamian,“Data Mining Applications in Customer Churn Management”, Proc. Of IEEE international conference on Intelligent Systems, Modelling and Simulation, 2010.
  7. Clement Kirui, Li Hong, Wilson Cheruiyot, Hillary Kirui, Predict the rotation of customers in the mobile sector using probabilistic classifiers in Data Mining, the International Journal of Computer Science.
  8. Vladislav Lazarov, Marius Capota, ―Churn Prediction‖, Technische Universität Munchen.
  9. Saad Ahmed Qureshi, Ammar Saleem Rehman, Ali Mustafa Qamar, Aatif Kamal, proposed a model of forecast of the abandonment of telecommunications subscribers using machine learning, IEEE, 2013.
  10. Ning Lu, Hua Lin, Jie Lu, ―A Customer Churn Prediction Model in Telecom Industry Using Boosting‖, IEEE Transactions on Industrial Informatics.
  11. Khalida, Sunarti, Norazrina, Faizin, ―Data Mining in Churn Analysis Model for Telecommunication Industry Journal of Statistical Modeling and Analytics.
  12. Hadden J., Tiwari A., Roy R., Ruta D. “Churn prediction: Does technology matter?” International Science Index, Engineering and Technology, 2008.
  13. Amal M. Almana, Mehmet Sabih Aksoy, Rasheed Alzaharni, "Survey of Data Mining Techniques in the Analysis of Customer Abandonment for the Telecommunications Industry", International Journal of Research and Engineering Applications.
  14. Umman Tugba Şimsek Gursoy, "Analysis of the abandonment of customers in the telecommunications sector", Journal of Istanbul University School of Business Administration.
  15. Subramaniam, Sakthikumar, Arunkumar Thangavelu and Hemavathy Ramasubbian. "Fact-AnAdaptive method to predict client abandonment rate using the widespread multi-criterion classification method for decision-making". Asian Journal of Science and Technology 4.11 (2013): 227-233.
  16. Richter, Yossi, Elad Yom-Tov, and Noam Slonim. "Predicting Customer Churn in Mobile Networks through Analysis of Social Groups." SDM. 2010.
  17. Shyam V.Nath, R.S.B. Customer churn analysis in the wireless industry: A data mining approach.
  18. Sowkarthika B., Akhilesh Tiwari, Uday Pratap Singh - Elephant Herding Optimization based Vague Association Rule Mining Algorithm, International Journal of Computer Applications ,Volume 164 – No 5, April 2017
  19. Bingquan Huang, B.Buckley, T.M.Kechadi, "Selection of multiple features through the use of NSGA-II for prediction of customer abandonment in telecommunications", Expert Systems with Applications 37 (2010) 3638-3646.
  20. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill Science/Engineering/Math.
  21. George, H. J., & Langley, P. (1995). Estimating Continuous Distributions in Bayesian Classifiers. Proceeding of the Eleventh Conference on Uncertainty in Artificial Intelligence (pp. 338-345). San Mateo: Morgan Kaufmann.
  22. Gutkin, M. (2008). Feature selection methods for classification of gene expression profiles. Tel-Aviv University.
  23. Cetnik, B. (1990). Estimating Probabilities: A crucial task in machine learning. Ninth European Conference on Artificial Intelligence, (pp. 147-149). London.
  24. S. Balaji, S.K. Srinivasta, -used Naïve Bayes Life Insurance Classification Approach Database for the actual prediction of customer preferences in life insurance products, International Journal of Computer Applications, Vol.51, No. 3, 2012.
  25. G.-G. Wang, S. Deb, X.-Z. Gao, and L. D. S. Coelho, “A new metaheuristic optimisation algorithm motivated by elephant herding behaviour,” International Journal of Bio-Inspired Computation, vol. 8, no. 6, pp. 394–409, 2016.
  26. MD.Rashid Farooqi, K. R. 2011. A comprehensive study of CRM through data mining techniques. Proceedings of the National Conference.
Index Terms

Computer Science
Information Sciences

Keywords

Churn Naïve Bayesian Classification Elephant Herding Optimization.