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A churn prediction model for prepaid customers in telecom using fuzzy classifiers

Author

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  • Muhammad Azeem

    (Shaheed Zulfikar Ali Bhutto Institute of Science and Technology)

  • Muhammad Usman

    (Shaheed Zulfikar Ali Bhutto Institute of Science and Technology)

  • A. C. M. Fong

    (Western Michigan University)

Abstract

The incredible growth of telecom data and fierce competition among telecommunication operators for customer retention demand continues improvements, both strategically and analytically, in the current customer relationship management (CRM) systems. One of the key objectives of a typical CRM system is to classify and predict a group of potential churners form a large set of customers to devise profitable and targeted retention campaigns for keeping a long-term relationship with valued customers. For achieving the aforementioned objective, several churn prediction models have been proposed in the past for the accurate identification of the customers who are prone to churn. However, these previously proposed models suffer from a number of limitations which place strong barriers towards the direct applicability of such models for accurate prediction. Firstly, the feature selection methods adopted in majority of the past work neglected the information rich variables present in call details record for model development. Secondly, selection of important features was done through statistical methods only. Although statistical methods have been applied successfully in diverse domains, however, these methods alone without the augmentation of domain knowledge have the tendency to yield erroneous results. Thirdly, the previous models have been validated mainly with benchmark datasets which do not provide a true representation of real world telecom data consisting of noise and large number of missing values. Fourthly, the evaluation measures used in the past neglected the True Positive (TP) rate, which actually highlights the ability of a model to correctly classify the percentage of churners as compared to non-churners. Finally, the classifiers used in the previous models completely neglected the use of fuzzy classification methods which perform reasonably well for data sets with noise. In this paper, a fuzzy based churn prediction model has been proposed and validated using a real data from a telecom company in South Asia. A number of predominant classifiers namely, Neural Network, Linear regression, C4.5, SVM, AdaBoost, Gradient Boosting and Random Forest have been compared with fuzzy classifiers to highlight the superiority of fuzzy classifiers in predicting the accurate set of churners.

Suggested Citation

  • Muhammad Azeem & Muhammad Usman & A. C. M. Fong, 2017. "A churn prediction model for prepaid customers in telecom using fuzzy classifiers," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 66(4), pages 603-614, December.
    • Handle: RePEc:spr:telsys:v:66:y:2017:i:4:d:10.1007_s11235-017-0310-7
    DOI: 10.1007/s11235-017-0310-7
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    References listed on IDEAS

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    1. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.
    2. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
    3. Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
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    Cited by:

    1. Hemlata Jain & Ajay Khunteta & Sumit Srivastava, 2021. "Telecom churn prediction and used techniques, datasets and performance measures: a review," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(4), pages 613-630, April.

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