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Churn Prediction for Telecommunication Industry Using Artificial Neural Networks
Abstract:Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts. Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 262
 P. Akhavan and S. Heidari, “Application of knowledge management in customer relationship management: a data mining approach,” Available at SSRN 2188230, 2012.
 S. F. Sabbeh, “Machine-learning techniques for customer retention: a comparative study,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 2, 2018.
 M. Hassouna, A. Tarhini, T. Elyas, and M. S. AbouTrab, “Customer churn in mobile markets A comparison of techniques,” CoRR, vol. abs/1607.07792, 2016. (Online). Available: http://arxiv.org/abs/1607. 07792
 A. Ahmed and D. M. Linen, “A review and analysis of churn prediction methods for customer retention in telecom industries,” in 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2017, pp. 1–7.
 M. A. H. Farquad, V. Ravi, and S. B. Raju, “Churn prediction using comprehensible support vector machine: An analytical crm application,” Applied Soft Computing, vol. 19, pp. 31–40, 2014.
 I. Brandusoiu and G. Toderean, “Churn prediction in the telecommunications sector using support vector machines,” Margin, vol. 1, p. x1, 2013.
 K. Dahiya and S. Bhatia, “Customer churn analysis in telecom industry,” in 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions). IEEE, 2015, pp. 1–6.
 F. Castanedo, G. Valverde, J. Zaratiegui, and A. Vazquez, “Using deep learning to predict customer churn in a mobile telecommunication network,” Wise Athena LLC, 2014.
 V. Umayaparvathi and K. Iyakutti, “Automated feature selection and churn prediction using deep learning models,” International Research Journal of Engineering and Technology (IRJET), vol. 4, no. 3, pp. 1846–1854, 2017.
 I. N. Da Silva, D. H. Spatti, R. A. Flauzino, L. H. B. Liboni, and S. F. dos Reis Alves, “Artificial neural networks,” Cham: Springer International Publishing, p. 39, 2017.