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Classification of Churn and non-Churn Customers in Telecommunication Companies
Tarik Rashid
Pages - 82 - 89     |    Revised - 30-10-2009     |    Published - 30-11-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
Artificial Neural Network, Classification, Prediction, Dynamic Training, Telecommunication
Telecommunication is very important as it serves various activities, services of electronic systems to transmit messages via physical cables, telephones, or cell phones. The two main factors that affect the growth of telecommunications are the rapid growth of modern technology and the market demand and its competition. These two factors in return, create new technologies and products, which open a series of options and offers to customers, in order to satisfy their needs and requirements. However, one crucial problem that commercial companies in general and telecommunication in particular, suffer from is a loss of valuable customers to competitors; this is called customer churn prediction. In this paper, the dynamic training technique is introduced. The dynamic training is used to improve the prediction of performance. This technique is based on two ANN network configurations to minimise the total error of the network to predict two different classes; names churn and non-customers.
CITED BY (5)  
1 Adebiyi, S. O., Oyatoye, E. O., & Kuye, O. L. (2015). An Analytic Hierarchy Process Analysis: Application to Subscriber Retention Decisions in the Nigerian Mobile Telecommunications. International Journal of Management and Economics, 48(1), 63-83.
2 Mahajan, V., Misra, R., & Mahajan, R. (2015). Review of Data Mining Techniques for Churn Prediction in Telecom. Journal of Information and Organizational Sciences, 39(2), 183-197.
3 Wilgenbus, E. F. (2013). The file fragment classification problem: a combined neural network and linear programming discriminant model approach (Doctoral dissertation, North-West University).
4 Saravanan, M., & Deepika, S. M. (2012, January). AUTOMATED PROVISIONING OF CAMPAIGNS USING DATA MINING TECHNIQUES. In Proceedings of the International Conference on Data Mining (DMIN) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
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Associate Professor Tarik Rashid
College of Computer Training - Ireland