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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Tuesday 25 September 2018

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Intelligent Churn prediction for Telecommunication Industry


Volume 4, Issue 1, September 2013, Pages 165–170

 Intelligent Churn prediction for Telecommunication Industry

Imran Khan1, Imran Usman2, Tariq Usman3, Ghani Ur Rehman4, and Ateeq Ur Rehman5

1 Department of Electrical Engineering, COMSATS Institute of Information Technology, Park Road, Islamabad, Pakistan
2 Center for Advance Studies in Telecommunication, COMSATS Institute of Information Technology, Park Road, Islamabad, Pakistan
3 Department of Computer Science, Khushal Khan Khattak University, Karak, Khyber Pakhtoon Khwa, Pakistan
4 Department of Computer Science, Khushal Khan Khattak University, Karak, Khyber Pakhtoon Khwa, Pakistan
5 Department of Electrical Engineering, CASE Islamabad, Pakistan

Original language: English

Received 28 May 2013

Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Customer churn is a focal concern for most of the services based companies which have fixed operating costs. Among various industries which suffer from this issue, telecommunications industry can be considered at the top of the list. In order to counter this problem one must recognize the churners before they churn. This work develops an effective and efficient model which has the ability to predict the future churners for broadband internet services. For this purpose Genetic Programming (GP) is employed to evolve a suitable classifier by using the customer based features. Genetic Programming (GP) is population based heuristic used to solve complex multimodal optimization problems. It is an evolutionary approach use the Darwinian principle of natural selection (survival of the fittest) analogs with various naturally occurring operations, including crossover (sexual recombination), mutation (to randomly perturbed or change the respective gene value) and gene duplication. The intelligence induced in the system not only generalizes the model for a variety of real world applications but also make it adaptable for dynamic environment. Comprehensive experimentations are performed in order to validate the effectiveness and robustness of the proposed system. It is clear from the experimental results that the proposed system outperforms other state of the art churn prediction techniques.

Author Keywords: Genetic Programming, Churn Prediction, Artificial Neural Networks, Support Vector Machines, Broadband Networks.


How to Cite this Article


Imran Khan, Imran Usman, Tariq Usman, Ghani Ur Rehman, and Ateeq Ur Rehman, “Intelligent Churn prediction for Telecommunication Industry,” International Journal of Innovation and Applied Studies, vol. 4, no. 1, pp. 165–170, September 2013.