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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Author 1: Adnan Anjum
Author 2: Saeeda Usman
Author 3: Adnan Zeb
Author 4: Imran Uddin Afridi
Author 5: Pir Masoom Shah
Author 6: Zahid Anwar
Author 7: Adeel Anjum
Author 8: Basit Raza
Author 9: Ahmad Kamran Malik
Author 10: Saif Ur Rehman Malik

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2017.080523

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 5, 2017.

  • Abstract and Keywords
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Abstract: Companies are investing more in analytics to obtain a competitive edge in the market and decision makers are required better identification among their data to be able to interpret complex patterns more easily. Alluring thousands of new customers is worthless if an equal number is leaving. Business Intelligence (BI) systems are unable to find hidden churn patterns for the huge customer base. In this paper, a decision support system has been proposed, which can predict the churning behaviour of a customer efficiently. We have proposed a procedure to develop an analytical system using data mining as well as machine learning techniques C5, CHAID, QUEST, and ANN for the churn analysis and prediction for the telecommunication industry. Prediction performance can be significantly improved by using a large volume and several features from both Business Support Systems (BSS) and Operations Support Systems (OSS). Extensive experiments are performed; marginal increases in predictive performance can be seen by using a larger volume and multiple attributes from both Telco BSS and OSS data. From the results, it is observed that using a combination of techniques can help to figure out a better and precise churn prediction model.

Keywords: Telco; Churn Prediction; Business Intelligence; Business Support Systems; Operations Support Systems; E-Churn Model (Ensembling Churn Model)

Adnan Anjum, Saeeda Usman, Adnan Zeb, Imran Uddin Afridi, Pir Masoom Shah, Zahid Anwar, Adeel Anjum, Basit Raza, Ahmad Kamran Malik and Saif Ur Rehman Malik, “Optimizing Coverage of Churn Prediction in Telecommunication Industry” International Journal of Advanced Computer Science and Applications(IJACSA), 8(5), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080523

@article{Anjum2017,
title = {Optimizing Coverage of Churn Prediction in Telecommunication Industry},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080523},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080523},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {5},
author = {Adnan Anjum and Saeeda Usman and Adnan Zeb and Imran Uddin Afridi and Pir Masoom Shah and Zahid Anwar and Adeel Anjum and Basit Raza and Ahmad Kamran Malik and Saif Ur Rehman Malik}
}


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