Abstract: To survive in the fierce competition of telecommunication industry and to retain the existing loyal customers, prediction of potential churn customer has become a crucial task for practitioners and academicians through predictive modeling techniques. The identification of loyal customers can be done through efficient predictive models. By allocation of dedicated resources to the retention of these customers would control the flow of dissatisfied consumers thinking to leave the company. This paper proposes artificial neural network approach for prediction of customers intending to switch over to other operators. This model works on multiple attributes like demographic data, billing information and usage patterns from telecom companies data set. In contrast with other prediction techniques, the results from Artificial Neural Networks (ANN) based approach can predict the telecom churn with accuracy of 79% in Pakistan. The results from artificial neural network are clearly indicating the churn factors, hence necessary steps can be taken to eliminate the reasons of churn.

Yasser Khan, Shahryar Shafiq, Abid Naeem, Sheeraz Ahmed, Nadeem Safwan and Sabir Hussain, “Customers Churn Prediction using Artificial Neural Networks (ANN) in Telecom Industry” International Journal of Advanced Computer Science and Applications(IJACSA), 10(9), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100918

@article{Khan2019,
title = {Customers Churn Prediction using Artificial Neural Networks (ANN) in Telecom Industry},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100918},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100918},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {9},
author = {Yasser Khan and Shahryar Shafiq and Abid Naeem and Sheeraz Ahmed and Nadeem Safwan and Sabir Hussain}
}