490 413
Full Length Article
American Journal of Business and Operations Research
Volume 4 , Issue 2, PP: 57-64 , 2021 | Cite this article as | XML | Html |PDF


Intelligent Optimal Deep Learning based Customer Churn Prediction Model in Telecom Industry

Authors Names :   Taif Khalid Shakir   1 *     Ahmed N. Al Masri   2  

1  Affiliation :  Ascencia Business School, College de Paris, France

    Email :  taif.shakir@cabling.att-mail.com

2  Affiliation :  American University in the Emirates, Dubai, United Arab Emirates

    Email :  ahmed.almasri@aue.ae

Doi   :   https://doi.org/10.54216/AJBOR.040202

Received: May 01, 2021 Accepted: August 29, 2021

Abstract :

Customer churn prediction (CCP) is a crucial problem in telecom industry which helps to improve the revenue of the company and prevent the loss of customers. Customer churn is an important issue in service sector with highly competitive services. At the same time, the prediction of users who are probably leaving the company can be identified at an earlier stage to prevent loss of revenue. Several works have used machine learning (ML) techniques for predicting the existence of customer churn in different industries. With this motivation, this paper presents an optimal long, short-term memory with stacked autoencoder (OLSTM-SAE) technique for CCP in telecom industry. The OLSTM-SAE technique encompasses three subprocesses namely preprocessing, classification, and parameter optimization. The OLSTM-SAE technique classifies the preprocessed data into churn and non-churn customers. In addition, the grey wolf optimization (GWO) technique is used to adjust the variables involved in the LSTM-SAE model.  For examining the enhanced performance of the OLSTM-SAE technique, an extensive simulation analysis takes place, and the outcomes are inspected with respect to various measures. The experimental results highlighted the betterment of the OLSTM-SAE technique in terms of different evaluation parameters. 

Keywords :

Customer churn , Prediction model , Machine learning , Deep learning , Intelligent models

References :

[1]      Ahmed, A.A. and Maheswari, D., 2017. Churn prediction on huge telecom data using hybrid firefly based classification. Egyptian Informatics Journal, 18(3), pp.215-220.

[2]      Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J. and Anwar, S., 2019. Customer churn prediction in telecommunication industry using data certainty. Journal of Business Research, 94, pp.290-301.

[3]      Idris, A., Iftikhar, A. and ur Rehman, Z., 2019. Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling. Cluster Computing, 22(3), pp.7241-7255.

[4]      Alboukaey, N., Joukhadar, A. and Ghneim, N., 2020. Dynamic behavior based churn prediction in mobile telecom. Expert Systems with Applications, 162, p.113779.

[5]      Azeem, M., Usman, M. and Fong, A.C.M., 2017. A churn prediction model for prepaid customers in telecom using fuzzy classifiers. telecommunication Systems, 66(4), pp.603-614.

[6]      Khan, Y., Shafiq, S., Naeem, A., Hussain, S., Ahmed, S. and Safwan, N., 2019. Customers churn prediction using artificial neural networks (ANN) in telecom industry. Editorial Preface From the Desk of Managing Editor, 10(9). 

[7]      Mishra, A. and Reddy, U.S., 2017, December. A novel approach for churn prediction using deep learning. In 2017 IEEE international conference on computational intelligence and computing research (ICCIC) (pp. 1-4). IEEE. 

[8]      Amin, A., Shah, B., Khattak, A.M., Moreira, F.J.L., Ali, G., Rocha, Á. and Anwar, S., 2019. Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods. International Journal of Information Management, 46, pp.304-319.

[9]      Mishra, A. and Reddy, U.S., 2017, November. A comparative study of customer churn prediction in telecom industry using ensemble based classifiers. In 2017 International Conference on Inventive Computing and Informatics (ICICI) (pp. 721-725). IEEE.

[10]   Zhao, L., Gao, Q., Dong, X., Dong, A. and Dong, X., 2017. K-local maximum margin feature extraction algorithm for churn prediction in telecom. Cluster Computing, 20(2), pp.1401-1409.

[11]   Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A. and Huang, K., 2017. Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, pp.242-254.

[12]   Prashanth, R., Deepak, K. and Meher, A.K., 2017, July. High accuracy predictive modelling for customer churn prediction in telecom industry. In International Conference on Machine Learning and Data Mining in Pattern Recognition (pp. 391-402). Springer, Cham.

[13]   Spanoudes, P. and Nguyen, T., 2017. Deep learning in customer churn prediction: unsupervised feature learning on abstract company independent feature vectors. arXiv preprint arXiv:1703.03869.

[14]   Cenggoro, T.W., Wirastari, R.A., Rudianto, E., Mohadi, M.I., Ratj, D. and Pardamean, B., 2021. Deep Learning as a Vector Embedding Model for Customer Churn. Procedia Computer Science, 179, pp.624-631. 

[15]   Pustokhina, I.V., Pustokhin, D.A., Aswathy, R.H., Jayasankar, T., Jeyalakshmi, C., Díaz, V.G. and Shankar, K., 2021. Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms. Information Processing & Management, 58(6), p.102706.

[16]   Domingos, E., Ojeme, B. and Daramola, O., 2021. Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector. Computation, 9(3), p.34.

[17]   Tariq, M.U., Babar, M., Poulin, M. and Khattak, A.S., 2021. Distributed model for customer churn prediction using convolutional neural network. Journal of Modelling in Management. 

[18]   De Caigny, A., Coussement, K., De Bock, K.W. and Lessmann, S., 2020. Incorporating textual information in customer churn prediction models based on a convolutional neural network. International Journal of Forecasting, 36(4), pp.1563-1578. 

[19]   Kozak, J., Kania, K., Juszczuk, P. and MitrÄ™ga, M., 2021. Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management. International Journal of Information Management, p.102357.

[20]   Jin, Z., Yang, Y. and Liu, Y., 2020. Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications, 32(13), pp.9713-9729.

[21]   Li, Y. and Cao, H., 2018. Prediction for tourism flow based on LSTM neural network. Procedia Computer Science, 129, pp.277-283.

[22]   Mirjalili, S., Mirjalili, S.M. and Lewis, A., 2014. Grey wolf optimizer. Advances in engineering software, 69, pp.46-61.

[23]   Zareie, A., Sheikhahmadi, A. and Jalili, M., 2020. Identification of influential users in social network using gray wolf optimization algorithm. Expert Systems with Applications, 142, p.112971.

[24]   Dadashzadeh, S., Aghaie, M. and Zolfaghari, A., 2021. Implementation of Gray Wolf Optimization algorithm to recycled gas centrifuge cascades. Progress in Nuclear Energy, 137, p.103769.


Cite this Article as :
Taif Khalid Shakir , Ahmed N. Al Masri, Intelligent Optimal Deep Learning based Customer Churn Prediction Model in Telecom Industry, American Journal of Business and Operations Research, Vol. 4 , No. 2 , (2021) : 57-64 (Doi   :  https://doi.org/10.54216/AJBOR.040202)