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Deep Churn Prediction Method for Telecommunication Industry

Author

Listed:
  • Lewlisa Saha

    (School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Hrudaya Kumar Tripathy

    (School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Tarek Gaber

    (Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt
    School of Science, Engineering, and Environment, University of Salford, Salford M5 4WT, UK)

  • Hatem El-Gohary

    (College of Business and Economics, Qatar University, Doha 2713, Qatar)

  • El-Sayed M. El-kenawy

    (Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt)

Abstract

Being able to predict the churn rate is the key to success for the telecommunication industry. It is also important for the telecommunication industry to obtain a high profit. Thus, the challenge is to predict the churn percentage of customers with higher accuracy without comprising the profit. In this study, various types of learning strategies are investigated to address this challenge and build a churn predication model. Ensemble learning techniques (Adaboost, random forest (RF), extreme randomized tree (ERT), xgboost (XGB), gradient boosting (GBM), and bagging and stacking), traditional classification techniques (logistic regression (LR), decision tree (DT), and k-nearest neighbor (kNN), and artificial neural network (ANN)), and the deep learning convolutional neural network (CNN) technique have been tested to select the best model for building a customer churn prediction model. The evaluation of the proposed models was conducted using two pubic datasets: Southeast Asian telecom industry, and American telecom market. On both of the datasets, CNN and ANN returned better results than the other techniques. The accuracy obtained on the first dataset using CNN was 99% and using ANN was 98%, and on the second dataset it was 98% and 99%, respectively.

Suggested Citation

  • Lewlisa Saha & Hrudaya Kumar Tripathy & Tarek Gaber & Hatem El-Gohary & El-Sayed M. El-kenawy, 2023. "Deep Churn Prediction Method for Telecommunication Industry," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4543-:d:1086814
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    References listed on IDEAS

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    Cited by:

    1. Hugo Ribeiro & Belem Barbosa & Antonio C. Moreira & Ricardo Rodrigues, 2024. "Customer Experience, Loyalty, and Churn in Bundled Telecommunications Services," SAGE Open, , vol. 14(2), pages 21582440241, April.

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