Deep Churn Prediction Method for Telecommunication Industry

Lewlisa Saha, Hrudaya Kumar Tripathy, Tarek Gaber (), Hatem El-Gohary and El-Sayed M. El-kenawy
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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
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

Sustainability, 2023, vol. 15, issue 5, 1-21

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.

Keywords: telecommunication industry; churn prediction; data analytics; customer relationship management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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