Deep Churn Prediction Method for Telecommunication Industry
Saha, Lewlisa and Tripathy, Hrudaya Kumar and Gaber, Tarek and El-Gohary, Hatem and El-kenawy, El-Sayed M. (2023) Deep Churn Prediction Method for Telecommunication Industry. Sustainability, 15 (5). p. 4543. ISSN 2071-1050
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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.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.3390/su15054543 | ||||||
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| Uncontrolled Keywords: | telecommunication industry, churn prediction, data analytics, customer relationship management | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Gemma Tonks | ||||||
| Date Deposited: | 10 Oct 2023 09:21 | ||||||
| Last Modified: | 10 Oct 2023 09:21 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/14828 |
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