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AuthorSaha, Lewlisa
AuthorTripathy, Hrudaya Kumar
AuthorGaber, Tarek
AuthorEl-Gohary, Hatem
AuthorEl-kenawy, El Sayed M.
Available date2024-04-30T08:58:17Z
Publication Date2023-03-03
Publication NameSustainability (Switzerland)
Identifierhttp://dx.doi.org/10.3390/su15054543
CitationSaha, L., Tripathy, H. K., Gaber, T., El-Gohary, H., & El-kenawy, E. S. M. (2023). Deep churn prediction method for telecommunication industry. Sustainability, 15(5), 4543.
ISSN2071-1050
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149983115&origin=inward
URIhttp://hdl.handle.net/10576/54510
AbstractBeing 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.
Languageen
PublisherMultidisciplinary Digital Publishing Institute (MDPI)
Subjectchurn prediction
customer relationship management
data analytics
telecommunication industry
TitleDeep Churn Prediction Method for Telecommunication Industry
TypeArticle
Issue Number5
Volume Number15
ESSN2071-1050
dc.accessType Open Access


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