A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case

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dc.authoridBASARSLAN, MUHAMMET SINAN/0000-0002-7996-9169;
dc.contributor.authorBasarslan, Muhammet Sinan
dc.contributor.authorUnal, Aslihan
dc.contributor.authorKayaalp, Fatih
dc.date.accessioned2025-10-11T20:47:52Z
dc.date.available2025-10-11T20:47:52Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractCustomer churn is an important issue in increasing both the long- and short-term revenues. If companies identify customers' churn behavior, they can prevent churn, ensure customer loyalty, and, in turn, gain better financial returns. The telecommunications sector is a customer-oriented sector that requires customer retention to survive in the market. In this sector, customer churn is observed at a high level. In recent years, artificial intelligence-based customer churn analysis has been widely used to predict customer churn behavior. In this study, a customer churn analysis was conducted using publicly shared Telco telecommunications data. Predictive models were constructed using machine learning (LR, KNN, SVM, DT, RF, ANN), ensemble learning (XGBoost, Majority Voting), and deep learning (LSTM) methods. In addition, a 3-layered LSTM model was proposed. Accuracy (Acc), F1-score (F1), Precision (Prec), and Recall (Rec) rates were used to evaluate the models. As a result, the novel3-layered LSTM model achieved 91.90% Acc, 91.49% Prec, 92.31% Rec, and 91.90% F1 values. The proposed model is competitive with the existing models.en_US
dc.identifier.doi10.26650/acin.1584030
dc.identifier.issn2602-3563
dc.identifier.issue1en_US
dc.identifier.urihttps://doi.org/10.26650/acin.1584030
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21623
dc.identifier.volume9en_US
dc.identifier.wosWOS:001433919400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIstanbul Univen_US
dc.relation.ispartofActa Infologicaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectCustomer Churn Analysisen_US
dc.subjectEnsemble Learningen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Learningen_US
dc.subjectTelecommunicationen_US
dc.titleA Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Caseen_US
dc.typeArticleen_US

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