Obalı, EmirÇalışkan, Sibel KırmızıgülKarani Yılmaz, VeyselKara, ErkanMeşe, Yasemin KürtcüÇakar, TunaYıldız, AyşenurHataş, Tuğce Aydın2024-02-282024-02-282023Hatas, T.A.,Obali, E.,Yildiz, A., Caliskan, S.K., Yilmaz, V. K., Kara, E., Mese, Y.K., Cakar, T. (Eylül 2023). Analyzing customer churn: A comparative study of machine learning models on Pay-TV subscribers in Turkey. IEEE. pp.1-6.9798350318036https://doi.org/10.1109/IISEC59749.2023.10390998https://hdl.handle.net/20.500.11779/2255Understanding the reasons for customer churn provides added value in terms of retaining existing customers, as customer attrition leads to revenue loss for companies and incurs marketing costs for acquiring new customers. In this study, the 6-month historical data of a Pay-TV company operating in Turkey was used, and due to the imbalanced nature of the dataset on a label basis, the oversampling method was applied. During the model development phase, various artificial learning algorithms (Random Forest, Logistic Regression, KNearest Neighbors, Decision Tree, AdaBoost, XGBoost, Extra Tree Classifier) were utilized, and their performances were compared. Based on the evaluation of success criteria for each model, it was observed that the tree-based Random Forest, Extra Tree Classifier and XGBoost achieved the highest performance for this dataset.eninfo:eu-repo/semantics/closedAccessPay-tv industryCustomer retentionMachine learningChurn predictionCustomer churnConference Object10.1109/IISEC59749.2023.103909982-s2.0-85184666022N/AN/A