CHURN ANALYSIS FOR PAY TV WITH MACHINE LEARNING

Recently, Pay TV services are diversifying. With diversified services, competition between companies increases. According to a study, the size of Pay TV is $183 million in 2021. According to the same study, this is estimated to reach $210 million in 2030. [1] In addition, according to Netflix's research, it is stated that acquired or retained subscribers are more valuable. [2] Predicting customers who are considering terminating their subscriptions has become very important for companies. In recent years, ML and artificial neural network techniques have made significant progress in prediction. They provide significant contributions to companies' decision-making processes. In this study, live data from a company that provides services in Turkey was used, and predictions were made with 8 different machine learning. Finally, by adding information about the last subscription cancellation request and the most frequently made cancellation requests of customers who may request subscription cancellation, the aim is to contact the customer before the subscription cancellation request is realized and to prevent cancellation by offering a suitable campaign. In this study, supervised learning techniques were used using unlabeled data. Within the scope of the study, Random Forest, XGBoost Classifier, KNeighbors Classifier, Logistic Regression, Ada Boost Classifier, Decision Tree Classifier, Extra Tree Classifier and Linear Discriminant Analysis models were used. Success criteria were found and compared for all models. When the data used in the study and the results of 8 different models were compared, the Extra Tree model was selected from among the few models that gave the best results. The purpose of choosing this model is to obtain better results than other models for records with fewer samples.