Date of Award


Document Type


Degree Name

Master of Science in Information Systems and Technology


Information and Decision Sciences

First Reader/Committee Chair

Dr. William Butler


Subscriber churn is a critical issue for companies that rely on recurring revenue from subscription-based services like the OTT platform. Machine Learning algorithms can be used to predict churn and develop targeted retention strategies to address the specific needs and concerns of at-risk subscribers. The research questions are 1) What Machine Learning algorithms are used to overcome subscriber churn? 2) How to predict subscribers’ churn in the OTT platform using Machine Learning? 3) How to retain subscribers and improve customer targeting? The dataset was collected from the Kaggle repository and implemented it into the various prediction algorithms used in previous research. Then, evaluate the performance of each algorithm to find out the highest accuracy model. The findings and conclusion for each question are 1) Logistic regression, multi-layer perceptron, random forest, decision trees, and gradient boosting machines were identified as effective algorithms for churn prediction analysis. 2) By sending the test data to a trained model by their historical dataset, customers are likely to leave a company (i.e., churn) based on their characteristics can be predicted. 3) Personalized offers and promotions, improving customer service, developing loyalty programs, and optimizing pricing strategies were suggested strategies for retaining subscribers. The gradient boosting machine model was found to have the highest accuracy and maximum AUROC, making it a powerful tool in the fight against customer churn. Areas for further study include incorporating unstructured data sources, deep learning techniques, and integrating real-time data sources to improve the accuracy and effectiveness of churn prediction models.