Predictive Modeling of Customer Churn in Subscription-based Services using Machine Learning Algorithms

Project Abstract

The increasing competition in subscription-based services has heightened the importance for companies to implement effective strategies to retain customers and minimize churn rates. This research project focuses on developing a predictive modeling framework using machine learning algorithms to forecast customer churn in subscription-based services. The study aims to explore the use of historical customer data, such as demographic information, purchase history, and usage patterns, to train machine learning models for predicting future customer churn. The research begins with a comprehensive introduction, providing the background of the study and highlighting the significance of addressing customer churn in subscription-based services. The problem statement emphasizes the challenges faced by companies in retaining customers and the potential benefits of implementing predictive modeling techniques. The objectives of the study include developing accurate predictive models for customer churn and evaluating the effectiveness of machine learning algorithms in this context. The literature review chapter presents an in-depth analysis of existing research on customer churn prediction, machine learning algorithms, and their applications in subscription-based services. The review highlights the strengths and limitations of previous studies and sets the foundation for the research methodology chapter. The research methodology chapter outlines the approach taken to collect and analyze data, select machine learning algorithms, and evaluate the performance of predictive models. Key components of the methodology include data preprocessing, feature selection, model training, validation techniques, and performance evaluation metrics. In the discussion of findings chapter, the research presents the results of the predictive modeling experiments, including the accuracy, precision, recall, and F1-score of the machine learning models. The chapter also discusses the impact of different features on the predictive performance and provides insights into factors influencing customer churn in subscription-based services. Finally, the conclusion and summary chapter offer a comprehensive overview of the research findings, highlighting the significance of predictive modeling in addressing customer churn in subscription-based services. The chapter concludes with recommendations for companies to implement data-driven strategies for customer retention and discusses potential avenues for future research in this field. Overall, this research project contributes to the growing body of knowledge on customer churn prediction and demonstrates the effectiveness of machine learning algorithms in improving customer retention strategies in subscription-based services.

Project Overview