Predictive Modeling of Customer Churn in E-commerce Industry using Machine Learning Algorithms

Thesis Abstract

Abstract
The rapid growth of the e-commerce industry has led to intensified competition among online businesses, making customer retention a critical focus for sustaining profitability and growth. Customer churn, the phenomenon of customers discontinuing their relationship with a business, poses a significant challenge for e-commerce companies. In this thesis, we explore the application of machine learning algorithms to develop predictive models for customer churn in the e-commerce industry. The objective is to leverage data-driven insights to identify customers at risk of churn and implement targeted retention strategies to enhance customer loyalty and reduce churn rates. The research begins with a comprehensive review of existing literature on customer churn, machine learning techniques, and their applications in the e-commerce sector. The literature review highlights the importance of predictive modeling in understanding customer behavior and facilitating proactive churn management strategies. The methodology section outlines the research design, data collection process, and model development approach. A large dataset containing customer transactional history, demographic information, and behavioral attributes is used to train and evaluate various machine learning models, including logistic regression, decision trees, random forests, and gradient boosting. Feature engineering techniques are employed to extract relevant insights from the data and improve model performance. The findings from the predictive modeling process reveal valuable insights into the factors influencing customer churn in the e-commerce industry. Key predictors of churn include order frequency, average order value, customer tenure, and customer engagement metrics. The developed models demonstrate high accuracy in predicting customer churn, enabling businesses to proactively identify at-risk customers and implement targeted retention campaigns. The discussion section delves into the implications of the research findings for e-commerce businesses. Strategies for leveraging predictive models to optimize customer retention efforts and enhance overall customer lifetime value are explored. Practical recommendations for implementing churn prediction models in real-world e-commerce settings are provided, emphasizing the importance of data-driven decision-making and continuous model refinement. In conclusion, this thesis contributes to the growing body of research on customer churn prediction in the e-commerce industry. By harnessing the power of machine learning algorithms, businesses can gain a competitive edge by effectively managing customer churn and fostering long-term customer relationships. The study underscores the significance of predictive modeling as a strategic tool for driving business growth and enhancing customer satisfaction in the dynamic landscape of e-commerce.