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

Thesis Abstract

Abstract
Customer churn is a critical challenge faced by businesses in the e-commerce industry, as retaining customers and reducing churn rates are key to sustainable growth and profitability. Predictive modeling techniques using machine learning algorithms offer a powerful tool for businesses to anticipate and mitigate customer churn. This thesis focuses on exploring the application of machine learning algorithms in predicting customer churn in the e-commerce industry. The study begins with a comprehensive review of existing literature on customer churn, machine learning algorithms, and their applications in the e-commerce sector. The literature review highlights the significance of predictive modeling in identifying factors influencing customer churn and the potential of machine learning algorithms to enhance predictive accuracy. In the methodology chapter, the research design and data collection methods for the study are outlined. The research methodology includes data preprocessing, feature selection, model training, and evaluation processes to develop an effective predictive model for customer churn prediction. The study utilizes a dataset containing historical customer transaction and interaction data from an e-commerce platform to train and test the predictive models. The findings chapter presents the results of applying various machine learning algorithms, such as logistic regression, decision trees, random forests, and neural networks, to predict customer churn in the e-commerce industry. The evaluation metrics used to assess the performance of the models include accuracy, precision, recall, and F1 score. The findings reveal the effectiveness of machine learning algorithms in predicting customer churn and identifying key factors influencing churn behavior. The discussion chapter provides an in-depth analysis of the findings, discussing the implications of the results for businesses in the e-commerce industry. The discussion highlights the importance of understanding customer behavior and preferences to implement targeted retention strategies and reduce churn rates effectively. In conclusion, this thesis contributes to the growing body of knowledge on customer churn prediction in the e-commerce industry using machine learning algorithms. The study demonstrates the potential of predictive modeling techniques to aid businesses in proactively managing customer churn and improving customer retention strategies. The findings of this research offer valuable insights for businesses seeking to leverage data-driven approaches to enhance customer relationship management and optimize business performance in the competitive e-commerce landscape.