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.

Thesis Overview

The project titled "Predictive Modeling of Customer Churn in E-commerce Industry using Machine Learning Algorithms" aims to address the critical issue of customer churn within the e-commerce sector. Customer churn, the phenomenon where customers stop doing business with a company, is a significant concern for e-commerce businesses as retaining existing customers is often more cost-effective than acquiring new ones. By leveraging machine learning algorithms to predict and understand customer churn patterns, this research seeks to provide valuable insights that can help e-commerce businesses develop proactive strategies to reduce churn rates and enhance customer retention. The research will begin with a comprehensive literature review to explore existing studies on customer churn prediction, machine learning algorithms, and their applications in the e-commerce industry. This review will provide a solid theoretical foundation for the study, highlighting key concepts, methodologies, and findings that will guide the research methodology. The research methodology will involve the collection and analysis of historical customer data from an e-commerce platform, including customer demographics, purchase history, browsing behavior, and other relevant factors. Various machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks will be applied to build predictive models that can forecast customer churn probabilities accurately. The findings from the analysis will be discussed in detail in the results chapter, focusing on the performance and effectiveness of different machine learning algorithms in predicting customer churn. The research will also identify key factors that influence customer churn in the e-commerce industry, providing actionable insights for businesses to improve customer retention strategies. In conclusion, the study will summarize the key findings, implications, and recommendations for e-commerce businesses looking to leverage predictive modeling and machine learning algorithms to address customer churn effectively. By enhancing their understanding of customer behavior and preferences, businesses can proactively engage with at-risk customers, personalize marketing strategies, and ultimately improve customer loyalty and retention rates.