Thesis Abstract

Abstract
This thesis explores the application of predictive modeling techniques in predicting customer churn in the e-commerce industry. As customer retention is crucial for the success of e-commerce businesses, understanding and predicting customer churn can significantly impact decision-making processes and strategic planning. The study focuses on leveraging machine learning algorithms to develop predictive models that can identify customers at risk of churning, allowing businesses to implement targeted retention strategies. The research begins with an introduction that provides background information on customer churn in e-commerce, highlights the problem statement, objectives of the study, limitations, scope, significance, and the structure of the thesis. A detailed review of existing literature on customer churn, e-commerce industry trends, machine learning techniques, and predictive modeling forms the basis of the theoretical framework in Chapter Two. Chapter Three outlines the research methodology, including data collection methods, data preprocessing techniques, feature selection, model development, and evaluation metrics. The study employs a dataset containing customer transactional data, demographic information, and behavioral patterns to train and test the predictive models. Various machine learning algorithms such as logistic regression, random forest, and gradient boosting are applied to build and compare the performance of the predictive models. Chapter Four presents a comprehensive discussion of the findings, including the evaluation of model performance, feature importance analysis, and insights gained from the predictive modeling process. The results highlight the effectiveness of machine learning techniques in predicting customer churn and provide actionable recommendations for e-commerce businesses to improve customer retention strategies. In the final chapter, Chapter Five, the thesis concludes with a summary of the key findings, implications for practice, limitations of the study, and suggestions for future research. The study contributes to the existing body of knowledge on customer churn prediction in the e-commerce industry and provides practical insights for businesses to enhance customer retention efforts using machine learning techniques. Overall, this research emphasizes the importance of leveraging data-driven approaches to address customer churn challenges and optimize business performance in the competitive e-commerce landscape.

Thesis Overview

The research project titled "Predictive Modeling for Customer Churn in E-commerce Industry using Machine Learning Techniques" aims to address the crucial issue of customer churn, specifically within the e-commerce sector, by leveraging advanced machine learning methods. Customer churn, which refers to the phenomenon of customers ceasing their relationship with a business or service provider, poses a significant challenge for e-commerce companies due to its potential negative impact on revenue and profitability. By developing predictive models using machine learning algorithms, this study seeks to provide e-commerce businesses with proactive strategies to identify and prevent customer churn, ultimately enhancing customer retention and loyalty. The research will begin with a comprehensive literature review to explore existing studies and methodologies related to customer churn prediction, machine learning techniques, and their applications in the e-commerce industry. This review will provide a solid foundation for understanding the current state of research in the field and identifying gaps that the present study aims to address. The methodology section of the research will outline the data collection process, feature selection, model development, and evaluation metrics employed in building the predictive models for customer churn. Various machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks will be considered and compared to identify the most effective approach for predicting customer churn in the e-commerce industry. The main focus of the study will be on analyzing real-world e-commerce data to train and test the predictive models. Key factors influencing customer churn, such as purchase history, browsing behavior, customer demographics, and customer interactions, will be examined to identify patterns and predictors of churn. By leveraging these insights, the research aims to develop accurate and reliable predictive models that can anticipate customer churn and enable e-commerce businesses to implement targeted retention strategies. The discussion of findings section will present a detailed analysis of the results obtained from the predictive models, highlighting the performance metrics, feature importance, and model interpretation. The findings will be critically evaluated in the context of existing literature and practical implications for e-commerce businesses looking to reduce customer churn and improve customer satisfaction. In conclusion, this research project on "Predictive Modeling for Customer Churn in E-commerce Industry using Machine Learning Techniques" seeks to contribute to the growing body of knowledge on customer churn prediction and machine learning applications in e-commerce. By developing effective predictive models and providing actionable insights, the study aims to empower e-commerce businesses with the tools and strategies needed to proactively address customer churn and enhance overall business performance and customer satisfaction.