Thesis Abstract

Abstract
Customer churn, the rate at which customers stop doing business with a company, is a critical challenge faced by organizations across industries, including the telecommunications sector. As competition intensifies and customer expectations evolve, understanding and predicting customer churn has become essential for businesses to retain customers and maintain profitability. In this context, machine learning algorithms offer a powerful tool for analyzing customer data and predicting churn patterns. This thesis focuses on the application of predictive modeling and analysis using machine learning algorithms to address customer churn in the telecommunications industry. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The chapter sets the stage for the subsequent chapters by outlining the importance of addressing customer churn through predictive modeling and machine learning techniques. Chapter 2 presents a comprehensive literature review that delves into existing research and studies related to customer churn, machine learning algorithms, and predictive modeling in the telecommunications industry. The review synthesizes key findings and insights to provide a solid theoretical foundation for the research study. Chapter 3 details the research methodology employed in this study, including data collection methods, data preprocessing techniques, feature selection, model selection, evaluation metrics, and validation procedures. The chapter outlines the steps taken to build and validate predictive models for customer churn analysis, ensuring the reliability and validity of the research findings. In Chapter 4, the findings from the application of machine learning algorithms to predict customer churn in the telecommunications industry are discussed in detail. The chapter presents the results of the analysis, including model performance metrics, feature importance, and insights gained from the predictive models. The discussion provides valuable insights into the factors influencing customer churn and the effectiveness of machine learning algorithms in predicting churn behavior. Chapter 5 concludes the thesis by summarizing the key findings, implications for the telecommunications industry, contributions to existing knowledge, and recommendations for future research. The chapter highlights the significance of predictive modeling and analysis in addressing customer churn and emphasizes the potential benefits for businesses in reducing churn rates and enhancing customer retention strategies. Overall, this thesis contributes to the growing body of research on customer churn prediction in the telecommunications industry by demonstrating the effectiveness of machine learning algorithms in analyzing customer data and predicting churn patterns. The study underscores the importance of proactive churn management strategies and the role of advanced analytics in driving customer retention and business growth in a competitive market environment.

Thesis Overview