Thesis Abstract

Abstract
This thesis explores the application of predictive modeling techniques in addressing the challenge of customer churn in the telecommunications industry. The study focuses on utilizing machine learning algorithms to develop predictive models that can identify customers at risk of churning, enabling proactive retention strategies. Customer churn, the phenomenon where customers cease their relationship with a service provider, poses significant financial implications for telecommunications companies. By predicting and preventing churn, companies can enhance customer retention, profitability, and overall business performance. Chapter 1 introduces the research topic, providing a background of the study on customer churn in the telecommunications industry. The problem statement highlights the significance of addressing customer churn, followed by the objectives, limitations, and scope of the study. The chapter concludes with the structure of the thesis and the definition of key terms used throughout the research. Chapter 2 presents a comprehensive literature review on customer churn, machine learning techniques, and predictive modeling in the telecommunications industry. The review explores existing studies, methodologies, and findings related to customer churn prediction, highlighting the importance of leveraging machine learning algorithms for accurate and efficient prediction models. Chapter 3 details the research methodology employed in this study. It includes sections on data collection, data preprocessing, feature selection, model development, evaluation metrics, and validation techniques. The chapter also discusses the selection of machine learning algorithms, parameter tuning, and model optimization for customer churn prediction. Chapter 4 presents an in-depth discussion of the findings obtained from the application of predictive modeling techniques in predicting customer churn. The chapter analyzes the performance of different machine learning algorithms, identifies key predictors of churn, and evaluates the effectiveness of the predictive models developed in this study. Chapter 5 concludes the thesis by summarizing the key findings, implications, and recommendations for telecommunications companies seeking to reduce customer churn using predictive modeling techniques. The chapter discusses the contributions of the study, its limitations, and potential areas for future research in the field of customer churn prediction. In conclusion, this thesis contributes to the growing body of knowledge on customer churn prediction in the telecommunications industry. By harnessing the power of machine learning algorithms, companies can proactively identify at-risk customers and implement targeted retention strategies to reduce churn rates and improve customer loyalty. The findings of this study have practical implications for telecommunications companies aiming to enhance customer satisfaction, profitability, and long-term business sustainability.

Thesis Overview