Thesis Abstract

Abstract
The telecommunications industry is highly competitive, with companies striving to retain customers in order to maintain profitability and market share. Customer churn, the phenomenon of customers switching to competitors or discontinuing services, presents a significant challenge to telecommunication companies. This study focuses on developing a predictive modeling framework using machine learning algorithms to forecast customer churn in the telecommunication industry. The aim is to assist companies in proactively identifying customers at risk of churning, enabling targeted retention strategies to be implemented. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the stage for the research by highlighting the importance of addressing customer churn in the telecommunication industry through predictive modeling. Chapter 2 presents a comprehensive literature review that examines existing research on customer churn prediction, machine learning algorithms, and their applications in the telecommunication industry. This chapter synthesizes relevant studies to provide a theoretical foundation for the research and identify gaps that the current study aims to address. Chapter 3 outlines the research methodology employed in this study, including data collection, preprocessing, feature selection, model development, evaluation metrics, and validation techniques. The chapter describes the steps taken to build and validate the predictive models using machine learning algorithms. Chapter 4 presents the findings of the study, detailing the performance of different machine learning algorithms in predicting customer churn. The chapter discusses the key factors influencing churn prediction accuracy and provides insights into the predictive modeling process in the context of the telecommunication industry. Chapter 5 offers a conclusion and summary of the research, highlighting the key findings, implications, and recommendations for telecommunication companies seeking to leverage predictive modeling for customer churn management. The chapter also discusses the limitations of the study and suggests directions for future research in this area. Overall, this thesis contributes to the growing body of knowledge on customer churn prediction in the telecommunication industry by demonstrating the effectiveness of machine learning algorithms in forecasting churn behavior. The research findings have practical implications for telecommunication companies looking to reduce customer churn rates and enhance customer retention strategies through data-driven approaches.

Thesis Overview