Thesis Abstract

Abstract
The telecommunications industry is highly competitive, with customer retention being a critical factor for business sustainability and growth. Customer churn, the phenomenon where customers switch from one service provider to another, poses a significant challenge for telecommunications companies. In this study, we focus on predictive modeling of customer churn in the telecommunications industry using machine learning algorithms to help companies proactively identify customers at risk of churn and implement targeted retention strategies. Chapter 1 provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter also includes definitions of key terms relevant to the study. Chapter 2 presents a comprehensive literature review on customer churn in the telecommunications industry, covering relevant theories, models, and previous studies on predictive modeling, machine learning algorithms, and customer retention strategies. Chapter 3 outlines the research methodology employed in this study, including data collection methods, data preprocessing techniques, feature selection, model selection, and evaluation metrics. The chapter also discusses the ethical considerations and limitations of the research methodology. Chapter 4 delves into the detailed analysis and discussion of the findings from the predictive modeling of customer churn using machine learning algorithms. The chapter highlights the performance of different models, the importance of various features in predicting churn, and the implications of the findings for telecommunications companies. Chapter 5 concludes the thesis by summarizing the key findings, implications, and contributions of the study. The chapter also discusses the limitations of the research, suggests areas for future research, and provides recommendations for telecommunications companies looking to leverage predictive modeling for customer churn prediction and retention strategies. Overall, this thesis contributes to the existing body of knowledge on customer churn prediction in the telecommunications industry by demonstrating the effectiveness of machine learning algorithms in identifying customers at risk of churn. The findings of this study can help telecommunications companies enhance their customer retention efforts, improve customer satisfaction, and ultimately drive business growth and profitability in a highly competitive market environment.

Thesis Overview