Predictive modeling and analysis of customer churn in the telecommunications industry using machine learning algorithms

Thesis Abstract

Abstract
Customer churn, or customer attrition, is a critical challenge faced by companies in the telecommunications industry. In this digital age, where customers have numerous options and are more empowered than ever, understanding and predicting customer churn has become a key focus for businesses seeking to enhance customer retention and profitability. This thesis presents a comprehensive study on predictive modeling and analysis of customer churn in the telecommunications industry using machine learning algorithms. The research methodology employed in this study includes a detailed literature review to establish a theoretical foundation for understanding customer churn, machine learning algorithms, and their applications in predictive modeling. Data collection involved gathering historical customer data from a telecommunications company, which was used to train and test various machine learning models. Chapter 1 serves as an introduction to the research topic, providing background information on customer churn, stating the problem statement, objectives, limitations, scope, significance of the study, and defining key terms. Chapter 2 presents a thorough literature review on customer churn in the telecommunications industry, machine learning algorithms, and relevant studies on predictive modeling. Chapter 3 outlines the research methodology, including data collection, preprocessing, feature selection, model selection, and evaluation metrics. The methodology also includes discussions on the implementation of machine learning algorithms such as logistic regression, random forest, and neural networks for predicting customer churn. Chapter 4 delves into the detailed discussion of the findings from the predictive modeling and analysis of customer churn. The chapter evaluates the performance of different machine learning algorithms in predicting customer churn and provides insights into the key factors influencing customer attrition in the telecommunications industry. Finally, Chapter 5 presents the conclusion and summary of the project thesis. The chapter discusses the implications of the research findings, highlights the contributions to the field of customer churn analysis, and suggests recommendations for businesses to improve customer retention strategies based on predictive modeling insights. Overall, this thesis contributes to the growing body of knowledge on customer churn analysis in the telecommunications industry by leveraging machine learning algorithms for predictive modeling. The research findings offer valuable insights for businesses to proactively address customer attrition and optimize their retention strategies to enhance customer satisfaction and profitability in a highly competitive market environment.