Thesis Abstract

Abstract
Customer churn, the phenomenon of customers discontinuing their services with a company, is a critical issue faced by companies in the telecom industry. To address this challenge, this thesis focuses on developing a predictive modeling framework using machine learning techniques to forecast customer churn in the telecom industry. The study aims to leverage historical customer data to identify patterns and factors that contribute to churn, thereby enabling telecom companies to proactively take measures to retain customers. The research begins with a comprehensive literature review to explore existing studies on customer churn prediction, machine learning techniques, and their applications in the telecom industry. The study then delves into the research methodology, detailing the data collection process, preprocessing steps, feature selection, model development, and evaluation metrics used to assess the performance of the predictive model. The findings of the study reveal the significant predictors of customer churn, such as customer demographics, usage patterns, and customer service interactions. Through the application of machine learning algorithms such as logistic regression, decision trees, and random forests, the predictive model demonstrates high accuracy in forecasting customer churn. The results highlight the importance of leveraging advanced analytics to gain insights into customer behavior and preferences, enabling telecom companies to tailor their retention strategies effectively. The discussion section provides a detailed analysis of the findings, emphasizing the implications for telecom companies and the potential benefits of implementing the predictive modeling framework. The study concludes with a summary of key insights and recommendations for future research, emphasizing the importance of continuous monitoring and refinement of predictive models to adapt to changing customer dynamics in the telecom industry. Overall, this thesis contributes to the growing body of research on customer churn prediction in the telecom industry and underscores the value of machine learning techniques in enhancing customer retention strategies. By developing an effective predictive modeling framework, telecom companies can proactively address customer churn, improve customer satisfaction, and drive sustainable business growth in a competitive market environment.

Thesis Overview

The research project titled "Predictive Modeling of Customer Churn in the Telecom Industry Using Machine Learning Techniques" aims to address the critical issue of customer churn within the telecom industry through the application of advanced machine learning methods. Customer churn, or the rate at which customers switch from one telecom service provider to another, is a significant concern for telecom companies as it impacts revenue and market competitiveness. By leveraging machine learning techniques, this research seeks to develop predictive models that can identify patterns and factors leading to customer churn, enabling telecom companies to proactively implement retention strategies. The project will begin with a comprehensive literature review to explore existing studies and methodologies related to customer churn prediction and machine learning applications in the telecom industry. This review will provide a foundation for understanding key concepts, trends, and challenges in the field, guiding the development of the research methodology. The research methodology will involve collecting and analyzing customer data from telecom companies, including demographic information, service usage patterns, customer feedback, and churn history. Various machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks will be applied to build predictive models based on the input features. The findings of the study will be presented and discussed in detail in Chapter Four, highlighting the performance and accuracy of the developed predictive models in identifying customers at risk of churn. The discussion will also explore the key factors influencing customer churn in the telecom industry and provide insights into potential strategies for improving customer retention. In conclusion, this research project seeks to contribute to the telecom industry by offering actionable insights and practical solutions for reducing customer churn rates through the implementation of advanced machine learning techniques. By enhancing the predictive capabilities of telecom companies, this research aims to help improve customer satisfaction, loyalty, and overall business performance in a competitive market environment.