Thesis Abstract

Abstract
Customer churn, the phenomenon where customers discontinue their relationship with a company, presents a significant challenge for businesses across various industries. The ability to predict and prevent customer churn is crucial for maintaining a loyal customer base and ensuring long-term business success. In recent years, machine learning techniques have emerged as powerful tools for analyzing large datasets and predicting customer behavior. This thesis explores the application of machine learning in predictive modeling of customer churn, with a focus on developing accurate and efficient models to identify customers at risk of churn. Chapter 1 provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 presents a comprehensive literature review, covering ten key research articles and studies related to customer churn prediction, machine learning techniques, and applications in the business context. In Chapter 3, the research methodology is detailed, including data collection methods, selection of machine learning algorithms, feature engineering techniques, model training, evaluation metrics, and validation procedures. The chapter also discusses the ethical considerations and potential biases that may impact the research findings. Chapter 4 presents the findings of the predictive modeling of customer churn using machine learning techniques. The chapter includes a detailed analysis of the model performance, feature importance, and insights gained from the predictions. The discussion delves into the practical implications of the findings for businesses seeking to reduce customer churn rates and improve customer retention strategies. Finally, Chapter 5 provides a conclusion and summary of the thesis, highlighting the key findings, contributions to the field, limitations of the study, and recommendations for future research. The thesis concludes with a call to action for businesses to leverage machine learning techniques for predicting customer churn and implementing targeted retention strategies to enhance customer satisfaction and loyalty. Overall, this thesis contributes to the growing body of research on customer churn prediction and underscores the importance of integrating machine learning techniques into business decision-making processes. By harnessing the power of predictive modeling, businesses can proactively address customer churn, optimize resource allocation, and drive sustainable growth in an increasingly competitive marketplace.

Thesis Overview

The research project titled "Predictive modeling of customer churn using machine learning techniques" aims to investigate and develop predictive models to anticipate customer churn in a business setting. Customer churn, or customer attrition, is a critical concern for businesses across various industries as it directly impacts revenue and profitability. By applying machine learning techniques, this research seeks to enhance the accuracy and effectiveness of predicting customer churn, ultimately enabling businesses to proactively address customer retention strategies. The project will start with an introduction that provides a background on the significance of customer churn in business operations and the potential impact it can have on the bottom line. The problem statement will highlight the challenges businesses face in identifying and retaining customers at risk of churning. The objectives of the study will outline the specific goals and outcomes the research aims to achieve, including the development of predictive models and the evaluation of their performance. The limitations of the study will acknowledge any constraints or restrictions that may impact the research process or the generalizability of the findings. The scope of the study will define the boundaries and focus areas of the research, including the specific industry or sector under investigation. The significance of the study will emphasize the practical implications and benefits of developing accurate customer churn prediction models for businesses. The structure of the thesis will outline the organization of the research document, including the chapters and sections that will be included. Definitions of key terms will be provided to ensure clarity and understanding of the terminology used throughout the research. The literature review will explore existing studies, methodologies, and technologies related to customer churn prediction and machine learning techniques. This section will provide a comprehensive overview of the current state of research in the field and identify gaps or opportunities for further investigation. The research methodology will detail the processes, tools, and techniques that will be employed to collect, analyze, and interpret data for the development of predictive models. This chapter will include information on the data sources, data preprocessing, model selection, and evaluation metrics used in the study. The discussion of findings chapter will present the results of the predictive models developed in the research, including model performance, accuracy, and predictive power. This section will also analyze the factors that contribute to customer churn and identify potential strategies for improving customer retention. The conclusion and summary chapter will provide a comprehensive overview of the research findings, implications, and recommendations for future research and practical applications. This section will highlight the contributions of the study to the field of customer churn prediction and machine learning applications in business. Overall, the research project on predictive modeling of customer churn using machine learning techniques aims to address a critical business challenge by developing accurate and effective predictive models that can help businesses anticipate and mitigate customer churn, ultimately improving customer retention and business performance.