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Introduction

The rapid growth of subscription-based business models in recent years has led to an increased focus on predicting and reducing customer churn. Customer churn, or the rate at which customers stop subscribing to a service, can have a significant impact on the profitability and sustainability of a subscription business. Therefore, the ability to accurately predict customer churn and implement effective retention strategies is crucial for the long-term success of subscription businesses.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of subscription-based business models
2.2 Factors influencing customer churn in subscription businesses
2.3 Existing methods for predicting customer churn
2.4 Challenges in predicting customer churn
2.5 Importance of customer retention strategies
2.6 Case studies on successful customer retention strategies
2.7 Machine learning techniques for predicting customer churn
2.8 Customer lifetime value and its impact on churn prediction
2.9 The role of data analytics in predicting customer churn
2.10 Ethical considerations in customer churn prediction

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Sampling techniques
3.5 Model development
3.6 Validation and testing of models
3.7 Ethical considerations in research
3.8 Limitations of research methodology

Chapter 4: Discussion of Findings
4.1 Analysis of customer churn data
4.2 Comparison of different churn prediction models
4.3 Identification of key factors influencing customer churn
4.4 Evaluation of the effectiveness of retention strategies
4.5 Recommendations for reducing customer churn
4.6 Implications for subscription businesses
4.7 Future research directions
4.8 Conclusions

Chapter 5: Conclusion and Summary
In conclusion, this thesis provides a comprehensive overview of the challenges and opportunities in predicting customer churn in subscription businesses. By examining the factors influencing customer churn, exploring existing prediction methods, and proposing new retention strategies, this research aims to contribute to the body of knowledge on customer churn prediction. It is hoped that the findings of this study will help subscription businesses improve customer retention and ultimately enhance their long-term profitability.

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