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Title: Predicting Customer Lifetime Value for Subscription-Based Businesses Using Transactional Data and Machine Learning

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 Introduction to Customer Lifetime Value
2.2 Subscription-based Business Models
2.3 Machine Learning in Customer Analytics
2.4 Predictive Modeling Techniques
2.5 Previous Studies on CLV Prediction
2.6 Data Sources for CLV Prediction
2.7 Challenges in CLV Prediction
2.8 Importance of CLV in Business Decision Making
2.9 Applications of CLV in Marketing Strategies
2.10 Future Trends in CLV Prediction

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Model Selection
3.6 Model Evaluation
3.7 Performance Metrics
3.8 Ethical Considerations

Chapter 4: Findings and Discussion
4.1 Descriptive Analysis of Transactional Data
4.2 CLV Prediction Models
4.3 Comparison of Different Machine Learning Algorithms
4.4 Impact of Feature Selection on Model Performance
4.5 Interpretation of Results
4.6 Practical Implications for Subscription-Based Businesses

Chapter 5: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Implications for Business and Academic Research
5.3 Future Research Directions
5.4 Conclusion

Thesis Overview:

The rapid growth of subscription-based businesses has led to an increased focus on customer lifetime value (CLV) as a key metric for business success. Predicting CLV accurately is crucial for businesses to make informed decisions regarding customer acquisition, retention, and marketing strategies. In this thesis, we aim to explore the use of transactional data and machine learning algorithms to predict customer lifetime value for subscription-based businesses.

Chapter 1 provides an introduction to the topic, including the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive review of the existing literature on CLV prediction, subscription-based business models, machine learning techniques, and previous studies in the field. Chapter 3 outlines the research methodology, including data collection, preprocessing, model selection, evaluation, and ethical considerations.

Chapter 4 discusses the findings of the study, including descriptive analysis of transactional data, CLV prediction models, comparison of machine learning algorithms, and practical implications for businesses. Finally, Chapter 5 presents the conclusion and summary of the thesis, highlighting key findings, implications for business and academic research, future research directions, and overall conclusions.

Overall, this thesis aims to contribute to the growing body of literature on CLV prediction by utilizing transactional data and machine learning algorithms to provide accurate and actionable insights for subscription-based businesses.

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