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Introduction

Customer Lifetime Value (CLV) prediction is a crucial aspect of marketing research that helps businesses understand the value of their customers over time. By predicting how much revenue a customer will generate over their lifetime, businesses can make informed decisions about customer acquisition, retention, and marketing strategies. This thesis aims to explore the various methods and techniques used for CLV prediction and their effectiveness in different industries.

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 Two: Literature Review
2.1 Introduction to Customer Lifetime Value Prediction
2.2 Traditional methods for CLV prediction
2.3 Machine learning techniques for CLV prediction
2.4 Challenges and limitations in CLV prediction
2.5 Industry applications of CLV prediction
2.6 Relationship between CLV prediction and customer segmentation
2.7 Importance of CLV prediction in marketing strategy
2.8 Case studies on successful CLV prediction implementation
2.9 Future trends in CLV prediction
2.10 Summary of literature review

Chapter Three: Research Methodology
3.1 Introduction to Research Methodology
3.2 Research Design
3.3 Data Collection Method
3.4 Data Analysis Technique
3.5 Sampling Method
3.6 Variable Measurement
3.7 Model Development
3.8 Model Validation
3.9 Ethical Considerations

Chapter Four: Discussion of Findings
4.1 Introduction to Discussion of Findings
4.2 Analysis of CLV prediction models
4.3 Comparison of different CLV prediction techniques
4.4 Interpretation of results
4.5 Implications for businesses
4.6 Recommendations for future research
4.7 Conclusion of findings

Chapter Five: Conclusion
5.1 Summary of the Thesis
5.2 Contribution to the field of CLV prediction
5.3 Implications for businesses and marketers
5.4 Limitations of the study
5.5 Recommendations for future research
5.6 Conclusion

Thesis Overview on Customer Lifetime Value Prediction

Customer Lifetime Value (CLV) prediction is a crucial tool in marketing that helps businesses understand the future value of their customers. This thesis aims to explore the various methods and techniques used for CLV prediction and their effectiveness in different industries. The literature review discusses traditional methods, machine learning techniques, challenges, and industry applications of CLV prediction. The research methodology chapter outlines the research design, data collection, analysis techniques, and model development process. The discussion of findings chapter analyzes different CLV prediction models, compares techniques, and interprets results. The conclusion chapter summarizes the thesis, discusses implications for businesses, and provides recommendations for future research. Overall, this thesis provides a comprehensive overview of CLV prediction and its importance in marketing strategy.

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