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Introduction

Customer Lifetime Value (CLV) has become increasingly important in the retail industry as companies strive to maximize profitability and customer retention. Predicting the CLV of customers allows retailers to tailor their marketing strategies, improve customer satisfaction, and ultimately increase revenue. This thesis aims to explore the factors that influence CLV prediction in the retail sector and develop a model to accurately forecast customer lifetime value.

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 Customer Lifetime Value
2.2 Factors influencing Customer Lifetime Value
2.3 Methods of CLV prediction in the retail industry
2.4 Importance of CLV prediction in retail
2.5 Challenges in CLV prediction
2.6 Relationship between CLV and customer loyalty
2.7 Comparison of CLV prediction models
2.8 Impact of technology on CLV prediction
2.9 Case studies on successful CLV prediction implementation
2.10 Future trends in CLV prediction

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Sampling techniques
3.4 Data analysis techniques
3.5 Model development process
3.6 Validation of the CLV prediction model
3.7 Ethical considerations
3.8 Limitations of the research

Chapter 4: Discussion of Findings
4.1 Model performance evaluation
4.2 Factors influencing CLV prediction accuracy
4.3 Comparison with existing CLV prediction models
4.4 Practical implications for retailers
4.5 Recommendations for future research
4.6 Case study analysis
4.7 Managerial implications
4.8 Implications for retail strategy

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to the field of CLV prediction
5.3 Practical implications for retailers
5.4 Recommendations for implementation
5.5 Limitations of the study
5.6 Future research directions

Thesis Overview:

Customer Lifetime Value Prediction for Retail

This thesis explores the prediction of Customer Lifetime Value (CLV) in the retail industry and its implications for marketing strategies and customer retention. The research aims to develop a model that accurately forecasts CLV by analyzing various factors that influence customer value. The study begins with an introduction to CLV prediction, its importance in the retail sector, and the problem statement that motivates this research.

The literature review provides an overview of CLV, factors influencing CLV prediction, methods of prediction, challenges faced by retailers, and the relationship between CLV and customer loyalty. Case studies and future trends in CLV prediction are also discussed to provide a comprehensive understanding of the topic.

The research methodology chapter details the approach taken in developing the CLV prediction model, including research design, data collection techniques, sampling methods, and data analysis procedures. The validation process of the model and ethical considerations are also addressed.

The discussion of findings chapter evaluates the performance of the CLV prediction model, factors influencing accuracy, comparisons with existing models, practical implications, and recommendations for retailers. Case studies and managerial implications are also presented to illustrate the real-world application of CLV prediction.

In the conclusion and summary chapter, key findings are summarized, contributions to the field are highlighted, practical implications and recommendations for implementation are provided, limitations of the study are acknowledged, and future research directions are suggested. This thesis aims to provide valuable insights for retailers seeking to optimize their marketing strategies and enhance customer relationships through CLV prediction.

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