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Introduction

Customer lifetime value (CLV) is a key metric for e-commerce businesses that allows them to understand the potential profitability of their customers over their entire relationship with the company. Predicting CLV accurately can help businesses make informed decisions on customer acquisition, retention, and marketing strategies.

In recent years, with the advancement of technology such as machine learning and big data analytics, predicting CLV has become more accurate and effective. This thesis aims to explore the use of browsing and purchase data combined with machine learning algorithms to predict CLV for e-commerce 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 Concept of Customer Lifetime Value
2.2 Importance of Predicting CLV
2.3 Traditional Methods of Predicting CLV
2.4 Machine Learning in CLV Prediction
2.5 Browsing and Purchase Data in CLV Prediction
2.6 Previous Studies on CLV Prediction
2.7 Challenges in CLV Prediction
2.8 Emerging Trends in CLV Prediction
2.9 Gap in Literature
2.10 Theoretical Framework

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 Training
3.7 Model Evaluation
3.8 Ethical Consideration

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of Data
4.2 Model Performance Evaluation
4.3 Comparison of Different Models
4.4 Interpretation of Results
4.5 Implications for E-commerce Businesses
4.6 Recommendations for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Contribution of the Study
5.4 Practical Implications
5.5 Limitations of the Study
5.6 Recommendations for Practitioners
5.7 Recommendations for Future Research

Thesis Overview

In this thesis, we aim to explore the use of browsing and purchase data in predicting customer lifetime value (CLV) for e-commerce businesses using machine learning algorithms. The introduction provides a background to the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms.

The literature review discusses the concept of CLV, importance of predicting CLV, traditional methods, machine learning in CLV prediction, browsing and purchase data in CLV prediction, previous studies, challenges, emerging trends, gap in literature, and theoretical framework.

The research methodology details the research design, data collection, preprocessing, feature selection, model selection, model training, model evaluation, and ethical considerations. The discussion of findings includes descriptive analysis, model performance evaluation, comparison of different models, interpretation of results, implications for e-commerce businesses, and recommendations for future research.

The conclusion and summary provide a summary of findings, conclusion, contribution of the study, practical implications, limitations, recommendations for practitioners, and recommendations for future research. Overall, this thesis aims to contribute to the existing body of knowledge on CLV prediction for e-commerce businesses using browsing and purchase data and machine learning.

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