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Introduction

With the increasing competition in the market, it has become imperative for businesses to understand their customers better in order to improve customer retention and overall profitability. One way to achieve this is by predicting customer lifetime value (CLV), which is a key metric that quantifies the value that a customer brings to a business over their entire relationship with the company. By predicting CLV, businesses can effectively allocate resources, tailor marketing strategies, and enhance customer satisfaction.

This thesis focuses on predicting customer lifetime value using transactional data and machine learning techniques. The study aims to explore how transactional data can be leveraged to predict CLV accurately and efficiently, and how machine learning algorithms can be used to achieve this goal. By harnessing the power of data and advanced analytical techniques, businesses can gain a competitive edge by making informed decisions that drive customer loyalty and profitability.

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 CLV
2.2 Importance of Predicting CLV
2.3 Traditional Approaches to CLV Prediction
2.4 Machine Learning in Customer Lifetime Value Prediction
2.5 Transactional Data and CLV Prediction
2.6 Challenges in CLV Prediction
2.7 Data Preprocessing Techniques
2.8 Feature Engineering for CLV Prediction
2.9 Evaluation Metrics for CLV Prediction
2.10 Recent Advances 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 Training
3.7 Model Evaluation
3.8 Hyperparameter Tuning
3.9 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of Transactional Data
4.2 Model Performance Evaluation
4.3 Feature Importance Analysis
4.4 Comparison of Different Machine Learning Models
4.5 Interpretation of Results
4.6 Implications for Business Strategy
4.7 Limitations of the Study
4.8 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contribution to the Literature
5.3 Practical Implications
5.4 Recommendations for Businesses
5.5 Conclusion

Thesis Overview

Predicting customer lifetime value (CLV) using transactional data and machine learning techniques is a critical area of research that holds immense potential for businesses seeking to enhance customer relationships and drive profitability. This thesis aims to explore the application of advanced analytical techniques to predict CLV accurately and efficiently, leveraging transactional data to gain insights into customer behavior and preferences.

The thesis begins with an introduction that provides an overview of the research topic, background information, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review examines the existing literature on CLV prediction, traditional approaches, machine learning techniques, transactional data, challenges, data preprocessing, feature engineering, evaluation metrics, and recent advances.

The research methodology chapter details the research design, data collection, preprocessing, feature selection, model selection, training, evaluation, hyperparameter tuning, and ethical considerations. The discussion of findings chapter presents the descriptive analysis of transactional data, model performance evaluation, feature importance analysis, model comparison, interpretation of results, implications for business strategy, limitations, and future research directions.

In conclusion, this thesis summarizes the key findings, contributions to the literature, practical implications, recommendations for businesses, and suggests areas for further research. By predicting CLV accurately and leveraging transactional data effectively, businesses can optimize their marketing strategies, improve customer retention, and maximize profitability in a competitive market landscape.

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