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Introduction

In today’s competitive market, customer lifetime value (CLV) has become a crucial metric for businesses to understand the long-term value of their customers. CLV is a prediction of the net profit attributed to the future relationship with a customer, providing valuable insights for customer acquisition, retention, and marketing strategies. With the evolution of big data and machine learning algorithms, businesses have the opportunity to leverage transactional data to predict and optimize customer lifetime value.

Background of study

The rise of e-commerce and the digital age has generated an unprecedented amount of transactional data, presenting an opportunity for businesses to harness this data to better understand their customers. Traditional methods of calculating customer lifetime value have limitations, such as being static and not accounting for individual customer behavior. Machine learning algorithms offer a dynamic and personalized approach to predicting customer lifetime value by analyzing patterns in transactional data.

Problem Statement

Despite the potential benefits of using transactional data and machine learning for predicting customer lifetime value, there is a lack of research on the application of these techniques in real-world business scenarios. Businesses struggle with understanding how to effectively implement these methods and interpret the results to drive actionable insights.

Objective of study

The primary objective of this study is to explore the use of transactional data and machine learning algorithms for predicting customer lifetime value in a business context. The study aims to develop a predictive model that can accurately estimate CLV and provide actionable recommendations for improving customer relationships and increasing revenue.

Limitation of study

This study is limited by the availability and quality of transactional data, as well as the scope of machine learning algorithms that can be applied. Additionally, external factors such as market trends and competitor strategies may impact the accuracy of the predictive model.

Scope of study

This study focuses on the application of machine learning algorithms, such as regression analysis, decision trees, and neural networks, to predict customer lifetime value using transactional data. The research will be conducted in the context of e-commerce businesses but can be generalized to other industries.

Significance of study

The findings of this study will provide valuable insights for businesses looking to optimize their marketing and customer relationship strategies by leveraging transactional data and machine learning algorithms. The results can help businesses improve customer retention, acquisition, and overall profitability.

Structure of the Thesis

Chapter One: 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 Two: Literature Review
2.1 Overview of customer lifetime value
2.2 Traditional methods of calculating CLV
2.3 Applications of machine learning in marketing
2.4 Predictive modeling techniques
2.5 Transactional data analysis
2.6 Previous studies on CLV prediction
2.7 Challenges in predicting CLV
2.8 Benefits of using transactional data and machine learning
2.9 Current trends in CLV prediction
2.10 Gaps in existing research

Chapter Three: Research Methodology
3.1 Data collection and preprocessing
3.2 Feature selection and engineering
3.3 Model selection and evaluation
3.4 Cross-validation techniques
3.5 Performance metrics
3.6 Implementation of predictive model
3.7 Data visualization techniques
3.8 Ethical considerations

Chapter Four: Discussion of Findings
4.1 Analysis of prediction results
4.2 Interpretation of model performance
4.3 Comparison with traditional CLV methods
4.4 Implications for business strategy
4.5 Limitations of the predictive model
4.6 Future research directions

Chapter Five: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to literature
5.3 Practical implications for businesses
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview

The thesis “Predicting customer lifetime value using transactional data and machine learning” focuses on the application of advanced data analytics techniques to predict customer lifetime value in a business context. The study aims to develop a predictive model that can accurately estimate CLV and provide actionable recommendations for improving customer relationships and increasing revenue.

Chapter One provides an introduction to the topic, outlining the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter Two presents a comprehensive literature review on customer lifetime value, traditional CLV methods, machine learning applications, predictive modeling techniques, transactional data analysis, previous studies, challenges, benefits, trends, and research gaps.

Chapter Three details the research methodology, including data collection, preprocessing, feature selection, model selection, evaluation, cross-validation, performance metrics, implementation, and ethical considerations. Chapter Four discusses the findings of the study, analyzing prediction results, model performance, comparisons with traditional methods, implications for business strategy, limitations, and future research directions.

Chapter Five concludes the thesis with a summary of key findings, contributions to literature, practical implications, recommendations for future research, and a final conclusion. This research aims to bridge the gap between academia and industry by providing practical insights into predicting customer lifetime value using transactional data and machine learning.

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