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Introduction:

Subscription-based software companies rely on customer retention and maximizing customer lifetime value (CLV) for sustainable growth and profitability. Predicting customer lifetime value is crucial for these companies to make informed decisions on customer acquisition, retention, and monetization strategies. Machine learning techniques have shown promise in predicting CLV by analyzing usage data and predicting future customer behavior.

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 CLV and subscription-based software
2.2 Theoretical frameworks for CLV prediction
2.3 Previous studies on CLV prediction using usage data and machine learning
2.4 Challenges in predicting CLV for subscription-based software
2.5 Importance of customer segmentation in CLV prediction
2.6 Role of feature selection in CLV prediction
2.7 Evaluation metrics for CLV prediction models
2.8 Comparison of different machine learning algorithms for CLV prediction
2.9 Emerging trends in CLV prediction for subscription-based software

Chapter three: Research Methodology
3.1 Research design and approach
3.2 Data collection and processing
3.3 Variable selection and feature engineering
3.4 Model building and evaluation
3.5 Cross-validation and hyperparameter tuning
3.6 Validation and interpretation of results
3.7 Ethical considerations
3.8 Limitations of the methodology

Chapter four: Discussion of Findings
4.1 Overview of data analysis results
4.2 Comparison of different machine learning models
4.3 Insights from feature importance analysis
4.4 Implications for subscription-based software companies
4.5 Recommendations for future research
4.6 Practical implications for CLV prediction
4.7 Challenges and limitations of the study

Chapter five: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to the literature
5.3 Practical implications for subscription-based software companies
5.4 Recommendations for future research
5.5 Conclusion and final remarks

Thesis Overview:

Predicting customer lifetime value is a critical task for subscription-based software companies to enhance customer retention and maximize profitability. This thesis aims to explore the use of usage data and machine learning techniques for predicting CLV in the context of subscription-based software. The thesis will begin with an introduction that provides background information on the topic, highlights the problem statement, outlines the objectives, limitations, scope, and significance of the study, and introduces the structure of the thesis.

The second chapter will review relevant literature on CLV prediction, theoretical frameworks, previous studies, challenges, customer segmentation, feature selection, evaluation metrics, machine learning algorithms, and emerging trends. The third chapter will detail the research methodology, including research design, data collection and processing, variable selection, model building and evaluation, cross-validation, ethical considerations, and limitations.

The fourth chapter will present a comprehensive discussion of the findings, including data analysis results, comparisons of machine learning models, insights from feature importance analysis, implications for software companies, recommendations for future research, and practical implications. The final chapter will provide a conclusion and summary of key findings, contributions to the literature, practical implications, recommendations for future research, and final remarks.

Overall, this thesis will contribute to the existing literature on CLV prediction for subscription-based software companies and provide valuable insights for practitioners in the field.

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