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Introduction

Customer Churn Prediction for Subscription Services is an important area of research in today’s business landscape. With the rise of subscription-based services in various industries such as telecommunications, software, media, and retail, it has become crucial for companies to understand and predict the likelihood of customers canceling their subscriptions. Customer churn, also known as customer attrition, can have a significant impact on a company’s revenue and profitability. By accurately predicting customer churn, companies can take proactive measures to retain customers and improve customer satisfaction.

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 Introduction to Customer Churn Prediction
2.2 Customer Churn Models
2.3 Data Mining Techniques for Customer Churn Prediction
2.4 Machine Learning Algorithms for Customer Churn Prediction
2.5 Factors Influencing Customer Churn
2.6 Customer Retention Strategies
2.7 Case Studies on Customer Churn Prediction
2.8 Critiques of Existing Customer Churn Prediction Models
2.9 Best Practices in Customer Churn Prediction
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Introduction to Research Methodology
3.2 Research Design
3.3 Data Collection Methods
3.4 Sampling Techniques
3.5 Variable Selection
3.6 Model Development
3.7 Data Analysis Techniques
3.8 Model Evaluation
3.9 Ethical Considerations
3.10 Summary of Research Methodology

Chapter 4: Discussion of Findings
4.1 Introduction to Discussion of Findings
4.2 Data Analysis Results
4.3 Model Performance Evaluation
4.4 Comparison of Different Models
4.5 Predictive Power of Variables
4.6 Practical Implications of Findings
4.7 Recommendations for Further Research
4.8 Conclusion of Findings

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Practice
5.4 Limitations of the Study
5.5 Recommendations for Future Research
5.6 Conclusion of the Thesis

Thesis Overview on Customer Churn Prediction for Subscription Services

Customer churn prediction is a critical area of research for companies offering subscription services. The ability to accurately predict customer churn can help companies take proactive measures to retain customers and improve their bottom line. This thesis provides a comprehensive overview of customer churn prediction, including the background of the study, problem statement, objectives, limitations, scope, significance, structure, and definition of terms.

The literature review covers various aspects of customer churn prediction, including churn models, data mining techniques, machine learning algorithms, influencing factors, retention strategies, case studies, critiques of existing models, and best practices. The research methodology section describes the research design, data collection methods, sampling techniques, variable selection, model development, data analysis, model evaluation, and ethical considerations.

The discussion of findings chapter presents the data analysis results, model performance evaluation, comparison of different models, predictive power of variables, practical implications, recommendations for further research, and a conclusion of the findings. The conclusion and summary chapter summarizes the findings, contributions to the field, implications for practice, limitations of the study, recommendations for future research, and concludes the thesis.

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