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Introduction:

In recent years, the gaming industry has experienced exponential growth due to technological advancements and changing consumer preferences. With millions of players engaging in online gaming platforms daily, companies are facing the challenge of retaining their customers and preventing churn. Customer churn, defined as the rate at which customers stop doing business with a company, is a critical issue for gaming companies as it directly impacts revenue and profitability. To address this challenge, many companies have turned to big data analytics to predict customer churn and implement strategies to retain customers.

This thesis aims to investigate the use of big data analytics for customer churn prediction in the gaming industry. By analyzing large volumes of data collected from online gaming platforms, this study seeks to uncover patterns and trends that can help gaming companies identify customers at risk of churning and implement targeted retention strategies. The findings of this research will provide valuable insights for gaming companies looking to improve customer retention and maximize profitability.

Table of Contents:

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
2.2 Big Data Analytics in the Gaming Industry
2.3 Previous Studies on Customer Churn Prediction
2.4 Machine Learning Algorithms for Churn Prediction
2.5 Customer Retention Strategies
2.6 Data Collection and Processing Techniques
2.7 Customer Behavior Analysis
2.8 Customer Segmentation
2.9 Predictive Modeling
2.10 Evaluation Metrics for Churn Prediction Models

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Model Development
3.6 Model Evaluation
3.7 Statistical Analysis
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Data Analysis Results
4.2 Model Performance Evaluation
4.3 Insights for Customer Retention Strategies
4.4 Comparison with Previous Studies
4.5 Implications for the Gaming Industry
4.6 Recommendations for Future Research

Chapter 5: Conclusion and Summary
5.1 Conclusion
5.2 Summary of Findings
5.3 Practical Implications
5.4 Limitations of the Study
5.5 Contributions to Knowledge
5.6 Future Research Directions

This thesis will provide a comprehensive analysis of the use of big data analytics for customer churn prediction in the gaming industry, offering valuable insights for gaming companies looking to enhance customer retention strategies and improve profitability.

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