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Introduction

Customer churn prediction is a crucial aspect of business management, particularly in the online gaming industry. With the rapid growth of online gaming platforms, retaining customers and predicting their behavior has become more challenging than ever before. Customer churn refers to the phenomenon where customers of a service or product cease their relationship with a company, leading to potential revenue loss. In the context of online gaming platforms, customer churn can significantly impact a company’s bottom line due to the high competition and low barrier to entry in the industry.

Background of Study

The online gaming industry is a multi-billion-dollar sector that continues to experience exponential growth. With millions of users worldwide, companies are constantly looking for ways to improve customer retention and engagement. Customer churn prediction has emerged as a critical tool for online gaming platforms to identify at-risk customers and implement targeted retention strategies. By leveraging data analytics and machine learning algorithms, companies can gain valuable insights into customer behavior and preferences, allowing them to proactively address issues before they lead to churn.

Problem Statement

Despite the importance of customer churn prediction, many online gaming platforms still struggle to effectively identify and address churn risk factors. The existing methods and tools for predicting customer churn are often limited in scope and accuracy, leading to missed opportunities for retention. Additionally, the dynamic nature of the online gaming industry poses unique challenges in predicting customer behavior, making it essential for companies to adopt cutting-edge techniques for churn prediction.

Objective of Study

The primary objective of this thesis is to develop a comprehensive customer churn prediction model specifically tailored for online gaming platforms. By leveraging advanced data analytics techniques and machine learning algorithms, this study aims to improve the accuracy and effectiveness of churn prediction in the online gaming industry. The specific goals include identifying key churn risk factors, developing predictive models, and evaluating the performance of the model in real-world scenarios.

Limitation of Study

While this study aims to provide valuable insights into customer churn prediction for online gaming platforms, there are certain limitations that should be acknowledged. These limitations include the availability and quality of data, the complexity of customer behavior in the online gaming industry, and the generalizability of the findings to different types of online gaming platforms.

Scope of Study

This study focuses specifically on customer churn prediction for online gaming platforms, with a particular emphasis on data analytics and machine learning techniques. The scope includes identifying key churn risk factors, developing predictive models, and evaluating the performance of the model using real-world data from online gaming platforms.

Significance of Study

The findings of this study are expected to have significant implications for the online gaming industry, providing valuable insights into customer churn prediction and retention strategies. By improving the accuracy and effectiveness of churn prediction models, companies can reduce churn rates, increase customer loyalty, and ultimately enhance their bottom line.

Structure of the Thesis

This thesis is structured as follows:

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 customer churn prediction
2.2 Customer churn prediction in the online gaming industry
2.3 Data analytics techniques for churn prediction
2.4 Machine learning algorithms for churn prediction
2.5 Factors influencing customer churn in online gaming
2.6 Retention strategies for online gaming platforms
2.7 Evaluation metrics for churn prediction models
2.8 Comparative analysis of existing churn prediction models
2.9 Challenges and future directions in customer churn prediction
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection and preprocessing
3.3 Feature selection and engineering
3.4 Model development
3.5 Model evaluation
3.6 Performance metrics
3.7 Experimental setup
3.8 Data analysis techniques
3.9 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Results interpretation
4.2 Key findings
4.3 Implications for online gaming platforms
4.4 Comparison with existing literature
4.5 Recommendations for future research
4.6 Practical implications
4.7 Limitations of the study
4.8 Conclusion of findings

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contribution to knowledge
5.3 Practical implications for online gaming platforms
5.4 Limitations and future research directions
5.5 Conclusion

Definition of Terms

– Customer churn: The phenomenon where customers of a service or product cease their relationship with a company.
– Online gaming platforms: Websites or applications that provide online games for users to play.
– Data analytics: The process of analyzing raw data to extract valuable insights and inform decision-making.
– Machine learning: A subset of artificial intelligence that enables systems to learn from data and improve their performance over time.
– Churn risk factors: Variables or features that are indicative of potential customer churn.
– Retention strategies: Techniques and tactics employed by companies to retain customers and prevent churn.
– Predictive models: Mathematical models that predict future events or outcomes based on historical data and patterns.
– Evaluation metrics: Performance metrics used to assess the effectiveness and accuracy of predictive models in churn prediction.

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