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Introduction

In today’s competitive market, it is crucial for companies in the mobile app industry to retain their customers. Customer churn, which refers to the loss of customers or users, poses a significant challenge for app developers and marketers. Identifying factors that lead to customer churn and predicting it in advance can help companies develop targeted strategies to reduce churn and increase customer loyalty. In this study, we aim to predict customer churn in the mobile app industry using user engagement data and machine learning.

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 Customer Churn in the Mobile App Industry
2.2 Factors Influencing Customer Churn
2.3 User Engagement Data and Customer Churn
2.4 Machine Learning in Predicting Customer Churn
2.5 Existing Models and Approaches
2.6 Case Studies in Customer Churn Prediction
2.7 Challenges and Limitations
2.8 Theoretical Framework
2.9 Summary of Literature Review
2.10 Gaps in Existing Literature

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Model Selection
3.6 Model Training and Evaluation
3.7 Performance Metrics
3.8 Ethical Considerations
3.9 Validity and Reliability

Chapter Four: Discussion of Findings
4.1 Data Analysis
4.2 Model Performance
4.3 Feature Importance
4.4 Comparison with Existing Models
4.5 Implications for Industry
4.6 Recommendations for Future Research

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications for Practice
5.3 Contributions to Knowledge
5.4 Limitations of the Study
5.5 Recommendations for Further Research
5.6 Conclusion

Thesis Overview: Predicting Customer Churn in the Mobile App Industry Using User Engagement Data and Machine Learning

In this thesis, we aim to investigate the problem of customer churn in the mobile app industry and propose a predictive model using user engagement data and machine learning techniques. The study is divided into five chapters, starting with an introduction that provides background information, problem statement, objectives, limitations, scope, significance, structure, and definition of terms.

Chapter two presents a comprehensive review of the literature on customer churn, user engagement data, machine learning, existing models and approaches, case studies, theoretical framework, challenges, and gaps in the literature.

Chapter three outlines the research methodology, including research design, data collection, preprocessing, feature selection, model selection, training, evaluation, performance metrics, ethical considerations, validity, and reliability.

Chapter four discusses the findings of the study, including data analysis, model performance, feature importance, comparison with existing models, implications for industry, and recommendations for future research.

Chapter five provides a summary of the project, implications for practice, contributions to knowledge, limitations, recommendations, and a conclusion. Overall, this thesis aims to contribute to the understanding of customer churn in the mobile app industry and provide valuable insights for addressing this critical issue using user engagement data and machine learning.

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