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Introduction

In today’s digital age, mobile apps have become an integral part of people’s daily lives, providing convenience and access to a wide range of services and information. With the increasing number of mobile apps available in the market, companies are constantly seeking ways to attract and retain customers. Customer engagement in mobile apps has become a critical factor for the success of app-based businesses, as higher levels of engagement often lead to increased customer loyalty, retention, and revenue.

Predicting customer engagement in mobile apps is a complex and challenging task, as it involves understanding user behaviors, preferences, and motivations. By analyzing user data and interactions within the app, companies can gain valuable insights into customer engagement patterns and trends. This thesis aims to explore the factors that influence customer engagement in mobile apps and develop predictive models to forecast user engagement levels.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of the study
1.3 Problem Statement
1.4 Objective of the study
1.5 Limitation of the study
1.6 Scope of the study
1.7 Significance of the study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of mobile apps
2.2 Customer engagement in mobile apps
2.3 Factors influencing customer engagement
2.4 Predictive modeling in customer engagement
2.5 User behavior analysis
2.6 User retention strategies
2.7 Mobile app analytics
2.8 Machine learning techniques for predictive modeling
2.9 Customer segmentation
2.10 Personalization in mobile apps

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Sampling techniques
3.5 Variable selection
3.6 Model development
3.7 Validation of predictive models
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of user data
4.2 Identification of key factors influencing customer engagement
4.3 Development of predictive models
4.4 Evaluation of predictive models
4.5 Comparison with existing literature
4.6 Implications for app-based businesses
4.7 Recommendations for future research

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to the field
5.3 Practical implications
5.4 Limitations of the study
5.5 Future research directions
5.6 Conclusion

Thesis Overview

The increasing competition in the mobile app market has made customer engagement a crucial aspect for businesses to focus on. This thesis aims to explore the factors influencing customer engagement in mobile apps and develop predictive models to forecast user engagement levels. The study begins with an introduction to the topic, providing background information, a problem statement, objectives, limitations, scope, significance, and the structure of the thesis.

The literature review examines the current state of research on mobile apps, customer engagement, predictive modeling, user behavior analysis, and machine learning techniques. The research methodology section outlines the design, data collection, analysis, sampling, variable selection, model development, validation, and ethical considerations of the study.

The discussion of findings chapter presents the analysis of user data, identification of key factors influencing customer engagement, development and evaluation of predictive models, comparison with existing literature, implications for businesses, and recommendations for future research. The conclusion and summary chapter provides a summary of key findings, contributions to the field, practical implications, limitations, future research directions, and a conclusion.

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