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Introduction

The media industry is undergoing rapid transformations due to the advent of digital technologies and changing consumer preferences. One of the key challenges faced by media companies is customer churn, where subscribers cancel their subscriptions or stop using their services. Customer churn not only leads to loss of revenue but also affects the overall business performance and growth of the organization. In order to mitigate the impact of customer churn, media companies are increasingly turning towards data analytics and machine learning techniques to predict and prevent subscriber attrition.

This thesis focuses on customer churn prediction in the media industry using subscriber data and machine learning. By analyzing historical subscriber data and applying machine learning algorithms, media companies can identify patterns and factors that influence customer churn. This enables companies to proactively intervene and implement targeted strategies to retain customers and improve their overall retention rates.

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 Customer churn in the media industry
2.2 Factors influencing customer churn
2.3 Data analytics and machine learning in customer churn prediction
2.4 Previous studies on customer churn prediction
2.5 Subscriber segmentation and targeting strategies
2.6 Customer relationship management in the media industry
2.7 Predictive modeling techniques
2.8 Evaluation metrics for churn prediction models
2.9 Ethical considerations in customer churn prediction
2.10 Future trends in customer churn prediction

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection and preprocessing
3.3 Feature selection and engineering
3.4 Model selection and evaluation
3.5 Implementation of machine learning algorithms
3.6 Cross-validation and hyperparameter tuning
3.7 Performance evaluation metrics
3.8 Ethical considerations in data usage

Chapter 4: Discussion of Findings
4.1 Descriptive analysis of subscriber data
4.2 Identification of key churn indicators
4.3 Performance evaluation of machine learning models
4.4 Comparison of different predictive models
4.5 Interpretation of model results
4.6 Recommendations for customer retention strategies
4.7 Limitations and challenges faced
4.8 Future research directions

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to the field
5.3 Practical implications for media companies
5.4 Limitations and recommendations for future research
5.5 Conclusion

Thesis Overview

Customer churn prediction is a critical issue in the media industry, as subscriber retention is crucial for the long-term success of media companies. This thesis aims to address the problem of customer churn by leveraging subscriber data and machine learning techniques to predict and prevent subscriber attrition. By analyzing historical subscriber data, identifying key churn indicators, and developing predictive models, media companies can implement targeted strategies to improve customer retention rates and enhance their overall business performance.

The literature review will provide a comprehensive overview of customer churn in the media industry, factors influencing churn, data analytics, and machine learning techniques for churn prediction, previous studies in this area, subscriber segmentation, and targeting strategies among other topics. The research methodology will outline the research design, data collection and preprocessing, feature selection, model selection, implementation of machine learning algorithms, performance evaluation metrics, and ethical considerations.

The discussion of findings will present a detailed analysis of subscriber data, identification of key churn indicators, performance evaluation of machine learning models, interpretation of results, recommendations for customer retention strategies, limitations faced, and future research directions. The conclusion will summarize the key findings, highlight contributions to the field, discuss practical implications for media companies, address limitations, and provide recommendations for future research.

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