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Introduction

In today’s highly competitive telecommunications industry, retaining customers is crucial for business success. Customer churn, which refers to customers leaving a company for a competitor, is a significant challenge that telecom companies face. To address this issue, many telecom companies are turning to big data analytics to predict customer churn and take proactive measures to prevent it.

This thesis aims to investigate the use of big data analytics for customer churn prediction in the telecommunications industry. By analyzing large amounts of data collected from various sources, such as customer behavior, usage patterns, and demographics, telecom companies can identify customers at risk of churning and implement targeted retention strategies.

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 Evolution of customer churn prediction
2.2 Importance of customer churn prediction in the telecommunications industry
2.3 Traditional methods vs. big data analytics for customer churn prediction
2.4 Challenges in customer churn prediction
2.5 Success stories of big data analytics in customer churn prediction
2.6 Factors influencing customer churn
2.7 Machine learning algorithms used for customer churn prediction
2.8 Data collection and preprocessing techniques
2.9 Evaluation metrics for churn prediction models
2.10 Implementation challenges and best practices

Chapter 3: 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 Validation techniques
3.8 Performance metrics
3.9 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of customer churn prediction models
4.2 Comparison of different machine learning algorithms
4.3 Interpretation of results
4.4 Recommendations for telecom companies
4.5 Implications for future research
4.6 Limitations of the study
4.7 Replicability of findings
4.8 Managerial implications

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to knowledge
5.3 Practical implications
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview

The telecommunications industry is facing increasing pressure to retain customers due to fierce competition. Customer churn, or customers leaving a company for a competitor, is a major concern for telecom companies. To address this issue, many companies are turning to big data analytics to predict customer churn and implement targeted retention strategies.

This thesis explores the use of big data analytics for customer churn prediction in the telecommunications industry. It begins with an introduction that sets the context for the study, followed by a literature review that examines the evolution of customer churn prediction, the importance of churn prediction in telecom, and the challenges and success stories of using big data analytics for churn prediction.

The research methodology chapter outlines the design of the study, data collection and preprocessing techniques, model selection, and evaluation metrics. The discussion of findings chapter analyzes the results of customer churn prediction models, compares different machine learning algorithms, and provides recommendations for telecom companies.

The conclusion and summary chapter summarizes the key findings, contributions to knowledge, practical implications, and recommendations for future research. This thesis aims to provide valuable insights for telecom companies looking to leverage big data analytics for customer churn prediction and improve customer retention rates.

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