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Introduction

Customer attrition, also known as customer churn, is a critical issue faced by companies in various industries, including the telecommunications sector. With intensifying competition and increasing customer expectations, retaining existing customers has become a top priority for telcos. Predicting customer attrition plays a significant role in developing effective retention strategies and minimizing revenue loss. This thesis aims to explore the factors influencing customer attrition in telecommunications and develop a predictive model to identify customers at risk of churn.

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 Two: Literature Review
2.1 Overview of customer attrition in telecommunications
2.2 Factors influencing customer churn
2.3 Customer retention strategies
2.4 Data mining techniques for predicting customer churn
2.5 Machine learning algorithms for churn prediction
2.6 Customer lifetime value modeling
2.7 Customer segmentation and targeting
2.8 Customer satisfaction and loyalty
2.9 Customer relationship management
2.10 Industry best practices in customer retention

Chapter Three: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Feature selection
3.5 Model development
3.6 Model evaluation
3.7 Performance metrics
3.8 Validation and testing
3.9 Ethical considerations

Chapter Four: Discussion of Findings
4.1 Descriptive analysis of customer data
4.2 Identification of key churn drivers
4.3 Development of predictive model
4.4 Evaluation of model performance
4.5 Comparison of different algorithms
4.6 Interpretation of results
4.7 Implications for telcos
4.8 Recommendations for future research

Chapter Five: 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 Directions for future research
5.6 Conclusion

Thesis Overview

Predicting customer attrition in telecommunications is a critical issue facing companies in the industry. Customer churn not only leads to revenue loss but also damages brand reputation and customer loyalty. This thesis aims to explore the factors influencing customer attrition in telecommunications and develop a predictive model to identify customers at risk of churn. The study will review relevant literature on customer churn, explore data mining techniques and machine learning algorithms for churn prediction, and discuss industry best practices in customer retention. The research methodology will involve data collection, preprocessing, feature selection, model development, and evaluation. The findings will be discussed in detail, and implications for telcos will be provided. The thesis will conclude with a summary of key findings, contributions to the field, limitations, and recommendations for future research.

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