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Introduction

Customer churn, also known as customer attrition, is a critical issue for telecommunications companies around the world. In an increasingly competitive market, retaining customers is more important than ever before. Customer churn prediction has become a popular topic in the telecommunications industry, as companies strive to identify and retain customers who are at risk of leaving.

Background of Study

The telecommunications industry is highly competitive, with numerous companies vying for market share. Customer churn can have a significant impact on a company’s bottom line, as acquiring new customers can be costly. Therefore, it is crucial for telecommunications companies to predict which customers are likely to churn, so that they can take proactive measures to retain them.

Problem Statement

Despite the importance of customer churn prediction, many telecommunications companies struggle with accurately identifying at-risk customers. Traditional methods of predicting churn, such as customer surveys and demographic analysis, are often ineffective. As a result, companies are turning to advanced analytics and machine learning techniques to improve their churn prediction models.

Objective of Study

The main objective of this thesis is to develop a customer churn prediction model for telecommunications companies. By analyzing customer data and using machine learning algorithms, we aim to accurately identify customers who are likely to churn. Additionally, we seek to provide insights into the factors that contribute to customer churn in the telecommunications industry.

Limitation of Study

It is important to note that this study has some limitations. Firstly, the accuracy of the churn prediction model may be influenced by the quality of the data available. Additionally, external factors such as economic conditions and competitive landscape may also impact the results of the study.

Scope of Study

This study will focus on customer churn prediction for telecommunications companies, specifically in the context of a large multinational telecommunications provider. The analysis will be based on customer data collected over a period of several years, and will utilize advanced analytics techniques to predict churn.

Significance of Study

The findings of this study can have significant implications for telecommunications companies, helping them to improve customer retention strategies and reduce churn rates. By accurately predicting customer churn, companies can proactively address the needs of at-risk customers and improve overall customer satisfaction.

Structure of the Thesis

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 Overview of Customer Churn
2.2 Factors Influencing Customer Churn
2.3 Traditional Churn Prediction Methods
2.4 Advanced Analytics and Machine Learning in Churn Prediction
2.5 Previous Studies on Customer Churn Prediction
2.6 Theoretical Framework
2.7 Research Gaps
2.8 Summary of Literature Review

Chapter 3: 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 Ethical Considerations
3.8 Limitations of the Research Methodology

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of Customer Data
4.2 Churn Prediction Model Results
4.3 Factors Contributing to Customer Churn
4.4 Comparison with Traditional Methods
4.5 Implications for Telecommunications Companies
4.6 Recommendations for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications for Practice
5.3 Limitations of the Study
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview

Customer churn prediction is a critical issue for telecommunications companies, as retaining customers is essential for long-term success in a competitive market. This thesis aims to develop a customer churn prediction model using advanced analytics and machine learning techniques. By analyzing customer data and identifying at-risk customers, telecommunications companies can proactively address the factors contributing to churn and improve customer retention strategies. The findings of this study can have significant implications for the telecommunications industry, helping companies to reduce churn rates and improve overall customer satisfaction.

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