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Introduction

In today’s highly competitive telecommunications industry, customer lifetime value (CLV) has become a critical metric for businesses to understand and quantify the long-term value of their customers. By predicting CLV, telecommunications companies can optimize their marketing strategies, retention efforts, and customer segmentation to maximize profitability and customer satisfaction. The use of call detail records (CDRs) and machine learning algorithms provides a unique opportunity to extract valuable insights from customer behavior and predict their future value to the business.

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 Understanding Customer Lifetime Value
2.2 Importance of Predicting CLV in Telecommunications
2.3 Traditional Methods of CLV Calculation
2.4 Use of Call Detail Records in CLV Prediction
2.5 Machine Learning Algorithms for CLV Prediction
2.6 Previous Studies on CLV Prediction in Telecommunications
2.7 Challenges in CLV Prediction
2.8 Emerging Trends in CLV Prediction
2.9 Gaps in Literature
2.10 Theoretical Framework

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 Research Limitations

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of CDR Data
4.2 Feature Importance in CLV Prediction
4.3 Comparison of Machine Learning Algorithms
4.4 Model Performance Evaluation
4.5 Implications for Telecommunications Businesses
4.6 Managerial Recommendations
4.7 Future Research Directions

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

Thesis Overview

The telecommunications industry is characterized by fierce competition and rapid technological advancements, making it essential for companies to adopt innovative strategies to retain and maximize the value of their customers. This thesis focuses on predicting customer lifetime value (CLV) for telecommunications businesses using call detail records (CDRs) and machine learning techniques. By leveraging the rich data available in CDRs and applying advanced machine learning algorithms, telecommunications companies can gain valuable insights into customer behavior and predict their future value to the business.

Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive review of the literature, covering key concepts related to CLV, the use of CDRs in CLV prediction, machine learning algorithms, previous studies in the field, challenges, and emerging trends. Chapter 3 outlines the research methodology, including research design, data collection, preprocessing, model development, evaluation, and ethical considerations.

Chapter 4 delves into the discussion of findings, including descriptive analysis of CDR data, feature importance, comparison of machine learning algorithms, model performance evaluation, implications for telecommunications businesses, managerial recommendations, and future research directions. Finally, Chapter 5 presents the conclusion and summary of the thesis, highlighting key findings, contributions to knowledge, practical implications, limitations, and recommendations for future research.

Overall, this thesis aims to provide valuable insights into predicting CLV for telecommunications businesses using CDRs and machine learning, offering practical recommendations for companies to enhance their marketing strategies, retention efforts, and customer segmentation. By understanding and predicting the lifetime value of their customers, telecommunications companies can ultimately improve profitability and customer satisfaction in today’s competitive market environment.

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