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Introduction

Telecommunications companies are faced with increasing competition and the need to retain customers in a highly dynamic and saturated market. Predicting customer lifetime value (CLV) has become a crucial aspect of marketing strategy in the telecommunications industry. CLV is a key metric that allows companies to forecast the future value of a customer based on their past behaviors and interactions with the company. By accurately predicting CLV, telecommunications companies can identify high-value customers, tailor their marketing strategies to maximize customer retention, and improve overall profitability.

This thesis aims to investigate the predictive modeling techniques that can be applied to forecast CLV in the telecommunications industry. By understanding the factors that influence customer loyalty and retention, companies can develop more targeted and effective marketing campaigns that lead to increased customer satisfaction and long-term profitability.

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 CLV in Telecommunications
2.2 Factors Influencing CLV
2.3 Predictive Modeling Techniques for CLV
2.4 CLV Calculation Methods
2.5 Customer Segmentation Strategies
2.6 Relationship between CLV and Customer Retention
2.7 Importance of CLV in Marketing Strategy
2.8 Case Studies on CLV in Telecommunications
2.9 Challenges in Predicting CLV
2.10 Future Directions in CLV Research

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Sample Selection
3.4 Variable Selection
3.5 Data Analysis Techniques
3.6 Model Evaluation Criteria
3.7 Ethical Considerations
3.8 Research Limitations

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of Data
4.2 Predictive Modeling Results
4.3 Comparison of Different Models
4.4 Identification of High-Value Customers
4.5 Impact of CLV on Marketing Strategies
4.6 Recommendations for Telecommunications Companies
4.7 Practical Implications of Study
4.8 Future Research Directions

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

Thesis Overview

The telecommunications industry is facing increasing competition and the need to retain customers in a highly dynamic and saturated market. Predicting customer lifetime value (CLV) has become a crucial aspect of marketing strategy in this industry. This thesis aims to investigate the predictive modeling techniques that can be applied to forecast CLV in telecommunications, with the goal of helping companies identify high-value customers and improve overall profitability.

Chapter 1 provides an introduction to the topic, including background information, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review on CLV in telecommunications, covering factors influencing CLV, predictive modeling techniques, CLV calculation methods, customer segmentation strategies, and the relationship between CLV and customer retention.

Chapter 3 outlines the research methodology, including research design, data collection methods, sample selection, variable selection, data analysis techniques, model evaluation criteria, and ethical considerations. Chapter 4 discusses the findings of the study, including descriptive analysis of data, predictive modeling results, identification of high-value customers, impact of CLV on marketing strategies, and recommendations for telecommunications companies.

Chapter 5 concludes the thesis by summarizing the findings, discussing implications for theory and practice, addressing limitations of the study, and providing recommendations for future research. Through this thesis, it is hoped that telecommunications companies will be able to effectively predict and leverage CLV to enhance customer relationships and drive long-term profitability.

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