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Introduction:
The insurance industry is one of the most competitive sectors in the financial services industry, with companies constantly looking for ways to attract and retain customers. Customer Lifetime Value (CLV) prediction is a vital tool that helps insurance companies understand the value of their customers over their entire relationship with the company. By accurately predicting CLV, insurers can make informed decisions about marketing strategies, customer segmentation, and retention efforts.
This thesis explores the use of predictive analytics techniques to predict CLV in the insurance industry. Specifically, this study aims to develop a model that can accurately predict the CLV of individual customers based on their historical data and behavior. By doing so, insurance companies can optimize their marketing efforts, improve customer satisfaction, and ultimately increase profitability.
Table of Contents:
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 Concept of Customer Lifetime Value
2.2 Importance of CLV in the Insurance Industry
2.3 Predictive Analytics in CLV Prediction
2.4 Previous Studies on CLV Prediction in Insurance
2.5 Factors Influencing CLV
2.6 Methods for CLV Prediction
2.7 Challenges in CLV Prediction
2.8 Customer Segmentation and CLV
2.9 CLV and Customer Retention
2.10 CLV and Marketing Strategies
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 Performance Metrics
3.8 Ethical Considerations
Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of Data
4.2 Model Performance Evaluation
4.3 Factors Influencing CLV Prediction
4.4 Customer Segmentation Analysis
4.5 Comparison with Previous Studies
4.6 Implications for Insurance Companies
4.7 Recommendations for Future Research
Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions to Knowledge
5.4 Practical Implications
5.5 Limitations of the Study
5.6 Suggestions for Future Research
This thesis will provide valuable insights into the prediction of Customer Lifetime Value in the insurance industry, offering practical recommendations for insurers to improve their business strategies and enhance customer relationships.
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