Thesis Abstract

Abstract
This thesis explores the application of predictive analytics in addressing customer churn within the insurance industry. Customer churn, the phenomenon where policyholders terminate their insurance contracts prematurely, poses a significant challenge for insurance companies. By leveraging predictive analytics techniques, such as machine learning algorithms and data mining, insurers can proactively identify customers at risk of churning and implement targeted retention strategies to mitigate the loss of valuable policyholders. The research begins with an introduction that sets the stage for the study, highlighting the importance of addressing customer churn in the insurance sector. A comprehensive background of the study provides an overview of the existing literature on customer churn, predictive analytics, and their relevance to the insurance industry. The problem statement identifies the gaps in current practices and emphasizes the need for predictive analytics solutions to enhance customer retention strategies. The objectives of the study are to develop a predictive model for customer churn prediction, evaluate the effectiveness of the model in real-world scenarios, and provide recommendations for insurance companies to reduce churn rates. The limitations of the study, such as data availability and model accuracy, are acknowledged, along with the scope of the research, focusing on a specific segment of the insurance market. The significance of the study lies in its potential to help insurance companies improve customer retention, enhance profitability, and strengthen customer relationships. The structure of the thesis outlines the organization of the research, guiding readers through the chapters and sub-sections that delve into the various aspects of predictive analytics for customer churn in the insurance industry. Definitions of key terms used throughout the thesis are provided to ensure clarity and understanding. The literature review chapter synthesizes existing research on customer churn, predictive analytics, and their applications in the insurance sector. Ten key themes are explored, including customer segmentation, predictive modeling techniques, and customer lifetime value analysis. The chapter critically evaluates the strengths and limitations of previous studies, laying the foundation for the research methodology chapter. In the research methodology chapter, the approach to developing the predictive model for customer churn prediction is detailed. Eight key components are discussed, including data collection methods, variable selection, model development, and performance evaluation techniques. The chapter outlines the steps taken to preprocess the data, train and test the predictive model, and validate its accuracy and reliability. Chapter four presents an in-depth discussion of the findings obtained from applying the predictive analytics model to real customer data. The analysis of the results highlights the effectiveness of the model in identifying customers at risk of churn and provides insights into the factors influencing churn behavior. The chapter also discusses the implications of the findings for insurance companies and offers recommendations for improving customer retention strategies. In the concluding chapter, the key findings of the research are summarized, and the implications for the insurance industry are discussed. The thesis concludes with a reflection on the contributions of the study to the field of predictive analytics and customer churn management in insurance. Future research directions are suggested to further enhance the predictive modeling techniques and address evolving challenges in customer retention. In conclusion, this thesis contributes to the growing body of knowledge on predictive analytics for customer churn in the insurance industry. By developing and evaluating a predictive model for customer churn prediction, this research provides valuable insights and practical recommendations for insurance companies seeking to reduce churn rates and enhance customer loyalty.

Thesis Overview