Thesis Abstract

Abstract
The insurance industry faces increasing challenges in retaining customers due to the competitive market environment and evolving customer preferences. Customer churn, the phenomenon where policyholders switch insurers, poses a significant threat to the sustainability and profitability of insurance companies. In response to this challenge, predictive analytics has emerged as a powerful tool for identifying and predicting customer churn behavior. This thesis explores the application of predictive analytics in addressing customer churn in the insurance industry. Chapter One introduces the research study by providing an overview of the problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter also defines key terms relevant to the study. Chapter Two presents a comprehensive literature review on customer churn in the insurance industry, covering topics such as the impact of churn on insurers, factors influencing churn, traditional churn prediction methods, and the role of predictive analytics in churn management. Chapter Three outlines the research methodology employed in this study, including data collection techniques, data preprocessing, variable selection, model building, and evaluation metrics. The chapter also discusses the ethical considerations and limitations of the research methodology. Chapter Four presents the findings of the study, including the results of predictive models developed to forecast customer churn in the insurance industry. The chapter discusses the key predictors of churn identified through the analysis and provides insights into the implications of these findings for insurance companies. Chapter Five concludes the thesis by summarizing the key findings, implications, and recommendations for insurance companies looking to leverage predictive analytics for customer churn management. The chapter highlights the significance of the study in advancing the understanding and application of predictive analytics in the insurance industry. Overall, this thesis contributes to the growing body of knowledge on customer churn management in the insurance industry and provides practical insights for insurers seeking to proactively address customer churn through the use of predictive analytics.

Thesis Overview

The project titled "Predictive Analytics for Customer Churn in Insurance Industry" aims to explore the application of predictive analytics in addressing customer churn within the insurance sector. Customer churn, or the rate at which customers stop doing business with a company, poses a significant challenge for insurance companies as it impacts revenue and profitability. By leveraging predictive analytics, which involves using data mining, machine learning, and statistical techniques to predict future outcomes, insurance companies can proactively identify customers at risk of churning and implement targeted retention strategies. The research will begin with a comprehensive review of the existing literature on customer churn in the insurance industry. This literature review will examine previous studies, frameworks, and methodologies related to customer churn prediction and retention strategies. By synthesizing the findings from these studies, the research aims to identify gaps in the current knowledge and propose a novel approach to addressing customer churn using predictive analytics. The methodology chapter will outline the research design, data collection methods, and analytical techniques employed in the study. Data will be collected from insurance companies, including customer demographic information, policy details, claims history, and interactions with the company. Machine learning algorithms such as logistic regression, decision trees, and neural networks will be used to develop predictive models that can forecast customer churn with high accuracy. The findings chapter will present the results of the predictive analytics models developed in the study. The research will evaluate the performance of these models in terms of predictive accuracy, sensitivity, specificity, and other relevant metrics. Insights gained from the analysis will be used to identify key factors influencing customer churn in the insurance industry and recommend targeted interventions to reduce churn rates. In the conclusion and summary chapter, the research will provide a comprehensive overview of the key findings, implications, and recommendations for insurance companies looking to leverage predictive analytics for customer churn management. The study will highlight the potential benefits of using predictive analytics in improving customer retention, enhancing customer satisfaction, and increasing profitability within the insurance sector. Overall, this research project on "Predictive Analytics for Customer Churn in Insurance Industry" seeks to contribute to the growing body of knowledge on customer churn management and provide practical insights for insurance companies looking to enhance their customer retention strategies through data-driven approaches.