Customer Lifetime Value Prediction of Motor Insurance Company using Regression Model

• Author(s): Swayam Prakash Jena ; Thanish Shekar ; Dhivya Tefella ; Yashas B S
• Paper ID: 1704532
• Page: 667-674
• Published Date: 29-05-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 11 May-2023

Abstract

Historically auto insurance companiesput more focus on policy sales as an important guiding metric when it comes to measuring their marketing success. New customers are the lifeline of any growing business. But while sales remain an important result of a successful customer acquisition effort, it is important to make sure that policy sales aren’t the only metric used to measure performance. Not all customers purchased insurance are equal. Someone who purchases an inexpensive policy is going to be less valuable for business than someone who purchases an expensive one, and longtime customers will bring in more money than those who buy a one-year policy and do not renew. This concept is called customer lifetime value (CLV). And if a company is not paying attention to it, it is going to wind up overpaying for low-value customers and losing out on high-value customers it might have had. As it turns out, modern companies can analyze their historical data to determine the lifetime value of their customers and determine the factors that can affect the CLV.

Citations

IRE Journals:
Swayam Prakash Jena , Thanish Shekar , Dhivya Tefella , Yashas B S "Customer Lifetime Value Prediction of Motor Insurance Company using Regression Model" Iconic Research And Engineering Journals Volume 6 Issue 11 2023 Page 667-674

IEEE:
Swayam Prakash Jena , Thanish Shekar , Dhivya Tefella , Yashas B S "Customer Lifetime Value Prediction of Motor Insurance Company using Regression Model" Iconic Research And Engineering Journals, 6(11)