Home / Current Issue / Paper 1704532
Customer Lifetime Value Prediction of Motor Insurance Company using Regression Model
Subject area: Science,Engineering and Technology · Area of research: Data Analytics
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.
References
[1] Junxiang Lu, Ph.D. Overland Park, Kansas, Modeling Customer Lifetime Value Using Survival Analysis − An Application in the Telecommunications Industry, Paper 120-28
[2] Ms. Ramamani Venkatakrishna, REVA Academy of Corporate Excellence, Reva University, Bengaluru, India, Mr. Pradeepta Mishra, Director of AI, Lymbyc, LTI, Ms. Sneha P Tiwari, REVA Academy of Corporate Excellence, Reva University, Bengaluru, India, Customer Lifetime Value Prediction and Segmentation using Machine Learning, International Journal of Research in Engineering and Science (IJRES), ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 , www.ijres.org Volume 9 Issue 8 ǁ 2021 ǁ PP. 36-48
[3] Albert Graf, Peter Maas, University of St. Gallen, Customer value from a customer perspective: A comprehensive review, Journal für Betriebswirtschaft 58(1):1-20(April2008), https://www.researchgate.net/publication/22599 8618
How to cite this paper
@article{1704532,
author = {Swayam Prakash Jena, Thanish Shekar, Dhivya Tefella, Yashas B S},
title = {Customer Lifetime Value Prediction of Motor Insurance Company using Regression Model},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {11},
pages = {667-674},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704532.pdf},
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.},
month = {May},
}