Predictive Analytics: Bringing Price Elasticity Concepts to P/C Insurance

September 3, 2013 by Deepak Ramanathan, Ed Combs and William Wilt

The concept of price elasticity of demand has not received enough attention in the world of property/casualty insurance. Executive SummaryMany industries have harnessed the power of Big Data and predictive analytics to enhance revenues and profits by incorporating consumers’ price elasticity of demand into their price determinations. Like these other industries, insurers need not remain stuck in the cost-plus pricing grind any longer, experts at Fractal Analytics reveal in this article, which describes the obstacles that have been holding insurers back.

Executive Summary

Many industries have harnessed the power of Big Data and predictive analytics to enhance revenues and profits by incorporating consumers' price elasticity of demand into their price determinations. Like these other industries, insurers need not remain stuck in the cost-plus pricing grind any longer, experts at Fractal Analytics reveal in this article, which describes the obstacles that have been holding insurers back.

Regulatory hurdles, a tradition of cost-plus pricing and maybe even an outright aversion to the word “discrimination” combine to hold insurers back from reaping the full benefits of applying some of the basic economic principles that are commonly used in other industries—industries that charge consumers different prices for the same product or service.

Microeconomic theory teaches us that thoughtful selection of prices, or price discrimination, is a key to maximizing revenue and profit. Our research, in fact, reveals that if P/C insurers adopt advanced pricing strategies that consider customer elasticity differences, they can boost their revenues by roughly 3 percent and returns-on-equity by 1 percent, on average.