The Importance of Price Optimization and Predictive Analytics in Online Insurance

For online insurance shoppers, price is the most important parameter and in mature markets aggregators exert strong pressure on insurers and online players need to improve product pricing. To do so, insurers should invest in pricing optimization and sophisticated analytic tools. Predictive analytics and price optimization both together support insurers’ capability to adapt to customer behavior changes, new pricing signals on the market and ranking on aggregators websites.

Price optimization helps insurers maximize data set to perform testing and refine models and pricing strategies. If there is a business sector where insurance companies can take advantage of price optimization, it is certainly in online insurance. Indeed, online sales platforms represent a great opportunity to gather instant information from the market. Implementing real time price optimization consists notably in performing real life field tests that allow insurers to capture trends in customers’ behavior directly from the market. Price optimization also contributes to shorten the time needed to implement new tariffs by using scenarios and pricing strategy models. Overall, real time pricing optimization engine allows for daily pricing scheme changes while helping insurers better capture market data and modify price strategies.

With the strengths of business analytics tools offered on the market, insurers are able to refine their analysis and the evaluation of certain risk’s elements. But the biggest value these tools bring to the industry is the democratization of risk evaluation principles (actuaries have no longer the monopoly of data and risk evaluation elements), which in turn contributes to generate more discussions about identification of new key parameters impacting the risk pricing. We are now in an era where specific teams are built within insurance companies, whose objective is to ask questions that have never been asked and then build models that include dynamic parameters (and not only static parameters) to improve pricing algorithms. According to me, time has come now for insurers to shift from a reactive analytics approach to a proactive analytics approach.