Predictive analytics is a trending topic as of late, and though the concept might seem complicated, most people interact with predictive analytics on a daily basis. For example, after you purchase an item on Amazon, a list of “featured recommendations” pops up. Amazon is recommending additional products to buy based on what you already bought — this is predictive analytics. They are predicting an unknown event (your next purchase) based on current data (the purchase you just made).
The global predictive analytics market is growing quickly and is estimated to reach $10.95 billion by 2020, which means an annual growth rate of 21 percent between 2016 and 2020. Companies across all industries are investing in this technology to gain better customer insight, forecast sales more accurately, and remain competitive in the market. Especially insurance companies.
Consumers are demanding lower insurance prices, and therefore the auto insurance market is becoming more competitive. Most insurance companies, whether home, auto, or health, have a wealth of data that they can gather from buyers and analyze to serve their customers better. In other words, they can utilize predictive analytics to stay competitive.
Investing in predictive analytics can help auto insurance companies discover new, sustainable channels for growth because big data allows them to optimize pricing, assess risk and fraud claims, and improve customer targeting and personalization. Here are a few other ways predictive analytics is changing car insurance for the better.
Price Optimization
In the car insurance industry, competition is high, and companies cannot compete on bottom-line price alone. Predictive analytics tools can help companies determine the optimal price for a new product or service, and avoid losing money by guessing or testing different pricing options. Predictive tools can automatically adjust insurance prices in real-time based on predefined specifications. Furthermore, predictive tools help companies take into account competitor’s pricing, current sales data, and even customer driving practices to come up with the optimal offer.
For example, an insurance company could optimize its pricing based on buyer behavior, capturing a driver’s speed, braking, and acceleration patterns in real time. Using this information, the company could offer its customers personalized pricing and usage-based insurance.
Risk Assessment and Fraud Claims
Insurance fraud is one of the biggest crimes in the United States. The Coalition Against Insurance Fraud estimates at least $80 billion is stolen each year, which has detrimental impacts on not only insurance companies but on policyholders as well.
Predictive analytics, however, can help to flag potential auto insurance fraud or duplicate claims. Instead of relying on individuals to find anomalies in fraud claims, algorithms can detect fraudulent patterns quickly and without the risk of human error.
With predictive analytics tools, auto insurance companies can create behavioral models using customer and satellite data to understand and evaluate risk better. Predictive analytics can also help forecast market instabilities and generate fraud propensity scores to help reduce insurance fraud.
Customer Targeting and Personalization
Predictive marketers are 2.9x more likely to report revenue growth at rates higher than the industry average. These tools can help auto insurers retain customers as well by helping them monitor buyer behavior and forecast customer actions. Furthermore, predictive analytics can help companies anticipate how a specific customer will react to bundled packages or discounts.
Customer acquisition costs can be high for some car insurance companies, and a policy might not be profitable for a few years after a customer signs on. Predictive analytics can not only help target new customers, but the tools can also predict churn or customers who are likely to leave. With predictive analytics, companies no longer have to guess what the buyer will do. Instead, they can forecast customer behavior.
The Impact of Predictive Analytics on the Auto Insurance Industry
The most significant hindrance to the growth of predictive analytics is the lack of awareness about its power and potential, which stresses the need for more education on the topic. Predictive analytics can anticipate vehicle failure, saving costs and resources for companies that invest in this technology.
Most importantly, predictive analytics can improve the safety of cars and trucks on the road. Many fleet managers have begun to install this technology on vehicles to aggregate data on elements such as tire pressure, engines, and more. Now, instead of reacting to a problem, companies can monitor their data and alert drivers to a potential problem. For auto insurers, this means fewer accidents and claims, and, more importantly, more safety on the road.
