Data has unquestionably changed the way property and casualty insurers function in today’s marketplace, namely by offering access to more information about customers and prospects than ever before. What insurers do with their data is often the subject of discussion, especially when it comes to defining strategic goals and objectives and then executing on them. In fact, strategy in a big data world is often tied to the popular catch-phrase being “data driven,” and when an insurer identifies with this phrase, it’s usually because a formal data collection, management and governance plan has been officially approved and acted upon.
It follows that insurers that are still hesitant to commit budget dollars to big data projects may lack an understanding of what is entailed, or they may not understand that their big data initiative needs to be tied to a larger organizational strategy (usually around digital transformation) with specific goals and objectives.
And insurers that operate in a “data driven” environment can feel pressure to move forward with a focus tied more to the potential challenges associated with big data (dealing with data functions and capabilities such as architecture, integration, analysis and even master data management) than to the benefits of a data-enhanced, or data-driven customer strategy.
So, while some insurance companies may consider big data and strategy in a data-driven world a work in progress, the good news is that, regardless of what you call it, insurers are starting to better understand the potential benefits of the vast sources of data now available, such as an improved customer experience, faster claims resolution and improved underwriting to name a few.
“More insurers are investing in big data and unstructured data environments, but many of these projects remain in the area of ‘innovation,’ meaning that the intended ROI is not always clear,” notes Novarica’s Jeff Goldberg in his blog Three Steps to Achieving Real Big Data Value. “Even when insurers don’t have a specific goal or use case in mind, there’s a general understanding that the ability to process and analyze unstructured data will provide value over time.”
For insurers that do have a specific goal in mind, for example, collecting driver-related data for usage based insurance, the benefits can be experienced quickly. Insurers can build models based on the data it collects, and by querying the data across multiple data sets, inform risk calculations for larger groups of drivers. In addition, data that measures driver behavior can be used to provide valuable feedback and premium discounts to policyholders in order to incentivize them to improve their actions while behind-the-wheel if a UBI program is in place. For example, incorporating mobile data collection technology from TrueMotion, American Family is offering smartphone programs such as “Know Your Drive,” which offers discounts based on the policyholder’s driving score and will use the TrueMotion Family app to focus specifically on helping teens become better and safer drivers.
The obvious benefits here are multifaceted. With improved communications targeting the policyholder/driver, the insurer establishes itself as an entity that cares about their wellbeing, a true value add against the changing demands of the customer and a boost to American Family’s reputation as an organization that wants to protect its policyholders’ dreams.
Assuming the driver’s behavior improves behind the wheel, the insurer will also see a reduction in loss frequency and severity, which further supports big data’s return on investment. And by the way, additional communications are opportunities–to up-sell or cross-sell based on the data that’s been collected and the unique policyholder insights it’s provided.
There’s no doubt that a big data strategy is only as good as the organization attempting to execute it, and American Family is a great practice example of a company that while embracing big data, has made the best possible use of its driver data in order to affect a number of positive business outcomes.
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