But all the time, if you’ve got a wonderful castle, there are people out there who are going to try and attack it, and take it away from you. ... I want a castle with a moat around it.—Warren Buffett (University of Florida, 2007)
Twenty-five years ago, insurers controlled 95 percent of the data they had on their customers—their data moat. And the industry has always been protective of this data. When consolidated or shared, data is within industry-controlled cooperatives, and only the organizations that contribute have access. Furthermore, these contributions are generally aggregated, with insurers keeping their granular and proprietary data for their own uses.
Take auto insurers, which have massive amounts of data on their customers—including their cars’ model and year, how often and far they drive, where they live, whether they’re married or single, and how many claims they’ve filed. The huge data moat that auto insurers have amassed out of this demographic and behavioral information has long represented a competitive advantage to sustain their business models and thwart potential disrupters.
But today, as the data universe has expanded—propelled by the rise of cloud computing and analytics—there are more and more ways to collect and analyze data on insurance customers. Within a car, for example, myriad devices can track driving performance. This new data is not only outside the insurer’s moat, it is also potentially a more powerful leading indicator than traditional pricing information. Weather conditions, route choices, and the manner of acceleration or deceleration can all be predictive of actual accidents. Meanwhile, what were once considered leading indicators—such as whether a customer had previously been in an accident—are comparatively lagging behind.
Moreover, the past five years have seen a rapid rise in the number of companies selling relevant data. Data brokers consolidate these new data sources, so companies can go through one source instead of negotiating contracts with hundreds of companies that are collecting data on their customers.
Data in the age of connectivity: Collaboration is key
The question is whether the insurance industry has reached a tipping point. Is the amount and quality of external data available sufficient to enable the same level of predictive accuracy as the industry’s century-worth of internal customer data? Is the data moat no longer the advantage it once was?
If so, then insurance executives will need to reposition their understanding of their data moats and consider the potential of rapidly scaling their use of external data. Staying behind the castle walls will no longer be seen as barricading but as willful isolation in the face of changing times.
In such a world, data sharing will become much more common. Imagine a day when your device prompts you to purchase your insurance via an app’s reverse auction that offers the lowest policy price to the most responsible driver. With the click of a button, you could turn on the app for 30 days to collect and summarize driving data. The data is then fed to the auction site, where insurers bid to provide the coverage needed for the next six months. Prompted to choose, your second click locks in coverage with a provider that is calibrated to optimize your mobility needs.
Far-fetched? Maybe. But the deeper strategic question is, has the moat become a trap?