“Mirror, Mirror – on the Internet — who is the fairest of them all?”
The Internet is a mirror of sorts — a data mirror. Right now it is a sort of fuzzy data mirror, but the pictures grow clearer as the available data grows. Soon, for example, the image of an insurers’ customer service, pricing, and claims experiences will grow crisp. How will it happen? How will insurers respond and remain competitive?
In Part 1 and Part 2 of our series, we discussed data symmetry — the leveling of the playing field that is currently happening because insurers are gaining access to many of the same streams of data. It runs in contrast to data asymmetry, which allowed insurers to comfortably differentiate themselves by being good at the analysis of their own in-house data. As insurers use more and more of the same data and some of the same analytics tools and methodologies, they will find themselves in a pool of sameness. Differentiation by price and service will be less about introspective analysis and more about finding and delivering on real brand promises.
So, in today’s blog we are crossing a bridge of sorts. We are done discussing how external data flowing into the organization will cause data symmetry and we are now going to look at how external and internal data flowing outward into the consumer information space is also going to cause data symmetry by giving the consumer a clear view of the real insurer.
Changes in scrutiny are causing data symmetry
Insurers are the subjects of constant scrutiny. The NAIC, the Federal Insurance Office, the Department of Labor, every state and every consumer protection organization have an interest in watching insurers. Yet all of that scrutiny may pale in comparison to the impact of the coming wave of individual consumer scrutiny.
Consumers are using ratings, stars, comments and shopping patterns to give instant feedback to all service providers. Feedback (real experience) is a sales tool for aggregators and retailers. It is a reason for consumers to choose particular channels or pipelines. Amazon and eBay don’t have to build trust for any one product. They only have to facilitate feedback and let the products, services and suppliers speak for themselves.
These outside views are the result of symmetrical data availability. Prospects are now able to compare any product or service, including insurance, with greater real data from which to draw, including both sources that are verifiable and those that contain unstructured data. Consumers may look at an insurer through the lens of an insurance aggregator, such as Insure.com or The Zebra or through simple search terms such as “worst auto claims experience in my entire life.” They may also witness an insurance interaction through their relationships with friends on social media.
Reputation analysis will hold tremendous power to validate or invalidate brand promises. Does the insurer make it simple to file a claim? Do they have a poor track record in paying claims? Are renewal rates much higher or lower than competitors? These bits of information weren’t as public in the past. Today, they are common and easy to find.
Data symmetry’s effect upon the insurer will operate much like a looking glass. The insurer will begin to see itself, not as it has attempted to portray its brand, but as it is perceived during real interactions. This will lead some insurers to make course corrections.
The good news is that data symmetry will supply healthy doses of reality. Insurers will know and understand their competition. They will have an unprecedented, timely idea about what customers really want and how well they are supplying it. If they are prepared for the coming levels of data symmetry, insurers will also be able to make agile shifts and meaningful steps toward selling insurance through many different channels. Many of these details are still food for our insurance visions. One thing is certain, however. Data and analytics will continue to unlock the secrets of market positioning to keep insurers competitive. Data’s relevance to business decisions will always grow.