When it comes to integrity, money and shame seem to be the motivators that matter for people and businesses. With enough greed or fear, lapses in behavior and judgment may arise.
When it comes to data integrity, accurate and identifiable context matters most. It helps remove the risk of relying on fluctuations in behaviors and motivations. Verified is a virtue.
You might believe everything you read on the internet, HA! You might believe every promise made, HA! You might believe there are no bad uses for good data, HA! But the world of risk transfer is built on staid provenance and reliability, with a "rule of law" expectation. The only funny thing about misuse of data is that it’s not illegal until there is a law that's been broken, so trust is all we have.
In a business built on promises, only those “made and kept” create trust and value.
Saving time and money with trusted services and providers is a mutual goal for policyholders, agents and insurers. Each needs data to be exchanged and used for specific problems and use cases to achieve those goals. So eliminating any questions of integrity oils the path for friction-free trust – and governed, actionable, accurate, contextual data is that oil.
For consumers, they have a bottom line at home. They seek value for their money and appreciate good service experience with a minimum of friction and time-wasting. Above all, customers with integrity have little to hide and are ready to engage with valuable, empathetic and interesting propositions that bring peace of mind and make it easier to interact day-to-day as well as in crisis-and-recovery mode. Convenience, continuous access and customer-preference awareness are the new expectations that consumers have with the connected world around them.
See also: Don’t Neglect the Politics of Analytics
For businesses, they have a bottom line at work. They know not all customers are equal and not all cars, homes, phones, drivers, routes, territories, businesses, buildings, etc. are the same. Yet often frustratingly, businesses cannot capture the value of that knowledge. Frequently, better data becomes available, but it may be device-specific, so the cost of collecting new data may be prohibitive, or it may be talent-enabled, so a capability gap needs to be overcome. Turning data into products for scalable customer interaction and business system integration frequently takes "oil pipelines" (a.k.a. application programming interfaces, or APIs) and new ways of working. Prioritizing these and shepherding them is a continuous improvement journey.
The best-in-class businesses are run by people who are customers, too, and think in a customer-centric fashion. They always look for better data for making better decisions as trusted advisers and guard against unscrupulous exploitation, unintended consequences, disparate impact and lack of fairness even across their supply chain of vendors and networks of suppliers. They consistently audit data quality and the cost of data (paid or collected) and innovate on things like eliminating steps and tasks for things they substantively can know (e.g. pre-fill everything) while always looking for new levels of analysis and new data features to improve customer and situational understanding (e.g. recursive segmentation). This search occurs for rating and risk factors alike in a progressive fashion of rate-to-risk fit.
Insurance is a “cost plus” business, and it is mostly a compulsory product, which is a good reason for all the regulatory structure. When costs rise, consumers don’t expect service to suffer. They expect that their data should maximize their value from a company, and they want the same level of service as they get when costs flatten or go down. And they never want to feel that their trust has been mislaid. They will forget the price changes eventually, but they will remember how you made them feel about those changes. Double that for data skullduggery – blatant or inadvertent or accidental.
The interplay of customers and businesses creates a market dynamic often laden with mistrust – where what you don’t know you don’t know is a blind spot you must avoid. The other boxes in that Johari Window of shared/unshared knowledge explain the dynamic more implicitly.
Customers with motivations (fear and greed) to avoid transparency are different than those who simply don’t trust what will become of their data. The latter get information about businesses from friends, family, advertising, the internet, agents and the companies they frequent. When their "radar" creates anxiety that their money or data is being exploited, their concerns and outrage cause them to churn. So, too, when their data is NOT being used to improve their experience: Concerns and outrage cause them to churn.
It seems that in exchange for their data, they expect it to be used as intended, for their greater good or not at all. Increasingly, this last option is dissipating as companies and products wrap “always and forever” expansive tendrils on any and all current or future "blue sky" ownership rights to everything that might be data and any inferences that those might create. This lingering infringing specter is a work in progress as is the pushback, oversight, audit, regulation and practice of law.
As for businesses, the examples of firms monetizing data in all sorts of freemium, premium and pay-per-use avenues continue to astound investors while stoking the venture forge fires. The short list of BFOs (blinding flashes of the obvious) are amazing in both the commonplace uses as well as the novel and exotic. But innovations aren’t real if no one buys them.
In a hard market, cost takeouts, "good customer" retention and focus on risk appetite get more play on the profitability jukebox than the soft market tune of "just get growth." When companies fail to understand the customer at new business, or the changes in the customer over time for renewal, they dig their own grave by assuming all customers are equal – they are not.
What happens to brand and reputation if you make promises and assurances and then abandon them? Will customers learn they cannot rely on you and your data practices? Or will you commit to a way of working to always be segmenting and improving experiences for personalized risk transfer, safety, prevention and loss management.
The cost of doing nothing with data is your future. Handling your data poorly accelerates your ex-date.
