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Everybody Loves Gelato

I start this blog on a hot summer day waiting for another grandchild to be born. Reflecting on one career and anticipating the careers that the next generation may have ahead. They all involve gelato (data science).

Apropos to the transition of the old to the new and what’s next, I recalled a conversation I had recently with the CEO of a data science insurtech who said, “Every claim deserves better than the old way of working.”

Profound and provocative. And true. Same for every link in the insurance value chain.

This blog takes a tack for the future of the industry and as well many of our careers -- one where “literacy” in data, AI, and tele-everything is the only way forward.

Top leadership is investing heavily in analytic literacy to get ahead of competition and widen the gap to make modernization a moat. But as fast as they move today, customers are moving even faster. They have computers in their pockets, data in their edge sensors, and cloud accounts of their own.

If you're on LinkedIn, you can’t have missed all the new postings at the largest insurers for everything data, AI, and cloud – especially new top executive roles. Same for all the top integrators, consulting firms, data players, software companies, cloud vendors, and OEMs. New top executives are like gelato flavors of the month – digital, data, analytics, AI, omni, cyber – the ones that deliver delight get added to every menu.

Nothing satisfies customers more than delightful and expectation-busting experiences. Whether a claim, a purchase, advice on flavors and choices, variety of serving sizes and cone or cup options, we want our gelato Apple intuitive, Amazon easy, Facebook friendly, Google fast, and with a Netflix recommendation for what we want next. FAANG is our competition, standard setter, and exemplar when it comes to what great gelato tastes like.

The metaphor (“Data Science is Gelato”) spawned the gelato blog trilogy that spanned how data, AI, and cloud were changing the insurance industry and now every industry. The series covered: how data science and actuarial science do and don’t look alike; how to fix what is broken around delivering more value with data, AI, and cloud with product thinking versus project-ism; and, how the entirety of the connected-anything world is demanding more of everything, especially a smooth digital experience.

Across the last year of those blogs and up to now this year, I have embarked on a continual refresh of what it means for me to be analytically literate. It’s feels like a full-time job by itself.

I have stayed in touch with many of you as I researched and authored reports on connectedness, personalization, customer-centricity, geospatial analytics, observable location data, telematics/mobility, new data, all things cloud, becoming product-minded, working remotely, accepting the new next normal now with “virus weather”, working from anywhere, and thinking about ethics when thinking about data, AI, and serving customers.

We jointly described what we saw customers now demanding: better experiences; quicker, more available, and convenient processes (robots please); clearer digital transactional transparency with trust; and, more value for their loyalty. It has been an exciting year.

And what a year it has been.

Billions in discounts, billions in buyouts, billions in capital raised. SPACs and M&A and full scale "aqui-hire" (acquisitions of companies with their staff to build capabilities). Big companies are buying little companies. Big companies are buying big companies. Some to grab the customers. Some as talent acquisition. Some as additional channels. Ecosystems and embedding insurance schemes seek to scale channels farther, wider, deeper, and with the simplicity of an API call. Many customers now expect a technology first option.

The biggest theme of the year of the pandemic is that value-seeking customers are bringing fresh expectations on what a leading technology company should do for them.

Insurtechs of all kinds are helping incumbents meet customer needs with a speed to market like never before, while some insurtechs are becoming insurers themselves to grow faster. Some bring filed models as advisory service organizations, while others just bring data, new data, or new ways of working with data. Some tech companies are dipping into insurance as are more than a few OEMs of vehicles and appliances, goods, and services.

Almost everyone is raising unicorn money or pre-unicorn mega-rounds. The merger of titans in telematics is stirring competitive forces to buy telematics companies, build more scores, ingest more data, and operationalize price-to-risk segmentation for each customer.

Insurers of every size are finding their total addressable market directly tied to digital offerings and personalized pricing. For customers that only want that, it is a binary shopping moment – they only want a product that considers their better data and they expect a better risk-based price with modern digital services.

Like in the gelato photo above, delivering value is not black and white (chocolate or vanilla), but loving gelato means customers only want data science capable insurance solutions because only those can deliver the modern digital experiences they crave. Old-fashioned insurance (regular ice cream) simply will not do. Only gelato (AI plus people) slakes their hunger.

Contextualized risk-based pricing should create value for everyone. Increasingly, that value is being driven by better data – both new features from old data sources as well as entirely new streaming data for continuous observational data. Add new features, streaming, and continuous to your literacy to do list.

Streams of data are fused into continual risk-assessment, behavioral feedback, and ‘right pricing’. Continuous observation permits slicing risk into smaller than six, or twelve, month time periods. It creates more engagement for in-the-moment risk management, safety, prevention, and as needed, claim detection, reporting, management, mitigation, adjudication, resolution, and recovery. Data from each step can be valuable to every step.

Pay attention to the word "value," because I did not say price.

All that risk-based pricing innovation has finally found the audience that strategists have been predicting for a decade or more. It may have taken a pandemic for the ultimate nudge – “you drove zero last month and paid the same as before.” This is a doozy of a cognitive dissonant shopper regret. For many it is a regret with decades of loyalty remorse on top.

Matching risk to price creates value for customers and companies alike. But only data, AI, and cloud literate companies can capture the full value of contextualized engagement with their customers, agents, employees, and vendors. Blending AI and people creates value.

Not blending AI and people destroys value. It can be a death spiral – you can’t win back a "right-priced" customer on price. And if you don’t invest in modern experiences built on data, AI, and cloud, you cut yourself off from that ever-growing market – it’s a lose-lose proposition. Laggard companies are disappearing if small and withering if large.

In the best case, laggard executives acknowledge they are out of touch with their customers and vow to adopt and adapt newer value-adding products, prices, and services. But that takes precious time. Getting data and AI literate is required to reduce the learning curve.

Speed to market and timely introduction of more value to customers is essential, now more than ever. Price is a big issue, but “right price” is the bigger strategic imperative. Then comes digital clarity, where ease of doing business matters most when prices are similar.

Smarter and faster-acting companies have seized this opportunity to provide the same or better product with greater value by using observational data to track actual usage and behavior. Creating customer-centric channel capabilities then lets companies act on preference-based personalization of services – especially digital self-service. Data science makes serving customers easier, like a point-and-order instant-pay gelato delivery robot.

Data science is gelato and every modern experience loves it.

Omnichannel means listening to, and engaging with, customers however they like. "Omni-" is another literacy “to do” term. It takes a data- and AI-literate organization to let a variety of links in the insurance value chain collaborate. And of course, that means literacy at the top, because company culture won’t adapt without the permission and promotion to do so.

No line of business is escaping accelerated digital expectations where data, AI, and cloud play a part in delivering outsized customer and corporate value and new ways of working.

It appears in commercial lines and personal lines -- and in property, auto, and other coverage as well. Foot traffic relates to sales and proximity. Mileage relates risk directly. Occupancy relates to active monitoring and mitigation of perils in cars and on premises. Connected sensors of all sorts can pass data, scores, and feedback to all parties concerned. End-to-end processes are now occurring as self-service, robot-service, and a hybrid of self/robot/people services.

For example, a miles-based or behavior-based telematics program can use smart phones, smart tags, and/or smart cars streaming data instead of classical rating programs which are blindered from facts on where and when driving is happening, exist on vague presumptions on who is driving, and assume a one-size-fits-all broad prediction on how much they drive.

During COVID-19 so far, companies have learned that they can move from poor and harsh assumptions of risk to refined and accurate continuous monitoring and scoring of risks.

This last year has taught us that personalization in pricing is the opposite of a one-size-fits-all gold-plated offering. Designing for an apex customer and making everyone else pay to that standard is like only offering vanilla ice cream. It does not have a future.

We are exiting the historical flat-term-rate-for-a-bloated-product era. This will vacate a situation where the singular product offering would be the best product for the wealthiest households, yet not a very good product for many households. A persona is not a person.

Customers are learning they can use their own data to understand and price their own risk. Then they can shop for a better fitting insurer. With embedded insurance, maybe just a taste of risk transfer is all they need. Shopping is easier than ever. Perhaps soon, an on-phone, in-cloud, continuous shopper synthetic reality avatar will do the scooping for them?


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