Like Netflix, Amazon and Apple, property/casualty insurers have been using analytics to understand their customers, but the similarity probably ends there for most insurers, one carrier analytics officer suggested recently.

“We’re extremely customized in the prices we offer consumers. We’re going to get 40 or 50 pieces of information on [each of them], so we can get extremely specific in the prices we offer, [and] then we turn around and we offer one-size-fits-all service for all of those customers,” said Eric Huls, chief data science officer for Allstate.

Huls delivered the observation during the FC Business Intelligence Third Annual Insurance Analytics USA Summit in Chicago earlier this month, indicating that insurers are waking up to the idea that they should be using analytics not simply for risk selection and pricing but also to understand customer needs and to create value for customers going forward. Unlike insurers today, Netflix and Amazon tailor customer experiences based on behavioral patterns revealed through data and analytical algorithms.

“If Netflix were an insurance company, [then] instead of understanding how I’ve interacted with it in the past, it would see me as this 37-year-old white guy who is an actuary,” Huls said, noting that movie titles like “A Beautiful Mind” would be suggested solely based on an insurer classification system instead. “Other people who met the same profile would get the exact same recommendations from the insurance company—not based on their own historical patterns.”

Huls referred to later speakers, including Barry Powers, director of Cognitive Insurance Solutions for IBM, who would talk about “cognitive learning and looking at things from a behavioral, attitudinal perspective” instead of a demographic one. “That’s a hugely important thing that the industry needs to adopt,” Huls said.

Imagining an insurance company in the role of online retail giant Amazon, Huls said that instead of recommending Disney DVDs like “The Little Mermaid” and “The Prince and the Pauper”—consistent with recent purchases he’d made for his three-year-old—Amazon Insurance “would always recommend life insurance because we’re always behind plan selling life insurance.

“It wouldn’t be taking the customer’s perspective. It would be looking at what are our own financial goals and how do we recommend something that meets our goals and not necessarily the goals of the customer.”

Finally, turning to his iPhone and noting that Apple has made it easy for him to control his entire life with his thumb, he said, “If the iPhone was an insurance company, not only would I have to type in my policy number to get into the phone [but] every time I switched apps, I would need to type it in.”

The bottom line: “Even though we don’t think of these companies as competitors, these are the types of companies that are setting customer expectations,” he said, echoing a popular insight delivered at the Summit and other recent insurance conferences. “It would be wise not to look around and assess the people in the room as our competition. It’s really these guys [Netflix, Amazon and Apple] that are setting the bar in terms of what customers come to expect from the people they do business with.”

Insurance Analytics: It’s Not Just About Risk Selection Anymore

Huls, who heads Allstate’s Quantitative Research and Analytics department of roughly 125 data scientists in five locations (with the most recent addition of a team in downtown Chicago), said the good news is that insurers are catching on.

The Allstate executive spoke after some introductory remarks from Stephen Applebaum, principal of Insurance Solutions Group and the conference chair, who reviewed key findings from a recent Willis Towers Watson survey about P/C insurers’ current use of predictive analytics and near-term aspirations for broadening usage beyond risk selection.

17 percent of insurers use predictive analytics for claims triage today; 69 percent expect to do so within two years.

10 percent of carriers use telematics data today; 42 percent expect to do so two years from now.

2 percent of carriers analyze data from agent interactions; 10 percent use data from customer interactions. Those numbers rise to 27 percent and 33 percent in two years, with information from both types of interactions coming from web, clickstream, phone and email data.

19 percent of surveyed insurers use big data to support management decisions today; 60 percent expect to do so two years from now.

Like the insurers surveyed for the Willis Towers Watson report, Huls said the history of his group at Allstate “is really routed in risk selection—understanding who is going to have claims and how much those will cost, helping us make decisions about whether we wanted to insure them and at what price. That’s very much been our bread and butter over the years.”

But in the last four or five years, his team’s focus has broadened beyond this narrow product focus and is evolving toward the customer vision of the noninsurance companies that are setting customer expectations. For example, he reported that Allstate is “moving into things like agency analytics. Who is going to make a good Allstate agent? Where should we locate their office to give them the highest chance of succeeding? How do we provide them with analytical tools so they can best meet their own financial goals effectively and efficiently?” The company is also working on customer experience, studying telematics data and the prospects of connected car data.

Still, “although the insurance industry is evolving quickly, the world itself is evolving even faster,” he reported. “When you actually look at the insurance industry, even though we have evolved and will continue to evolve, if you actually call to buy an auto insurance policy, your experience is not too unlike what it was 10 or 20 years ago,” he said.

IBM’s Powers gave a glimpse of things to come in terms of changing customer experiences later in the session when he demonstrated how IBM Watson would respond to an open-ended customer query. Responding to an insurance policyholder who typed the statement “I just got married” on a mobile device, Watson inferred the possible concerns on the mind of the new husband, supplying information about personal belongings coverage on a homeowners policy and fielding an unexpected question about jewelry coverage, among other things.

Before Powers took Summit attendees on that leap forward, Huls delivered a more basic message for insurers: “One of the most important things that folks in the industry need to figure out is how to actually start using analytics to create value for customers as opposed to do[ing] things that from their [the customers’] perspective may be seen as penalizing them.

“‘I’m going to give you a bunch of information so that you can charge me a lot of money. Or so that you can decide not to insure me. Or so that you can sell me more stuff.’ That analytics from the customer’s perspective all seems like it’s being driven from the company’s self-interest rather than the customer’s interest,” Huls said. “When we sell an intangible good that most people don’t use, that is a recipe for not really building a lot of good will,” he said.

He added that while carriers like Allstate are using static models today, they need to catch up to online companies like Netflix and Amazon in their interactions with customers—where they “click a button and [we’re] going to recalculate all the math behind the scenes to best meet what that customer is needing.”

“We need to recreate that type of experience regardless of how we actually deliver our product—online or through a call center or through an agent.

“It’s really about using analytics to better understand customers, better understand what those unique customers’ unique needs are, and then turning around [and] creating value on behalf of that customer and making them understand that we actually understand their specific needs.”

Beyond the Cow Path

“The No. 1 reason why customers leave insurance companies isn’t price and it isn’t service. It’s the company’s inability to predict and understand what the customer wanted,” Powers reported before displaying Watson in action. That conclusion was based on a 2014 survey of insurance customers from the IBM Institute of Business Value.

“Just having a knowledge of what’s in the household, having a knowledge of life events, having a knowledge of external events—being able to put those pieces together to understand how to interact in context with the customer” will make a difference, he said, noting that price came in at No. 4 among the top five reasons that customers gave for switching insurers.

“As we talk about partnering with customers or penetrating wallet share more deeply, there’s also a flip side problem of customer retention. [If] you can’t stop the leaking bucket from losing customers, you’re going to have that much more of a difficult time going out and acquiring customers,” Powers said.

Reporting results from a different study—this one not specific to insurance but instead a 2013 study of marketing professionals across various industries—he said that “the companies that actually do this right, those that can use analytics for interactions with customers, have enormous differences in their profitability.” Among the findings he shared from the IBM State of Marketing 2013 survey were that “leading companies,” which use technology to influence customer experience, had a net income compound annual growth rate that was 3.4-times higher than other companies for the three-year period studied (7 percent of all other companies vs. 23.5 percent for “leading” marketers from 2009-2012).

“It’s not even acceptable to just have a web presence 24/7. You have to actually be able to understand and interact with your customers either in real time with a CSR or have some automation capabilities that make you look as if you’re having that human interaction with the customer. It’s becoming table stakes as opposed to game-changing.”

Throughout his presentation, even when he demonstrated Watson in action, Powers said there’s more to be done than simply automating human interactions that are taking place today between insurers and their customers. “Analytics is definitely changing the insurance industry,” but what’s going on today “is almost like paving the cow path,” he said. “It’s taking an interaction that a human did and making it faster, a little bit more consistent,” he said, suggesting that following a well-trodden path instead of creating a new and improved path won’t cut it.

“By paving the cow path,” he said, insurers are also “losing the human interaction. We can ‘path’ people through something, but there’s nothing better than your best customer service rep listening to a person’s voice and understanding when they’re reaching a point of frustration,” he said.

“What would it be like if we could take the best CSR, the best insurance agent, the best financial adviser and create that person—create the ability to do that—[and] not just create that person in an automated fashion but also be the angel on the shoulder of that best person, or maybe the lower person who doesn’t have the skill sets,” he said, hinting of the goals now in sight for marrying analytics and automated customer response processes.

Reviewing some highlights of the Watson demonstration, Powers also noted that Watson understands that the just-married customer would want to talk to a human agent about life insurance needs (based on past customer interactions in his knowledge base) while an action like increasing limits on personal belongings coverage was completed entirely during the automated session—again, within the customer’s comfort zone.

The Future: Business Analytics

During his presentation, Huls talked about another change that needs to happen in insurance companies beyond the evolution from pricing analytics to customer analytics. He said insurers also need to restructure their company culture toward business analytics.

Huls illustrated the point by referring to the story behind the book (and later movie) “Moneyball,” about how Oakland Athletics general manager Billy Beane used an analytical approach to assemble a competitive baseball team.

“‘Moneyball’ is the most trite, overused example of business analytics,” Huls admitted, also noting the insights of a professor at Northwestern University, Joel Shapiro, who puts a unique—and useful—spin on the story. Shapiro notes “Moneyball” didn’t come about because of some sudden improvement in data. Baseball statistics have been around since the 1960s. “It wasn’t new statistical or mathematical techniques” either, since the regression-based methods used have been around for a hundred years. “It wasn’t even [attributable] to some large advance in computing power. By and large, they were doing this on desktop computers that anyone could buy.”

“The innovation was really that a business person, in this case Billy Beane, actually asked a question in a way that it could be solved with analytics,” Huls said, reporting the professor’s insight in his own words. “This was not the analytics folks pushing a solution. This was a business person who realized that in order to compete effectively with less money, they had to do a better job of understanding that the value of a player was relative to their net worth.

“That was a business insight, not an analytics insight,” he concluded, going on to ask the Summit attendees a key question: “How do we get the business to understand that analytics isn’t something that is separate over here, that folks like me and many others in the room do?” he said, gesturing to indicate the separation.

“It’s actually something that is part of the business, drives the business and needs to be embedded in the business going forward,” he said.