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Big Data? How About Quality Data?

While talking about data has become trendy — with terms like big data, small data, dirty data all good candidates for buzzword bingo — the reality is that, big or small, even if it is clean, data is useless unless it drives actionable insights.

Data is the foundational layer that underpins almost every industry, and it is a survival requirement for business today. Industries like retail, travel and finance have all tapped into the power of data to drive consumerization. And, while the health insurance sector may have fallen behind these industries, it is catching up, as today’s health insurance consumers are demanding actionable information in the form of choice, transparency and simple user experiences.

The good news is that innovators in the space are responding to these consumer demands by creating ground-breaking tools — tools that leverage modern technology, powerful software design and quality data. Today’s health insurance innovators are making waves in the insurtech industry by building products and features that truly solve consumer pain points.

So, who are these innovators? What are they creating? And why is the underlying data so crucial?

Who are the innovators?

According to CB Insights, $1.7 billion was invested in insurtech in 2016. Essentially, the insurtech category encompasses all new technologies, companies, apps and business models that are pushing to revolutionize the insurance industry itself. And, thanks to advances in technology and funding, this category is seeing rapid growth.

See also: Why to Refocus on Data and Analytics 

Jump Capital recently pulled together its view of the movers and shakers that are disrupting the insurance industry. With so many new vendors in the market, it is safe to say that there is definitely a lot of moving and shaking going on in insurtech.

These companies are finding different ways to meet consumer demands and remove the complexity associated with the insurance industry. The companies included in the healthcare segment of this infographic are, for the most part, delivering new ways to help consumers find, buy and receive health insurance. Collectively, health insurtech platforms answer a consumer cry for help. But none of these platforms are functional, let alone useful, without a foundational layer of quality data on which to build.

What are the innovators creating?

Health insurtech innovators are working to answer a variety of consumer demands. They’re creating tools that simplify the health insurance shopping experience. Tools that help doctors find prescription drugs that are covered by a patient’s insurance plan. Tools that help consumers find doctors that are in-network. The problems that these platforms solve are many.

Here are just a few examples of innovative tools and features being created to deliver value to health insurance consumers:

1. Decision Support Tools for the Individual Market

A variety of web-based entities (WBEs) have popped up to help individual consumers find and purchase a health insurance plan that is right for them. PolicyGenius is a good example of an innovative platform doing just that. The company prides itself on delivering simple benefits that are personally designed for the individual. A consumer enters a few bits of information, and PolicyGenius recommends health insurance plans for that individual — all delivered through a seamless digital experience.

2. Analysis Tools for the Small Group Market

Certain broker-facing platforms are starting to build analytical tools that help strengthen group health plan recommendations. These tools allow platforms to compare and contrast different health plans, including each plan’s network, aiding in disruption analysis and delivering value to their employer customers.

3. In-Network Provider Search and Notification Features

Many health insurtech platforms offer customers provider-network search and notification features. Stroll Health, for example, delivers personal recommendations for imaging centers based on a patient’s insurance plan. And some HR and benefits administration platforms now have the ability to notify employees if an employee’s preferred doctor drops out of network. Thoughtful features like these save consumers time and money.

See also: Next Step: Merging Big Data and AI  

Why is the data so crucial?

While data may not be the sexiest element of a tech platform, the data layer enables all features. For example, for those broker-facing analysis tools to be useful, the broker platform must have access to accurate and timely data on the benefits and rates of every health plan they’ll compare. For an HR and benefits administration platform to alert an employee when a doctor drops out of network, the platform must first know when that doctor drops out of network. This means the platform must have access to an accurate and extremely granular database of providers in the specific network being tracked. Quality data is what informs today’s innovators, pushing them to take action and build exciting applications that solve real problems.

Health is a complex space, but there are many brilliant minds working to improve the health insurance industry. Putting the right data into the hands of these innovators allows them to do what they do best — solve problems with creative technology solutions. Continuing to do this will allow today’s innovators to respond to consumer pain points and transform the health insurance industry.

Data Opportunities in Underwriting

For more than a decade, Americans have been trained to assess and buy insurance products as commodities. This is partly thanks to commercials by Geico, the biggest advertising spender in insurance for many years, which has pushed the concept that “Fifteen minutes can save you 15%,” portraying policies as “the same,” where the only differentiator is the price. Some have dubbed insurance’s being viewed as a commodity as the industry’s biggest challenge.

On top of price-centric buying behavior, most consumers who are required to purchase certain insurance products — such as medical and auto — expect to have a wide selection and may switch insurance carriers at a blink of an eye. With competition increasing, big data and associated technologies provide timely opportunities by reshaping the modern insurance landscape.

The insurance business model typically comprises four parts:

  • Underwriting — where insurance companies make money.
  • Investment — where insurance companies invest money.
  • Claims — where insurance companies pay out (the cost factor).
  • Marketing — where insurance products and services are promoted and often advertised.

Insurance companies have always used data in each part of the business model — to assess risk, set policy prices and to win/retain consumers. Previously, insurers would formulate policies by comparing customers’ histories, yielding a simplistic and not-very-accurate assessment of risk. Today, our increasing ability to access and analyze data as well as advancements in data science allow insurers to feed broader historical, continuous and real-time data through complex algorithms to construct a much more sophisticated and accurate picture of risk. This enables insurance companies to offer more competitive prices that ensure profit by covering perceived risk and working within customers’ budgets. Such prices, or setting policy premiums, come from underwriting.

In this post, we will focus on an underwriting use case in the highly competitive auto insurance space, where accuracy of risk assessment and rate setting ultimately drive the insurers’ profitability. Future posts will address other parts of the insurance business model.

More accurate (and competitive) pricing for auto insurance underwriting

Auto insurance may be the most competitive part of the insurance marketplace. Customers shop around (often marketed to by price-comparison services) and change insurers at will. To offer competitive premiums that allow profitability, auto insurers have no choice but to assess risk as accurately as possible.

See also: Why Data Analytics Are Like Interest  

In auto insurance, insurers use both “small” and “big” data. David Cummings explains the two as:

“Traditionally, underwriters have developed auto insurance prices based on smaller data — such as the car’s make, model and manufacturer’s suggested retail price (MSRP). But ‘bigger data’ is now available, providing far more information and allowing insurers to price policies with a better understanding of the vehicle’s safety. From manufacturers and third-party vendors, insurers can learn about a car’s horsepower, weight, bumper height, crash test ratings and safety features. That big data helps insurers create sophisticated predictive models and more accurate vehicle-based rate segmentation.”

As data increasingly becomes the lifeblood for insurance companies, the combination of big data and analytics is driving a significant shift in insurance underwriting. For example, faster processing technologies such as Hadoop have allowed insurers like Allstate to dig through customer information — quotes, policies, claims, etc. — to note patterns and generate competitive premiums to win new customers.

The data and analytics movement has also made room for newcomers like Metromile to enter the market. Although the company started out with no proprietary data of its own, Metromile has quickly gained customers and collected data with a new model: auto insurance by the mile.

This entrance of Metromile into the auto insurance space has both disrupted the industry and put pressure on incumbent insurance providers to make advances with their own models.

In auto insurance underwriting, a number of ways to use new data to achieve more accurate pricing have gained attention:

  • Using usage-based insurance (UBI)
  • Leveraging external data
  • Leveraging real-time data

Usage-based insurance

UBI can be used to more closely align premium rates with driving behaviors. The UBI idea is not new — there have been attempts to align premiums with empirical risk based on how the insured actually drives for a couple of decades. In 2011, Allstate filed a patent on a UBI cost determination system and method. Progressive, State Farm and The Hartford are just a few examples of other companies that are embracing UBI methods in underwriting.

Technological evolutions like the Internet of Things (IOT) and all its attendant sensors provide new ways to capture and analyze more data. The UBI market has flourished and is expected to reach $123 billion by 2022. The U.S., the largest auto insurance market in the world, will lead the way in UBI marketing and innovation in 2017. With UBI’s market potential, there has also been a rise in business models such as pay-per-mile insurance for low-mileage drivers using UBI methods in underwriting. Embracing UBI methods in underwriting is no small feat, because of the huge amounts of data that must be collected and integrated. Progressive collected more than 10 billion miles of driving data with its UBI program, Snapshot, as of March 2014. For the most part, the data focuses on mileage, duration of driving and counts of braking/speeding events. These are all “exposure-related” driving variables, which are considered secondary contributors to risk. They can be bolstered with external data such as traffic patterns, road type and conditions, which are considered primary contributors to risk, to create a more accurate picture of an individual driver’s risk.

Leveraging external data

The idea of using external data is also not new. As early as the 1930s, insurance companies combined internal and external data to determine the rate for policy applicants. However, more recently, the speed of technological advancements has allowed insurers to dramatically redefine and improve their processes.

For example, customer applications for insurance today are significantly shorter than before, thanks to external data. With basics like name and address, insurers can access accurate data files that will append other necessary information — such as occupation, income and demographics. This means expedited underwriting processes and improved customer experiences. Some speculate that all insurers will purchase external data by 2019 to streamline their underwriting (among other things).

Another consideration is that the definition of external data has been evolving. Leveraging external data in an auto insurance risk assessment today may mean going beyond weather and geographic data to include data on shopping behaviors, historical quotes and purchases, telematics, social media behaviors and more. McKinsey says, “The proliferation of third-party data sources is reducing insurers’ dependence on internal data.” Auto insurers can incorporate credit scores into their underwriting analysis as empirical evidence that those who pay bills on time also tend to be safer drivers.

Better access to third-party data also allows insurers to pose new questions and gain a better understanding of different risks. With the availability of external data like social data, insurers can go beyond underwriting and pricing to really managing risks. External data doesn’t just go beyond telematics and geographic data; it may also have real-time implications.

Leveraging real-time data

Real-time data is a subset of the rich external data set, but it has some unique properties that make it worth considering it as a separate category. The usage of real-time data (such as apps that engage customers with warnings of impending weather events) can cut the cost of claims. Insurers can also factor data such as weather into the overall assessment at the time of underwriting to more accurately price the risk. In the earlier example of using external data to shorten the underwriting process, accessing external information in real-time and checking with multiple sources makes the information in auto insurance application forms more accurate, which, in turn, leads to more accurate rates.

Underwriters can also work with integrated sales and marketing platforms and can reference data such as social media updates, real-time news feeds and research to provide a more accurate assessment for those who seek to be insured. Real-time digital “data exhaust” — for example, from multimedia and social media, smartphones and other devices — has offered behavioral insights for insurers. For example, Allstate is considering monitoring and evaluating drivers’ heart rate, electrocardiograph signals and blood pressure through sensors embedded in the steering wheel.

See also: Industry’s Biggest Data Blind Spot  

Insurers can influence the insured’s driving behaviors through real-time monitoring, significantly altering the relationship with each other. A number of insurance companies, such as Progressive — in addition to the pay-per-mile insurer Metromile — are monitoring their customers’ driving real-time and are using that data for underwriting purposes. Allstate filed a patent on a game-like system where drivers are put in groups. Those in the same group could monitor driving scores in real-time and encourage better driving to improve the group’s driving score. Groups can earn rewards by capturing better scores.

Conclusion

There’s no doubt that the risky business of insurance is sophisticated. The above examples of leveraging UBI, external data and real-time data merely scratch the surface on data-driven opportunities in auto insurance. For example, what about fraud? Efficiency and automation? Closing the loop between risk and claims? Because only 36% of insurers are even projected to use UBI by 2020, those that embrace data-driven techniques will quickly find themselves ahead of the game.

While it’s outside the scope of this post, we should note that leveraging data and methods shouldn’t be done without careful consideration for consumers. As consumers enjoy easier insurance application processes, as well as having more products to choose from and compare prices on, increasingly they will want to understand how these data and analytics techniques affect them personally — including their data privacy and rights.

As we pause and reflect on how data and analytics have driven changes in auto insurance underwriting, we welcome questions and discussions in the comments section below. In the future, we’ll examine other ways the insurance market is becoming more data-driven, including the changes that data and analytics are driving in auto insurance claims and the rising focus of marketing.

This article first appeared on the site of Silicon Valley Data Science

The Case for Personalization

The relationship between the insured and the insurance company isn’t just business — it’s also personal. It’s important for insurance companies to be there when customers need them the most, but, over the past decade, “there” has been redefined, and too many insurance companies haven’t adjusted.

See also: 5 Personal Traits of Great Leaders  

Consumers today communicate on a variety of platforms, including: online, mobile, email and social media. Insurance company leaders who want to gain a competitive advantage must monitor shifting communication patterns and adjust their outreach strategies so they can be where their customers are.

Insurance companies need data — both large and small. Big data has become an increasingly central component of modern business operations across all sectors, including the insurance industry. But insurance company leaders who want to implement a visionary approach and build a closer relationship with customers should think beyond the typical use cases, such as using big data to detect fraud. They need to consider “small data,” too — such as using social media and SMS contact information to build relationships.

That’s a tall order for insurance companies, which typically don’t have that type of contact information in customer files and often struggle to maintain accurate phone numbers and addresses because many insurers only interact with customers when it’s time to process a claim or add a family member to a policy. But to truly modernize their approach to customer service, and make it more immediate and personal, insurance companies have to bridge the information gap, clean up existing data and secure the additional contact information they’ll need to reach customers where they are.

Insurers will also need to ask customers about their communication preferences and obtain consent for future contact early in the customer journey and relationship lifecycle — or as soon as possible for their existing customer base. Insurers can analyze the communication channels available to customers and ask customers which platforms they prefer, then abide by the customers’ stated preferences. In this way, insurers are implicitly demonstrating that they respect their customers.

See also: Personal Effectiveness – The Continuing Challenge  

But following this strategy is much more than just a sign of respect or a signal that the company is tech-savvy. It opens up possibilities for relationship-building through technology. For example by using a secure, compliant platform that integrates data from multiple sources — and automates messaging via voice, text or email — insurance companies can engage in proactive communication, such as sending out alerts when a weather event threatens a customer’s area. And by integrating data from connected home products, like sensors in smoke detectors and appliances that connect to the Internet of Things (IoT), insurers can communicate with customers and their preferred providers to alert them of issues, as they arise, reducing property damage.

A personalized approach like this not only reduces risk for both insurer and insured, it builds trust. As insurers create new lines of communication with customers, insurers can become an important part of the customer’s support network — truly looking out for the customer.

The technology to make it happen exists today. All it takes to put data to work for a higher purpose is the vision to change the way the company communicates and make it more immediate and human. Because with insurance, it’s not just business — it’s personal.

Forget Big Data — Focus on Small Data

In their rush to jump on the big data bandwagon, many organizations have lost sight of a much simpler yet effective source of customer insight: “small data.”

Big data is about synthesizing, mining and analyzing mounds of seemingly unrelated information to derive actionable insights about your customer. It’s a complex science but one that can be leveraged to understand and engage customers in new, surprising and sometimes even creepy ways. (Consider the well-documented case where retailer Target figured out that a teenage girl was pregnant before her father even knew—merely by analyzing her purchase history data. See “The Challenges Around Big Data and the Lessons to Be Learned.”)

In contrast, small data is about listening to and observing your customers intently, picking up on simple cues that allow you to better personalize and customize your interactions with them.

Small data doesn’t require supercomputers to decipher. It’s not really a new concept, either—it’s just a new moniker for a tried and true approach that the best sales and service people have employed for decades, if not centuries.

That might make small data sound quaint and old-fashioned, but don’t be fooled. Using it can actually enhance your business’ customer experience in very material ways, without the expensive overhead associated with big data solutions.

See Also: To Go Big (Data), Try Starting Small

To get a flavor of how small data can influence your customer experience, consider these examples of the strategy put into practice:

• Delta Airlines’ 800-Line Greeting

Presuming a customer calls Delta from a phone number the airline has on record, the 800-line voice response system will skip the standard pleasantries and prompt you with a question such as “Are you calling about your delayed flight?” If the answer is yes, then Delta immediately routes the caller to an automated service or a live representative who can help, obviating the need to navigate through a series of menu options.

Once the incoming phone number is identified, Delta’s systems check to see if the customer has reservations coming up, or if perhaps a flight that day has been delayed or canceled.

That’s not a terribly complex undertaking from a data perspective, as it is a relatively simple look-up exercise, rather than a full-blown analytics task.  Yet it yields a much better and more efficient customer experience, particularly at a time when passengers may be frazzled about unexpected changes in their travel plans.

• Ritz-Carlton’s Personalized Guest Experiences

The Ritz-Carlton luxury hotel chain is renowned for its ability to create highly personalized guest experiences. If the Ritz in Boston learns that a guest is allergic to feathers, then the Ritz in Dubai—half a world away—will de-feather that same guest’s room prior to arrival.

How does the company do that? Ritz staff are trained to listen carefully for guests’ likes, dislikes and general preferences. These are small pieces of data (such as a favorite newspaper or snack, or a preferred room location) that Ritz-Carlton employees dutifully record in a customer database dubbed “Mystique.”

They’re also trained to consult that database prior to a guest’s arrival and act on any relevant information they find. This helps ensure that any previously captured small data is used to create an unusually customized guest experience during subsequent visits.

These two examples are from outside of the insurance industry, but the approaches they illustrate are easily transferable. It’s simply a matter of putting your antennae up and looking for small pieces of data that can be used to deliver a more personalized, relevant and anticipatory customer experience.

Consider the small data that’s available to insurance carriers—data that, if captured and capitalized on, could generate some very positive customer impressions:

• Children’s Ages

By recording information about a customer’s children during an initial needs analysis, insurers can engage the policy owner to assist in stressful parenting periods, such as when a child approaches driving age.

While identifying households with youthful drivers isn’t a new idea for insurers, using that information to strengthen the customer relationship is. Historically, such data has been used by insurers to address situations where a new, uninsured driver may be behind the wheel (to adjust premiums).

However, the identification of a youthful household driver shouldn’t just be an exercise in rate adjustment. It’s also an opportunity for the insurer to demonstrate the value it provides—in this case, by communicating relevant information to parents that helps them navigate a difficult family transition (e.g., determining what resources are available to teach their son/daughter how to drive or how they can best ensure their child’s safety while they learn to drive).

Using small data in this way creates a customer experience that appears strikingly prescient to the policy owner, essentially addressing their concerns and questions before they even have a chance to raise them.

• Sales

For certain types of commercial lines coverages, insurers have visibility into business performance measures for their clients (such as sales), which are recorded annually via premium audits.

Here again, as with youthful drivers, the industry has traditionally used such data exclusively to adjust premium rates for coverages that are tied to these business metrics. But this small data can be far more useful.

Consider the first time a commercial lines customer crosses over the $10 million revenue threshold. That’s a milestone that would be reflected in the small data most insurers collect, yet few do anything with it, other than raise premiums.

Imagine if that customer received a handwritten note from his insurer (or agent) a month after renewal, congratulating him on reaching that milestone. Imagine how that small token of recognition would make the customer feel.

Business owners, after all, don’t really care about their business insurance—but they do care about their business. When their business grows, that affords an opportunity to celebrate alongside them, to give them a “pat on the back” that they likely weren’t expecting from their insurance provider but will remember fondly.

• Recurring Information Requests

At Ritz-Carlton hotels, if a guest requests the same newspaper, snack or room location visit after visit, the staff will notice and use that small data to shape the customer’s future stays.

There is an analog for this in the insurance industry. Consider the reports and other information materials that a policy owner requests year after year—e.g., a commercial insured requesting updated certificates of insurance for her core set of clients, or a corporate risk manager requesting loss reports sorted by site.

Every recurring information request represents a piece of behavioral small data that can be used to customize the policy owner’s future experience.

Imagine if a policyholder didn’t even have to make those information requests, just as the Ritz-Carlton guest who’s allergic to feathers need not request a feather-free room.

Imagine if an insurance provider, based on a policyholder’s prior history of information requests, offered all of those reports and certificates to the customer at precisely the right time each year.

That would be the epitome of a more personalized and effortless customer experience, all made possible simply by acting on a piece of small data.

Small data may be less glamorous than its more buzz-worthy big data counterpart, but it’s no less important.

Big data has its merits, but as the “shiny new object” that every company covets it has unfairly eclipsed the value of simpler and more straightforward sources of customer insight.

Better understanding your customers and her needs doesn’t always require intense data crunching and sophisticated analytics. Often, what’s really needed is just a watchful eye, an attentive ear and the discipline to act on whatever insights you uncover.

Because when it comes to creating a positive, memorable and personalized customer experience, small data can have a really big impact.

This article first appeared at Carrier Management.

Helping Data Scientists Through Storytelling

Good communication is always a two-way street. Insurers that employ data scientists or partner with data science consulting firms often look at those experts much like one-way suppliers. Data science supplies the analytics; the business consumes the analytics.

But as data science grows within the organization, most insurers find the relationship is less about one-sided data storytelling and more about the synergies that occur in data science and business conversations. We at Majesco don’t think it is overselling data science to say these conversations and relationships can have a monumental impact on the organization’s business direction. So, forward-thinking insurers will want to take some initiative in supporting both data scientists and business data users as they work to translate their efforts and needs for each other.

In my last two blog posts, we walked through why effective data science storytelling matters, and we looked at how data scientists can improve data science storytelling in ways that will have a meaningful impact.

In this last blog post of the series, we want to look more closely at the organization’s role in providing the personnel, tools and environment that will foster those conversations.

Hiring, supporting and partnering

Organizations should begin by attempting to hire and retain talented data scientists who are also strong communicators. They should be able to talk to their audience at different levels—very elementary levels for “newbies” and highly theoretical levels if their customers are other data scientists. Hiring a data scientist who only has a head for math or coding will not fulfill the business need for meaningful translation.

Even data scientists who are proven communicators could benefit from access to in-house designers and copywriters for presentation material. Depending on the size of the insurer, a small data communication support staff could be built to include a member of in-house marketing, a developer who understands reports and dashboards and the data scientist(s). Just creating this production support team, however, may not be enough. The team members must work together to gain their own understanding. Designers, for example, will need to work closely with the analyst to get the story right for presentation materials. This kind of scenario works well if an organization is mass-producing models of a similar type. Smooth development and effective data translation will happen with experience. The goal is to keep data scientists doing what they do best—using less time on tasks that are outside of their domain—and giving data’s story its best possibility to make an impact.

Many insurers aren’t yet large enough to employ or attract data scientists. A data science partner provides more than just added support. It supplies experience in marketing and risk modeling, experience in the details of analytic communications and a broad understanding of how many areas of the organization can be improved.

Investing in data visualization tools

Organizations will need to support their data scientists, not only with advanced statistical tools but with visualization tools. There are already many data mining tools on the market, but many of these are designed with outputs that serve a theoretical perspective, not necessarily a business perspective. For these, you’ll want to employ tools such as Tableau, Qlikview and YellowFin, which are all excellent data visualization tools that are key to business intelligence but are not central to advanced analytics. These tools are especially effective at showing how models can be used to improve the business using overlaid KPIs and statistical metrics. They can slice and dice the analytical populations of interest almost instantaneously.

When it comes to data science storytelling, one tool normally will not tell the whole story. Story telling will require a variety of tools, depending on the various ideas the data scientist is trying to convey. To implement the data and model algorithms into a system the insurer already uses, a number of additional tools may be required. (These normally aren’t major investments.)

In the near future, I think data mining/advanced analytics tools will morph into something able to contain more superior data visualization tools than are currently available. Insurers shouldn’t wait, however, to test and use the tools that are available today. Experience today will improve tomorrow’s business outcomes.

Constructing the best environment

Telling data’s story effectively may work best if the organization can foster a team management approach to data science. This kind of strategic team (different than the production team) would manage the traffic of coming and current data projects. It could include a data liaison from each department, a project manager assigned by IT to handle project flow and a business executive whose role is to make sure priority focus remains on areas of high business impact. Some of these ideas, and others, are dealt with in John Johansen’s recent blog series, Where’s the Real Home for Analytics?

To quickly reap the rewards of the data team’s knowledge, a feedback vehicle should be in place. A communication loop will allow the business to comment on what is helpful in communication; what is not helpful; which areas are ripe for current focus; and which products, services and processes could use (or provide) data streams in the future. With the digital realm in a consistent state of fresh ideas and upheaval, an energetic data science team will have the opportunity to grow together, get more creative and brainstorm more effectively on how to connect analytics to business strategies.

Equally important in these relationships is building adequate levels of trust. When the business not only understands the stories data scientists have translated for them but also trusts the sources and the scientists themselves, a vital shift has occurred. The value loop is complete, and the organization should become highly competitive.

Above all, in discussing the needs and hurdles, do not lose the excitement of what is transpiring. An insurer’s thirst for data science and data’s increased availability is a positive thing. It means complex decisions are being made with greater clarity and better opportunities for success. As business users see results that are tied to the stories supplied by data science, its value will continue to grow. It will become a fixed pillar of organizational support.

This article was written by Jane Turnbull, vice president – analytics for Majesco.