Tag Archives: data

Context Is Key to Unlocking LTC Data

Long-term care (LTC) insurance is no stranger to large amounts of data. However, in my 10-plus years in an LTC claim operations role, there is a piece of data I’m surprised continues to be shared without the proper context – claim terminations for people labeled “recovered.” Across the industry, this piece of data is used in actuarial assumptions and operational processes — but not just for claims where the insured has recovered.

Before I explain further, a little background:

Claims data is a crucial piece of the overall risk management puzzle, especially for LTC insurers. The reserves associated with future claims represent a huge amount of the liability they are holding separate. Claim termination rates are closely watched.

Insurers generally have three main designations for terminations for closed claims:

1. Death

This one is pretty easy to understand; the insureds stopped receiving benefits because they are now deceased. This occurs 73% of the time based on the recent study conducted by the American Association of Long Term Care Insurance.

2. Exhaustion of Benefits

Again, another simple concept. The insureds ran out of benefits before they died. This occurs 14% of the time, according to the AALTCI study.

3. Recovery

Here is where we find the complexity. The very nature of the word implies the claimant in this category is now healthier and no longer needs to receive benefits. According to the same study, this occurs 14% of the time.

See also: Using Data to Improve Long-Term Care  

The problem with this category lies not with the study, which accurately reflects what insurers report, but rather the context and consistency of how this data is classified. What’s suggested is not quite the reality. But it requires a little digging to understand what I mean.

Now for the Context

Insurers and the claim administration systems they use require their data be categorized into larger buckets. It’s much easier, after all, to analyze and predict variables when there are fewer varieties of those variables. Instead of having many claim termination reasons, let’s find a way to just have three. Sounds simpler, right?

Unfortunately, this approach changes the recovery designation into more of an “Other” category. Any claim that is closed where the insured isn’t deceased and still has benefits remaining ends up in this classification. Some examples:

Preservation of Benefits

Some insureds have limited benefits (and thus can run out of them). These claimants tend to be in their 60s, 70s and lower 80s. Given they’ll potentially fall short of benefits, they sometimes choose to stop receiving benefits to save them for future needs.

Respite Care

Most policies allow for several weeks of respite care per year. This benefit is independent of the elimination period and allows families to open a claim for a short time while the primary caregiver takes a much deserved break. Again, when these short claims close, they are coded as recovery.

Moving Abroad

Many policies do not cover care received outside the country. So, when insureds move overseas at the end of their life, the claims unfortunately must be closed, and their policy then lapses by their choice.

Spouse Retires/Family Member Becomes Caregiver

This one is close to the preservation of benefits status. Some policies exclude family members from providing the care. When the claim is initially filed, the spouse is still working or family members are unavailable to assist. These factors can change and cause a family to close the claim while the family member is able to care for the insured and save the rest of the money for later.

Lack of Contact

As odd as it sounds, sometimes claimants just stop sending in bills. The company attempts to contact them over several months, they search online databases for proof of their passing and they contact every phone number and e-mail address they have in connection to the claimant. At a certain point, they have to stop trying and close the claim.

Unreported Death

Related to lack of contact are deaths that are not reported to the insurance company and don’t get picked up by the search techniques most insurers use. Even if the companies later find out that the insured passed away and close the policy as a death, they generally don’t go back and change the termination status of the claim, so it remains, a recovery.

Less than $100 left on the policy

This one adds a final bit of humor to the list. The benefits available on an LTC policy are often not used in the exact amounts intended, so the policy is not exactly exhausted by the final benefit payment. I have seen situations where the amount left on the policy is so small, the insured (or the family) doesn’t send an invoice to request the final amount.

All of these examples have something in common. The claimant didn’t die, and there were benefits remaining on the policies. So every one of these situations would be reported as a recovery.

So what?

So what am I trying to say here? All data is inaccurate? No, the data isn’t inaccurate, it just requires the proper context before it is used for analysis. Without the proper context, statistics could be used to suggest that, 14% of the time, an insured who qualifies for long-term care benefits will improve enough to regain independence and no longer require assistance.

See also: Time for a ‘Nudge’ on Long-Term Care  

The reality is much harder to know. While you would expect some recovery on acute conditions (think hip replacements), would it surprise you to know that as many as 25% of these recoveries are claims where the insured has been certified with cognitive impairment? Did those claimants really get better and no longer require care? Another 25% of the recoveries most likely fall into one of the categories above. So that means about half the recoveries reported, aren’t really recoveries.

Recommendations:

  • Talk to your internal claims team to get their input. Involve them in the collection and analysis phases, not just at the read-out of the final product. By working together with some of the key claims experts, you will gain better context around the data.
  • Understand your internal processes and procedures. Learn the details of your company’s processes associated with opening, approving, paying and closing claims.
  • Be careful when using industry-wide data. Not every company’s processes are the same, and data elements may have different definitions. Only rely on and draw conclusions when you understand the contextual factors surrounding the data.

Turning Data Into Action

Over the past decade, insurers have focused heavily on improving the customer’s journey. This task can be particularly challenging because a customer’s engagement with them could be as little as one annual wellness visit with no other claims for that year.

In an effort to create engagement and build loyalty while working toward better health status, insurers have gamified biometric device interactions, launched semi-automated communications platforms and established group wellness challenges for employer groups and individual coverage plans.

But here’s the challenge: If the data gathered from these engagements that is fed back to insurers is not clean, readable and available in the format and time in which it is needed, then a carrier is unable to optimize its application. If this challenge can be solved, high-quality data that does meet those parameters can be used for CRM modeling tools, experience and loyalty measuring systems, enhanced communications applications, cross-sell offers and lifetime customer value formulas.

So how does one begin to solve this challenge? The key lies in using information obtained from reputable sources to fill in some of the gaps in the data you are already gathering.

See also: How Agencies Can Use Data Far Better  

Here are some of the benefits of using third-party data to inform your analytics:

  1. You can enhance the bland data you already have. You could fill volumes with the amount of information you have about your customers’ basic demographics such as age, geography and household income. But what about their risk for certain health conditions and their history of disease? Including these details can support better communications, closer engagement and efficient transaction processing with care providers and administrative systems managers.
  2. You can improve both the quantity and quality of your data. Quality of data can make or break processing and downstream analytics. When you use a third party to obtain your data, you may experience a more reliable return on investment in your marketing and communications spend. You can also make more informed decisions when you are pricing the risk of catastrophic losses. High-quality data can mean the difference between automated workflow decision making or manual and costly processes. It does not have to be a lot of data — but it does have to be clean, understandable, reliable and available when needed.
  3. You can diversify ways of turning data into actionable insights. Information might be engineered or derived from big datasets that are curated in a way that a payer can ingest, making it useful for activities including workflow automation, risk management assessments, price modeling exercises, population health management or sales and marketing activities.

Of course, it’s important to be able to efficiently manage data from multiple sources. To do that, you need to create a master data management plan. Often, a centralized location for several datasets makes sense, although a connected, decentralized arrangement can work, as well. Establish a standard data dictionary within your company to ensure that your staff understands external data in the right way and can more precisely define even internal data. In other words, break down data silos and functional barriers that may be preventing a standard dictionary that all can leverage.

How can you determine whether you are getting the most out of your use of data? A three-step approach may be helpful:

  1. Evaluate the data you have and verify whether it is clean, reliable and accessible in the manner you need it.
  2. Identify the areas in which external data could complement your own and structure a data management approach for all of your data — both internal and external.
  3. Establish a cross-functional executive team that can prioritize where you need the data most, and start on one initiative now. If you are not doing something, your competitors most probably are.

See also: Role of Unstructured Data in AI  

Well-organized data can help you engage your current customers, attract new customers and ultimately improve your company’s bottom line. But too much data, that is not optimized for your business needs, may not help the organization meet its goals. When you focus on high-quality and reliable data, you can see some tangible results when you adapt its use into platforms all along the lifecycle of your business.

Understanding New Generations of Data

To effectively acquire customers, offer personalized products and provide seamless service requires careful analysis of data from which insights can be drawn. Yet executives cite data quality (or lack thereof) as the chief challenge to their effective use of analytics. (Insurance Nexus’ Advanced Analytics and AI survey).

This may, in part, be due to the evolving nature of data and our understanding of how its changing qualities affect how we use it — as technology changes and different data sources emerge, the characteristics of data evolve.

More data is all well and good, but more isn’t simply…more. As new and more contextual streams of data have become available to insurance organizations, more robust and potent analytical insights can be drawn, carrying with them huge implications for insurance as a whole.

See also: Data, Analytics and the Next Generation of Underwriting  

Insurance Nexus spoke to three insurance data experts, Aviad Pinkovezky (head of product, Hippo Insurance), Jerry Gupta (director of group strategy, Swiss Re Management (US)) and Eugene Wen (vice president, group advanced analytics, Manulife), for their perspectives on what each generation of data means for the insurance organization of today, and how subsequent generations will affect the industry tomorrow.

See full whitepaper here.

While there is disagreement regarding which generational bucket data should fall into, current categorizations appear to be largely aligned. Internal, proprietary data is generally agreed to form first-generation data, with the second-generation comprising telematics and tracking device data. There is some contention over the categorization of third-party data, but these are largely academic distinctions.

Experts agree that we are witnessing the arrival of a new classification of data: third-generation. As Internet of Things (IoT) data becomes more commonplace, its incorporation with structured and unstructured data from social media, connected devices, web and mobile will constitute a potentially far more insightful kind of data.

While this is certainly on the horizon, and has been successfully deployed with vehicular telematics, using “IoT, including wearables, in the personal lines space [and elsewhere], is still not widely adopted,” says Jerry Gupta, senior vice president, digital catalyst, Swiss Re. Yet, he is confident that third-generation data will “be the next wave of really big data that we will see. Wearables will have a particular relevance to life and health products as one could collect lot of health-related data.”

Download the full whitepaper to get more insights.

Despite this promise, there are significant roadblocks to effectively leverage third-generation data. According to Aviad Pinkovezky, head of product at Hippo Insurance, the chief problem is one of vastly increased complexity: “This sort of data is created on demand and is based on the analysis of millions of different data points…algorithms aren’t just generating more data streams, they are taking new data, making decisions and applying them.” Clearly, this requires a change in how data is handled, stored and analyzed. Most significantly, third-generation data has the potential to change the nature of insurance.

See also: 10 Trends on Big Data, Advanced Analytics  

Given that data is no longer the limiting factor for insurance organizations, our research suggested five areas on which insurance carriers should focus to turn data into real-time, data-driven segmentation and personalization: cost, technical ability, compliance, legacy systems and strategic vision.

A challenge, certainly, but the potential rewards to both insurance carrier and insureds are hugely promising, especially the change in relationship between carrier and insured. The potential to not only predict, but mitigate, risk has huge implications for insurance.

Efficient, accurate and automated data gathering is a clear benefit for insurance carriers, and the potential to provide value-added services (by mitigating risk altogether) greatly enhances their role in the eyes of the customer. Measures that reduce risk to the insured increase trust and strengthen the bond between the carrier and the insured. Customers are less likely to view insurance as a service they hope to never use but, rather, a valuable partner in keeping themselves secure, both materially and financially.

The whitepaper, “Building the Customer-Focused Carrier of the Future with Next-Generation Data,” was created in association with Insurance Nexus’ sixth annual Insurance AI and Analytics USA Summit, taking place May 2-3, 2019, at the Renaissance Downtown Hotel in Chicago. Expecting more than 450 senior attendees from across analytics and business leadership teams, the event will explore how insurance carriers can harness AI and advanced analytics to meet increasing customer demands, optimize operations and improve profitability. For more information, please visit the website.

3 Ways to Use Data to Optimize Mobile Ads

The average U.S. consumer spends five hours a day on mobile devices. Two of those hours are spent consuming social media. Advertising on these channels is a highly effective yet often underused way for companies to engage consumers and grow their business.

However, taking a “spray-and-pray” approach — blasting your message out to everyone and hoping it will reach someone likely to respond — no longer cuts it. Today’s digital consumers expect the companies they do business with to anticipate their needs and deliver highly relevant and personalized experiences. That’s where data comes in.

Every time a consumer takes an action — a page visit, click, like, comment, share, post — he or she leaves a data trail. Following that trail tells a rich story about the consumer and his or her path to purchase.

Below are three ways to activate that data trail to optimize your mobile and social media advertising.

1. Develop personas.

Personas, or detailed representations of audience segments, are much more effective than broad demographic descriptors because they humanize target audiences. Fueled by data-driven research that maps out the who behind buying decisions, customer personas can help inform everything from marketing messages to product development efforts.

For insurance and financial services companies, a great way to start developing personas is by analyzing data around life events. We know consumers make insurance purchases during major life events, such as buying a home or car, getting married or having a child. Beyond reaching the right consumer with the right message at the right time, life-event marketing puts it in the right context. Understanding the context of a consumer’s behavior is incredibly valuable and can help you adjust your marketing message to inspire action.

See also: Data Opportunities in Underwriting  

Social media provides marketers access to global conversations, bringing together droves of data to better understand consumer behavior, trends and opinions. And the more a brand advertises on mobile and social media, the more data it has access to and the more it can test and refine its personas to achieve even better results in the future.

2. Track the entire conversion journey.

In today’s multi-channel world, data on cross-channel behaviors allows you to track a customer’s entire journey to conversion. As that journey increasingly involves multiple devices, browsers and mobile apps, it cannot be accurately measured using only cookies. While cookies may provide insight into the last touchpoint before the conversion, a cookies-only approach overlooks what happens earlier in the customer journey.

A recent study by Facebook and Datalicious found marketers who only measure campaign success via cookies overlook nearly 40% of all digital touchpoints in the customer journey to conversion. And because people use multiple devices, each person has an average of three unique cookie identifiers. In other words, one individual is seen as three different people through the lens of cookie-only measurement. Because only one of those cookie identifiers will actually convert (and the other two appear to go cold), the data on engagement with your content becomes skewed.

According to a Facebook IQ article, cookies force marketers to rely on guesswork to justify media investment, leading to wasted ad dollars: “So how do we tackle this complex reality? By understanding consumers as people rather than cookies…. To help drive results and sustainable business growth, more accurate methods based on people insights can give marketers the ability to measure campaigns on and off Facebook, on both desktop and mobile devices. When looking at attribution and reach, this approach offers a more holistic look at the ad performance — something not possible before.”

Implementing Facebook Pixels to track conversions across devices is a great way to start building a cross-channel, people-based campaign measurement model.

3. Find the right partners.

One way to greatly enhance the value of your own consumer engagement data is to combine it with complementary datasets. The easiest way to do that is to partner with organizations that have access to large amounts of data.

Denim, for example, has aggregated more than 1 billion data points on consumer engagement with mobile and social media ads powered for insurance and financial services companies. We’re proud to have more data on consumer engagement with insurance- and financial services-related mobile and social media ads than anyone.

See also: 4 Benefits From Data Centralization  

At the same time, we recognize Denim’s dataset is not the only dataset the industry will ever need, nor is anyone else’s dataset the only dataset the industry will ever need. Denim’s database is a contributory database. In other words, it’s the contribution of Denim’s data along with a variety of other datasets that will ultimately paint a vivid picture of today’s consumer market and target it in a way that’s never been done before.

Denim has partnered with a variety of organizations, including a global actuarial consulting firm, to perform extensive analyses of Denim’s data in contribution with other datasets. The results will be used to help our customers make even smarter mobile and social media marketing decisions in the future. Watch for exciting announcements to come!

How to Earn Consumers’ Trust

Let’s talk about trust. The insurance industry is built on it. So why is there so little trust between consumers and the insurance industry?

According to the 2018 Edelman Trust Barometer, financial services as an industry has improved in the percentage of those surveyed who trust the industry from 48% in 2014 to 54% in 2017. While the level of trust is at least moving in the right direction, financial services does rank dead last among all of the sectors polled. Last.

Trust is not something that comes easily these days anywhere, much less in the world of insurance and other financial services. This is not great news for an industry in which we literally sell a promise to be there when bad things happen to consumers and businesses, such as car accidents, fires or deaths.

Many insurers may think of data along these lines:

Consumers trust and understand that, at the end of the day, insurance carriers are in the business of data. It’s at the core of what we do, the data is how premiums are decided, how to best protect assets and develop the fastest solutions when there is a loss, how products are marketed and much, much more. Of course, carriers can be trusted to protect that data and consumers’ privacy.

As a regulator who often hears from consumers, I wouldn’t bank on that. Simply put, there is a lot of uncertainty around data these days. Cyber-attacks are in the news seemingly endlessly, from Home Depot, to Target, to Equifax. And if consumers know one thing, it’s that their data is out there, often on old systems that may or may not be properly maintained, and many big-name companies may not have succeeded in protecting that data, and thereby their privacy.
Consumers also often are bombarded with long applications or questionnaires, sometimes with rather personal questions. Often, they are left baffled trying to understand, “Why would these people need this information?”

See also: When Not to Trust Your Insurer  

Many agents or brokers requesting the data may not know themselves. Data collected by insurance companies is input into complex algorithms in trade-secret black boxes to which few have access, much less full access.

Simon Sinek provides great insight into why leaders and companies need to focus on answering the question of “why” to maintain the focus as anyone—leaders, product managers, agents and brokers—starts the process and as any of us review whether that vision is working. Sinek says that people should consider whether “Starting With Why” in innovation will instill trust and cooperation.

If companies are transparent about exactly why data is collected, consumers can understand how it affects them. Transparency also can allow agents, brokers, consumers and others collecting the data to ensure it is as accurate as possible.

This issue is being discussed inside insurers, at insurance departments and among consumers. There can be scary downsides to secret data black boxes in insurance and otherwise.

Insurers could also use the data to provide feedback to help consumers better manage their risks. It’s important that, as new technology brings new opportunities, those asking for the information fully explain the “why” behind requests for data.

Insurance is global, and changes in other countries may cause changes that affect U.S. consumers and companies. As the General Data Protection Regulation (GDPR) is on the eve of its effective date of May 25, 2018, in the E.U., the U.S. has the opportunity to learn from the experiences.

When the Iowa Insurance Division addresses these topics with companies, we point out the obvious. These are your consumers. If consumers ask the question about what data is being used and from what point, they should gain a clear response so they can understand fully before they consummate the transaction.

See also: 6 Lessons in Trust From Retailers 

Those in the insurance industry are given and trusted with much data. Because of that, much is expected.

It’s an incredible time to be in the business of insurance, and the expectations are high. The Iowa Insurance Division will continue to work with companies and consumers to discuss the proposed “why” for the benefit of all affected. After all, the insurance industry is built on trust.