See through the smoke and mirrors.
We’ve all heard the hubbub about big data for HR. We’ve all witnessed the insurance industry’s continuing move into the world of benefits enrollment technology and decision support tools.
Yet most employee benefits election engines fail to meet the expectations of employees and HR.
Most benefits enrollment technology falls short of expectations because their engines are built by developers, consultants, and technology companies that rely on false premises.
They believe in six myths that eventually lead them to fail HR — and the employees HR is responsible for.
You can make better decisions about your benefits administration technology partner if you learn:
Let’s get started:
1. The Myth: People want to shop for benefits – including healthcare insurance. Therefore, benefits can be sold like other online consumer products.
The Truth: “Most consumers dread shopping for health coverage. It’s a difficult task [that involves] complex information, and it’s fraught with important implications for their family.” — Kleimann Communication Group and Consumers Union, Choice Architecture: Design Decisions That Affect Consumers’ Health Plan Choices.
“To engage consumers, most designers agree that the most important strategy is speed. Every site wants to get people to results [a list of health plans, including some information about cost] as quickly as possible.”— L. Quincy, What’s Behind the Door: Consumer Difficulties Selecting Health Insurance, Consumers Union
Let’s face it – we want to spend time on the things that we enjoy.
For most people, investing hours comparing the trade-offs between a PPO and an HDHP is not on that list. Neither is thinking about critical illness and hospital indemnity.
Businessolver sees how people behave when they need to choose benefits during onboarding, at annual enrollment, and when they have changes in their lives, like a marriage or the birth of a child.
Banish the myth with choice architecture and a recommendation engine.
“Choice Architecture is broadly defined as the way information is organized to help people make decisions. …It may make it easier for the consumer to navigate complex choices.” — Richard Thaler and Cass Sunstein, Nudge
Remember, people are influenced by the way their benefits options are presented and the steps they need to take to make buying decisions. The right solution will take this into mindful consideration. An optimal design will reflect what matters most to the user, not to the insurance company or the benefits administrator.
How can you help employees make solid decisions when they don’t want to spend a lot of time poring over complex details?
Help them through the process with a recommendation engine, not just by giving them benefits selection or enrollment options. But by asking employees a few simple questions, a recommendation engine built on choice architecture can do two things:
2. The Myth: When they have more choices, people will make better decisions.
The Truth: More choice doesn’t give people more freedom. Eventually, it paralyzes them.
“As the number of choices keeps growing, negative aspects of having a multitude of options begin to appear. As the number of choices grows further, the negatives escalate until we become overloaded. At this point, choice no longer liberates, but debilitates.” — Barry Schwartz, author of The Paradox of Choice
We’ll take care of it tomorrow, or the next day, or the next. For benefit decisions, the effects can be devastating.
A study about the retirement decisions of nearly 1 million Americans from about 650 retirement plans found that the more funds a plan offered, the fewer people participated in the plan.
“Further, when presented with too many options, the quality of people’s decisions was negatively affected.” — Sheena Iyengar, author of The Art of Choosing, paraphrased from a TED Talk
Banish the myth with choice architecture: find the sweet spot.
Where is the sweet spot when it comes to benefits choice? It’s the place where employees and their families can benefit from variety but not be paralyzed by it — even when they are traveling the ever-expanding product landscape of health insurance options.
For example, an employee’s healthcare costs are a major concern — the cost of their premiums plus what they’ll pay out of pocket for copayments and deductibles. Plan information is important, but research shows that too much information is counterproductive. The best design will strike the right balance between too little information and information overload.
Again, the answer is a recommendation engine with capabilities not found in traditional benefits administration technology. For example, choose one that is built on a recommendation engine that asks employees a few questions to determine:
Then we scour the available choices and deliver the best fit to that employee.
3. The Myth: People make rational decisions when they buy health insurance because it’s an important decision.
The Truth: People are often risk averse, and that comes into play when they make benefits decisions. As a result, how plans are designed, how they are communicated, what things are called, and how they are explained all contribute to what employees choose.
“Often, people choose [healthcare benefits] on the basis of essentially irrelevant features of plans, just because the relevant features are too complex to evaluate.” — Barry Schwartz, author of The Paradox of Choice
“Consumers’ purchasing decisions are often emotionally based, as they are seeking peace of mind in their choices.” — J. Cordina; T. Pellathy; and S. Singhal, The Role of Emotions in Buying Health Insurance, McKinsey Insights.
Banish the myth with choice architecture: rational decision-making.
Give employees and their families opportunities to make benefits decisions that are based more on what really matters to them and less on emotion. Give them recommendations that fit their finances, risk tolerance, healthcare needs, and their emotional perspective.
By asking a handful of questions about risk tolerance and lifestyle, a recommendation engine can calibrate each possibility, to truly find the best fit.
The financials might suggest that a high-deductible health plan is the right choice for the employee. But the engine might uncover that the employee would be frightened by the prospect of meeting a large deductible amount. A recommendation engine can factor in the needs with the emotions to give employees confidence in their benefits elections. It’s all about providing peace mind.
An optimal recommendation engine should ask targeted questions covering emotional, financial, and health risks to help users find the best products for their individual circumstances.
4. The Myth: Avatars and videos are the best ways to convey information, because they’re friendly and appeal to the multigenerational workforce.
The Truth: The effectiveness of videos and avatars depends on their context. If users prefer speed over engagement, the context is wrong for videos and avatars to be learning tools.
“It’s a distraction, and it takes control away from the user.”— L. Quincy, What’s Behind the Door: Consumer Difficulties Selecting Health Insurance, Consumers Union
Friendly avatars and cheerful video narration alone won’t change this simple fact: Consumers dread shopping for health insurance. Speed is the key to engagement. Avatars and videos can slow down the experience. In order to be useful, avatars and videos must be contextually relevant, which is extremely difficult to achieve.
Banish the myth with choice architecture: clarity and plain language.
Straightforward questions and clearly displayed information trump cute avatars and lengthy video scripts (no matter how great the voice track is). First things first, get rid of insurance jargon. Only 14% of respondents in a national survey of people with employer-sponsored health coverage could correctly define these four essential terms:
An optimal recommendation engine will use 10 to 15 questions to gain an understanding of the employee and their circumstances.
5. The Myth: Decision support is the key to helping employees.
The Truth: It’s not about supporting a decision. That’s the old way. It’s about making recommendations to help people make better decisions.
“The way information is organized and displayed can help people make a decision. The goal: Make it easier for employees to navigate complex decisions.” — M. Renee’ Bostick, Leveraging a Standards-based Architecture for Health Insurance and Medicaid Enterprise, Principal Health Management Associates
The initial recommendation that an engine offers is hugely important. It “radically affects” the consumer.
The ‘default choice’ architecture becomes the employee’s anchor, the baseline to which they compare everything else that follows. In addition, the users don’t know what they aren’t seeing. — Kleimann Communication Group and Consumers Union, Choice Architecture: Design Decisions That Affect Consumers’ Health Plan Choices
Banish the myth with choice architecture: user-based recommendations.
Decision support is replaced by supportive, member-based recommendations. Incorporate members’ responses to benefit and enrollment questions, then use that data to form the most solid recommendations for each user. At the end of the day, it’s about the user experience, ensuring the employee has the tools to make the best possible choice.
6. The Myth: Past use (aka claims data) is the best predictor of future use.
The Truth: Many more factors than past use influence future projections. Decision support tools that rely only on past history:
When it’s aggregated, claims data can be a great tool for analyzing and predicting future claims for a group of people. However, claims data for an individual is a different matter. It’s not optimal for determining the best health plan choice for an employee.
Why? Because, as we showed earlier, selecting health insurance is an irrational buying process. So making a recommendation based only on past history fails to account for the entire emotional side of the equation.
Banish the myth with choice architecture: multiple data points.
Recommendation engines that rely only on a single source of data (e.g., past claims) to recommend coverage can often lead to employees buying too little or too much insurance coverage.
A recommendation engine needs to include multiple data points to do the job in the best way possible. Now, we’re not saying claims data is bad or irrelevant. We’re saying it simply fails to address the whole person, including how they feel about their current situation and their life down the road.
We believe in looking out through the windshield, rather than in the rearview mirror.
An optimal recommendation engine will project use based on data points that include the user’s:
The recommendation engine will be able to combine that data and more to provide the best coverage recommendations to each employee and their family. A holistic recommendation starts with understanding what the employee needs in the now, not the past.
It gets more challenging every year to understand health insurance and benefits. More than ever, healthcare consumers need trusted partners to help them make wise choices. Businessolver works hard to understand the user’s perspective, and we’ve designed our recommendation engine with the user’s needs and goals in mind.