Why predictive analytics is the key to better benefits guidance

Choosing a health care plan is all about finding a balance between cost and coverage. But these days, that task is a lot easier said than done. With so many options to choose from and confusing jargon at every turn, figuring out how to spend less while securing the right level of care can feel like an endless maze.

Employers have increasingly been looking for ways to alleviate that burden, and help their workforce answer the question: How do I get the right coverage for the right cost? But unfortunately, it’s a problem that no human can possibly answer accurately.

After all, how could any one benefits pro anticipate each and every health need a given employee will have, understand their financial situation, account for family members who need care, and make spot-on, personalized recommendations? You’d be up all night, every night — and that’s just not sustainable.

Luckily, new technology has emerged to solve this very problem, in the form of predictive analytics. Leading benefits pros are relying on predictive analytics to streamline the enrollment process, make better recommendations than any human could, and improve their client relationships.

But what is predictive analytics, and how can it help you provide a better experience for your clients? Let’s jump in.

What is predictive analytics?

Predictive analytics uses historical data and machine learning to identify how likely it is that a future event will happen. By analyzing historical information, predictive analytics algorithms can spot patterns and trends, giving end users a better idea of how a given scenario will play out.

These days, it seems like predictive analytics is everywhere. This type of technology can be applied to an endless number of fields, like marketing, finance, retail or sports.

In fact, if you’re an online shopper, you probably encounter predictive analytics every day without even realizing it. Remember the “Customers also like…” sections on e-commerce sites? That’s right, its predictive analytics at work — assessing your shopping behavior, comparing it to other shoppers like you, and outputting recommendations for other products to buy.

How does predictive analytics apply to the world of benefits?

Within the past decade, the health care industry has increasingly applied predictive analytics to a variety of problems, with the goal of improving patient outcomes, reducing costs, and optimizing how resources are used.

For example, predictive models can be used to identify patients at risk of developing certain conditions, predict hospital readmission, and inform personalized treatment plans.

In other cases, predictive analytics can be used to decide where health care resources should be allocated. For example, health care professionals can forecast patient demand, identify bottlenecks in the supply chain, and optimize staffing schedules to improve efficiency and reduce costs.

And in our world of employee benefits, predictive analytics has revolutionized how we connect employees with the health care plans that are best for them specifically. Making health care decisions is incredibly complicated and includes endless factors — from cost and coverage to finding in-network providers and deciphering medical lingo. And while we can’t give employees a crystal ball to flawlessly forecast their health care journey, we can give them the right technology — in the form of predictive analytics — to help ease those decisions.

Why is a data-driven approach better than traditional benefits guidance?

Employees have to weigh a lot of factors when deciding which benefits give them the best value. What health care will I need? What will it cost? How much risk am I willing to take on? What is this benefit, and how does it work? Everyone has to consider these questions when choosing health plans, and in the absence of decision support, most people make the wrong choice. They overestimate their risk and end up spending too much money.

That’s where the right technology and data can help. Here at Jellyvision, we’re finding that a combination of predictive analytics, economics, and behavioral science is the key to helping employees anticipate their upcoming needs, avoid wasted spending, and secure better coverage for themselves and their families.

We’ve covered predictive analytics, but what do these other two terms mean?

Economics: No, we’re not talking about money here. Microeconomics is the study of individuals, quantifying how people behave in different conditions. So with economics, we have a way to evaluate uncertain events — like how many employees will complete their preventative care check-up this year, or how many folks will visit a chiropractor.

Behavioral science: Behavioral science studies human actions — and how our own biases, lack of knowledge or previous experience can influence our decision-making. We can leverage behavioral science to identify common benefits pitfalls, and steer employees towards the right plans and decisions for them — based on their own personal health, finances, and other factors.

The power of these three data-driven approaches combined can catapult our industry into a new era: where we’re not just offering our best guess as to which benefits an employee should choose — we know which benefits are the right choice for them.

An added bonus? A digital decision support tool is simply what employees want. According to Gartner, 42% of employees would rather use a self-service HR system than speak with a specialist. So providing a benefits engagement platform that relies on these new technologies not only gives employees better guidance — it gives them the benefits experience they expect.

Conclusion

If you’ve made it to this point, I know what you might be thinking. Whenever we talk about new technology, a big question always arises: “Are the robots coming to take our jobs?”

But as we’ve seen time and time again, predictive analytics and other emerging benefits tech isn’t here to eliminate our roles as brokers. It’s here to make us better.

Read more: Meeting workforce demands through the convergence of technology, analytics and value-based care

By streamlining the decision-making process and providing employees with stronger recommendations, we can improve our relationships with clients, win more business, and ultimately focus on what really matters: big-picture strategic initiatives that will help our clients offer the best possible benefits programs to their workforces, year-round.

I hope you’ll join me in embracing our industry’s evolution — because it’s an exciting time to be a broker.

Keith Vallera serves as Jellyvision’s VP of Channel Sales.