As employee benefits technology has advanced, every platform uses similar promises: “personalized recommendations,” “powered by AI,” “smarter algorithms,” or “predictive claims data,” “claims data analytics dashboard.”
And on the surface, that all sounds great…right? After all, the modern workforce expects personalized experiences everywhere else in their lives. They don’t want to sift through pages of benefits information or guess which healthcare plan is right for their family. They want guidance that feels relevant to their own unique situation, and they expect their employer to deliver it.
The issue? Most benefits decision support tools are still thinking too small when it comes to how they actually define “personalization.” Many rely on claims data as their main way to guide employees, estimating what they might spend on healthcare next year based on their prior habits. But that’s not enough to appropriately guide every employee towards the right choices.
Is claims data a piece of the puzzle? Absolutely. But it’s only one piece. Benefits decisions are shaped by far more than historical claims, because choosing a health plan has never been just a data exercise. It’s a human decision.
And that’s exactly why most benefits platforms are limited when it comes to defining “personalization,” and how ALEX is doing it differently.
The benefits decision support landscape has changed
When benefits decision support tools first emerged, simply helping employees compare plans was considered a huge step forward.
For years, recommendation engines focused on one primary goal: estimate a user’s annual healthcare spending, compare their plan options, and recommend the lowest-cost choice. And as technology evolved, many vendors started incorporating medical and pharmacy claims data to improve those projections, helping employees understand how their past healthcare use might impact their future spending habits.
Somewhere along the way, the industry started calling these advancements a “personalized benefits experience.” And it made sense at the time! Using the information available to better understand employees, predict their future behavior, and help them keep costs down.
But today’s workforce (and benefits landscape) look very different than they once did.
Healthcare (and all) costs continue to soar. Expensive specialty medications are a bigger part of employees’ financial planning. Families navigate increasingly complex provider networks. Younger employees may have little or no claims history, while others make benefits decisions around life events that historical data simply can’t predict.
At the same time, HR teams and benefits brokers like you face mounting pressure to deliver a better employee experience without creating more administrative work.
Employees want more reassurance. HR teams want less confusion, fewer repetitive questions during open enrollment, and greater appreciation for the benefits your organization invests in.
Unfortunately, those goals can’t be achieved by looking in the rearview mirror alone.
What’s wrong with relying on claims data?
Claims history tells you what happened in the past. But it doesn’t tell you what matters to someone now.
Let’s think about two employees with nearly identical medical claims last year. We’ll call them Rick and Emily.
Rick
Comfortable taking on a higher deductible in exchange for lower monthly premiums.
Wants to keep seeing a specialist he’s trusted for years.
Knows he’s starting an expensive medication next year.
Emily
Prefers more predictable costs—even if it means paying more each paycheck.
Perfectly happy choosing a narrower provider network if it saves them money.
Starting to think about family planning, and will be adding a spouse to their coverage for the first time.
None of those upcoming priorities show up in claims data analytics. That’s why historical healthcare use alone can’t determine the “right” plan for each employee. In fact, relying too heavily on prior claims introduces several blind spots:
- Claims look backward. They reflect what happened last year, not what an employee is planning for next year.
- Claims can’t measure preferences. Personal financial priorities, risk tolerance, and provider preferences never appear in a medical claim. (And moreover, preferences change year-to-year depending on personal life circumstances).
- Claims don’t capture household dynamics. Dependents often have entirely different healthcare needs than the employee making the enrollment decision.
- Claims aren’t always available or predictive. Younger employees, healthier populations, or employees who are new to an organization may have little to no meaningful claims history.
Your employees don’t need a recommendation engine that simply processes what they’ve done in the past. They need a truly personalized experience that acknowledges how they’re growing and evolving over time.
What does a personalized benefits experience actually look like?
A truly personalized benefits recommendation starts with a different question. Instead of asking:
“What did this employee spend last year?”
It asks:
“What does this employee need this year?”
Those two questions start a very different conversation. One assumes your employees are going to act the same way they did last year, and only care about paying the lowest possible cost for their healthcare. The other considers your employees as a whole person, factoring in their changing health and financial situation, evolving family needs, and all of the emotions that come along with making benefits decisions.
The right health plan depends on dozens of individual considerations. Your employees are asking themselves:
- How much will healthcare likely cost me this year?
- How much risk am I comfortable taking on with my deductible?
- Do I need access to specific providers or networks?
- Am I budgeting for a specialty drug or GLP-1 medication?
- Am I planning to expand my family this year?
- Do I have dependents whose needs look very different from mine?
- What tradeoffs am I actually willing to make between premium and out-of-pocket costs?
None of these factors exist in isolation. Each employee weighs these questions differently because each individual has a different definition of what’s the “best” for them.
That’s why personalization can’t be built around a single variable like claims data. Yes, a great benefits decision support tool should consider historical data when making recommendations. But a truly personalized one? It’ll also get to know employees on an individual level—accounting for their future goals, current financial situation, changing family dynamics, and evolving feelings about their health plans.
How ALEX gets personalization right
At Jellyvision, we’ve never believed that the “best” health plan is simply the cheapest one. The best plan is the one that aligns with each employee’s unique healthcare needs, finances, and personal priorities—and gives them real confidence in the decision they’re making.
That’s why ALEX approaches personalization differently.
Instead of relying on a single data source like prior healthcare claims, ALEX gets to know your workforce through a human-like conversation, asking a robust set of questions that paint a complete picture of each employee—from their health goals and spending preferences to their family makeup and caregiving responsibilities.
In other words, ALEX combines several sources of truth to capture each user as a whole person. The result? Our platform makes more intelligent benefits recommendations in a way that no one data set could do on its own.
Here’s how:
Dollars: Looking beyond last year’s claims
Healthcare costs are always part of the equation, but they’re not the entire thing.
ALEX projects expected out-of-pocket healthcare costs based on anticipated use, while also helping employees budget for major expenses (like high-cost specialty drugs and GLP-1 medications). Instead of simply showing projected totals, ALEX puts those numbers into context so that employees understand how different plan designs could affect their finances throughout the year.
For example, ALEX asks users if they could afford to cover a surprise $1000 medical bill. The goal isn’t just to estimate upfront costs. It’s to help employees choose a plan they can realistically pay for if life doesn’t go exactly as expected.
Behavior: Recognizing that real life isn’t a spreadsheet
Two health plans may look similar on paper, but employees experience them very differently depending on their unique situation. Deductibles, copays, coinsurance, and out-of-pocket maximums don’t exist in isolation. They affect monthly budgets, financial stress, and day-to-day decision-making.
ALEX models how healthcare spending actually happens throughout the year, helping employees understand how different plans impact their monthly cash flow instead of focusing solely on year-end totals.
In short, ALEX offers recommendations that better reflect how people manage their healthcare expenses in the real world—not just in a spreadsheet that they look at during open enrollment.
Preferences: Because the “right” answer is personal
This is where many recommendation engines stop. Unfortunately, most decision support tools don’t capture enough data about each individual’s unique preferences when it comes to the type of coverage they’d like (and how much they’re willing to pay for it).
Through a series of structured questions, ALEX learns what matters most to each employee.
Maybe sticking with a trusted physician is non-negotiable. Maybe predictable monthly costs are more valuable than the potential to save more long-term. Maybe someone is willing to switch provider networks for lower premiums. (Or maybe they aren’t!)
Those preferences don’t appear in claims history, but they’re often the deciding factors behind a benefits decision. By understanding what employees actually value, ALEX delivers recommendations that feel personalized…because they are. Not because an algorithm guessed—but because it specifically asked.
Education: Recommendations only matter if employees understand them
Even the smartest benefits recommendation loses value if an employee doesn’t understand why it was made.
Benefits terminology has always been a major barrier to confident decision-making. From confusing acronyms to endless jargon (👀 looking at you, deductibles, premiums, coinsurance, out-of-pocket maximums, provider networks…), it’s enough to make even the most seasoned employees’ heads spin.
Research consistently shows that many employees struggle to understand key health insurance concepts, and people who better understand their options report feeling more confident in the decisions they make. That’s why education isn’t an add-on in ALEX—it’s part of the recommendation itself.
Throughout the experience, ALEX explains tradeoffs in plain language, defines unfamiliar terms when they appear, and helps employees understand not only what they’re choosing, but why one option may fit their unique situation better than another.
That educational approach transforms each recommendation from a giant question mark into a decision that employees can trust.
The world of benefits isn’t static—and neither is ALEX
If one truth has emerged in the benefits industry in recent years, it’s that nothing ever stays the same.
Employees’ needs change. Healthcare costs rise. New medications become more common. Family situations shift. Financial priorities change from one year to the next.
That means our approach to benefits decision support should be constantly evolving, too. That’s why we’ve continued to invest in ALEX’s recommendation engine with upgrades that strengthen personalization across every dimension. Here are just a few of our most recent improvements:
- More accurate budgeting guidance for high-cost specialty medications and GLP-1 therapies
- Refined behavioral modeling that better reflects how employees experience deductibles and out-of-pocket costs throughout the year
- Improved guidance around provider networks and plan structures, helping employees better understand the tradeoffs between flexibility, access, and affordability
Each enhancement we make is backed by the same defining principle: great benefits recommendations come from better understanding your people—not simply processing more data.
The future of benefits decision support starts with the whole person
Here’s the truth: when it comes to the tools you offer your workforce, personalization has become an expectation—not a nice-to-have.
Your employees aren’t spreadsheets. They aren’t claims histories. And they certainly aren’t all looking for the same exact health plans. They bring different financial realities, healthcare needs, family situations, and priorities to open enrollment every year.
And they’re looking to you—their trusted benefits advisors—to offer a decision support platform that reflects their complex realities.
That’s what ALEX was built to do.
By combining projected healthcare costs with behavioral modeling, employee preferences, and personalized education, ALEX delivers recommendations that feel genuinely personal—not because they rely on more data, but because they rely on the right data.
For HR leaders and benefits brokers, that means more confident employees, fewer enrollment questions, and a benefits experience that feels less transactional and more human.
