Project Meridian

Project Meridian was a ground-up rebuild of the digital experience for plan participants, sponsors, and advisors at a Fortune 500 U.S. retirement provider. As B2C design lead, I led an international team of 10 through design and testing of the full participant experience, from enrollment through withdrawal.

This case study zooms into one piece of that redesign: enrollment, the moment where decision anxiety costs both the participant and the business the most. We focused on reducing complexity and decision anxiety at the point where participants face the critical decision of how much to save and where to invest, building simple tools that helped people see trade-offs clearly and make that decision with confidence.

The product is set to launch in Fall 2027.

*For confidentiality purposes, client name has been withheld.

Within the full redesign, one challenge stood out for its size and urgency: enrollment abandonment. Participants were leaving the enrollment flow before making a savings or investment election, with real costs to both their retirement outcomes and the business’s assets under administration.

That’s the pain point this case study walks through. It sat alongside other known pain points across the broader redesign but enrollment carried the clearest opportunity for near-term engagement and revenue impact, and the clearest line from research insight to shipped design.

Analytics on the existing site revealed the primary cause of enrollment abandonment was decision anxiety, with many users feeling unsure about their choices. A significant 47.5% of users indicated they needed more time to make a decision, while 25.3% reported requiring assistance. This struggle to decide was the leading factor in users abandoning the enrollment process.

These analytics were confirmed and deepened in a round of in depth interviews with 7 research participants 22–64 years old, all enrolled in an employer sponsored retirement plan. Many described themselves as overwhelmed or confused during key enrollment decision points, like decided how much to contribute or where to invest. Most participants expressed a desire for guidance through these enrollment decisions.

“I feel fairly confident, but there are attributes that I definitely could get help with in terms of an investment strategy that works for the stage that I’m currently in”

— Participant, 58

“As an overwhelmed new hire, I need additional support so I don’t fall through the cracks”

— Participant, 23

While mapping the plan enrollment customer journey with industry experts, our team identified another high-impact design opportunity: automatic enrollment, where employees are enrolled in their employer’s 401k unless they manually opt out. In 2021, 37.1% of plan participants were in plans with automatic enrollment, a number expected to rise. Despite this adoption, the digital experience for auto-enrolled participants has remained largely overlooked, often leaving participants with low savings rates and default investment vehicles—limiting their chances of building healthy retirement savings.

Simply put, our research sprint identified two fundamental insights for improving the enrollment experience: (1) simplify enrollment decisions and contextualize them for each participant’s individual circumstances, and (2) build a dedicated experience for auto-enrollees that lets them quickly understand their default elections and act on them, rather than leaving those decisions on autopilot by default.

We started the enrollment design phase with an eagerness to empower users with tools to understand their contributions impact on their long term future. Our first wave of enrollment designs threw every modeling tool we could build at the problem: dynamic charts, real-time projections, sliders for every variable a participant could tweak to watch their future balance shift.

It backfired. In internal testing and client reviews, 100% of testers reported feeling confused when shown these early wireframes. What we thought were powerful, personalized modeling tools instead heightened the analysis paralysis we were trying to design away.

Our next round of designs took inspiration from pricing pages instead: three simple contribution rate options, with one pre-selected based on the participant’s age and income. For most users, these three buckets gave them an easy mental model for their retirement savings, and the system recommendation helped them quickly anchor on a healthy savings rate. For those who wanted to go deeper, we offered the option to customize their contribution rate — surfacing a simplified savings-estimate range to keep that decision grounded, even without the full modeling toolkit.