Unum
Research Strategy • Systems Thinking • Journey Measurement • Evidence-Based Decision Making
While consulting at Unum, I joined a cross-functional team working on a digital maternity leave experience designed to help expectant parents navigate the professional and personal responsibilities of pregnancy and parental leave.
The product supported employees throughout an emotional, highly personal journey—from planning leave and finding healthcare resources to communicating with managers and returning to work.
The team wanted to better understand the customer experience.
Their solution was straightforward.
Survey the users.
They had identified a population of roughly 500 users and planned to send one survey to people who had completed the experience and another to people currently using the product.
Before they finalized the plan, they asked for my perspective.
The survey design wasn't wrong.
It just assumed every user's feedback represented the same experience.
As I thought about the customer journey, I realized something.
Someone who had completed maternity leave months ago would answer through the lens of memory.
Someone in the middle of maternity leave would likely be experiencing entirely different emotions.
Someone just beginning the process would have different expectations and concerns altogether.
If we combined those perspectives into only two surveys, we'd average away the most valuable insights.
We wouldn't know where in the journey people were struggling.
Instead of organizing research around user populations, I proposed organizing it around customer journey stages.
Completed users would receive a single retrospective survey to provide an overall baseline, recognizing that their responses would naturally be influenced by hindsight.
Active users, however, would receive different surveys depending on where they were in their maternity leave journey.
I designed four stage-specific surveys:
Each survey focused on the experiences that mattered during that specific stage. Examples:
Although the questions changed, every survey measured the same core dimensions:
This gave us something far more valuable than isolated survey responses.
It gave us a way to compare the customer experience across the entire journey.
The framework allowed the team to correlate customer sentiment across different stages while still isolating the specific moments where users experienced confusion, stress, or uncertainty.
Instead of asking whether people liked the product, we could identify:
The team embraced the approach and implemented the surveys as an automated research program that continued after my contract ended.
Within the first week, nearly 270 responses had already been collected.
More importantly, the organization now had an ongoing measurement system rather than a one-time survey.
This project reinforced something I've come to believe throughout my career:
Research should follow the customer's experience—not the organization's reporting structure.
The quality of research depends as much on when you ask a question as what you ask.
By aligning research with the natural stages of a customer's journey, we gained richer, more actionable insights that informed future features, improved adoption, and created a clearer understanding of where the product delivered value—and where it still needed to improve.