The Evive Plan Choice team just wrapped up a few months of discovery work. Now just what does this mean? Discovery work is where we pursue activities that help us deeply understand user behavior. It’s a critical stage in product design to ensure the solution is meeting the needs of the users, and to prove or disprove any assumptions about the experience.
In this case, we were doing discovery work for our open-enrollment support product, Evive Plan Choice. The purpose was to glean insights that will guide changes to the product over time. It was part of a big-picture approach to user testing across products in a variety of ways, as we did last year with Evive 360 through empathy interviews.
When it came to Evive Plan Choice discovery work, we broke it up into a few different areas:
On the heels of the 2019-2020 open enrollment, we decided to interview 10 of our colleagues at Evive about their experience with open enrollment. Before jumping into those conversations, we created an interview guide to structure the questions in a way that led people to tell us stories, rather than just one-off answers. With permission granted from each interviewee, the discussions were recorded and transcribed so we could refer back to them in detail later. After all interviews were completed, we put together a rainbow report analysis of what we learned, pointing out the biggest trends.
Analyzing the user experience
Next, it was time to return to the users who did engage with Evive Plan Choice for their open enrollment. Our Data Science team began to pull as many relevant data points as possible from our user dashboard. This focused on what actions people were taking (or not taking) as they used Evive Plan Choice.
These statistics helped us identify the most challenging areas of the product and opportunities for improvement that we needed to focus on. It was helpful to compare those points of interest with the challenges shared with us in the one-on-one interviews with Evivers. Coupling these learnings together empowers us to make smarter decisions in future product design.
Categorizing the data
Using both the quantitative and qualitative data that we gathered, our team wrote “How might we” statements. These are statements that capture the more critical problems and opportunities that Evive Plan Choice can solve—for example, “How might we give people confidence in their HSA contribution amount?” Or, “How might we understand why a user says ‘other’ when selecting the reason for choosing the plan they chose?” We prioritized these statements, based on user impact and value.
Following that, the entire Evive Plan Choice team took part in an in-depth affinity clustering exercise. This allowed us to ideate on the “How might we” statements and bucket them into important themes.
Takeaways + next steps
Of course, what’s the purpose of discovery work if not to apply it to product development? That’s a key priority for us: We’re building enhancements to the product, in small chunks, based on what we’ve deemed most impactful to the user.
It’s worth noting that none of these activities are meant to be one-time occurrences. This round of discovery work was simply one example of how we practice pillars of The Evive Way—to embrace curiosity and learn continuously. We will continue to build on these approaches and apply them to our product, always keeping the goal in mind of creating a better open-enrollment experience for all.