It’s not easy to turn generative research into actionable insights—to transform mountains of input into clear, concise information that designers, product teams, stakeholders, and users can actually use to make better decisions and improve performance.
What many well-meaning researchers wind up with instead is a stack of reports and slide decks that are read once (if at all) before being filed away.
As a result, many researchers wind up trapped in a Sisyphean cycle of repeating the same research over and over—not in a useful, iterative way, but in a wasteful, duplicated-effort (and seriously frustrating) way.
When he was Head of UX at WeWork, Tomer Sharon and his team developed the “atomic research” approach to improve this process. The concept is based on his belief that the correct output of research is not reports, but “nuggets.” Read about atomic research nuggets in our UX Research Field Guide.
Atomic Research is an approach to managing research knowledge that redefines the atomic unit of a research insight. Instead of reports, slide decks, and dashboards, the new atomic unit of a research insight is a nugget. A nugget is a tagged observation supported by evidence. It’s a single-experience insight about a customer’s experience. - Tomer Sharon
Lucy Denton, Product Design Lead at Dovetail, used Sharon’s atomic method when she was tasked with running a large-scale, high-stakes opportunity research effort. And to make the project even more interesting (and daunting), she was researching researchers!
Lucy joined hosts Erin and JH on the Awkward Silences podcast to share how Dovetail turned over 300 atomic insights into an actionable roadmap that addresses users’ true needs, opens up new market opportunities for the product, and gets the Dovetail team excited about executing the product vision.
The research catalyst: What are people actually asking for?
Lucy’s research project emerged out of an all-too-common scenario: Dovetail was fielding a lot of requests on a particular feature, but the value to be gained from meeting those requests wasn’t clear.
The Dovetail team decided to dig deeper and think more holistically about the feature, its purpose (both intended and in practice, and what users were asking for.
“We wanted to really understand what users were trying to do. We wanted to make sure there wasn’t a better solution than what users were asking us to build.”
From those fairly modest beginnings, the project evolved to encompass a much broader scope of inquiry that considered the entire research landscape and ecosystem. Lucy and her team set out to learn how companies think about research, who is involved, which tools they use, and what processes they follow.
The team was careful to keep the scope broad, rather than focusing on Dovetail. By keeping the interview conversations more open, they were better able to understand the market opportunities, especially around how they might expand their product to meet previously unknown needs.
The research process: Interview, analyze, ideate
At the end of the day, Lucy and her team ended up interviewing 45 people over two short weeks. This intense effort was followed by in-depth analysis and two rounds of iterative ideation on where to go next.
First, Lucy identified the various groups of people she needed to talk with. This included people who do research, enable research, and are affected by the research, so:
- User researchers
- Research managers
- Research operations folks
- Product managers
- Product and UX designers
Lucy recruited five to eight people in each category, making sure to include a variety of current and churned customers as well as non-customers who had never used Dovetail.
As the team worked through the list of interviews, they recorded the conversations, uploaded them to Dovetail, and transcribed them.
Qualitative coding and analysis
It took teamwork to successfully tackle the almost overwhelming task of analyzing such a large volume of in-depth qualitative data. The project leaders created a taxonomy, and then the entire team helped tag all the interviews.
Once the tagging was done, the team had a huge quantity of data points, which they were able to view in a table form, sort the data, and organize it by themes and categories. By grouping related pieces of data together, they were able to identify the patterns that would later become their actionable insights.
In addition, the nature of the tagging and analysis allowed them to also categorize insights by the research life cycle phase. This helped shed light on the needs, wants, and general state of mind of a researcher at various stages of the researcher workflow. Getting this granular allowed the team to understand things like whether a particular insight was relevant during the research planning phase versus the analysis phase. Lucy explained:
“We used fields on the insights to categorize the phase in the research life cycle. So, for example, you could have an insight about privacy and know that is important during the analysis phase.”
Stepping back from the research
After conducting the interviews and running their analysis, the team had a wealth of ideas. Too many ideas, in fact.
At that point, the challenge was deciding which ideas to build. And it was a bigger challenge than anticipated.
“We didn’t really know how to progress. Some of us had gut feelings around which ideas made sense to investigate further, but we couldn’t really articulate that.”
The team realized that the real roadblock was the lack of an articulated product strategy—what they wanted to achieve with product growth at that particular point in time.
“Taking a design vision and turning it into shippable milestones or product iterations is the hardest part. You come up with this big, ambitious vision, but then how do you scope that down into something you can iteratively ship?”
So the Dovetail team hit the pause button. They set their inventory of great ideas aside, and turned their attention to running a product strategy workshop. They looked at competitors, evaluated their current product, and really focused on defining exactly where they wanted to grow. The point of the exercise was not only to uncover a clear path forward, but also to document that plan and align everyone around it.
“After the product strategy workshop, it was easier to come back to the ideas and identify which ones would help us grow in the ways we now know we want to grow.”
The team picked three to start with, and those ideas became the focus for 2021.
The research results: A clear path forward and an energized team
This research project represented a pretty big lift for such a small team. Researching researchers, executing at startup speed, and tackling a complex analysis process—all these factors added up to make the project more challenging than most. In the end, however, it paid off.
By boiling everything down to actionable “nuggets” instead of defaulting to a typical research report as an output, Dovetail was able to develop a clear and strategically sound plan for moving forward.
And while a lot of the ideas that emerged will result in big changes to the product or wholly new products (translation: a lot of work and time), the entire Dovetail team is really excited about bringing this well-informed vision to life.
Tune in to the full episode to hear more from Lucy about this project and some of the specific insights they gained about where researchers struggle.
Ready to kick off your own generative research project?
You’ll be wanting to conduct some user interviews…