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We'll cover the first step to processing qualitative data, organizing raw data with synthesis frameworks, and communicating your findings.
Before beginning your analysis, the first step is to become familiar with your raw data. It’s difficult to come to any valuable conclusions until you do.
For most UX designers, this isn’t a solo mission. It’s ideal to collaborate with others while working through your data sets.
“You have to work as a team to help your partners in crime get realigned with what they heard and what it might mean,” Timothy said.
Timothy’s go-to method for this first step of qualitative analysis is a gallery walk. Here's how it works:
Gallery walks stifle any analysis paralysis in qualitative research because they're easy to execute. Depending on how much raw data you have, the entire exercise might only take 15 minutes to complete, not including the time it takes to transcribe field notes.
Working remotely? Not a problem. Timothy suggests using Miro, an online tool that operates like a virtual sticky-note board. Using this analysis software, you can easily familiarize yourself with raw data from qualitative studies while working with a distributed team.
Once you're familiar with your raw data, you can start to arrange them.
To do this, Timothy recommends using synthesis frameworks: structured analysis methods that help you examine your raw data to find common themes. Some researchers have used them before without knowing what they’re called; others in Timothy’s workshops ask, “Where has this been all my life?”.
Synthesis frameworks offer structure by putting your qualitative research in a visual context. And by using a combination of them to organize your data sets, you can walk away with different learnings.
In qualitative data analysis, there are many synthesis frameworks you can use. Timothy gave the following three analysis methods:
Also known as a 2x2, a matrix is one of the most commonly used synthesis frameworks. It can help you organize raw data based on overlap between the key metrics you seek to measure. It’s most helpful when you and your team want to find unexpected relationships between users or ideas.
Have you collected qualitative data that seems contradictory? Analyzing them with a spectrum might be the best solution. With a spectrum, you can chart opposing user data sets onto a linear scale.
In contrast to spectrums, this analysis method is useful when there isn’t tension among your raw data. The exercise is simple: Group or cluster together the data points with common themes you’re noticing.
More often than not, qualitative data is nonlinear, which makes it hard to follow. Synthesis frameworks help you arrange raw data in an understandable way. But even then, to come away with your final insights will take some added effort.
The final step of qualitative research — relaying your results to your team — deserves as much, if not more, of your attention. After using synthesis frameworks to organize data sets, Timothy likes to turn to insight mad libs.
“It’s a declarative sentence that summarizes your users,” Timothy said. “It summarizes their needs, and it also encapsulates a surprising insight, something you found by going out and interacting with them firsthand.”
If you’ve ever played mad libs, you know how these work. But for those who haven’t, think of insight mad libs as fill-in-the-blank statements for content analysis. Use them to frame into context what you've found from qualitative research like customer interviews.
Here are some common mad lib structures that Timothy shares in his workshops:
Arriving at a good insight requires a systematic analysis process. That’s why the structure provided by insight mad libs helps when sharing findings from qualitative research.
They're also unifying in the research process. Insight mad libs play a critical role toward bringing consensus between you and your team. When everyone's on the same page, you'll glean more meaningful takeaways from your qualitative data.
"It's not only utilizing those default mad lib structures," Timothy said. "What is the process, and what are you doing as a team to help you arrive at that declarative sentence?"
Qualitative data analysis doesn’t have to be a months-long research process. When you’ve learned how and when to use the analysis methods that Timothy teaches, results can come fast.
The public workshops that Timothy runs on behalf of The Design Gym are prime examples. These events take only 1-3 days. Participants don’t need any more time than that to make a few swift conclusions.
In one of his workshops, Timothy had participants conduct qualitative research for Shinola. It’s a Detroit-based company with a mission to bring manufacturing back to America. Shinola makes high-quality, premium products such as watches, jewelry, and bicycles.
When Shinola approached Timothy, it had a problem with brand perception: Many of its customers considered Shinola to be a company that created only products for men. But they wanted to sell to more women, too — without compromising the rapport they’d built with their male customers.
To prepare for the workshop, Timothy found people who met the demographic and behavioral characteristics of Shinola’s target audience via User Interviews. When the day came, workshop participants (researchers in training) gained experience with qualitative methods to answer Shinola’s research question.
They asked participants open-ended questions in empathy interviews and conducted brand-perception card sorting. They did some usability testing as well as in-store observational studies. The attendees also tried different synthesis frameworks to organize their data.
In rapid time, they discovered a few key insights that Shinola could use. They found that most customers understood Shinola's story while shopping in person. In contrast, Shinola’s core mission was less clear to those who bought online.
These insights proved valuable to Shinola as it moved forward. To solve its brand perception issue, Shinola needed to improve how it shared its story online. Attendees also recommended Shinola drive awareness of its values with more community involvement.
The analysis of qualitative data to discover these insights didn't take long. All researchers needed was one well-organized weekend.
Ask this to anyone who’s conducted user research, and you’ll receive a variety of answers. In Timothy’s experience, there are two related scenarios that come to mind.
Analyzing qualitative data isn’t a cakewalk. Timothy notes that many times, you only gain a handful of good nuggets from large amounts of raw data. But when successful, those few findings are often well worth the effort.
The best insights can often come from listening beyond what your users simply state. “You read between the lines, you take what somebody else said, and you keep drawing connections to it,” Timothy said.
Note: Looking for a specific audience to participate in your research? User Interviews offers a complete platform for finding and managing participants. Tell us who you want, and we’ll get them on your calendar. Find your first three participants for free.