Quant data is like a smoke signal that points to an interesting place. Qualitative user research uncovers the story from the ground.
As a UX strategist, I perform qualitative research to advocate for users’ POV. People sometimes ask:
You only spoke to 10 people, so, aren’t your findings just anecdotal?
If I chose 10 random people to talk about topics I chose on my own, yes, my findings would be anecdotal and quite biased. Instead I use quantitative insights culled from surveys, funnel tracking and other analytics to ensure I’m asking the right questions to the right people. The marriage of both Quantitative & Qualitative data ensures we’re having discussions that matter.
Quantitative Data (surveys, tracking & other analytics) provide the WHO, WHAT, WHEN, WHERE of an issue.
Qualitative User Research (interviews, diary studies, deep dives, product tests) provide insight into WHY and HOW things happen and clues to how to make a real impact.
Good research uses a Quant | Qual sandwich.
I start with Quant to identify the important populations, subjects and /or moments in our funnel to focus on. Leveraging analytics, I know that people of a certain demographic are who I should be speaking to.
The next problem to solve is what to talk to them about. Are there 25 topics on the table to discuss? Put out a survey to that target population and figure out what the top 5 topics are. Scratching heads on a complex product? Work closely with analytics to see where interesting things are happening. Where is the major lift or drop off? Focus user discussions on those areas, or the subjects that impact those areas.
Sometimes it’s an open faced sandwich. The things you learned in Qual make sense and you can make forward. Other times, it can be a head scratcher or a divisive finding. You need to follow up with another slice of Quant to validate anything you learned. For example, people kept bringing up X, but there are questions or doubts about how many people X really impacts. Run a survey and see if X scales. Better yet, see if you can find out what other factors correlate.
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Inductive vs Deductive
When people think about research they most commonly think about Deductive studies where they have some ideas in mind and they want to deduce which are the stickiest.
We have a list of features we’re thinking about and we’d like to run a survey to deduce which are the most important.
Before getting too attached to ideas to evaluate, it’s important to also include Inductive moments in your research that are open ended, making room for unanticipated concepts. This is where user research becomes very powerful.
For example, in user interviews I performed at Credit Karma I noticed an ongoing trend of people bringing up “stress spending” — spending they did to relieve stress that ultimately ended up becoming the source of stress, triggering an unending cycle. This unexpected concept surfaced because of open ended — or inductive — portion of interviews .
We needed to see how many people “stress spending” really impacted to make a call on how to address it. We deployed a survey. Not only did we find that stress spending is something many people experience, but we were able to inspire further surveys to understand more details. What were people stress spending on? How did different behaviors map to demographics? Our editorial team ended up writing a piece based on the study which was later covered by Forbes and NBC.
We used these insights to drive core features and marketing campaigns.
All because we took the time to listen to human size stories then examine the impact at scale.
