Starting with the Question: Sometimes it’s QUALity over QUANTity (a semi Journal Club post)
As a PhD neuroscientist, I spend a lot of time talking about data. Mostly from what people consider quantitative sources (though not always), like implicit scores, EEG signals, physiological readouts, and the statistical significance of all of it. But before any of that, I often find myself recommending something far less flashy: good old qualitative research.
Why? Because the hardest part of a study isn't usually the measurement. It’s figuring out what to measure… and why.
The Question Behind the Question
When clients come to me with a research question, it often sounds something like:
“We want to see if the experience is satisfying.”
That’s a good instinct, but also a fuzzy one. What kind of experience? Satisfying in what way? For whom, and in what context? And most importantly: what does "satisfying" look like in measurable terms… psychologically, physiologically, or behaviorally?
Too often, teams jump straight into study design or data collection, assuming the question is ready for testing. But if we don't define our variables precisely, even the most advanced tools won't save us from vague results.
Why Qualitative First
It’s not sexy. But this is where qualitative research shines. Not as a softer alternative to “hard” science, but as a powerful front-end tool for sharpening hypotheses.
Open-ended interviews, thematic coding, contextual observations. These are not just warm-up exercises. They’re diagnostic tools that help clarify what matters, what varies, and what to measure next. When done well, they set the stage for robust, neuroscience-informed testing.
It also gives us the space to consider something researchers and marketers often underestimate: the gestalt of the consumer experience. The full context of any brand or product encounter includes a tangled web of sensory cues, past experiences, cultural meanings, emotional associations, and environmental factors. Qual helps us sit with that complexity. Keeping things open-ended just long enough to truly understand it, before we drill down.
Journal Club Highlight: The Power of Framing
One article I recently came across in PLOS ONE makes this point beautifully. The authors explore how participants in qualitative studies often reinterpret or reshape the research question in their responses. That may sound like a problem. But it’s actually a clue.
Participants aren’t always answering your question. They’re answering the version that makes sense to them.
That insight has major implications for study design. It reminds us that research isn’t just about controlling variables. It’s about aligning frameworks. If your team asks, “Was this experience satisfying?” but your participants interpret that as “Did it meet expectations?” or “Did it spark joy?”… your measures (and conclusions) will miss the mark.
Use Behavioral Frameworks to Guide the Qual
That open-endedness doesn’t mean aimless. Some of the best qualitative work I’ve seen (and led) is structured using behavioral science frameworks, like COM-B (Capability, Opportunity, Motivation), the Jobs to Be Done lens, or decision science models like Fogg’s Behavior Grid or Kahneman’s System 1/System 2. These provide scaffolding for asking smarter, more targeted questions, even when responses are freeform.
They also help you see why a consumer might engage, hesitate, or abandon an experience, by anchoring your inquiry in real drivers of behavior. This approach helps qual go beyond storytelling into hypothesis generation.
And with recent tech advances, we're no longer limited to manual methods. I've been increasingly interested in using large language models (LLMs) in Wizard-of-Oz-style experiments, where an AI simulates part of the product or service experience to probe user expectations, emotional responses, or decision-making in real time. It’s not just a gimmick. When paired with qualitative prompts and behavioral framing, LLMs can help scale and simulate early interactions, revealing pain points, delight moments, or mismatches in expectations that would be hard to uncover otherwise.
From Fuzzy to Focused
Here’s what I often recommend instead:
Start qualitative. Run a few in-depth interviews. Ask open-ended questions. Let participants tell you how they talk about satisfaction, delight, disappointment.
Code and cluster. Look for recurring themes, emotional language, contextual cues.
Translate to constructs. Turn those themes into measurable variables, like “felt control,” “multi-sensory delight,” or “expectation violation.”
Now bring in the neuroscience. Only once we have a clear, behaviorally grounded framework do we introduce physiological or psychological tools.
💡What This Looks Like in Practice
For example, I’ve worked with clients testing “satisfaction” using biometric tools like GSR or heart rate variability, only to realize that what they really cared about was something closer to emotional resolution or relief. After a short qual phase, we reframed the study to test when and how that resolution occurred during the product experience. And suddenly, the physiological data made much more sense.
🧠 Let Qual Be the Compass
Even in the age of AI, neuroscience, and passive data collection, we can’t skip the messy, human part of research. Qualitative insights help us navigate the broader gestalt of consumer experience That rich, layered reality of how people engage with products in the real world.
That’s why I often start here when working with clients. Whether it’s refining a fuzzy research question, identifying the right behavioral framework, or integrating neuroscience tools meaningfully, I help teams move from curiosity to clarity.
If you're struggling with where to begin (or not getting the insights you hoped for from high-tech methods), I can help you step back, zoom out, and design smarter from the start.
