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Untangling the Deeper Concept

Author of Practical Empathy Indi Young breaks down why so many of us look for research patterns in the wrong places. 

Words by Carrie Neill, Visuals by Emi Tolibas

Indi Young doesn’t have anything against Post-its, per se—except, she admits, they might be the enemy.

“I hate to blame sticky notes,” says the author (most recently of Practical Empathy) and founding partner of UX agency Adaptive Path. “But I work with so many teams where people are trying to do things like affinity grouping by using a wall of sticky notes. And a sticky note is just too small. It requires us to reduce what people are saying into really short phrases, and those short phrases are too general. They don’t contain the actual person anymore, or any of their vocabulary. And they often center around a noun.”

Nouns, Young says, are another problem—too many researchers rely too heavily on nouns when they’re trying to synthesize data, and end up pulling out data points based on the what and not the why.

“One thing I find is that many people don’t actually know how to actually synthesize patterns from qualitative data,” Young says. “We don’t do a very good job of teaching it, so people just think about data insights in terms of what ‘stood out’ to them. And there’s inherent bias in that.”

Young, who also teaches a series of advanced courses and lectures widely about mental model diagrams and thinking-styles, says that in order to get to real, actionable insights, researchers have to start thinking in verbs. We sat down with Young to hear just why action words speak louder, why she advocates a “slow-food” approach, and what researchers can do to push our own boundaries even further.

dscout: You’ve said that sometimes, research “insights” are the junk food of product teams. What do you mean by that?

Indi: So many of the product teams that I work with understand the value of research, but they’re forced by external circumstances to go too fast. And often when teams are under pressure to go fast, they resort to the McDonald’s version of research. It’s not terribly nutritious. It’s not going to actually be informative. A lot of the time it just confirms what you suspect, because you’ve unknowingly set it up to do that. So teams make decisions based on those insights that lead them down the wrong path, or a very narrow path, or a circular path, and in six or twelve months they throw up their hands and say, “I don’t know why we’re not making any progress.” Well, that’s why.  

So I advocate for teams to try and take the time to get out of that strictly solution space. To try and really understand what it is that people are going through, not just whether they like a certain idea. How people feel about a certain idea is really just a way for companies to measure their own services. What we really need to do to figure out how to best support people—and to do that we have to come in with no assumptions, no set questions, and just listen. We need to think about things like, what’s the larger purpose this person has? What are they trying to accomplish? How are they getting it done? What are all the tools that they’re using to get it done? And possibly, there are no tools. Because this kind of slow-food listening is not about services or products, it’s about trying to understand what people are trying to achieve—and when you do that, it just might point out a gap or a weakness in your own product, or it might point out a new opportunity to innovate.

Ok, so break the slow food approach for us—how do you advise teams to go about doing that?

It all starts with listening sessions. Trying to get a handle of a person’s inner thinking while they’re trying to achieve a larger purpose. To do that, you want to be as close to telepathically connected to someone as possible, and listen or watch them in context as they’re going through the motions of what they’re trying to do.

For example, I did a study for an appliance manufacturer who wanted to innovate in the kitchen. Instead of talking about how people about they were using their stove or their fridge, we talked to people about how they cooked dinner. And we narrowed it down to people who thought of themselves as creative chefs. We started out with a remote interview via phone, and then a few days later we showed up in their kitchens while they were cooking dinner. For that group, being in context was really essential. There were so many things they were doing that had become a habit or second nature that they couldn’t really articulate all of it. But when we could see them and see what they were doing live, we were able to go much deeper, and get them beyond explanation level in some cases. For instance, at simply the explanation level, someone might have taken us through what they were doing this like: “First, I'm cracking the eggs. Now I'm waiting for them to get brown. Now they’re brown and crispy around the edges, so I'm going to take them out, because it's time to throw in the garlic.” And there are two places there where a researcher could go deeper. Why did they want the eggs to be brown and crispy around the edges? Where did that come from? And why the garlic in afterward? What’s the inner thinking happening there? That came from somewhere.

Or, there was one creative chef we spoke to who was using turmeric in a stir fry, and she was holding it above the pan and grating little flakes off. And we asked why she was doing it that way, and she told us it was because turmeric stains the countertop. It turned out she had recently been turned on to turmeric because of the health benefits. She’d tried it in pill form, and it seemed to make a difference for her well-being, but she really preferred fresh food. So she’d bought the root and took it home and starting cutting it up, and afterward realized she couldn’t get the stain out of the cutting board. She’d tried for a couple of days to get the stain out of the cutting board, putting baking soda on it and everything, but it hadn’t worked. That was especially frustrating for her as she really tried to keep her cutting boards very clean, because she worried about germs and contamination, which it turned out was because of a stomach problem her mother had gotten from some contaminated food. So we got so much more information about this woman than we would have if we’d just asked her to tell us how she was making a stir fry.

That’s the level you want to get to—to the level of what is going through a person’s mind as they solve the problems they encounter in the moment. And you get that through specifics, not generalizations—because when you talk about generalizations, you don’t get down to the actual inner thoughts that went flying through someone’s head, or the emotions that they felt. We need to get down to that level to get those inner thoughts, those real reactions in the moment, because that’s the only way to develop cognitive empathy. We have to listen, and try not to intrude our goals onto others. In these kinds of research sessions, we have no goal, except to understand someone’s stream of consciousness. That’s really key, because a lot of the time, we’re not even aware that we’re generalizing.

We’re unaware of our own bias.

Exactly. There are conventions and ruts that we all fall into when it comes to data, especially when we’re dealing with it on a daily basis. The whole point of a listening session is to try and get outside of our own heads—either our individual heads, or the heads of our collective team. Our team can only think of the ideas that are already somehow in our own brains—whether those are inspired by something we’ve read, or data we’ve seen, or our own lived experiences. But we’re limited by ourselves.

That’s true for researchers too, not just for others on the product team or clients. One thing that I find in my work is that many people have no idea how to actually synthesize patterns from qualitative data. They just do it based on what “stood out” for them. Well, there’s inherent bias in that.

One thing that I find in my work is that many people have no idea how to actually synthesize patterns from qualitative data. They just do it based on what “stood out” for them. Well, there’s inherent bias in that.

Indi Young

So how do we avoid that kind of bias? How do we really look at the research from the bottom up? Especially if it isn’t something we’ve been taught.

I think part of it comes from understanding where the work really is. Often, research teams are so exhausted after they’ve collected the data that they mistake that exhaustion for feeling like the hard part is over. They’ve done their recruiting, they’ve conducted their study, they’ve dealt with people who had problems, and they’ve collected a massive amount of data. But this is the point where I actually try to get teams to take a couple of days away from the data, because the hard part isn’t over.  

Hearing the data isn’t the same as understanding the data. For every hour of listening that I do, I generally do about eight hours of dwelling in the data. If you don’t actually spend that time with your transcripts, you’ll end up with generalizations, surface level information. What we really need to do is find the depth within the data, and untangle all of the concepts that are tangled up within it. And you have to separate them out not by concept meaning, but by concept type—by intent. Because two people might be talking about different subjects, but they might be focused on the same ultimate goal. It’s our job to understand that when we’re looking at the data.                      

Another way to think of it—when you’re looking at data, don’t group things together by noun. Group them together by verb. I’ve done a lot of work with the healthcare industry, and one thing I often see research teams do is bring together insights that are all about a noun—here is all of the data that we got about how people feel about the doctors. But when you do that the intent behind what people are really saying ends up all over the place. One person is complaining that they don’t trust their doctor. Another is asking, how do I find a doctor? Organizing your data that way will actually do you a total disservice—when you go back to solution land and try to ideate and come up with a product that will help address people’s concerns, you’re going to tear your hair out because there are a million concepts that call out the keyword “doctor.”                         

Instead, center your insights around verbs. If you think about the data that way, and line up concepts by intent, it becomes so much easier to wrap your mind around how people are feeling and what they want. Maybe what someone is saying when they’re complaining about their doctor is really that they just want to feel heard. And maybe that’s the same thing they really want in an interaction with their insurance company, or their pharmacist—to be able to vocalize their concerns to the person who is taking care of them. And when we’re looking at our data and trying to figure out what it can tell us, that’s a much easier concept for a product team to wrap their heads around.

When you’re looking at data, don’t group things together by noun. Group them together by verb.

Indi Young

So how do you go about finding that connecting verb, especially when two situations might be really different? How do you drill down to the heart of the intent?

Well, that’s why we need the human mind to do this kind of analysis. In the creative chef study, one woman told us that she wanted to bake something that her mother couldn’t ever successfully bake. And another person told us she liked going to the farmer’s market and choosing what to cook based on what was fresh at the market that day. She did that because, in her mind, that was how real chefs did it. She wanted to elevate her cooking beyond what a home cook would do. In both cases, the women were doing things that felt like they were a level above what had been done before. And that was important to them. Ultimately, that was the reason behind what they were doing, and so by drilling down and getting to that intent, we were able to understand what they really wanted from the cooking experience much more clearly. When we use data that way, we’re really standing in a user’s shoes in a way we can’t otherwise.

Carrie Neill is a New York based writer, editor, design advocate, bookworm, travel fiend, dessert enthusiast, and a fan of People Nerds everywhere.

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