Megan Ruxton on Mixed Methods in Market Research

In this episode of the Greenbook Podcast, host Karen Lynch sits down with mixed methods researcher Megan Ruxton—most recently with the FDA and previously with Vibrant Health—to explore how researchers can turn ambiguity into action. From academia to public health to tech, Megan’s career has been driven by a passion for applied behavioral research. She shares how mixed methods can uncover the “unpredictable magic” of human behavior—insights that AI alone can’t capture.

The conversation touches on building structure from chaos, navigating stakeholder resistance, the future role of AI in research, and why researchers must embrace both the messy and the measurable. Megan’s blend of optimism, experience, and human-centered thinking offers a refreshing perspective on what’s next for the insights industry.

Key Discussion Points:

• Why applied research brings more impact than theoretical work
• How to structure ambiguity using tools like logic models and the Five Whys
• Defining true mixed methods—and how to select the right sequence
• The irreplaceable role of “emotional squishiness” in a world leaning on AI
• What makes insights actionable in high-stakes decision-making

Resources & Links:

• Beneath the Why – Christopher Brace’s workshop referenced in the episode

You can reach out to Megan Ruxton on LinkedIn.

Many thanks to Megan Ruxton for being our guest. Thanks also to our production team and our editor at Big Bad Audio.

Transcript

Karen: Hello everyone. Welcome to another episode of the Greenbook Podcast I’m hosting today, Karen Lynch, happy to be hosting today, as I am most of the time when I do this. It’s just a pleasure to speak to all of you. Today I’m speaking with a woman, no lie, friends, we met on Tuesday, if you think about it, and here we are on Friday, four days into our relationship, and our conversation is so promising that we’re having it live with all of you. Just wanted you to know that. I’ll talk more about that in a minute. But I have Megan Ruxton on with me. She’s most recently with the FDA. Previously, she was with Vibrent Health. She’s a mixed-methods researcher who, you know, takes, kind of, human behavior and turns it into strategy, and she’s done this in public health and tech and different policy sectors. Megan, first and foremost, welcome to the Greenbook Podcast [laugh].

Megan: Thank you so much for having me, Karen. I’m so happy to be here. 

Karen: I’m so happy you are here. And I’m going to let you introduce yourself a little more thoroughly in just a moment, but friends, I just want to let you know, this is what happened. Megan reached out to connect with me on Tuesday. I said absolutely. And then on Wednesday because she showed up in my feed, she—and this will not make sense from when this gets published, these dates, but just trust me—day one, she published something on LinkedIn that caught my eye, and it was the concept of unpredictable magic in the world of kind of human insights and AI methodologies. So, I just held on to that and latched on and said, I really want to explore that with you. Can we do it live on the podcast? And here we are. So, before we get into that, that concept that just anybody who knows me knows I get intrigued by all things magical, before we dig into that, tell us a little bit more about, kind of, yourself and you know, and your background and how we got here today.

Megan: Yeah, absolutely. So, I am a mixed-methods researcher. I’m kind of a mixed-methods evangelist, which I think we’re going to touch on later. My background is actually political science. I did my PhD focusing on American politics and environmental politics and policy. I did find myself really, sort of, shifting away from that, really because I wanted to move from the theoretical to the applied. So, since then, I have worked at an applied research center, I had the entrepreneurial bug, I was an independent consultant in program evaluation, I was at a digital health technology company as a survey methodologist, and then, as Karen said most recently, at the FDA. Ended unexpectedly for reasons that I think we can all assume, and so now I’m reassessing where I want to go, and it’s really something that I want to pivot back into is that behavioral research, using those mixed methods to unearth the random, unpredictable things that people do, and use that to help make better decisions.

Karen: I love that. Yeah, and we’re going to dig in just a minute to all of that, but I do want to go back to one thing you just said, which was the idea you wanted to kind of move from the theoretical to the applied. Tell me the appeal of that for you. Like, what was it that kind of triggered that desire to shift into the application of all you knew?

Megan: It just felt so incomplete when I was doing it. I was uncovering all of these really interesting trends and all of these underlying behavioral, you know, aspects of really important policy issues and cultural issues, and then I was putting it out into the world, and then it stopped. You know, I had no control over what direction it took. I had no control whether anyone even recognized it or wanted to use it. And so, you know, I love that there are people doing theoretical work. I don’t want to cast aspersions on my academic colleagues because they’re doing such amazing things. I just, I wanted to take it to the next step. I wanted to have that additional impact where I was the voice in the room saying, “Here’s something that we saw. Here’s a direction I think we need to go.”

Karen: And, you know, it ties in really nicely to something that, again, I saw on your LinkedIn profile, where you kind of self-described yourself as somebody who brings structure to ambiguity. And in the world of research, so much of what we do, right, is translation [laugh] related. Like we take this, kind of, you know, ambiguous data that we have, and we turn it into something else. So, tell me a little bit more about that, and maybe give us an example, if you have one, about how you’ve done that.

Megan: Yeah because, you know, often what happens is that you’ll have clients come to you that want to either measure all of the things. They have, you know, 27 different directions they want to take, or they come to you with something very vague, of ‘I want to understand my customers better,’ or ‘I want to understand my clients better.’ And so, the first step is taking that ambiguity and distilling it down into okay, what are the actionable steps that we can take? So, I have a couple of things that I have borrowed from my previous career lives. You know, when I was teaching in the academic world, I used the Five Whys, where we start with ‘I want to understand my customers better.’ ‘Okay, tell me why. Why is that important?’ And then we, you know, keep asking why at every level down, and eventually we distill down to, ‘I want them to feel a certain way when they see my brand,’ or ‘I want them to continue from onboarding all the way through a process.’ And so, you know, you can use that structure just to get down to the nitty gritty of, okay, what is it we’re trying to do here and then you take that and work backwards. So, as a program evaluator, I used something called a logic model that really is so useful. I think everyone should be using it, even outside of program evaluation because it really just puts into buckets, okay, what are the outcomes that we’re looking for? Okay, well, what are the outputs that tell us that we have reached that outcome? What does that look like in practice? And then we can keep working backwards from there of what is it that we need to be doing, what’s our strategy, and what are the metrics that are going to tell us whether or not that will work? So, I actually, years ago, worked with a university that wanted to rebrand, and they came to us and said, “We want to understand what people think of our brand.” You know? “What is it they think of when they hear you this university name?” And it was sort of full stop there. There were no details. And so, it was a matter of digging into, okay, who does this matter to? Why now? Why are we asking about branding now? What is the problem that we’re looking for a solution to? And so, you know, we wound up doing focus groups with parents and prospective students and minority groups and media and a statewide survey of what does the general public think of this university brand. And so, it really was a matter of going through, distilling who it was and why it mattered, and then coming up with the different ways that we could actually measure that. And so, they did have a plethora of data and information that they were able then to use to start thinking, okay, what are the changes we actually need to make?

Karen: I’m so down for everything that you’re saying. And there’s a part of me that really wants to talk about, you know, kind of your Five Why Method because I’m like, oh, in creative problem solving, we use a ladder of abstraction all the time, and you know, we do that, and it’s really important to get out to that fifth why, too, right? Like, I could spend a lot of time talking about that, but that’s not necessarily why we’re here. Look up those things, right? Go ahead and do a Google search. Use those tools because I love that you shared them. But that last example that you gave me has me thinking about that is truly what mixed methods are, right? Like taking them all. A lot of people talk about hybrid methods, which I think it’s a little bit of qual and a little bit of quant. But let’s just talk about mixed methods specifically because, like what you just described, feels like… a buffet of research methods [laugh] and how you pull it all together. So, kind of define it for me, how you define mixed methods and then we’ll dig in further.

Megan: Sure. So, I think that the idea of mixed methods is a little bit scary to people because the question is, how am I going to bring this all together into a cohesive narrative that I can then actually work with? For me, mixed methods is ‘fit for purpose.’ You can get trends and you can get what people are doing and what their attitudes are through surveys, through other, you know, quantitative methods, but if you want to dig deep into the why that’s when you have to bring in those qualitative, in-depth interviews, those focus groups, you know, those ethnographic studies. And you can do it in a number of ways. You can do sequential. A really good idea is, if you’re still figuring out what it is you need to know, start with focus groups or interviews, then actually construct the survey based on what you find there, and you can always do a follow up of focus groups later, but you can also do them in parallel. You can get that big picture view of, you know, the statewide survey while also having those targeted groups that you’re getting the deeper information from. And so, is it a challenge to bring all of that together? Of course it is, but that’s why we do this. That’s why we’re here. And so, I think that’s where you get not only the big picture, but then those nitty gritty details that you start getting into those unexpected trends that are not captured in the quantitative.

Karen: Yeah, and I imagine that, you know, I think back to the days when I was executing research full service—which I had done, for your own background, you know, prior to joining Greenbook—and I was executing research full time with qua—I was kind of, you know, qualitative lead, either in my own business or at other businesses. And you know, how many proposals came our way that called for some sort of a mixed method approach? But then there’s the age old question of, well, how many? Like, and yes, how do we decide? Do we kick off with some one-on-ones, or do we kick off with some groups? And do we start with the survey and then do the qual or vice versa? So, talk to me a little bit about how you, kind of, decide on what method to kind of put—or what approach to put forth when you are balancing all of the different options in your mind all the time. Because personally, that was always one of the biggest challenges to me is, I have all these ideas; how do I converge on the right approach?

Megan: For me, especially in all of the positions that I’ve held, my first question is, what are our resources? Because that’s always going to be the guiding North Star of what you’re able to do. I’ve worked with a lot of nonprofits, a lot of academic centers, and so resources are always the biggest question. If resources are not an issue, then we just really need to focus on what is it that we’re trying to get out of each of these. You know, do we just need those big trends? You know, is this exploratory, you know, where we just need five to six focus groups, you know? It’s one of those things where you can’t be concrete and inflexible when you’re designing these things. You have to remember that, in research, things are going to go a way that you’re not expecting, and you need to be flexible, and you need to be adept. And, you know, we always use the word agile, and I think sometimes we forget what agile actually means. We need to make sure that we’re being agile in all of the ways that we approach research. And that means maybe we set out, we say we’re going to do five focus groups with eight people. We don’t get down to what it is that we really want. Let’s bring in another five, you know? So, just constantly asking the question, are we getting what we need? Are we getting what is actually going to move the needle farther down?

Karen: Yeah, yeah. Talk to me sidebar; you know, this is not in the brief—but as you’re discussing, I’m sitting there thinking, you know, like, there might be a difference between kind of being a social scientist and being a traditional market researcher, in that a lot of partners in the industry are like, I’ve been given a budget. I don’t have necessarily the ability to be agile when it’s like, gosh, you know, if we stray too much, we say we’re agile, but if we stray too much, then we’re going to be over budget, and we’re going to, you know, really, you know, be off track. We don’t have the flexibility to be able to do that. And I’m wondering if it’s different in kind of a social science world, you know, where maybe you can evolve it a little differently than somebody who’s kind of hired to run a project a certain way. So, what are your thoughts about that? 

Megan: I think it is two different mindsets. I would say, you know, as a social scientist, coming from more of a very, you know, broad research lens, I think there is this idea of, you know, I can kind of bob and weave my way to where I need to get to. You know, that doesn’t mean that resources are unlimited, but you at least have some leeway in you know how you’re going to actually use those. Whereas in market research, and especially, I know everything is so fast paced now. You know everything has to happen within a very short timeframe. So, I think there, it’s a willingness to take risks and a willingness to fail, which is scary when there’s money on the line. But you know, you are going to—it’s inevitable. You are going to have ideas that fail and you’re going to have things go sideways. You know, so you can attempt to build as much flexibility into your protocol as possible, but ultimately, you need that gut instinct, you need that experience to tell you when you need to shift and when it’s worth the risk of doing so. 

Karen: It’s interesting. I’ve had the conversation before with younger researchers who—younger meaning less experience in the field of insights and analytics for instance—they’ve said to me—I’m a mentor in the industry, so this is coming directly from people who are like, when do you trust yourself? Like, when do you trust that what you’re doing is actually spot on? And I was like, you know what? It was one of those things in my world that was somewhat, um… a question, until it wasn’t. And I couldn’t tell you the exact day, but there was a moment in time where I was always a little bit, like, unsure. And then there was a moment in time where I’m like, “Well, of course I know exactly what to do.” And so, it’s almost like if the moment could be celebrated, like, ah, I’m at that moment now. And you could say maybe it happens ten years in, or maybe it happens twelve years in, or maybe it happens eight years in, or you know, who knows what the magic number is, but it feels like something that should be honored when you suddenly feel that conviction of… starts with the methodology, right? It starts with the approach that you’re like, I believe this approach will work. And then there’s the other part of it, which is, like, I believe that these are the right recommendations to make, even if they do fail. Like, I trust that this is still what the research showed us. So, I don’t know if you have any experience with that, too, that kind of that moment, which you know, we could call magic, also, [laugh] of when that feeling hits you that you know what you’re doing.

Megan: You know, and it’s always—for me, it’s a scary moment because I suddenly realize, oh my goodness, I am the adult in the room. How did that happen? When did that happen [laugh]? Yeah, and I think for me, it happens again and again. You know, with each role I take on, you know, I’ve pivoted fairly often in terms of, you know, what sort of, you know, context and domain I’m working in, and so, you know, for me, it happens with every new area that I’m in, of suddenly, oh, I just, I didn’t second guess myself. I deserve a pat on the back. And so, you know, I hope others have that too, of that sort of sense of renewal every time of, I just became an expert. [laugh].

Karen: Yeah, yeah. I know exactly what you mean in the context of switching roles, also. I remember speaking to somebody when I took this role, which was very different for me because I’m not executing research anymore. You know, I’m leading content at a, you know, a media company that is in the industry that, you know, I’ve always known, but it was a very different role for me. And I remember saying to somebody, like, “When? When does that happen? Like, when do you feel like you are solidly in your role?” And she said, “Not the first year.” And I was like, “Are you kidding me?” I was like, “Not the first year?” And she’s like, “Just recognize you will not feel the expert the first year in a new role.” And I thought that’s an incredibly long time to feel a little, you know, rocky in a new position. But especially today, I think when our world is vastly different than it was, you know, even two years ago, three years ago, in terms of the tools at our disposal, it probably takes more than a year, maybe two, to feel that you’ve really settled into a position. I’m not sure what that number is. 

Megan: Well, and even now—and I know we’re going to be talking about, you know, the role of AI. I think that has a lot of people going back to that feeling of, I’m not quite sure what it is I’m doing here. I think it snuck up on us to a large degree. And so, I think there’s also a comfort there of knowing that we’re all in that same sort of shaky place, but also knowing that we’re all going to get to the point where we realize we can trust our instincts and know when it’s the right time to use the right tool.

Karen: Yeah, yeah. Yeah, let’s talk about that specifically in the context of kind of taking the insights that we’ve developed or generated or, you know, gathered, and then kind of really turning them into action. So, let’s hover there for a minute. When you think about turning insights into, like, a business strategy, or like, this is a working plan for the [laugh] future, which much of the research that we need to do today—and by today, I mean, you know, in 2025 when this is airing—many of the conversations that I have with, you know, people who are leading insights at large brands, for example, are saying, like, the function of insights right now is largely to inform business strategy, business decision making at a very large scale. So, in your opinion, what does it take to kind of make insights actionable at that higher level?

Megan: I think truly understanding what are the decisions to be made. And, you know, understanding not just okay, what are the decisions, but also understanding the context in which those decisions are happening. You know, what is the stress? What is the pressure of this particular decision? Is this something small, where we’re looking to do you know a label change, or is this something big, where we’re going to be starting off an entire new type of our brand, or a type of the service that we provide, and understanding that sometimes those big decisions also need to be broken up into a number of little bitty decisions that lead us to that big decision. So, I think it really takes a cooperative relationship with decision makers. We can no longer be the ones that put together the slide deck, present it, and then step away. We have to be in the room where it’s happening to help translate that data into all of those small and big decisions that are being made.

Karen: Yeah. And also asking the right questions up front in clarification. Because I think that, you know, to some of what you’re saying is, you know, you might think you understand the business, and you might think that you understand the business question at hand, but there might be a dynamic that’s really outside of your purview that you have no idea about. I remember one time talking with the team I was involved in package design for somebody in the supplement space, and you know, as we’re, you know, talking about the project ahead, one of the things she was saying is, “We really need to, you know, invite one of our kind of retail experts into the next conversation. We have to talk about what the shelving decision-makers are doing on our distribution side.” You know, what are the people at CVS and Walgreens, like, what are their points of view? How are they allotting shelf space? Because we could keep doing what we were doing for the package for the supplement, but if CVS isn’t going to put that on the middle shelf, it doesn’t matter. So, how do we show up when they’re only giving bottom row visibility to the product? Which is not necessarily—that’s just a constraint that is outside of their organization, but we had to really clarify that because it would definitely inform package design. Like, that would be a great design if it was right there at eye level, but it won’t be. So, I think that really underscores the importance of clarifying way up front before you go about all of this, about how all of that extraneous information also matters because that will inform the decisions you make on the tail end of what’s actionable and what you can do.

Megan: Yeah, we can no longer work in silos. And I think there are probably still some organizations that have been around for a long time that’s just sort of how things have been built, but when you recognize that, you know whether you’re offering CPG or if you are, you know, providing a service—you know, I was in a tech there for a while, we provided a service—and understanding all of those other details that we are a link in a very large chain, so you need to be aware of what those other links are, and bring all of those voices in. So, you know, I think if we’re moving towards—I know it’s kind of a cliche these days—but cross-functional teams, they are very important because once you get those people together and make sure you’re all speaking the same language, that’s when you get all of that contextual information that helps you take, you know, these little data points and turn it into, this is going to be a big win for the company overall.

Karen: Yeah, yeah. Let’s use that very important point as a segue into thinking about all of those kind of stakeholders and any resistance you have to any methods in particular, you know? And I think, like, obviously, as a qualitative researcher, I always have this, like, any resistance to qual, and now I’m sitting there thinking, like, any resistance to quant, like, we have data quality concerns and we have methodological changes. So, you know, what resistance have you been seeing out there in terms of methodologies, whether it’s qual or quant? And then, kind of, how do you overcome that resistance, to convince people this mixed methodology is actually the way to go to be well rounded. Talk to me a little bit about that. 

Megan: Well, you know, it’s so interesting because I come from, you know, my political science background, which I think was very heavily biased towards quant where, you know, really there was resistance against any sort of qualitative information. What I see now are these really interesting conversations of both resistance to quant and to qual. Because we understand that there are going to be trade-offs for each of them. We understand that there are pros and cons, and they’re always going to be pitfalls, but you know, I saw an article fairly recently that talked about how, you know, surveys are not of the highest quality anymore, that look like they were actually generated by AI. And you know, then—so there’s the actual production of those quantitative methods that maybe we’re sticking too stringently to the way that we did things in the past. In addition to, there are bots out there. I saw some really sophisticated bots when I was at the digital health technology company at Vibrent that could actually draw a picture as part of the check to see whether or not they were human. They were drawing these lovely butterflies. It—[laugh] you know? So there’s, there are all these pitfalls, and then with qualitative it’s messy, it’s hard to understand, it’s hard to bring those insights out. You know, how do you make sure that you have someone moderating who is really going to pull out those gotta-gottas, those nuggets that are going to help us get further down the road? And you know, it’s so interesting now because I think mixed methods—and this is why I say I’m a mixed-methods evangelist—is we can get all of the best parts of each, and that by doing so, we are mitigating the pitfalls that also come from each. And that’s what I always try to really sell people on, is, you know, be open minded. Let’s try some things. And you know, I’ve been very lucky in that I’ve worked with organizations and with clients that are open to that. And you know, I know that it would still take quite a bit of work to get through the resistance of some who just are not there yet. So, you know, I always tell them, if you’re worried, start small, you know? If you want to try the survey route, do a pilot. If you want to do the call route, do a handful of interviews. Use that to iterate. Get your moderator guide completely nailed down. Make sure that your survey is speaking to your target population. I saw one article the other day that was talking about how you make it work for Gen Z, and it’s no longer just a bunch of straightforward questions; it requires interaction. It requires open ended questions. So, you know, just make sure that you’re keeping with what your needs are, and making sure that you are adapting to, you know, the changing times, and just be willing to jump in, try something new. And, I mean, it goes back to our earlier points on taking risks and just knowing when it’s time to try something new. 

Karen: I want to hover on something for a minute that I was just thinking as you’re talking. I’m thinking in my head, obviously, you just talked about university work, and I’m sitting there thinking like, wow, it’d be really interesting—not to discuss anything proprietary, so please don’t think I’m leading you there because I’m not—but I’m now thinking like, imagine a university that has, you know, like, a huge alumni network, so they’re going to have opinions about a rebrand. But Gen Z coming in, they’re going to have different opinions. And instead of just using the same method for both demographics or psychographic segments, you might have to use different methods, which means you may not have a direct correlation of one segment saying this and one segment saying this, if the methods are different, right? But hopefully you’ve then, you know, put stuff together where you’re like, we extracted from each segment in such a way that we can glean findings from it. So, to me, that would be, like, super intriguing, and I’m fascinated with that work because, again, as a qualitative researcher, I’m like, yes, I love that [laugh].

Megan: And I think you can really get ahead of that, as long as you’re making sure that your thematic work is, you know, stretching across these different groups. You know, if you’re asking about one entire theme with one group and not focusing on that in the other group, make sure that you have those connective tissues, but just approach it differently. And I think that’s—you know, and that’s the great thing about qualitative work. You know, people disparage it as being squishy. Well, the squishiness works for you because then you can bring those very different conversations together and find the common points.

Karen: Yeah. I hope at this moment, to all qualitative research, you know, colleagues that are listening, they now embrace the word ‘squishy’ because, [laugh] like, I would definitely have been using that in my conversations when I was selling my services. You know, I’d be like, “Let’s talk about the squishy.” Because—anyway, I love it. That’s just more of me, Megan, as you get to know me and as I get to know you, so let’s come back to the moment—

Megan: [laugh]. I’m loving it. I’m loving it.

Karen: When suddenly I was like, all right, Megan, I see you. Now that we’re connected on LinkedIn, you shared this phrase—I’m going to read it because it stopped me in my tracks—it was about AI, and it’s about that time in the conversation we’re going to talk about it, right? So, you said, “Let AI handle the automation, but don’t lose sight of the unpredictable magic that only real messy, emotional people bring to the table.” Now, before I commentary on it—ohh, I just got goose bumps again because this just struck me and struck a chord with me—unpack what you meant by that to our listeners who weren’t there on the LinkedIn post where I saw it, and we’ll take it from there.

Megan: Absolutely. So, this was in reaction to a really great article that I read by Abby Auisomu. I’m probably butchering your name, Abby. If you’re hearing this, I am so sorry, but the article itself was wonderful. And it was about the debate over synthetic data. And what we’re finding is that when it comes to synthetic users and even AI moderators for qualitative work, they’re getting us about 90% there compared to what humans are capable of doing. And she flipped that in a really interesting way and said that means that the trends that we see from people, means that we’re about 90% automated, which, if you think about it, makes a lot of sense. I mean, when you get up in the morning, you go shower, you brush your teeth, and at no point ever will you switch those two things around. It’s just part of your unconscious routine. And you know, she talked about the automated 90%. Her phrasing was the wild 10% which I also really loved. And that made me think of these really random—and she talked about these too—these really random, sort of, viral moments that either were completely out of nowhere, or it was something that had sort of lain dormant and then all of a sudden had a cultural moment. And so, I referred to the question about, ‘How often do you think about the Roman Empire?’ Suddenly being the thing that everyone asked on a first date. Or the, really, [laugh] the fantastic one I loved was ‘girl dinner.’ You know, all of a sudden there’s a name for the thing that I’ve been doing for [laugh] I don’t even know how many years of just picking random snacks and making that a meal. And so, it got me thinking about that 10% and how it’s this wonderful quality of humanity that the most seemingly random things will suddenly just stick, you know? And it’s always a question of, okay, well, you know, this is a little pebble falling off a mountain. Is it just going to land, or is it going to start a rock slide, you know? And for me, I got to thinking about how some of the most impactful moments do come from those random—dare I say, squishy—moments that we didn’t see coming. But if we’re ready for it, you know, we can let AI do the work on that 90% that, you know, we know that these are the trends and these are the habits and these are the traditions, but that 10% is where we can really take the insights that we are able to gather as humans and turn it into something amazing. And so, you know, knowing that we were going to be talking about this, I kind of cycled through things that I thought would be really good examples. And I was thinking about several years ago, I think this was about 2018, if you did not follow the NFL, you had never heard of Colin Kaepernick. And then after a few games where he took a knee during the national anthem as a protest, all of a sudden, this became a huge cultural debate. Emotional, so emotional. And, you know, this was something that yet no one really saw coming because it was so risky because it could have, and ultimately did, end his career in the NFL. And, you know, one of the big reasons too, that I thought of this was, not only did we not see that moment coming, but you know who was able to really focus on that wild 10% was Nike. They had a campaign that they did with Colin, and it was, I’m going to butcher the tag, but it was something like, ‘believe in something, even if it costs you everything.’ And there was backlash. There was absolutely there was backlash. There were people burning shoes and everything, but within a few days, their online sales were up about 30%. And within weeks, their stocks had hit sky-high record levels. And you know that if AI had said, “What’s a safe bet in terms of marketing strategy,” it would not—it would have been in that 90%, and said, “Do something else.” But they, those market researchers knew the brand, knew that this fit, and knew that they could, the emotion of it fit with what they wanted to be. And so, you know, we need people to help capture that 10% and be ready for those cultural moments.

Karen: Yeah, yeah. I know. As you’re talking, you know more and more of them keep happening, but I did a lot of work in the… in food and beverage [laugh]. I was, like this food and beverage qualitative researcher for most of my career. It’s just so interesting to me. But anyway, the girl dinner thing, when I had seen that online, that was another thing where I was like, “Oh, my gosh, that’s so funny,” because I had heard a million people—maybe not quite a million; I exaggerate, but probably a thousand women—talking about meals and the relief they felt when they didn’t have to make an entire meal for their whole family. I mean, I can’t tell you how many hours of, you know, of groups that I might have listened to. And, you know, and we—you know, or interviews that I had, or whatever, but you know, that was never part of the objective, right? But at some point, somebody, you know, goes viral with girl dinner on TikTok or something, and it’s like, instantly understandable. That is the most insightful thing that had happened, right? I was like, oh, gosh, in that category, absolutely because it opens up the possibilities for a whole different way of marketing food [laugh].

Megan: Right. Exactly. Yeah and, you know, I was actually in this really great webinar the other day called, “Beneath the Why,” and it was led by Christopher Brace, and it was really focused on the emotion behind, you know, what the decisions that we’re making, and that the difference between sort of unconscious mind versus conscious mind. And, you know, we’re no longer in a need-based landscape; we’re in a want-based landscape. And that was one of the things that really stood out to me. So, you know, do we, you know, need the convenience of, you know, cheese crackers and some salami for [laugh] dinner? No, but we want the, you know, taking away from the emotional load that we have as, you know, women in particular when it comes to making meals, still very prominently a woman-dominated area in households. And so, you know, taking that emotional load away, that resonated and that became, you know, a cultural moment. And, you know, I think there are even some fast food places that—

Karen: Oh, they leaned in.

Megan: —you know, created, you know, girl dinner menus where you could pick three sides [laugh].

Karen: Absolutely. I know. I know. And I think what’s interesting is, again, I wasn’t doing work at this time, but, you know, I think about that, what you just said, kind of the emotional load coming off, and I’m like, yes, that could be discussed, right? Why does this resonate with you so much? And it’s because, you know, the pressure and the demands and the expectations and all of that stuff just disappeared, right, with one kind of movement. And that’s the kind of qualitative I would love to be doing, you know, still to this day is that, like, here’s a cultural moment. Why? You know, why did this happen? Why did this resonate so much? And what do we do with it? Making that actionable, that’s really, really good stuff. So, I just, I love the role that you have in this world of doing this kind of social research, too. So, let’s get back to AI before we wrap because I do want to kind of go there. Like, now that we know it’s like, okay, so we understand, kind of, you know, what AI is going to automate, and where we still need, you know, the squishy stuff, right, but talk to me about what you see happening, kind of as this rolls out further, where AI becomes more and more integrated as methods that we use. 

Megan: I think our role, as you know, for insights professionals, the role is really going to be a translator role where, you know, we’re going to be able to use AI for some of those tedious, you know, tasks that, you know, maybe find some trends, you know, or even do some preliminary work for us. So, I actually have another [LinkedIn 00:40:27] post today where I talk about a little experiment I did where I took an old survey, de-identified and gave it to ChatGPT, and said, you know, “Here’s a sample of 100. Create some user personas for me.” Because that the idea is that, you know, okay, well, you’ve put out a survey. You’re starting to get responses in. Let’s do some preliminary work. And then I compared that to what I saw in the full data set, and it missed some things. You know, there was some nuance to it. There were some trends that were more emerging, rather than actually fully there, but it gave me a starting point. I did not have to start, you know, from a blank slate. I was able to take what AI gave me and then validate it and, you know, build from it. And I think that’s going to be one of the ways that we really just make what we’re doing more efficient. And I think, you know, I’ve seen some really alarmist news that AI is going to eliminate all of these entry-level positions and I understand that is a real threat, but I caution people, and I urge people to remember that you still need someone doing that initial, tedious first step. AI cannot be the only thing that you’re utilizing, and you need to have someone in that role that you’re then training to become the translator themselves. You know, where those of us who are mid-career, we already have kind of, you know, the research chops that we can step in and understand that, but we’re still going to need those early-career people, those incoming professionals that they need to grow up with the AI. 

Karen: Yeah, I did just hear a statistic, though, that, you know, very quickly, you know, following Gen Alpha, you know what’s happening next is we will have a generation—so within a generation, we will have a group of people that, just like, there’s a group of people, Gen Z, for instance, that grew up with mobile phones as part of the their reality, we will have people with AI as a part of their reality. They won’t know Google search the way we did it, for example. They won’t even know social media search, the way Gen Z did it. They will just know a world with generative AI as a tool in their daily living. A generation away from that. So, incredible, actually. Incredible. So, the future of insights. What do you think the future is for insights professionals?

Megan: [sigh]. I think it’s… I think we’re going to have a lot of challenges. I think, you know, obviously, AI is the conversation of the moment. I don’t think that’s going to go away. I think there are probably going to be new challenges that come along that challenge us, whether it’s, you know, an economic reality, political reality, what have you. But I think we’re at an inflection point where things are only going to get more interesting. I think we’re going to be able to start asking better and more interesting questions as a result of a lot of the new tools and a lot of the new contexts that we are in. I don’t want to downplay any of those challenges that I alluded to. You know, obviously I—you know, not to make this all about me, but you know, I myself, am looking for my next permanent position because of the changing context. There are a lot of other people like myself out there. This is not a me thing; this is a very much a big thing. And you know, I think we saw that from the GRIT report, too, is that, you know, we’re going to see some challenges when it comes to just even the marketplace of professionals. So, I feel hopeful. I feel cautiously optimistic in general and I hope that everyone is just ready for a wild ride because I think it’s really going to get very interesting, maybe a little chaotic, but I think we can do it. I think we’re on our way to something great.

Karen: I love that. I love ending on such an optimistic note, also. I feel like I just said to somebody who saw themselves as someone who sees the possibilities in all things and all challenges and sees the opportunities that abound, so I love that you have that spirit as well. So, I am with you for the, I won’t call it a roller coaster, then. I’m going to call it, like, one of those, like, log rides or flume rides or something where there’s water and it might be rapid, but it’s a hot, sunny day, we’re getting wet, and it’s kind of refreshing [laugh].

Megan: It is. Absolutely. I love that [laugh].

Karen: Megan, thank you so much for joining me on this show. It was such a pleasure.

Megan: Absolutely. I hope I get to do it again sometime. It’s been so wonderful. Thank you.

Karen: Absolutely. And I’m so glad we’re connected. I feel that with everything. Thank you for joining me and for joining me and for being so responsive, and I’m so glad we’re connected [laugh].

Megan: Same. Absolutely.

Karen: That’s great. And thank you to Brigette for making this happen, especially on such short notice. I so appreciate you. Thank you to, gosh, our audio editor, Big Bad Audio who’s actually doing our video editing now, too. So, thank you for all that you do for us and the Greenbook Podcast. And of course, to all of our listeners, thank you for tuning in time and time again. We so appreciate you and are so glad you show up. We’ll keep on, keeping on. Have a great day, everyone. Bye-bye.