Redefining Market Research: Insights from Lenny Murphy on Innovation and AI

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Description

In this episode of The Curiosity Current: A Market Research Podcast, hosts Stephanie and Matt are joined by Lenny Murphy, a trailblazer in market research and a thought leader in leveraging technology for transformative business impact. Known for his work with GreenBook and Gen2 Advisors, Lenny discusses his unconventional entry into the industry, his passion for innovation, and how he has evolved to become a trusted advisor to industry leaders navigating disruption. Lenny shares his insights into the industry's evolution and gives his perspective on the role of AI in shaping the future of research. From the importance of asking the right questions to navigating the challenges of disruption, this episode is a must-listen for anyone in the market research space.

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Lenny Murphy is a recognized pioneer in the field of market research, with over two decades of experience at the intersection of insights, technology, and business strategy. As the driving force behind GreenBook and Gen2 Advisors, Lenny has played a pivotal role in shaping the industry, helping organizations navigate disruption and embrace innovation. He’s a champion of transformative technologies like AI, advocating for their thoughtful integration to amplify human expertise and unlock deeper insights. Lenny is a true visionary—guiding countless businesses in leveraging data and insights to drive smarter decisions and achieve sustainable growth. His expertise and influence have made him one of the most respected voices in the market research community.

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Transcript

Stephanie: Hi, today we're joined by Lenny Murphy, one of the most influential voices in market research. From Greenbook to Gen2 Advisors, Lenny has been at the forefront of the industry, shaping how we use technology and insights to drive innovation, the power and influence of insights, and to prioritize that consumer brand engagement and ultimately marketing ROI.

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Matt: We have a pretty high-level conversation lined up today. We'll dive into the current state of market research, the role of AI, and what's next for the industry. So this is one conversation you won't want to miss. Lenny, welcome.

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Leonard Murphy: Thanks. Glad to be here and really appreciate that intro.

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Stephanie: Well, to jump right into it and to go back to that sort of intro, Lenny, you have been at the forefront of market research for over two decades, shaping the conversation around innovation and technology in this space. Before we start to really get into some of those topics, current trends, future predictions, we would love to hear a little bit about what first drew you, you know, to the field, and then also how your perspective has kind of evolved over the years to be this change agent and champion of tech and innovation.

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Leonard: Thanks. Like so many folks, I kind of stumbled into the industry. I would love to say that it was some strategic decision. It was not. My background was primarily in operations and other industries, and I had been managing a regional call center for a paging company, I'm really aging myself now, called Weblink Wireless. Found myself looking for a new role, and there is a research company in Snellville, Georgia, the Myers Group, that needed a phone room manager. And I thought, well, I can do that. I've been running a call center for years, not knowing that it was radically different. Conducting satisfaction research for health plans was what the Myers Group did, and was smitten. Like, wow, this is really cool. I'm asking people questions. And so my start was running a phone room in market research. Then along came the internet, and I was recruited by actually a supplier of mine to help them as they were building out some new technology capabilities. And a few years later, I hung out my own shingle and then launched my own research company called Rockhopper Research. What through that path, and always attracted to the new. So in the early 2000s, when I launched Rockhopper, we made the decision to focus on online research, primarily global B2B online research. And that was when that was actually something that really was different and cool and unique, so not the standard. And we did that, developed one of the first online focus group capabilities off of Adobe Connect in 2006. Couldn't give it away, right? Nobody was interested in, what? An online focus group? So I guess it was baked into my DNA to do that. Unfortunately, the strategic error I made at Rockhopper is that we focused on the financial services industry. And then here came the Great Recession, and we took it on the chin. We went from- Well, we're the coolest great new company in the industry to how the hell am I going to pay people? And that started a process of introspection as the CEO of, wait, I thought we were doing all the right things, but we weren't doing the right things in terms of looking for disruption across multiple categories. I was friends with the folks at Greenbook. They said, hey, we want to try this whole blogging thing. You're struggling. Will you consult with us and we'll give you money? I said, yes, great, fine. And that started this transition into this role. What I found myself doing was blogging about what I knew, which was, wow, here's this industry we love, experienced that point roughly a decade in, but it's changing because that was also the onset of social media and mobile, right? The kind of Facebook and the iPhone all kind of exploded at the same time. And there were lots of other folks that were struggling to figure out what does the industry look like when the focus isn't on our tried and true methodologies, the foundations of research still there, but how we went about it, we're changing radically. And I had the time to think about it and talk about it. And that just started this whole thing that, again, I would love to say that I planned. I did not plan for it. It just happened. And since I was one of the voices out there leveraging social media, LinkedIn, Twitter at that point, talking about these things, people kind of wanted to listen to what I had to say, which was quite a pleasant surprise. And then folks started asking me, well, will you help advise us? Because we don't have time to do this either. And I think the core lesson there was just so often in our industry, we, especially founders, you get caught up working in the business instead of on the business, but yet disruption occurs. And a lot of folks don't have the focus on trying to think through what does the future look like? So, over the years, I stumbled into that role of being that outsourced partner through Green Book and through Gen2 Advisors, my consulting company, of being the one who's tasked with payingto, where the puck is going and helping to advise leaders on that. Here's where the puck is going, and here's, you know, because of my background as an operator, I could say this is what I think it means for the business. And what I found over the years, I'm pretty good at predicting where the puck is going. I'm not so good at predicting when it's going to get there. And that was what it usually meant is that I expected things to happen faster than they did until AI. And now I find myself in the opposite, where I'm still pretty good at predicting what's going to happen, but I think it's a year out and it's like tomorrow. So changes are happening faster and faster. So that's how I wound up in this role. And it's the coolest thing I've ever done. I love helping people. My kids ask me what I do for a living and I just say, I help people. Because fundamentally we need people to be thinking about these things so leaders can focus on running the business.

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Stephanie: Yeah, that's a very cool business model. As a kind of follow-up to that question, because you did touch on this in your answer there, but I would love to hear from you. You've talked about, and we know, that the industry has undergone some major transformations in recent years. What do you think have been the most impactful shifts? And I know AI is the big elephant in the room. In addition to that, and how have they redefined the way that researchers deliver value to the brands that they work for?

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Leonard: The first one was the transition from mail to telephone and the golden era of random digit dialing. Everybody had a phone. It was easy. Then the shift online was the first big one. But all we really fundamentally did was take what we did and just made it a little more visual. There was no real transformation. It was just kind of old wine in the new bottle kind of thing. Yeah. And I don't mean that as a dismissive thing. It's just that's just where we were. But that did have fundamental changes in sampling, which was the foundation of research. And that was why it took a long time for online to catch on because their people had to get online. So that was the first big one was taking what was a into a visual medium effectively. Then, of course, from that came DIY because these tools became easier for lots of people and it lowered the barrier to entry for researchers. The next big one was mobile. Again, a huge change in the form factor, but it also enhanced capabilities, allowed us to do some different things we could have done before. And as technology, ubiquity caught up, bandwidth, you know, all of those things, then suddenly research was global. We can incorporate video. We started to see where I couldn't give away online focus groups in mid 2000s. Now those became viable ways to engage with people. So the tool set started to expand, but the use cases started to expand as well. So those were the biggies. And then, of course, social media as well. A whole other feed of information. Where does that fit in? Where we could listen instead of ask and incorporating this whole process of digital exhaust into the insight flows. You know, those were really transformative. But what was always holding us back was technology availability. We could see the vision where things were going to go, but we had to wait for it to become saturated. That's really not where we are now. Just as in the 80s, everybody had a home telephone. Well, in, you know, as we head into 2025, everybody has a mobile device. That mobile device has a camera. It tells you lots of things about where the person is and the behaviors. And that is effectively a global saturation. Certainly, you know, some tribesmen in the Sahara may not have that, but most of them do. So those have been big changes. And now what AI has done is that it's now taking down all the barriers from a process in efficiency, standpoint particular data integration and synthesis that existed before Right? We're adapting. We're figuring out these cool new things. But fundamentally, there are still ways to make all the pieces fit together. That is the fundamental transformation of AI is that the pieces just fit together really well now because there's a technology that that's what it does. It connects dots, it synthesizes, it puts information together. And that has a whole other series now of follow on effects that we will have to adapt to because fundamentally we are a process driven industry. So the primary driver of revenue for a supplier is process. So design, data collection, field management, reporting, those are processes. And traditionally it's been people driving those processes. Increasingly, technology is taking over more and more. Now we see a world where the process is entirely driven by technology other than the human elements of what's the business question? And then what the hell do we do with the answer? Those are still inherently human connections, but everything else in between, it is we're moving rapidly in the place where everything else that enables those things is now driven by technology and very quickly, maybe driven even without human intervention in some components. And that is the next great era of transformation that the whole world is going. There's not just us.

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Matt: Definitely a lot to explore on the AI topic. I want to pause really quickly and go back to a perspective you shared, maybe more of a retrospective. And I think was really interesting on sort of contextualizing where we are in this moment with this looming promise slash threat of AI, however you want to view it in the broader context of all of these other transformations. Maybe they were transformative. Maybe they were smaller scale changes in the way we've done our work as market researchers that researchers have already made their way through. You don't know. You mentioned the change to digital, the adoption of social media analytics, the growth of. You know, greater sample scrutiny. There are a number of needs that drove each one of those, those changes. I'm curious to get your thoughts on thinking about all of the challenges that we have sought to use technology to overcome. How are we doing as an industry so far? You know, again, kind of setting AI aside for a moment, all of these other problems that we've been struggling with up until now, we've been tackling through technology in various ways. Where do we stand as an industry? Are we nailing it? Are we ready for the next big thing? Are we ready for AI or do we still have things that we need to shore up?

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Leonard: Now that's a question and no one has asked me that specific question before. Uh, so I'm going to have to kind of freewheel on this one. So I think the, the industry has overall adapted from a process standpoint. We have not done as well adapting to a, from a business model perspective. Not when you think about kind of the core sectors. So within some of your folks might know, and I'm sure you do the, the Grant report, right? Yeah. AYTM was a great partner for us for many years in conducting that. And that's our twice annual taking the pulse of the industry. So asking buyers and suppliers, what do you think is going on and, and getting their take on things. So in that we view a few different segments in the industry. So full service, which is the bulk of the industry from a revenue standpoint, at least traditionally was. Ipsos, Kantar, you know, the big full service agencies. The field services, which was always under the surface field services support full service. They do the grunt work of data collection and all of those things. Qualitative. Which still kind of stands on its own, although the lines are blurring now, strategy consulting and technology. Now, there is this very uncomfortable relationship for a long time between the full-service companies and the technology companies because fundamentally they have the same concern that we have now. You're going to take my job. And that has happened. The need for coders, for instance, has been steadily degrading for years because of tech analytics. The need for programmers of a survey has steadily declined because we have automation. We've templated and created kind of plug-and-play solutions in many cases. Report writers often replaced by platforms like AYTM that, well, you've got the gist here, right? Here's all your charts and graphs. Now it's just fine-tuning. So we have adapted to that. But that begs the question then of, well, where's the value at? So if you're a full-service, research company versus, let's say, an AYTM that is both service and technology, and you're a buyer, well, why do I go to a full-service company when I can pretty much get what I need from an AYTM? So we're still struggling with that. But the market speaks. The growth of the technology sector of the industry is now equivalent to, if not greater than, the traditional definition of the industry. That has happened rapidly in the last decade. So that's forced some reportment for folks to kind of think through how they fit together. And that's where we see now the growth of the strategy consultancies, because that's all they are. They are just focused on the human element. Where does that leave us? Where do we get to in the final kind of view of the market structure? My belief is that fundamentally there is technology-driven but service-enabled businesses. We'll be roughly half, if not more, of the business. And then there is human-driven but technology-enabled companies that fit on the other side. And everybody's going to fit in kind of a spectrum there. But the bulk of the revenue that defines from a market sizing standpoint, the industry, won't be on that tech side of things. And that's really kind of where we are now already. And buyers, because buyers speak, right? That is where they go. So now the question against the AI stuff is, if that's true, then what's the focus for the human element? And I think that that is some type of niche specialization, either vertically, healthcare, pharma, whatever. You know, that specialization. Or business issue. Specialization, advertising testing, new product development, you know, those type of things. And that's not different from where we are, per se, but it will be far more pronounced. And undergirding all of that will be the technology in it, one way or the other. It's just going to be a question of what's your tip of the spear? Are you leading with the human or are you leading with the tech? Most everything in the industry is going to be led by the tech.

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Matt: Great perspective. I think this is something that Stephanie and I were just talking about, in fact, and you alluded to it in your answer, which is just at a fundamental level, the researcher's role is going to shift. Whether you are excited about it or you're apprehensive or some mixture of the two, there are significant changes coming if they haven't already arrived. So given that, big changes in the industry, from the researcher's perspective, how do I pull it together? How do I start to integrate some of these AI tools that are available to me in my day-to-day practice?

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Leonard: There's another dimension of this. We've been kind of talking about the supplier perspective, but the buyer side is impacted by this as well. I think that's kind of where you're getting to, because the decentralization of research as well, Antoine, I'm sure you guys know that better than most. My bet is that if you look at your client list, that you have as many directors of marketing or head of product or head of brand as you do director of research titles. So it's interesting that this democratization of insights has occurred driven by technology. And that also means that the buyer needs, in some ways, are very fragmented because they have a different perspective. While at the same time, fundamentally, it always does come down to cheaper, faster, better. And better has different permutations, how we think about it. Quality is kind of at the top of that sub-list of better, but it's also going to be better insight that enables me to tell my boss, here's the right answer to the business decision that moves the needle, right, that informs this billion-dollar decision. So they're experiencing the same disruption. And the way to tie it together does go back to AI. So that is what we are seeing now. I know that one of the largest CPG companies in the world, well, they talk about this publicly. So P&G was kind of slow on the AI adoption front, as many other companies have been, until we don't want to be the guinea pig, right? We should make sure the data is secure, the stuff works, they've gone through all this IT compliance, all that good stuff. And as soon as that was there, they said, all right, well, we're going to invest like a jillion dollars into building their own internal LLMs based off of their data and leveraging their data. Now, this is stuff we talked about when I traveled and spoke. I would always use a clip from the Minority Report, right, where Tom Cruise is walking through the mall, you know, and there's these targeted ads coming through. It was like, that's big data. And that was the word we used back then. That was the promise of this. We didn't know that we needed something else to make it happen. Oh, yeah, we'll just put all the data together and look at this world we'll live in. It'll just happen. AI unlocked the era of big data. So, truly. So, now, what brands are now doing, and we see the public examples like P&G and Coca-Cola and some of the largest brands in the world, are recognizing that they are sitting on dated gold mines. They do not necessarily have to mine for new gold all of the time. They got a buttload of data that answers questions. They don't need to ask new questions. What they can do more efficiently is to look at what they do have and leverage AI to now say, okay, here's what's missing. Now we need to go get that missing data to fill this in. And that becomes a virtuous cycle of constantly going out to the market and asking information to fill in gaps, but not repeating what we already know. We don't need to ask the same questions over and over again. And when we can leverage the data effectively, that already exist, and that is the great adaptation we are moving into now, is the accessing the right technology. Because, sure, P&G can spend a billion dollars on their own custom LLMs, but, you know, if you're Joe's Smoke Shop in Kentucky, and I've got five branches, but I still want to be able to tap into those things, then I'm just going to use ChatGPT. So, it's just going through kind of where those tools fit within your organization, but the end result is going to be the same. You will be leveraging a combination of freely accessible information online with your own internal customer data, your own research initiatives, interrogating that first, then going out and getting new information to fill in gaps and to validate as well. I mean, by no means do I think anybody should trust what an LLM tells you today without validating it. This past weekend, I was doing some work with GPT-01, and it just flat out hallucinated a bunch of bull. And so, that was my first time seeing that. Ford's like, yeah, okay, that's right. That passes the sniff test. This was the not. It was a weird experience to go to talk to the AI and say, you were wrong. What the hell's wrong with you? Right? Why did you tell me this BS? Oh, I'm so sorry. You're right.

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Stephanie: I know, I love how quickly it's like, of course, you're right, I did.

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Matt: Very apologetic.

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Leonard: Right, apologetic, but what did you do to begin with? We're not there yet where we can trust it, so we're still going to be validating, etc., etc. But that is where all of us, both buyers and suppliers, is now thinking, how do we fit within that ecosystem? Fundamentally, suppliers are in the business of supplying information. So that is how we need to think about things. Our product is data. So our product is not a research study. So there will still be research studies. There's still going to be very specific things that brands want to understand in a different way. But that'll probably increasingly just be more strategic in nature, probably more iterative, more agile, as we think about that term. But kind of tactical stuff, that will basically be a subscription to ongoing information that is feeding the beast. And that ecosystem is being set up right now. We know OpenAI, for instance, they pay for access to information. So to feed their systems. That'll continue in a slightly different way. And we'll all just have to adapt on how that works. The train has left the station. There is no stopping this transformation. It's already well underway. And what I hope we find, and I'll give this weekend as I was working with an alum, I was doing a competitive assessment. So it was a really interesting experience where I treated this as a collaborator. So I fed it some information that I had that was proprietary, while also allowing it to do its thing. With external information, and went through a process of exploring these things together. And there was no denying the efficiency. I could have gotten to the same place, right. But it would have taken me 30 hours to get there. This took me five. And to have a fully baked, here's the deliverable that I was looking for. It wasn't pretty, but it had the right information there. So that process of, from a researcher standpoint, we still, we provide information, which means we need to know how to ask questions, and I think, if we're on the client side or on the supplier side, we have to think through, that the process is to provide the right information, then we need to know how to ask the right question, and to get to the right answer. Fundamentally, that's what we do already. Now the tools are just going to change in how we achieve that. What that should do is increase bandwidth. So we should be able to do more. We should be able to, rather than today, I know you guys have grown explosively, but you have 100 people and an X revenue, and you should be able to not have to put 200 people in to do the same number of projects.

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Stephanie: To scale without growing resourcing.

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Leonard: Yes, you should be able to scale up and handle greater volume. And brands will, on the buyer side, they'll consider the same thing. So, okay, we are resource-centric, we are data-centric, but you don't have to have a thousand people like P&G. Instead, it's how do you give every person superpowers? And I think that that is where we were going. We thought automation would get us there. It didn't really. There were hints of it, but we didn't quite get there. But I think that AI does now. It enables all of us to have these superpowers. If we're thinking about it correctly, to get the right information and ask the right question, deliver the answer. Next, let's move on. And our ability as subject matter experts to know when LLM is bull- If I wasn't an expert, I wouldn't have known that it was hallucinated. So that becomes a real value driver as well for us as researchers.

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Stephanie: I had also read, like, I think this is in the context of and across several different sources. So I'm kind of summarizing and synthesizing here. But as digital transformation in this age of AI for our corporate partners, that there's kind of two roles that ultimately researchers may end up falling into. And one is sort of this knowledge management system, repository owner, administrator, curator. And that the other one is this strategic interpreter of results that really pulls in the parts that AI is maybe less good at right now, like the macroeconomic context, the longer, more forward-looking view. So that's on the client side. And then on the supplier side, I've seen and I personally love this notion of researchers, people who have historically been in professional service departments, you know, moving into the world of product and building products. Based on their domain expertise that are repeatable, standardisable, and do all of those things that allow AI to work the most effectively and draw the cleanest insights. Do you agree that those are sort of like where you see researchers as the future of researchers? Are those places you see them sitting?

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Leonard: I do. And I think a little more nuanced. And we've tracked this via grid. We've asked what skills are necessary for the researcher of the future. And there's always technology that continues to be there. But we've also, over the years, we've seen the emergence of anthropologists, storytellers, social scientists, these kind of soft skills, as well as business consulting. So it's kind of the buckets. And I do think that that's true because we need to help whether you're a supplier side or buyer side, you have a client. And we need to help our clients understand all the dimensions necessary to make the right business decision. And, you know, AI is going to be really good at who, what, when, where, and how. So it already is. Because that's just data. I'm also good at Y. And. And I think we are keepers of the Y. I think that that would be a big piece of that. And whether that comes through domain expertise, through education, experience, through intuition. I think the role of intuition is important there. Just, I'm sure we've all experienced that. Sometimes you just, you just know. I mean, I made my living connecting dots. And I would love to say, here's how, you know, I did that. I just, I don't. I just, I go from A to Z. And I got to back into it of how I got there. And that's a valuable skill, doing that. So, yes. And the product stuff. And that, that's where it gets really interesting. Because here we just let the market speak. In the last year, there's probably been close to a hundred AI specific launches in the research space. Sorry, guys. I, I hate, you know, if you're one of those startups, I hate to tell you, there's not room for a hundred of you. There'll be room for 5 or 10, right? That will really grow, you know, to become large companies. So there may be a whole bunch of little lifestyle businesses. There's not room for that level of sustainability within a B2B ecosystem. And I think we can look at the advent of the app markets, right, for mobile phones. Not everybody's going to be in Angry Birds or whatever, right? There may be 10 knockoffs of Angry Birds, and they may make some money, but they're not going to make much. So I think we will see a very vital ecosystem of product developers, you know, creating new products. And some of those won't make it. Most of them won't make it. Some will get absorbed into other businesses, and they'll make it. And a few truly will be disruptive and will become the next AYTM or whoever, the next big company. And that's cool. There'll be a lot of excitement. I love startups. I do the startup competition as part of the IEX event series. I've been doing that for 10 years. Track record of picking winners that help companies disrupt and grow has been really great, and that's exciting. I love that because I'm an entrepreneur as well. But for every winner. There's 30 who never make it up on stage. There are variations on a theme. And that's just what we're going to have to accept. That's just playing the odds of being an entrepreneur.

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Matt: I want to make sure we get your perspective on this. You mentioned as researchers, we are keepers of the why. I love that. We are purveyors of the information. It's up to us to make sure the information is correct and that it's being utilized in appropriate ways to guide decision making. Everybody's talking about synthetic data. So if you have a data set that's trained on an AI model that's artificial at its core, is that information? Does that have a place in research today? Or if not, maybe it does. If not, when do you expect that transformation to happen?

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Leonard: So one, I think that there's a definitional issue here, what those terms mean. People have different meanings. When I say synthetic sample, what I mean is really the scenario that I was just talking about. It is an LLM overlaid with real first-party data that then you are creating personas off of real information. We've been doing that for years, right? This is cooler and easier now.

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Stephanie: Queryable, which is just cool, right?

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Leonard: Right, to have a conversation based on real information. So you don't need to go out and ask the same questions because you already have that. And it's real. It's valid. It's not imaginary. It has been trained off of an extensive amount of real individual data. So that is the definition that when I use that word, that is what I mean. I also know that there is a whole other component of that where it is, it's just made up.

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Stephanie: What you're talking about right now, sorry, I want to just clarify this for just a minute, is because we have people reach out who are, you know, companies, vendors, suppliers, reach out and say, hey, you wanted to try us out. And okay, we will. They're like, give us two thirds of a data set and then we'll add the final third. And I'm like, are you just interpolating? That's not AI. I don't want that. I can do that right now. Like, that's not what I'm looking for.

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Matt: Or they don't even have a starting point. They want to ask the question of a system rather than a person.

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Leonard: Yes. So to the core adoption that Qualtrics recently released, their annual research market report, it's a good report. And they asked that question. And I think the number was that within three years, 71% of their respondents, which are primarily buyers, would be utilizing a synthetic sample. That's a shocking number while not being a shocking number. Because, of course, how about fit for purpose? So there are some things where just like when social media emerged and people are, this will replace the focus group. No, it's not going to replace the focus group. But it's absolutely useful for specific use cases, 1,000%. And that's why we saw the growth of social media analytics and those things. So we will find the fit for purpose applications of the trained first-party data personas. We will absolutely have that. We will build ecosystems and businesses around it. I know for a fact that OpenAI is looking at buying a panel company. So they realize it. They realize they need access to first-party data. And a research panel is a pretty damn good way to get it. So there's that. While there's also what we have currently, which is just this, you're just sucking in everything online. And you're looking for commonalities and creating personas off of that. And that can be good and useful as well for the right business issue. I have used those type of things for early stage ideation and concept testing, throwing stuff against the wall.

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Stephanie: Yeah, yeah, yeah, yeah, yeah. I've seen that too. Be successful.

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Leonard: It helps provoke new thinking and, okay, now I want to go test it with real people. And it's easy to think, if I could test it with a trained LLM of personas that I would call synthetic sample, sure, let's do that. Then the next step would be, now I need to go validate it and fill in the gaps of information with real people. And that will be what we think of as the research project. So back to the Qualtrics thing, shocking, not shocking. The speed is what is, you know, it was like, whoa, three years. But at the pace of change that is occurring now.

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Matt: Totally believable.

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Leonard: Yes. It is either inertia on the buyer side because it takes time to get through procurement and do all of those things, right? They don't move fast. But it's not technology ubiquity. It's not that. It's not technology, you know, scalability. The barriers of adoption are just us doing it. But financially, why wouldn't you? The example I used earlier.

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Stephanie: It's like perceptual hurdles and use case hurdles to your point more than anything else.

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Leonard: Yes. And I struggled. So, you know, here I am, Mr. Futurist. I did not use AI for the first year, year and a half. I would not do it because perceptually I had the thought this is going to denigrate how I pay the bills. So it cheapens me. And that was my mindset. I saw it, saw all the benefits, be talking about it, go back to me for January of 2022. Yeah, all those are cool things to slice bread, you know, but I'm not touching it. That's not where I am now, right? I had to get over that and realize that the efficiencies, you know, the things that it could do for me to take away grunt work tasks that were not value add or not really how I make my money. Were, it was worthwhile to do that and then become a collaborative partner. And I think we're all on that journey at this point.

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Matt: I have a follow-up question that is probably going to have somewhat of a jaded tone. So just bear with me. Stephanie said I should lean into my contrarian nature. I'm still not sure what she meant by that. But when I think about synthetic data, I love your idea of, you have to start with the definition. I mean, we've talked about that with the general topic of AI in general. There's so much confusion over what is generative AI. Anyway, going back to things like synthetic data at a controversial stage right now, do you fear that as it's used, you were just kind of alluding to it there where, you know, your own personal use of AI tools has increased just as your familiarity has grown. Do you fear that as an industry, we might become more prone to adopting tools, technologies, approaches that seem to provide an ostensible effect? They provide something that looks like data. It looks like something that would be really, really useful in building a strategic plan or informing a decision. But you have to dig through several layers of dirt. To get to the definition that might be concerning, but it's hidden. It's in the minutia where most people aren't going to look. On the surface, it looks great. It looks like a great tool. It works well. And hey, it looks like a decision on paper. Is that a valid fear that we could find ourselves falling down that slope? Or is that nothing to worry about?

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Leonard: It's just as valid as, oh, look, I have a new survey engine that, you know, is connected to, you know, a million people and it's the crappiest sample in the whole world.

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Stephanie: It's all bots.

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Leonard: It's all bots. You can't trust any of the results. So fundamentally, that's what we're talking about is the quality debate, right? Which we've been grappling with for a long time. And I think the answer is going to be very similar. So you need to understand data providence. You need to understand where it's coming from. And that will make us the trusted arbiters. What we can't do, though, as an industry, is get so caught up in that we get into an ivory tower mentality and we lose opportunity. And that's happened to us as an industry. We lost the CX thing. We lost the UX thing. We lost the social media. And I was living through that. People would say, well, nobody's asking for social media. It's like, well, of course we're not because you're not offering it. So we lost arguments, right? We lost opportunities to do those things. That is not our foundation in science. I understand. But I'm also not a scientist. I'm a business guy, right? And I learned research through just working in the business, not because I have a degree in statistics, so which I do not have a degree in statistics. So I'm a little skeptical of that to begin with. So, yes, I think that is a legitimate concern. That is our opportunity. So look at a company like AYTM. You own your own panel. You can have that argument about data providence. You can say, look, we are building these things to be fit for purpose because you can see all the way through. And just like the example we used earlier, that doesn't mean that you can't use the general LLMs. It's like you couldn't use even a crap sample platform. I got a hundred bucks to spend and I just want to do a quick pulse check. Directional, it's not, I'm not making a million dollar decision on it. I think it's okay. You don't do that. It's cheap and it'll give you something to start structuring something bigger off of. That's just the way we have to think about it, right? Is what is the, it has a purpose. It has a role to play. And no, no solution that we offer today as an industry is a hundred percent solution. We've always been combining things quad and qual. And we're just going to keep doing that. We can just do it a lot faster and cheaper and more efficiently. And we can combine stuff on the backend in a much cooler way than we could before. And that's where we are. Now, it doesn't mean that, who knows, two years or even two weeks at the pace things are going now, you know, they could roll out something that just makes it. Just, we all just need to curl up and accept our robot overlords and, you know, just move on. Right. I mean, it's just the matrix. Just put me in the pod and I'll be a battery because that's all the hell that I'm good for now. I don't think we're there.

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Stephanie: Got a few years, maybe.

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Leonard: A few years. I mean, we did purposely buy a small farm in very rural Kentucky for a reason.

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Stephanie: You're like, it's not a hard recommendation, but...

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Leonard: You know, I mean, you know, most of my neighbors are Amish, Mennonites, and everybody's got guns. You know, if you're going to come after us robots, we're the last on the list. Anyway, we'll see. There will be a multi-million dollar decision misfire because somebody used bad data sources to drive it.

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Matt: High profile.

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Leonard: Right. I mean, just like, you know, we're recording this in December and during the run-up to the election and after the election, my point on that was, look, we just need to recognize it's not that polling is dead, it's that we have bad sample. And here's a visible example of why it was not highly predictive, because we weren't talking to the right people. We need more examples like that in business. There is a big miss on a decision, but businesses don't like talking about that. But they'll happen. There will be some, like there will be a new co-co-age myself again, that'll come out and we'll realize, oh, because you tried to run it all through ChatGPT instead of doing real research. Well. Won't do that again, will you? And we'll learn.

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Matt: We will learn. That is definitely what we do. And like you said, if we don't, we have at least a few years to get our leases on our farms set up and our fallout shelters built. Optimistic advice from Lenny. No, Lenny, genuinely, we've selfishly taken up so much of your time today, but this has been such a wonderful conversation. Have really enjoyed capturing your insights and perspectives on where we sit as an industry and where we're headed. Do you have any wrap up thoughts? Any parting words of wisdom for our audience members? Anything we didn't get to discuss today?

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Leonard: I don't know about wisdom, but that automatically puts me off because that's not what I do. But it's been a joy, really. One, compliments to AYTM. Again, I've seen your journey, and I think you guys have just done an amazing job from a business standpoint. And it's really exciting to be your inaugural guest on the podcast. I will say this, and this is not blowing smoke. The type of outsider thinking that drove the development of AYTM, we will continue to see more of that. And that's where the real innovation is going to come from. And that combined with that expertise, so even my own experience, right? Lev had a research experience, but then they brought me in as an advisor, and here's our resident research guy. And there's that combination of thinking through those things together that has helped driven the growth of that business. We'll see a lot more of that. And those are the companies that we're at the future. And that's fun and exciting to see. The last only wisdom I can say is the lesson that the pace of change is only increasing. And that's a trope, but it is true. It is demonstrably true. So hold on. We thought that the 2020 to now, I don't know if you guys are sure you've seen the memes, the jokes of, oh, 2022. I mean, now, as we're talking about this, is there an alien invasion happening in New Jersey? I don't know. Right? There's like, it's like the weirdness level is just increasing. Google says that their new quantum chip says that there's multiple universes. We live in really interesting times. Have fun with it. There's the wisdom. Have fun with these interesting times. They can be stressful, but we'll figure it out because that's what we do as humans, we adapt. And in the meantime, it's a really interesting time to be alive watching all of these changes happen.

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Stephanie: So Lenny, if people want to hear more about your perspective on AI, market research, the future of the industry, where can they find you?

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Leonard: Greenbook.org is my online home. My email address is lmurphy@reenbook.org. You can also find me on LinkedIn, Leonard Murphy. I'm on Twitter slash X @lennyism. So those are all good ways to find me. I'm pretty ubiquitous, I think. I'm probably the most overexposed man in the industry. So most people probably won't struggle trying to find me.

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Stephanie: Well, again, we appreciate your time so much. Thanks so much for recording this inaugural episode with us.

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Leonard: Thank you for having me.

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