Stephanie 0:04 Today, we're joined by Lenny Murphy, one of the most influential voices in market research. Uh, from Green Book to Gen 2 Advisors, Lenny has been at the forefront of the industry, shaping how we use tech and insights to drive innovation, the power of influence. Oh, I gotta start over. Sorry. Let me do this one more time. It's alright. Hi. Today, we're joined by Lenny Murphy, one of the most influential voices in market research. From Green Book to Gen 2 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.

Matt 0:50 We have a pretty high level conversation lined up today. Uh, we'll dive into the current state of market research, uh, the role of AI, and, uh, what's next for the industry. So this is 1 conversation you won't want to miss. Lenny, welcome.

Leonard 1:05 Thanks. Uh, glad to be here and, uh, really appreciate that that intro. I think I owe you a, uh, a Christmas present now for making me sound far better than I would ever describe myself.

Stephanie 1:18 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 2 decades, shaping the conversation around innovation and technology in this space. Before we start to really get into some of those topics, uh, 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 of tech and innovation? ce. Before we start to really get into some of those topics, uh, 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 of tech and innovation?

Leonard 1:52 Thanks. I I appreciate that. Like so many folks, uh, I kind of stumbled into, uh, into the industry. Uh, I would love to say that it was some strategic decision. It was not. I 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, um, called WebLink Wireless. Uh, the, uh, found myself looking for a new role, and there is a a research company in Snellville, Georgia, the Myers Group, uh, that needed a a phone room manager. And I thought, oh, I can do that. I've been running a call center for, you know, for years, uh, not knowing that it was radically different, uh, conducting satisfaction research for health plans, uh, was what the Myers Group did, uh, and was smitten. Right? Uh, like, wow, this is really cool and asking people questions. And so my start was running a phone room in 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. Blah, blah, blah. Went through that path, right, of and always attracted to to the new. Uh, so in the the early 2000, when I launched Rawhopper, we were we made the decision to focus on online research, um, primarily global b to b online research, and that was when that was actually something that really was different and cool and unique. Yeah. Not the standard. Um, and we, uh, we did that, uh, developed one of the 1st online focus group capabilities, uh, off of Adobe Connect in 2006. Couldn't give it away. Right? Nobody was interested in, uh, what, an online focus group? So so I guess it was baked into my DNA, uh, to do that. Unfortunately, the strategic, uh, error I made at Rockhopper is that we focused on the financial services industry, and then here came the great recession.

Stephanie 4:06 Right. ive it away. Right? Nobody was interested in, uh, what, an online focus group? So so I guess it was baked into my DNA, uh, to do that. Unfortunately, the strategic, uh, error I made at Rockhopper is that we focused on the financial services industry, and then here came the great recession. Right.

Leonard 4:07 Uh, and we took it on the chin. Uh, right? We went from, you know, well, we're the coolest, great new company in in the industry to how the hell am I gonna pay people? Um, and that started a process of introspection, right, uh, 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 Green Book. They said, hey. We want to try this whole blogging thing. Uh, you're struggling. Will you consult with us, and we'll we'll give you money. I said, yes. Great. Uh, fine. And that started this transition into this role because what I found myself doing was, uh, blogging about what I knew, uh, 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. Uh, 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. Those were still absolutely the foundations of research are still there, but how we went about it were 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, it did not, I did not plan for it. Uh, it just happened. Um, and since I was one of the voices out there, leveraging social media, LinkedIn, Twitter at that point, uh, talking about these things, people kind of wanted to listen to what I had to say, which was quite a pleasant surprise. Uh, and then folks started asking me, well, will you help advise us because, you know, we don't have time to do this either. And I think the the core lesson there was just so often in our industry, we, especially founders, uh, you get caught up working in the business instead of on the business, um, 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, right? Of, uh, being that outsourced partner through Green Book and through Gentoo Advisors, my consulting company, uh, of being the one who's tasked with paying attention to where the puck is going, um, and helping to advise leaders on on that. Here's where the puck is going and here's, you know, because of my background as an operator, uh, I could say this is what I think it means for the business. Um, 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. So Fair. Uh, 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 room. Still pretty good at predicting what's gonna happen, but I think it's a year out and it's like tomorrow. Right? So changes are happening faster and faster. Uh, so, yeah, long story short, uh, shorter than it could be. Uh, that's the how I wound up in this role. Uh, and it's the coolest thing I've ever done. Um, I I love helping people. My kids ask me what I do for a living, and I just say I help people, uh, because fundamentally, it we need people to be thinking about these things. So 100%. Leaders can focus on running the business. So, yeah, there we go. und up in this role. Uh, and it's the coolest thing I've ever done. Um, I I love helping people. My kids ask me what I do for a living, and I just say I help people, uh, because fundamentally, it we need people to be thinking about these things. So 100%. Leaders can focus on running the business. So, yeah, there we go.

Stephanie 7:46 Yeah. That's a very cool cool business model. Um, as a kind of follow-up to that question, because you did touch on this in in your answer there, but I would love to hear from you. You know, you've talked about and 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, uh, in addition to that, and how have they redefined the way that researchers deliver value to the brands that they work for?

Leonard 8:19 Well, the I mean, the first the first one was the transition, uh, from well, of course, it went from mail to telephone.

Stephanie 8:29 Yeah.

Leonard 8:31 And the golden era of random digit dialing, everybody had a phone, it was it was easy, then the shift online was was the first big one. But all we really fundamentally did was take what we did and just made it a little more visual. Um, there was no real transformation. Um, no it was just kind of old wine in a new bottle kind of thing.

Stephanie 8:57 Yeah. Yeah. Yeah. a phone, it was it was easy, then the shift online was was the first big one. But all we really fundamentally did was take what we did and just made it a little more visual. Um, there was no real transformation. Um, no it was just kind of old wine in a new bottle kind of thing. Yeah. Yeah. Yeah.

Leonard 8:58 Yeah. And I don't mean that as a dismissive thing, it's just that's just where we were. Um, but that did have fundamental changes in, uh, in sampling, which was the foundation of research. Um, 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, um, was taking what was a into a visual medium effectively. Um, and then of course, when that came DIY because these tools became easier for lots of people and it lowered the barrier to entry for researchers. Uh, 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, uh, and as technology ubiquity caught up, uh, bandwidth, you know, all of those things, then suddenly research was global. Um, we can incorporate video, we started to see where I couldn't give away online focus groups in the mid 2000s. Now those became viable ways to engage with people. So the toolset started to expand, but the use cases started to expand as well. Um, so those were the biggies. And then of course social media as well, a whole other feed of information, where does that fit in, uh, where we could listen instead of ask, uh, and incorporating this whole process of digital exhaust, um, into the insight flows. Those were really transformative. But what was always holding us back was technology availability, right? Um, 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. The, uh, you know, just as in the 80s everybody had a home telephone, well in 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 their behaviors, uh, and that is effectively a global saturation. Certainly, some tribesmen in the Sahara may not have them, but most of them do. So those have been big changes, and now I may queue up the next question. What AI has done is that it's now taken down all the barriers of, uh, from a process and efficiency standpoint, particularly on data integration and synthesis that existed before, right? We were adapting, we're figuring out these cool new things, uh, but fundamentally there were still ways to make all the pieces fit together. Um, that is the fundamental transformation of AI, um, 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. Um, and that has a whole other series now of 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 data design, data collection, field management, reporting, those are processes, and traditionally it's been people driving those processes. Increasingly technology has taken over more and more. Now we see a world where the process is entirely driven by technology, uh, other than the human elements of what's the business question, and then what the hell do we do with the answer, right? 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, um, in some components. And that is the next great arrow transformation that the whole world is going. There's not just us. lse 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, um, in some components. And that is the next great arrow transformation that the whole world is going. There's not just us.

Matt 13:01 Right. Definitely a lot to explore on the AI topic. I wanna pause really quickly and go back to a perspective you shared, maybe more of a retrospective that I think was really interesting on sort of contextualizing where we are in this moment with this this looming promise slash threat of AI, however you wanna view it

Leonard 13:24 Right.

Matt 13:24 In the broader context of all of these other transformations. Maybe they were transformative, maybe they were, uh, smaller scale changes in the way we've done our work as market researchers, that researchers have already made their way through. You know? You mentioned the change to digital, um, the adoption of social media analytics, the the growth of, um, you know, greater sample scrutiny. Um, there are a number of needs that drove each one of those those changes. I'm curious to get your thoughts on, you know, thinking about all of the 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?

Leonard 14:25 Uh, boy. That now that's a question, and no one has asked me that specific question before, uh, so I'm gonna have to kind of freewheel on this one.

Stephanie 14:33 Do it. ailing 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?

Leonard 14:25 Uh, boy. That now that's a question, and no one has asked me that specific question before, uh, so I'm gonna have to kind of freewheel on this one.

Stephanie 14:33 Do it.

Leonard 14:37 So I think the the industry has overall adapted from a from a process standpoint. Um, we have not done as well adapting to a from a business model perspective. Um, 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 grit report, right? Yeah. A by T M was a great partner for us for many years in conducting that. Um, and that's our twice annual taking the pulse of the industry. Right? So asking buyers and suppliers, what do you think is going on and getting their take on things. So in that we view a few different segments in the industry. So, uh, full service, which is the bulk of the industry, uh, from a revenue standpoint, at least traditionally was. Ipsos, Kantar, you know, the the big, you know, the full service agencies. The field services, which was always under the surface, field services support full service. Um, they they do the grunt work of data collection and all of those things. Qualitative, uh, which still kind of stands on its own, although the lines are blurry now, uh, strategy consulting and then technology. Now, uh, 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 gonna take my job. Right? Um, and that has happened. You know? The, uh, uh, the need for coders, for instance, has been steadily, uh, degrading for years because of text analytics. You know? The need for programmers of a survey, um, has steadily declined because we have automation. Um, you know, we've we've templated and created, you know, kind of plug and play solutions in many cases. Uh, you know, people report writers often replaced by platforms like a y t m that well, you've got you got the gist here. Right? Here's all your charts and graphs. You know? Now it's just fine tuning. So so we have adapted to that. Um, but that begs the question then of, well, where's the value add? So if you're a full service research company versus, let let's say, an an AYTM that is both service and technology, um, and you're a buyer, why do I go to a full service company when I can pretty much get what I need from a, you know, from an AYTM? So we're still struggling with that, but the market is the market speaks. Right? Um, the growth of the technology sector of the industry is now equivalent to if not greater than the traditional definition of the industry, Um, and that has happened rapidly, so in the last decade. So that's forced some reportment, right, for folks to kind of think through how they fit together. Um, and that's where we see now the growth of the strategy consultancies because that's all they are. Yeah. They're 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 will be roughly half, if not more, uh, of the business, and then there is human driven, but technology enabled companies that fit on the other side. Uh, and everybody is going to fit in kind of a spectrum there, but the bulk of the revenue that defines from a market sizing standpoint the industry will be on that tech side of things, and that's really kind of where we are now already. Um, and buyers because buyers speak, right? That is where they go. So now the question, I guess, 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, um, specialization, you know, advertising testing, uh, 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. Um, and, uh, and undergirding all of that will be the technology enablement one way or the other. It's just gonna be a question of what's 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. be far more pronounced. Um, and, uh, and undergirding all of that will be the technology enablement one way or the other. It's just gonna be a question of what's 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.

Matt 19:40 That's a great perspective. I I think this is something that Stephanie and I were just talking about, in fact, is and you alluded to it, um, in your answer, which is just at a fundamental level, the researcher's role is gonna shift. Whether, you know, you're you're you're excited about it or you're apprehensive or some mixture of the 2, there's there are significant changes coming if they haven't already arrived. Um, so given that, you know, big changes in the industry. From the researcher's perspective, how do I pull it together? Like, you know, how do I start to integrate some of these these AI tools that are available to me in my day to day practice?

Leonard 20:19 Yeah. Well, that so there's another dimension of this. I just we've been kind of talking about the supplier perspective, but, um, the buyer side, is impacted by this as well. I think that's kind of where you're getting to. The because the fragmentation the decentralization of research is well on its way, and I'm sure you guys know that better than most, right? The 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 that's so it's interesting that on the this democratization of insights has occurred driven by technology. Um, 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. Right? Um, quality is kind of at the top of that sub list of better. Um, 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 us $1,000,000,000 decision. Um, 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, uh, I know that one of the largest CPG companies in the world well, they talk about this publicly. So P and G was kind of slow on the AI adoption front as many other companies have been until they like, let's make sure we don't wanna be the guinea pig. Right? Let's say we should make sure the data is secure, you know, the stuff works, they're going through all this IT compliance, all that good stuff. And as soon as I was there, they said, alright. Well, we're gonna invest, you know, a gazillion dollars, right, uh, into building their own internal LLMs based off of their data and leveraging their data. Now this is stuff we talked about. Uh, I used to when I I traveled and spoke, I would always use a clip from the movie minority report, right, where Tom Cruise is walking through the mall, you know, and there's these targeted ads coming through. their own internal LLMs based off of their data and leveraging their data. Now this is stuff we talked about. Uh, I used to when I I traveled and spoke, I would always use a clip from the movie minority report, right, where Tom Cruise is walking through the mall, you know, and there's these targeted ads coming through.

Stephanie 22:41 All personalized ads. Yep.

Leonard 22:43 It was like, that's big data. Right? And that was the word we used back then. That was the promise of Mhmm. Of this. We didn't know that we needed something else to make it happen. Right? Oh, hell. Just put all the data together and look at this world where

Stephanie 22:55 we're happen.

Leonard 22:56 Yeah. Right. It'll just happen. Uh, you know, AI unlocked the era of big data. So truly, um, the, uh, so now I I get going guys, forgive me. The, uh,

Stephanie 23:09 that's good.

Leonard 23:10 Trying to see what we can on your question. The 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, um, are recognizing that, uh, they are sitting on data gold mines. They do not necessarily have to mine for new gold all of the time. Sorry, that analogy is stretching. They got a buttload of data that answers questions. They don't need to ask new questions. Um, 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 that missing data to fill and fill this in, and that becomes a a virtuous cycle of constantly, excuse me, going out to the market and asking information to fill in gaps, but not repeating what we already know. Right? We don't need to ask the same questions over and over again

Stephanie 24:06 Of course. Yeah.

Leonard 24:08 When we can leverage the data effectively that already exists. And that is the great adaptation we are moving into now is the accessing the right technology, um, if it because sure, P and G can spend $1,000,000,000 on their own custom LLMs, but, you know, if you're Joe's, uh, Joe's Smoke Shop in Kentucky, right, where I live, and I've got 5 branches, but I still want to be able to tap into those things, then I'm just gonna use ChatGPT.

Stephanie 24:41 You wanna subscribe. Yeah. Exact exactly. and G can spend $1,000,000,000 on their own custom LLMs, but, you know, if you're Joe's, uh, Joe's Smoke Shop in Kentucky, right, where I live, and I've got 5 branches, but I still want to be able to tap into those things, then I'm just gonna use ChatGPT. You wanna subscribe. Yeah. Exact exactly.

Leonard 24:43 Right. Right. Yeah. So, uh, 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 accessible and freely accessible information, uh, 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, I'm by no means do I think anybody should trust what an LLM tells you today without validating it. I was this past weekend, I was doing some work, uh, with, uh, GPT o one, and it just flat out hallucinated a bunch of bull. Right? And I and so, uh, that was my 1st time seeing that. Right? Uh, before it's like, yeah, okay. That that's right. That that passes a sniff test. This was this did not. It was like it was it was a weird experience to go to talk to the AI and say, you were wrong. What the hell is wrong with you? Right? Why did you tell me this b s? Um, you know? Uh, oh, I'm so sorry. You're right.

Stephanie 25:46 I know. I love how quickly it's like, of course. You're right. I did. Like, it's Very apologetic.

Leonard 25:51 Right. Apologetic is something we want to do to begin with. Right?

Stephanie 25:54 Yeah. Yeah. Yeah. Not great. this b s? Um, you know? Uh, oh, I'm so sorry. You're right. I know. I love how quickly it's like, of course. You're right. I did. Like, it's Very apologetic.

Leonard 25:51 Right. Apologetic is something we want to do to begin with. Right?

Stephanie 25:54 Yeah. Yeah. Yeah. Not great.

Leonard 25:56 So this is we're not we're not there yet, right, where we can trust it. So we're still gonna be validating, etcetera, etcetera, but that is where, uh, 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, uh, you know, there will still be research studies, there's still going to be very specific things that brands want to understand in a different way. Yeah. Uh, but that'll probably increasingly just be more strategic in nature. Yeah. Probably more iterative, um, more more agile as we think about that term. But kind of tactical stuff, that will basically be a subscription, um, to, uh, ongoing information that is feeding the beast. So and that ecosystem is being set up right now. Right? We know o OpenAI, for instance, they pay for access to information. So to feed their systems, that'll continue, um, in a slightly different way. And we'll all just have to adapt on how that works. The trains left the station, there is no stopping this transformation. It's already well underway. Um, the what I hope we find, and I'll give this this weekend as I was working with with Adele, and I was doing a competitive assessment. K? So, uh, 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, um, while also allowing it to do its thing with, uh, with external information and went through a process of exploring these things together. So, uh, and there was no denying the efficiency. I could have gotten to the same place. Right? But it would have taken me 30 hours, you know Yeah. To to get there. Um, this took me 5. Uh, and to have a fully baked like, well, here's the deliverable, right, that I was looking for. Um, one pretty, but it was it had the right information there. So that process of how from a researcher standpoint, we still we provide information, which means we need we need to know how to ask questions. And I think if we're on the client side or on the the supplier side, we have to think through, um, 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. And fundamentally that's what we do already, now the tools are just going to change, in how we achieve that. And what that should do is increase bandwidth, right? So we should be able to do more. We should be able to rather than today, I don't know, I know you guys have grown explosively, but you know, you have a 100 people and, you know, an x revenue, and, uh, you should be able to do not have to put 200 people in to do the same, you know, number o is increase bandwidth, right? So we should be able to do more. We should be able to rather than today, I don't know, I know you guys have grown explosively, but you know, you have a 100 people and, you know, an x revenue, and, uh, you should be able to do not have to put 200 people in to do the same, you know, number

Stephanie 29:14 of functions. Right. Yeah. Without You

Leonard 29:15 should be able to

Stephanie 29:16 sourcing. Yeah.

Leonard 29:17 Yes. You should be able to scale up, and handle greater volume. Um, and brands will on the buyer side, they'll consider the same thing. So, uh, okay, we want to we are resource centric, we are data centric, but you don't have to have a 1,000 people like P&G, right? 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. Um, 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. Um, and our ability as subject matter experts to know when the when the l m was was bullshitting. Right? If I wasn't an expert, I wouldn't have known that it was hallucinating. So, uh, that becomes a real valued driver as well for us as researchers.

Stephanie 30:17 I had also read, like I I think this is in the context of and across several different sources, so I'm kinda summarizing and synthesizing here. But, you know, as digital transformation in this age of of AI for for our corporate partners, you know, um, that there's kinda 2 roles that ultimately researchers may end up falling into falling into. And 1 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 the longer you know, more forward looking view. So that's on the on the client side. And then on the supplier side, I've seen and I I personally love this notion of researchers, you know, people who have historically been in professional serve professional service departments, you know, moving into the world of product and building products based on their domain expertise that are repeatable, standardizable, and do all of those things that allow AI to work the most effectively and draw the the cleanest insights. Do you agree that those are sort of, like, where you see researchers as you know, the the future of researchers of those places you see them sitting?

Leonard 31:39 I do, uh, and I think a little more nuanced. We've and we've tracked this, uh, via grid. We've asked what skills are necessary for the researcher of the future. Right? Um, and there's always technology that continues to be there, but we've also over the years have seen the emergence of anthropologists, storytellers, social scientists, uh, you know, these kind of soft skills as well as business consulting. So it's kind of the buckets. necessary for the researcher of the future. Right? Um, and there's always technology that continues to be there, but we've also over the years have seen the emergence of anthropologists, storytellers, social scientists, uh, you know, these kind of soft skills as well as business consulting. So it's kind of the buckets.

Stephanie 32:09 Yeah.

Leonard 32:09 And, uh, and I do think that that's that's true because we we need to help our 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. Um, and AI is going to be really good at the at who, what, when, where, and how. So, uh, it already is, right, because that's just data. Um, not so good at why, uh, and I think we are keepers of the why. Um, 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 know, right? Yeah. 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, you know, of how I got there. Uh, and that's a valuable skill. I mean, you know, uh, doing that. So 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 100 AI specific launches in the research space.

Stephanie 33:39 Yeah.

Leonard 33:42 Sorry guys, I hate if you're one of those startups I hate to tell you, there's not room for 100 of you. There will be room for 5 or 10, right? They will really grow uh, to become large companies. So there may be a whole bunch of little lifestyle businesses, um, but there's not room for, uh, for that level of 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 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 creating new products, and some of those won't make it, most of them won't make it. Um, some will get absorbed into other businesses,

Stephanie 34:47 and- Right. s 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 creating new products, and some of those won't make it, most of them won't make it. Um, some will get absorbed into other businesses, and- Right.

Leonard 34:48 And they'll make it, and a few will be truly will be disruptive, uh, and will become the next AYTM or whoever, right? 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 IX event series, 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 and I love that, because I'm an entrepreneur as well. But for every winner, there's 30 who never make it up on stage, Right. Um, there are variations on a theme and that's just what we're going to have to accept, right? That's just the odd. That's just playing the odds of being an entrepreneur.

Matt 35:37 I wanna make sure we get your perspective on this. You mentioned as researchers, we are keepers of the why. I love that. We are, you know, purveyors of the information. We it's up to us to make sure the information is correct and that it's being utilized in, you know, appropriate ways to guide decision making. Uh, everybody's talking about synthetic data now. Uh, here we go. So if you have a dataset that's trained on, um, you know, 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?

Leonard 36:25 So one, I think that there's a definitional issue here, uh, terms mean. People have different meanings. When I say synthetic sample, uh, what I mean is really the scenario that I was just talking about. Um, it is an LLM overlaid with real first party data, um, uh, that then you are creating personas off of real information that we've been doing that for years. Right. We can, this is cooler and easier now.

Stephanie 36:56 Variable, which is just cool, right?

Leonard 36:59 Right. Right. To have a conversation based on on, uh, 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. Um, so that is the definition that when I use that word, that is what I mean. Um, I also know that there is a whole other component of that where it is it's just made up.

Stephanie 37:31 It's been just with the what you're talking about right now sorry. I wanna just clarify this for just a minute.

Leonard 37:36 It's It's an important topic. So We use that word, that is what I mean. Um, I also know that there is a whole other component of that where it is it's just made up.

Stephanie 37:31 It's been just with the what you're talking about right now sorry. I wanna just clarify this for just a minute.

Leonard 37:36 It's It's an important topic. So We

Stephanie 37:38 have people reach out who are are, you know, companies, vendors, suppliers reach out and say, hey. You wanted to try us out? And and okay. We will. They're like, give us 2 thirds of a dataset, and then we'll add the final third. And I'm like, are you just interpolating? Like, what are you? That's not AI. Like, that's you're like, I don't want that. I can do that right now. Like, that's not a I don't that's not what I'm looking for.

Matt 38:02 Or they don't even have a starting point. They wanna ask a question of a system rather than a person.

Leonard 38:10 Yes. So so to the core adoption, uh, the Qualtrics recently released their annual report, uh, annual research market report. Sorry, I know they're a competitor, but you know they

Stephanie 38:23 Oh, no.

Leonard 38:24 Yeah. Okay. It's a good report, and they asked that question, and I think the number was that that within 3 years, 71% of their respondents, which are primarily buyers, uh, would be utilized as a synthetic sample. Um, that's a shocking number.

Stephanie 38:44 It is.

Leonard 38:45 While not being a shocking number. So because of course if we go back to the definite it's all about different purpose, right? So there are some things where just like when social media uh, emerged and people, uh, this will replace the focus group. No. It's not gonna replace the focus group, but it's absolutely useful for specific use cases. 1000%, and that's why we saw the growth of social media analytics and those things. Um, so the, uh, we will find the fit for purpose applications of the trained, you know, you know, first party data personas.

Stephanie 39:32 Mhmm.

Leonard 39:32 Um, we will absolutely have that. Um, we will build ecosystems and businesses around it. I know for a fact that OpenAI is looking at buying a panel company, so, you know, they they realize it. They realize they need access to first party data, uh, and the research panel is a pretty damn good way to get it. So, uh, so there's that. While there's also what we have currently, which is just this, you know, you're just sucking in everything online and you're looking for commonalities and, uh, and creating personas off of that, and that's that can be good and useful as well, um, for the right business issue. I I have used those type of things for early stage ideation and Mhmm. You know, kind of concept testing, throwing stuff against the wall.

Stephanie 40:18 Yeah. Yeah. Yeah. Yeah. Yeah. It's to be successful. Yeah. sonas off of that, and that's that can be good and useful as well, um, for the right business issue. I I have used those type of things for early stage ideation and Mhmm. You know, kind of concept testing, throwing stuff against the wall. Yeah. Yeah. Yeah. Yeah. Yeah. It's to be successful. Yeah.

Leonard 40:21 It it it helps provoke new thinking and and okay, now I want to go test it with real people. Um, and it's 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 would be what we think of as the the research project. Um, so so back to the Qualtrics thing, shocking, not shocking, um, the speed is what is you know, it's like, woah. 3 years?

Stephanie 40:58 Totally.

Leonard 41:00 Um, but at the pace of change that is occurring now okay. Because the only way to stop it yes. It it 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, uh, or some but it's not technology ubiquity. It's not that. It's not technology, you know, scalability. The the the barriers are of adoption are just us doing it. Right? Um, but financially, why wouldn't you? Again, the example I used to have.

Stephanie 41:43 It's like perceptual hurdles and and use case hurdles to your point more than than anything else.

Leonard 41:48 Yes. And I struggled, so, you know, here I am mister futurist. I did not use AI for the 1st year, year and a half. Um, I would not do it because perceptually, I had the thought, this is going to denigrate how I pay the bills, Right? So

Stephanie 42:09 Yeah.

Leonard 42:10 Um, it cheapens me.

Stephanie 42:12 Yeah. Yeah.

Leonard 42:14 And that was my mindset. But saw it, saw all the benefits, been talking about it, you go back to me for, you know, January of, you know, 2020 2. Uh, yeah. All those are the coolest things that sliced bread, you know, but I I'm not touching it. Um, that's not where I am now. Right? I had to get over that and realize that the efficiencies, you know, the the things that it could do for me, uh, to take away grunt work tasks that were not value add or not really how I made my money, um, where it was worthwhile to do that and then become a collaborative partner. And I think we're all on that journey, um, uh, at this point.

Matt 42:50 I have a follow-up question that is probably going to have somewhat of a of a jaded tone. So just bear

Leonard 42:59 with me. Stephanie I made my money, um, where it was worthwhile to do that and then become a collaborative partner. And I think we're all on that journey, um, uh, at this point.

Matt 42:50 I have a follow-up question that is probably going to have somewhat of a of a jaded tone. So just bear

Leonard 42:59 with me. Stephanie

Matt 43:00 Stephanie said I should lean into my contrarian nature. I'm I'm still not sure what she meant by that. But when I think about synthetic data, I love your idea of do you have to start with the definition? I mean, we talk about we've talked about that with, you know, the general topic of AI in general. There's so much confusion over what is generative AI, what what is, you know Right. 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 of AI tools has increased just as you as your familiarity has grown. Do you fear that, as an industry, we might become more prone to adopting tools, technologies, approaches that, um, seem to provide an in ostensible effect. You know, they 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. Mhmm. But you have to dig through several layers of dirt to get to the the definition, um, that that, uh, uh, you know, might be concerning. But it's hidden, you know. It's it's it's it's in it's in the minutiae where most people aren't gonna look. Right. Um, on the surface, it 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're that we could, you know, fall ourselves find ourselves falling down that slope or, you know, is is that nothing to worry about?

Leonard 44:41 Oh, I think it's it's just as valid as, you know, oh, look, I have a new survey engine that, you know, is connected to, you know, a 1000000 people and it's the crappiest sample in the whole world.

Stephanie 44:51 Exactly. It's all bots.

Leonard 44:52 It's It's all bots. You can't easily trust any of the results. Right? Um, so so fundamentally, it's what we're talking about is the quality debate. Right? Which we've been grappling with

Stephanie 45:01 for e crappiest sample in the whole world. Exactly. It's all bots.

Leonard 44:52 It's It's all bots. You can't easily trust any of the results. Right? Um, so so fundamentally, it's what we're talking about is the quality debate. Right? Which we've been grappling with

Stephanie 45:01 for

Leonard 45:01 a long time. Uh, and I think the, uh, 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, um, and that will make us the trusted arbiters. Um, the, uh, what we can't do though, as an industry, let's get so caught up in that, that we get into an ivory tower mentality and we lose opportunity. And that's happened to us as an industry. We lost, uh, we lost the c x thing. We lost the u x thing. Right? We lost the social media. Uh, and I was living through that. People would say, well, nobody's asking for social media. It's like, well, of course you're not because you're not offering it. Right? So so we lost arguments. Right? We lost opportunities, uh, to to do those things because we were too, you know, that 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, you know, just working in the business, uh, not because I have a degree in statistics, so which I do not have a degree of statistics. So, um, so I'm a little, you know, skeptical of that to begin with. Sorry. I was going off on a soapbox. The so, yes, I think that is it is a legitimate concern and that is our opportunity. So look at a company like AYTM, you own your own panel, you know, you you can have that argument about data providence. You can say, look, we are building these things, uh, to be fit for purpose because you can see all the way through.

Stephanie 46:39 Right.

Leonard 46:40 Uh, and just like the example we used earlier, that doesn't mean that you can't use the general LLMs just like you couldn't use, you know, even a crap sample platform. Right? If I just wanna get a, you know, I got a $100 to spend and I just want to do a quick pulse check directional. It's not, I'm not making a $1,000,000 decision on it. I think it's okay. Fine. You know, do that. It's cheap and it'll give you something to start structuring something bigger off of. The that's just the way we have to think about it, right, is what is the, uh, it has a purpose, it has a role to play, it's a no no solution that we offer today as an industry is a 100% solution. Uh, we all we've always been combining things, quant and qual, you know, um, and we just have to keep doing that. Um, we can just do it a lot faster and cheaper and more efficiently, and we can combine stuff on the back end in a much cooler, more way than we could before. Um, and, yeah, that's that's where we are. Now it doesn't mean that who knows, 2 years or even 2 weeks at the pace things are going now, you know, they could roll out something that just, you know, makes it just we all just need to curl up and accept our robot overlords and

Stephanie 48:02 That's right. Right. Right. nd, yeah, that's that's where we are. Now it doesn't mean that who knows, 2 years or even 2 weeks at the pace things are going now, you know, they could roll out something that just, you know, makes it just we all just need to curl up and accept our robot overlords and That's right. Right. Right.

Leonard 48:03 Right? I mean, it's just the matrix. Just put me in the pod, you know, and

Matt 48:07 That's right.

Leonard 48:07 And I'll be a battery because that's all the hell that I'm good for now. Um, I don't think we're there. So Had

Stephanie 48:14 a few years, maybe.

Leonard 48:16 A few years. I mean, I I did we did purposely buy a small farm in very rural Kentucky for a reason. Right? So Yeah.

Stephanie 48:23 Like, it's not a hard recommendation, but

Leonard 48:26 Uh, you know, I mean, you know, most of my, uh, neighbors are are Amish, Mennonites, and everybody's got guns. You know? If you're gonna come after us, uh, robots, we'll you know, we're we're we're the last on the list.

Matt 48:38 Sounds right.

Stephanie 48:39 Got it. Yeah.

Leonard 48:40 Anyway yeah. It we'll we'll see. Right. And and here's what'll do that. The last sorry. I'm against soapboxing. There will be a a multimillion dollar decision misfire because somebody used bad data sources to drive it.

Matt 48:58 High profile.

Leonard 48:59 Mhmm. Right. Uh, I mean, just like I you know, we're we're recording this in December, uh, you know, during the run up to the election after the election. My point, uh, on that was, look, we we just need to recognize this not that polling is data, so that we have bad sample. So, um, and 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, you know, that there was a there was a big miss on a decision, uh, but business don't like talking about that. So, um, but they'll happen. Uh, there will be some, like there will be a new coke, you know, age myself again, that'll come out and we'll realize, oh, because you tried to run it all through chat GPT instead of doing real research. Well Right. Won't do that again, will you? Um, and we'll learn.

Matt 49:53 We will learn. That is that is definitely what we do. Um, and and like you said, if we don't, we have at least a few years to get our, uh, our leases on our farms set up and our fallout shelters built. Optimistic advice from from Lenny. No. Lenny, uh, genuinely, we've selfishly taken up so much of your time today, but this has uh, been such a wonderful conversation. Have really enjoyed capturing, uh, uh, your insights and perspectives on, you know, where we sit as an industry and where we're headed. Um, do you have any wrap up thoughts? Do you have any any parting, uh, words of wisdom for, uh, for our audience members? Any, uh, anything we didn't get to discuss today? onversation. Have really enjoyed capturing, uh, uh, your insights and perspectives on, you know, where we sit as an industry and where we're headed. Um, do you have any wrap up thoughts? Do you have any any parting, uh, words of wisdom for, uh, for our audience members? Any, uh, anything we didn't get to discuss today?

Leonard 50:35 I, yeah, I don't know about wisdom. That that automatically puts me off because that's that's not what I do. But, um, it's been a joy, uh, really. Uh, 1, compliments to AYTM. Again, I've seen your journey, uh, and I think you guys have just done an amazing job from business standpoint, and it's really exciting to be your inaugural guest on, uh, on the podcast. Uh, I love you guys. I I I will say this, and and this is not, uh, not blowing smoke. The type of outsider thinking that drove the development of AYTM, um, we will continue to see more of that, and that's where the real innovation is going to come from. Um, and that combined with that expertise, so even my own experience, right, Lev had no research experience, but then they brought me in as an advisor and here's our our resident research guy, um, and there's that combination of thinking through those things together that has helped driven the growth of that business. Um, we'll see a lot more of that, and that's those are the companies that will write the future. Uh, 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. Um, and so hold on. Right? We thought that things were you know, 2020 to now, and I hope you guys are sure you've seen the memes, the jokes of, oh, 2022. What are you know? Here comes the you know? Right. I mean now as we're talking about this, is there an alien invasion happening in New Jersey? I don't know. Like, there's like it's like the the the weirdness level is just increasing. Google says that their new quantum chip says that there's multiple universes. I mean, you know, there it's we live in really interesting times. Have fun with it. There's the wisdom. Have fun with these interesting times. It can be stressful, um, but we'll figure it out because that's what we do as humans. We adapt. Um, and, uh, in the meantime, it's a really interesting time to be alive watching all these changes happen.

Stephanie 52:41 For sure. It is. Well, again, we we appreciate your time so much. Thanks so much for recording this inaugural episode with us.

Leonard 52:50 Thank you for having me.

Stephanie 52:51 Alright. Alright. Thank you. Everybody.

Matt 52:54 Uh, before we go, so that in postproduction, they can edit it, cut it right there. Um, anything else we wanna do over, retake, shoot in, statements. Lenny, I think we we covered, uh, basically everything.

Leonard 53:11 Uh, Yeah. I probably went on and on way too long. So sorry.

Matt 53:13 That was great. No. No. That's that's feel free

Leonard 53:16 to edit down for time. there. Um, anything else we wanna do over, retake, shoot in, statements. Lenny, I think we we covered, uh, basically everything. Uh, Yeah. I probably went on and on way too long. So sorry.

Matt 53:13 That was great. No. No. That's that's feel free

Leonard 53:16 to edit down for time.

Stephanie 53:17 That's why we have you on. No. No. Jess, did you hear anything that we missed, or do we need to do a more formal outro with with Lenny? No. I sing a song? I think it's perfect. The one thing we might wanna get is, um, if, uh, Stephanie, Matt, if you wanna prompt Lenny to share where folks can find him on the Internet. Uh, Lenny, if you wanna, uh, plug the the Green Book podcast or anything, anywhere anyone can go to find you.

Leonard 53:46 The do you want

Matt 53:49 If you want to, don't feel pressured.

Stephanie 53:51 But Yeah. Absolutely.

Leonard 53:52 Yeah. I mean, we can add that. It's yeah. You would would hit record, ask question, and I'll yeah.

Matt 53:58 Yeah. Sure. I think we're I think we're we're still recording. All of

Leonard 54:00 this stuff. Still recording? Okay.

Stephanie 54:02 I'll just

Matt 54:03 Yeah. Go for

Stephanie 54:03 it, Steph. So, Lenny, if people wanna hear more about your perspective on AI market research, the future of the industry, where can they find you?

Leonard 54:13 Well, assuming anybody really wants to hear anything more after this, uh, my tirade's here. But, uh, greenbook.org is, uh, my my online home. Uh, my email address is lmurphy@greenbook.org. Uh, you can also find me on LinkedIn, uh, Leonard Murphy. Uh, I'm, uh, on on Twitter slash x, uh, linearism, uh, at linearism. So, yeah, uh, those are all good ways to to find me. I'm pretty ubiquitous, I think. Um, probably the most overexposed man in the industry, so most people probably won't struggle trying to find me.

Matt 54:51 Amazing. Visibility is not a problem.

Leonard 54:54 No. Again, probably over, uh, overly visible in many ways. So but thank you. Great.

Stephanie 55:02 Thank you.

Matt 55:02 Perfect. Good call, Jess. Alright. I think that's it.

Leonard 55:08 Yeah. Guys, thank you. That was a fun conversation. Those are some good questions I hadn't, uh, hadn't really thought about before, so I appreciate you keeping me on my toes.

Stephanie 55:16 We appreciate the the wealth of knowledge. Very interesting. So thanks again.

Leonard 55:20 Yep. Alright. Well, happy holidays to everybody.

Matt 55:23 Take care. Likewise.

Leonard 55:24 Yeah. Send me the link when it goes live. We'll make sure Of course. On the the Green Book side to amplify it and, uh, and get it out there.

Matt 55:30 Yeah. Of course. Uh, Yeah. We'll we'll definitely make sure you have access to it. I think, uh, we we were even talking about, um, post podcast guest swag might be headed your way. So be on the lookout.

Stephanie 55:42 Get ready for that swag. k side to amplify it and, uh, and get it out there.

Matt 55:30 Yeah. Of course. Uh, Yeah. We'll we'll definitely make sure you have access to it. I think, uh, we we were even talking about, um, post podcast guest swag might be headed your way. So be on the lookout.

Stephanie 55:42 Get ready for that swag.

Leonard 55:44 You you know, I I have not had a y t m slag slag swag in a while. So, yeah, send it on. So alright. Alright. Alright. Please, I give the team my best. Okay? Will do. Will

Stephanie 55:54 do. Alright.

Leonard 55:55 Thanks, everybody. Take care.