Why 85% of Thought Leadership Fails with Mike Nash of KS&R

Mike Nash: You need something that has some tension in it, um, to really grab the reader. And I think that's where we spend as much time as we can up front with our client in workshops or in different conversations to try to figure out what is that tension. Because again, you know, the client has a point of view that they're trying to get out there because they're trying to sell something or make their. Make their point. And that's fine, but that's not your lead or that's not going to be your hook, right? So you got to find out how does that tie in to something that we can make a bit of an argument or a case for.

Narrator: Guided by over 25 years in the data and research industry and assisting innovators with investment banking and advisory services, Sima Vasa brings you Data Gurus, a leading market research podcast that offers actionable insights for business acceleration and value creation. Join her as she speaks with key innovators in the space to bring you up to speed with the current state and the future of data analytics and data ecosystems. This is Data Gurus need support on your market research projects. Paradigm Sample is a full service market research solutions provider. Whether you need help with questionnaire design, survey programming, or online data collection, we are ready to assist. Paradigm can do as little or as much as you need, saving you time so that you can focus on insights. Learn more@paradigmsample.com

Sima Vasa: welcome to another episode of Data Gurus. I'm so excited to welcome Mike Nash, who is the Chief Growth Officer of ksnr. Thank you, Mike, for joining me. Thank you for being part of this conversation. I also should mention you're also on the board of the company as well, so welcome, Mike.

Mike Nash: Thank you. Thanks for having me. Looking forward to it. I've seen a few of your, uh, more recent ones and, uh, looking forward to the conversation.

Sima Vasa: Awesome.

Narrator: Awesome.

Sima Vasa: Ah. So, um, you know, I'd love to ask guests a little bit about their background into how they got to this point in the journey. Briefly. I think people, um, you know, it's always interesting because I think young people always have this plan of where the future looks like. And the reality is you weave and dip and dive and you eventually, um, you know, still figuring out what you're going to do anyway. Yeah. So, Mike, tell us a little bit about your journey.

Mike Nash: I think that weave and dip and dive is particularly true in this industry. Right. In the market research industry. I am on the advisory council at the, uh, University of Texas at Arlington program. And some of those folks went into that master's program saying, hey, I want to do research. But it's a small number of people that sort of grow up wanting to be a market research analyst or in this space. Right. So I went to a small liberal arts college here outside of Syracuse, upstate New York. Uh, got an economics degree. So certainly was in the data world, for lack of a better word. Uh, went to Albany State for a master's, thinking I was going to do, uh, civil service, be in politics, that kind of thing. Um, and I got my master's in public administration. So again, not nice research. Heavy. But there was some research elements. And then the only thing that chased me out of that world was I was on two or three mayoral campaigns. And, uh, my guys lost every time. So I was like, wait a minute. Oh, man, if I can't. If I can't pick a winner, this might not be a good career for me. Right. So you get close to the family candidates, and then they lose. It's like, oh, I can't go through this. Uh, so I bailed and answered a tiny little, uh, want ad in a newspaper, which was again, 30 years ago. That's how you found the job. Right. And, uh, unbranded. And walked in and met four or five of the owners and said, this sounds interesting. Let's give it a shot. And that was 30 plus years ago.

Sima Vasa: Wow. And just for reference, what was your first job when you joined the company?

Mike Nash: In the company, I was a research assistant. Right. Just starting out, ground level, just sort of looking up data. We. I think we were sharing a computer at the time. Right. This was like 93 or something. Right. 94. So they didn't. Everyone didn't even have their own computer. Did a lot of time copying, uh, big, giant binders. Right. Photo binders and running. We. We weren't doing the punch card stuff. We were past that, but it was. It was old school.

Sima Vasa: Yeah, it was kind of lived and died by the FedEx stuff deadline.

Mike Nash: A lot of Fed. We knew every FedEx spot and where, where he went in what order. So if you missed them here, we could go downstream and catch him down there. And then we always had to make the run to the airport. There was always an airport. FedEx. That was the last pickup. And we'd be hustling to the airport to. To meet a driver, uh, before they got on the plane. Yeah.

Sima Vasa: So cool. So what do you. I mean, obviously, you've been with the company for so long. What do you love about. I'll say research, first and foremost. Like, what gets you excited about It,

Mike Nash: Yeah, I think there's a couple things that. One kept me in Syracuse. Right. I was always off to the big city or wanted to go do something else, but I found that I could still play with major brands. Right. When you look at the people we support, they are the big hitters of the Fortune 500 for the most part. Um, so that's exciting to me. And I could still do it in the comfort of Syracuse. Again, pre Covid, that was not a common thing. Right. We had to go to New York or had to go to Boston or go to D.C. to sort of be in the heart of things. But here with ksnr, I could still work with companies out of those areas and support them. And you know, except for our health care group, that probably does save some lives in some ways with the research they do, we're not saving lives, but we are solving meaningful problems. Right. These, these tend to be things that are moving markets, particularly the places I play with my business services, clients. And in our technology space, um, you know, you can't have many conversations in the office or outside the office without talking about AI, uh, or talking about synthetic data or talking about cloud and, and we're on the forefront of all that stuff. Right. One of my first projects way back in the 90s was, was starting to understand would people even pay for the Internet. What was this thing? It wasn't even called the Internet.

Sima Vasa: Right.

Mike Nash: What would we do with that? So I think being on the, the leading edge of some of those more macro shifting, macro moving kind of ideas, um, is exciting to me.

Sima Vasa: Yeah. And watching and helping your clients, um, traverse those trends and try to figure out where they're going and be able

Mike Nash: to deal with it and not have to live in Silicon Valley to do it.

Sima Vasa: Right.

Mike Nash: That's the beauty of it, that we can still live in Syracuse and enjoy all the things that Syracuse brings from a smaller lifestyle or a smaller, uh, community than being in the major metro area.

Sima Vasa: Yeah, that's cool. Um, so talk to me a little bit about your perspective on, um, AI. And I know you're really passionate about thought leadership. So in some ways they're a little bit, um, I shouldn't say contradicting, but thought leadership is really deep critical thinking from my perspective. And AI can do that, but not necessarily using your full brain to do it, but using a machine. Um, give me a perspective on, uh, actually let me start from the basics. Give me a definition of thought leadership from your perspective.

Mike Nash: Yeah, yeah, I think you're right. There is a paradox there with AI. And thought leadership, Right? And I think, again, I think they, they do meld together at some point sometimes maybe they meld too easily for some people than others. But really when we think about thought, uh, leadership, and one of the reasons why we're positioning it in some ways as defense from, from AI is that it's really about original insight. Right? And so for us, it's not just scraping the web or it's not just scanning the, the last 10 articles written by company acts and trying to improve it. It's really coming up with something original. And, and the other part that, that's a real kicker for us on thought leadership is that it changes how people see the problem. It's not just the answer to a problem, but it changes the framework or the, the paradigm that they're looking at with that problem. And then the third part that really does matter, and again, we even still fall short from this sometimes and our clients do as well, is that how do they do, what do they do next with it? Right? How do you activate on that? Or how do you, how do you take a step because of that? Right? And I think all three of those pieces need to be clear and successful to have a successful piece of thought leadership. And they don't, all three of them don't always come together at the same time or, or easily. Right? So the, the original insight though, is the one where it gets you out of the AI space, right? You can use AI to sort of see what's out there. You can get a sense of, of where the white space might be. Um, but, but you can't just write, in our opinion, you can't just write quality thought leadership, um, using AI tools. It, just by definition, they're not original, right? They're not, they're not provocative. They're not putting a new perspective on something. They are just kind of regurgitating what's already out there and often kind of, um, simplifying it to the mean. Right. If you're thinking about, of a numeric, which not all thought leadership has to be numeric, but if you're thinking about a numerical, that the role of AI is to kind of bring you to consensus or bring you to that median point. Um, and that's not usually what makes quality thought leadership.

Sima Vasa: So let's peel it back a little bit. Um, first of all, is the trend of thought leadership, has that changed? Has it increased, decreased? Like, what are you seeing, uh, what's happening right now in terms of thought leadership as companies are investing in it? Um, yeah, that'd be great.

Mike Nash: We're certainly seeing an uptick in the sense that again, it is one of those last stanchions of original thought. Right. So again, no matter what your marketing budget is, no matter how many TV spots you try to grab, if everyone's kind of recycling the same ideas, um, it gets hard to stand out. Right. So we see thought leadership particularly on the, on the B2B side. Not exclusively, but that's where I spend a lot of my personal space is more on the B2B kind of technology side. Um, thought leadership becomes one of the last places where you can really differentiate in a unique way. And we are seeing. I'd encourage everyone to look at the Global Thought Leadership Institute. They put together a nice program over the last couple years, um, really putting together, ah, a group that really thinks deeply about this as an industry space. Um, and they are putting forward a lot of good data, a lot of good examples of what is quality versus not quality, um, to ferret out some of those kind of weaker, you know, the AI slop dreaded term. But I think the Global Thought Leadership Institute is really doing a nice job putting some guardrails on this sub industry, if you will, to really make sure it's legitimate. And uh, I would encourage everyone to take a look at what they're putting out and some of the data they're delivering. But we know for a fact that executives are looking at thought leadership. We know for a fact that they're admitting that at uh, a very high level, up over 90% that they're admitting that thought leadership helps influence their purchases. Again in this B2B primarily technology space, but not exclusively. And the paradox is also that we know that they admit that there's only about 15% of that thought leadership that they're deeming is very good or excellent. Right. So there is a gap between how much they're leaning or want to lean on thought leadership activity versus what's really breaking through and helping them in a positive way. So we think that, that you know, 90 plus percent that are using it and only 15% indicating that it's quality. There's a big gap there to play that KSNR is trying to take advantage of and offer some, some greater quality for firms that want to close that gap a little bit and be more meaningful. And again, we see that as a way to use, um, AI to help us do this work, but also defend against. You just can't use AI for, from start to finish and pretend that it's going to be quality.

Sima Vasa: And who typically at These companies is um, making decisions about hey, I need to develop original content. Thought leadership. Are you working with the C suite? Uh, it feels different than the traditional consumer insights group, um, that a lot of agencies work with.

Mike Nash: Yeah, no, it's a mess. But you are starting to see titles with thought leadership in them. Right. Which again is new and that's a change in the last five years. Right. And you're seeing them in companies, companies that you wouldn't naturally think of. Again. Our, the bulk of our thought, uh, leadership work is done for various consulting firms, the, the big tech manufacturer, software producers. That, that's kind of expected. They've always been out there doing that kind of thing. But now you'll see thought leadership departments or individuals uh, popping up in you know, construction firms, you know, major, major manufacturing firms. Right. So I think it's, it's definitely spreading as people start to see that this is a, a place where we can differentiate and be a little bit different. Um, and, and I think that's, that's sort of the, the beauty of this wave that we're riding right now around thought leadership. Um, and I think hopefully that'll continue for a while. But we're definitely seeing this as a, a, a you know, trending towards a high water mark in, in activity that maybe five, ten years ago it wasn't talked about as much and thought about in the same way because firms had other ways to, to get their word out. Right now it, it's becoming more uh, and more difficult to, to sort of show what you're all about. Leadership.

Sima Vasa: Yeah. And also I think there's this fine balance and you tell me about being self serving versus really helping uh, an industry or a uh, segment of clients or customers to say irrespective if ultimately if you buy from me or not, here's a objective piece of thought leadership that could help you drive business.

Mike Nash: Yeah, again I would say that was harder to communicate to our clients a few years ago. Again, if it's, if it's coming out of the marketing department, we get a lot of people that want a sales brochure. They don't know it.

Narrator: Right.

Mike Nash: They say they want thought leadership but they end up describing some uh, sort of salesy kind of thing. And you're right, there's nothing more that will kill somebody. Ah, the desire to read an article. If you're overwhelming them with you know, sales brochures and you know, uh, you know, click, click here for our product description. It really does need to be more about the issue at hand. The challenge at hand. And then by default, they start to think of you as the deliverer of that solution. Right. So I think that the sell is kind of the soft, you know, the soft secondary result. Uh, you really are trying to help solve a problem or think about a problem differently. Remember, not, not every thought leadership piece has to solve a problem per se, but you're trying to highlight some, some issues to be aware of that these executives can make better decisions and be, be smarter about it and use that dynamic wisdom in a powerful way for their own business.

Sima Vasa: Yeah. Um, and you mentioned, like the three pillars in terms of successful thought leadership. Obviously activation is a big component. And do you guys play in that space or. Yeah, okay.

Mike Nash: Yeah, we're doing, we're doing more and more of that. Um, we certainly have, uh, a design element within ksnr. Um, and this is a space that, again, a lot of times the ad firms that we're working with will do some of this work, but we're certainly taking more and more of it. But the big thing for us, uh, and again, this is shifting over the last couple years is that traditionally we would go out, do a big study, come back, and either we'd write something, the client would write something somewhere between, and you'd have this massive, whatever, 20, 30, 50 page white paper.

Sima Vasa: Right.

Mike Nash: That was the phrase that was always used, you know, I want to do a white paper. And that's still fine, right? That we still do a lot of that work. Our communication to our clients is to think about it more as a compounding asset that it should not be just a white paper and done right, it should be used in multiple areas from all your different social media channels to obviously the big, you know, horizon piece, if you will, or the big landmark piece that you want to put out there. But then how do you distribute that within your own sales force, within your own company, and then within your prospects and targets? So I think we look at more of a bit of a holistic, um, campaign kind of notion to sort of say, how do we reuse these insights beyond the, uh, big white paper? Right. Because that's certainly still a leading element, but it shouldn't be the only element. And I think we do everything from workshops we started doing Lego creative play where we bring in executives and start having them sort of move Lego blocks around in ways to think about systematically thinking how they might change their business based on this data and insight. So there's a lot of different qualitative, creative ways to actionize this data. Um, and then I think it's got to get people out of the head of, of just doing a white paper and moving on to the same, the next things that even, even if you just keep it in a, a narrative, uh, place, you can still chunk things up in a way and sort of keep that, that conversation going for months and months or even years. Um, and not just kind of run to the next topic, but that, that's the, that's the biggest thing we see with our clients is that we, we get the work done, we have the paper, a paper gets published and they're on to the next thing. And we're trying to counsel more and more of them to keep reusing that data and think about creative new ways of using that data that will again compound your investment in a more positive way.

Sima Vasa: So it's a long tail, like use the data. It can be used over a longer time period.

Mike Nash: Yeah, it's really a systemic approach as opposed to a one and done kind of approach. Right. So it's really kind of how do you activate this in different environments and different touch points with different users, both internal to your company and customers and prospects externally or partner channels even. Right. To, to keep that, that, that dialog moving.

Sima Vasa: Yeah, I find that, uh, people have really low attention to like, to your point, like you could do it, you could do the white paper and then you could chunk it up and use it over a period of time. But I don't know if you deal with this in terms of trying to create memory for, um, the client, but also obviously even potential prospects or customers. Is that something that's different now than it was a few years ago? And how are you dealing with it?

Mike Nash: Yeah, no, I think a lot of that comes down to the framing and the delivery of the piece. Right. Whether it be a three panel, uh, carousel on a social media feed to a 50 page, very academic, 10 pages of footnotes, kind of white paper, and anywhere in between. So I think a lot of that has to do with the narrative that you're writing, the style that you're using, and not to forget the graphic elements. Right. Again, sometimes we do that with our clients. Sometimes they have their own internal agencies, um, that are doing that. But there is a lot to the art of making things memorable and making things intriguing to read. Even if you have a great topic. I think the standard 10 pages of bar charts. Yeah. People start to lose their mind by the end of that. Right. So you really got to think about, um, how do you want to use this data? Um, how can you make the data a bit Interactive. Right. We've got a lot of clients that on their website now, you know, you can sort and filter that white paper to your specific industry or part of the world or size of company. Um, we're doing a lot of almost kind of like quick uh, polls. Right. You know, what do you look like, right. What, what is your kind of characteristics? And that serves up a certain part of the, of the study for them that may, they don't have to read the whole 50 pages. It'll take them to the five pages that they are likely to care most about based on some predictive model that we can build for them. Uh, doing a lot of indices. Right. Where, where we're sort of combining elements to sort of create almost like a consumer price index. Right. Let's check, let's check the security, the security index for quarter two and let's see what happens to that security index six months from now or 12 months from now. Right. So I think the indices world is also one where there's more to be done to create something that's uh, a little bit ongoing and, and gives people a bit of a summary of the data that again they're not forced to read that 50 page um, diatribe although it still needs to exist to show the validity. Right. That this wasn't just made up through AI or just click and dirty. Right. So I think there is home for the, the, the, the more, the, the more thorough rigorous white paper. But then it's how do you disting, distinguish that from some other sort of ways, um, to deliver the same message uh, in a more user friendly environment.

Sima Vasa: Yeah, no, I love it. It's, it's, it's, it's being able to create deliverables or engagement based on target audience, whoever's going to engage with the content that, that's been developed. Um, I kind of, I actually in this day and age, I love meaty content. Like it's to, to pour through data and to really understand connections. Uh, um, and even to your point about defining the problem differently, like that doesn't happen overnight.

Mike Nash: No.

Sima Vasa: Talk to me a little bit about a client comes to you and says hey, I want to develop thought leadership and they might have an exact prescription of what they want to do. And you're like wait a second, let's talk about what you're trying to accomplish. How long is that problem definition take?

Mike Nash: So that is a critical element and again we talk about uh, AI all the time. Right. Um, um, it wasn't long when I was pulling my own hair I was saying, how much more could we say about AI that's new or different or fun? Right? So, so now, now that world shifted to synthetic a little bit. Right. So we're off the hook now. We're writing a lot about synthetic and not as much about AI, but, but even in the AI world, we spend, ah, a lot of time up front with a client pushing, um, workshops or at least literature reviews where again, you can then use AI to discour the, you know, every piece ever written about, uh, cad, cam design or something. Right. And just see what's out there that helps you find that tension of what could be new or differentiated or unique. And I think that the tension, words are big. When you hear about that a lot in, uh, in writers schools or authors or, you know, people writing scripts for, for plays or shows, same idea here you need something that has some tension in it, um, to really grab the reader. And I think that's where we spend as much time as we can up front with our client in workshops or in different conversations to try to figure out what, what is that tension? Because again, you know, the client has a point of view that they're trying to get out there because they're trying to sell something or make their, make their point. And that's fine, but that's not your lead or that's not going to be your hook, right? So you got to find out how does that tie in to something that we can make a bit of an argument or a case for. Right? Think about even a trial lawyer, lawyer world, right? You're trying to, you're trying to build a case for something. Um, and that's where we do some, some workshops and some interviews with, with the client or with customers even. We do a literature search, try to figure out what's out there in the world already that we can again, make sure we don't replicate by mistake. And so just say something that Company X said two years ago. And more importantly, how do we build on that argument or build on that debate, if there is such a thing in the space? Um, and again, in places that I spend more of my time, things like AI, uh, and cybersecurity and technology development, there often is a debate, is this really good for me? Do I need to do this? Um, and so we can find places where we can have that tension, uh, to feed off. And you almost don't care what the answer is, which side of the argument that the answer is going to fall on. But you need something in there that's going to give people A little bit of a hook and a reason to read the piece.

Sima Vasa: Very cool. I'm fascinated by everything you just said and I'm conscious of the fact that traditional market research skills aren't necessarily honed in that way. It almost feels a little bit like, uh, management consulting skills or business skills. So how did you evolve your team in that way to, to be able to do. That's, that's a hard pivot.

Mike Nash: We're still evolving, right? We, we are still evolving. I think you're right. There, there is a, this is going up the ladder to more of a consultative role. I, um, think the, the power that we lean on as researchers and, and, and sort of trained as researchers that we are sitting on the data. That is the answer. Right. So again, as opposed to if I was going to take a negative view to management consultants. Right. It's not, it's not my opinion on this frictional issue. Right. Whatever the, whatever the issue is, I'm serving up. Here's what a thousand CIOs said about this issue. You can not believe them if you want to, but it's not my idea. So you take it, you take the personal out of it a bit and we lean on that, on that data. So we're still leaning on the research element as the biggest asset, uh, to make our case. But then yes, you do need to bring in a bit of a creative writer kind of element which again is, is hard to find. We sometimes use stringers, we sometimes write some of our own. Um, but there are people out there that have more of that skill set than maybe you would find in your traditional market research, um, house. And then again, on the activation side, like I said, we are building our, our own group internally and sort of doing more and more of that creative activation to compound that asset in a more positive way going forward. But, but you're right, it's not easy. It's not the, the first knee jerk reaction that someone coming out of a, uh, market research, uh, program has. Um, but I think as they see the results and you see what you're trying to solve for, um, people coming along.

Sima Vasa: Yeah. Cool. Let's uh, switch topics. We kind of talked about this briefly, AI But I know you guys have launched a new initiative as it relates to synthetic data. Um, I believe Echo.

Mike Nash: Yeah, Echo. Echo is our synthetic data play. We've started with, uh, what we call amplify, which is where we think about from our overall AI philosophy. Um, the human needs to be in the loop, right? We, we. And you've heard that phrase from other people. Um, but that, that is a big part of our world. We, we never use any AI tools, uh, un, un. Unaccompanied, uh, without the human vibe. So I think that's critical for, for how we look at, at AI. It's a support, it's an assist. It's not the answer, it's not the primary. And then building off of that, going more specific to the, the latest craze in synthetic, uh, where everyone's sort of being pushed to, to do traditional research, you know, cheaper and faster. We still don't hear a lot of better. Right? We hear a lot of good enough. Right. If I heard one more client tell me it's, it's good enough, I might lose my mind. But we're trying to make the case that it might not be good enough. And our Echo product is, is a way to look at governance, look at transparency and look at quality of that synthetic model that you might want to use. Right. So we have, we have built a tool and we've launched that. And there'll be another, um, a webinar in the middle of June that you'll see from, from ksnr, sort of explaining and talking a little bit more about KSNR's Echo, which is meant to be the governance source, to sort of put some guardrails on kind of what is and is not quality, uh, synthetic data.

Sima Vasa: Got it. Sorry, I just would be explicit. Governance source. What does that mean? Uh, specifically, it's. You're the arbiter of December.

Mike Nash: We would like. Yes, we were looking to be the arbiter of, of the quality of that data set. Right. So, you know, send us what, Send us what you're using. We can look at that and try to point out where we think the synthetic is creeping in. Short answer, if we can, if we can point to the synthetic data that's probably not great data. Right. If you can tell the difference that easily between the two. So, so we have a lot of, a lot of work to do there as an industry. Um, even just defining digital twins versus synthetic versus AI, right. Those terms all start to get jumbled up, um, by executives just wanting to go faster and cheaper. And we're trying to create something that some of these market research folks can push back with and say, wait a minute, that that's not really going to give us a great answer. Um, and that's where we think there's a lack in the market that everyone's rushing to build these models that may or may not be correct. And we have Some tools that can help them make, uh, better choices on which of these models to use.

Sima Vasa: Cool. Are you, are you seeing from clients that there's a mandate to somehow say, use synthetic or AI and. Yes. Yeah.

Mike Nash: And again, it comes at the price of cheaper and quicker. Right. And again, we're not hearing the. They'll give me a better answer. Um, and that, that's, that's a bit disappointing. So we are trying to still fight for there are good places to use synthetic and there are good synthetic models and there are not so good. And I think you just want to be kind of eyes wide open as to which you're using and, and what you're using it for. Right. There's a lot of it too.

Narrator: Right.

Mike Nash: There's some things that good enough probably is good enough.

Sima Vasa: Right.

Mike Nash: And that's, that's okay. Um, we just want to make sure people are making those decisions consciously with intent.

Sima Vasa: Right.

Mike Nash: I think intentionality is a big part of this whole AI space and subsequently the synthetic space. Not, um, just to do it, to do it, but to do it intentionally in the right places.

Sima Vasa: Yeah. There's two things that I, I still question is, you know, everybody wants speed, quality and price. Lower price. Um, and I think we have to be realistic of what is feasible out of those three. Probably two out of the three is the, uh, maybe I don't want to sound like a cynic, but the two out of three are probably going to get you where you're where you want to go. But to say I can get all three is really difficult. Unless you say quality is good enough, which is a different, different motto. Right.

Mike Nash: Yeah. And I think that, I think there's something to that, that three legged stool. And I get disappointed when I'm not even sure. Not so much our clients, but our clients, bosses are not even considering the quality answer. Right. They're considering exclusively the speed and cost answer. Um, and so I think we're just trying to be a reminder that there is a quality lever to consider. And again, if some cases you don't need that, then that's fine. But don't just dismiss it out of hand without thinking about what that might cost you. Because I think we're going to start to see, and maybe we already have seen in some of the AI world, it only takes one or two bad decisions to lose that ROI that you thought you gained by doing something faster and quicker. Right. I mean, maybe even only one decision could sort of wreck that math. So I think companies have to be cognizant of that. And just be again intentional about where they place these tools. And I think our Echo product can help them make better decisions on which synthetic models to lean towards and to think about.

Sima Vasa: That's awesome. Well, I think it's great and I also think as an industry it's great to develop these tools that allow um, clients, agencies, whomever to understand exactly where does the data land in terms of synthetic or non synthetic. I did hear that a lot of the synthetic data or digital twin data, like the open end responses are so clean. Um, and it's, it's a bifurcation because I know traditional research with human respondents, they don't like when ChatGPT is used in open ends. But yet digital twins have these pristine, clean open ends. And um, we got to really figure

Mike Nash: all that out because no, it's a, it's a problem. It's even that, that's, that's one of our, our sanity checks or data quality checks. That, that's one thing we didn't talk a lot about in the, in the, the thought leadership world is that we spend a lot of time still both with AI, uh, tools and with just old fashioned hand scouring data to make sure it's quality. Right. So we're still at a point where we're throwing back 20, 30, 35% of completes as being illegitimate, either intentionally from fraud or just somebody falling asleep at the wheel and not paying attention. But yeah, when you start asking questions like uh, I don't know, how big is the Empire State Building? And you're getting exact answers, you're like wait a minute, this person doesn't know that.

Sima Vasa: Right.

Mike Nash: This isn't jeopardy. Right. So that's one way to spot out these AI induced chatbots or something that' not quite a human activity. Um, and you can get too good an answer and that doesn't do you any better either. Right. So I think there, there's a middle ground to find that we're looking for authentic real answers. Again, especially in the thought leadership world, we want people to, to understand or tell us what is going on with this particular problem. Um, and, and, and, and that, that's sort of a challenge.

Sima Vasa: Yeah. And I think my, my second point is, is that we just don't know the cost yet because we don't know how much we need to hydrate the models with real. Right. That's tbd.

Mike Nash: Yeah. You start getting into the first time use and then all of a sudden two or three years or even two or three months later you get this drift kind of notion. Right. That's a buzzword. You're going to start hearing more about this model drift and then do you have to recalculate the entire model and is it still any good? And yeah, there's a lot that we still don't know. Um, we're all for the experimentation and sort of kicking the tires on all these things. Um, you just hope people are doing it with sort of the best intentions behind them and not just trying to go fast.

Sima Vasa: Yeah, I totally agree. Well, Mike, it's always been a pleasure. It's always a pleasure to talk to you. I feel like we, we have so many other topics we, we've talked about, but I appreciate you joining the show and sharing your perspective. Thank you.

Mike Nash: It's been great. I appreciate it. I appreciate what you do for the industry and keep pumping these things out here. I, I did listen to your, uh, your professor from a few weeks ago. Right. So the, the. What's. Whatever's going down at Columbia is interesting because that. I follow one of the, uh, the golf writers there, Mark Brody's a professor there, did the whole shots, Golf, uh, shot stuff. Uh, uh, and that's a fascinating statistical view if you're a real stat nerd. Um, and the last guy you had on about some of this AI stuff was interesting too. So I think, uh, or synthetic stuff. So I think, um, there's, there's good conversations happening and we, uh, certainly want to be a part of those and then drive those where it makes sense and thank, uh, you for putting their voice out there.

Sima Vasa: Of course. I appreciate it. And I also like, um, having academia, um, be part of the conversation because there's no commercial intent. It's just, what does the data actually say? Um, gives us a little bit of a balancing effect in terms of they've

Mike Nash: gotten a bad rap in the past. Now they're starting to come back a little bit as being relevant to the party again. Yeah, that's good to see. Correct.

Sima Vasa: Thank you so much.

Mike Nash: Thank you. Have a great day. Appreciate it.

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