Emerging trends and watchouts in market research

Ryan Barry: Hi everybody. And welcome to this episode of Inside Insights, a podcast powered by Zappi. My name is Ryan and I'm your host. And I'm joined by a dear friend of mine, an industry legend and a beacon of the future, Lenny Murphy, the Editor in Chief and Chief Advisor for Insights and Development at Greenbook. Lenny, what's up, my man. 

Leonard Murphy: Thank you. Right. I don't think I've ever been introduced quite that way. And this is why I love you. I appreciate it, man. So, living the dream, buddy. Interesting times in our industry. 

Ryan: It sure is. It's an interesting time. And so today's episode, folks, what we're going to do is Lenny and I are going to have a conversation around trends and where the industry is going and specifically how that's going to impact your job and your company's ability to represent consumers as they make growth oriented or strategic decisions.

Ryan: So, you're in for a treat today. And, I was on his podcast very recently. So we're having a little reciprocation here, but Lenny, what's the name of your podcast again? 

Leonard: The Greenbook podcast. 

Ryan: Greenbook podcast. Make sure you like and subscribe. It's a great podcast. Lenny, myself and Steph Gans had a wonderful discussion.

Ryan: I think it was about a month ago and it's available, but there's also like amazing speakers all the time on the podcast. If you've been living under a rock and you're not familiar with the GRIT report or Greenbook, um, welcome to the industry. It's definitely well regarded Lenny's been the author of that report for how long now, Lenny?

Leonard: Well, this was the 34th edition. So, uh, it's coming up on 20 years. 

Ryan: Wow, that's incredible. I mean, I really do recommend, like, this industry doesn't have anything even remotely close to better in terms of where buyers are spending dollars, where there's friction in organizations, where there's trends.

Ryan: And, um, you know, don't be surprised when you see, you know, grit. Trends come to life via startups that disrupt markets. Um, uh, you might not know this, uh, but Zappi is one of those startups that disrupted a market. We won, I think it was the, was it the first innovation exchange? 

Leonard: It was the first competition.

Ryan: So, tell us about that, the competition in general, like what's the thesis behind it and how do you apply to it and get into it just for those who don't know. 

Leonard: Yeah, let me, uh, actually give a little bit of backstory, uh, for those who, who don't know me, I've been in the industry almost 25 years, uh, first hint of that building research companies, um, the last 15, uh, in this weird hybrid role, uh, Greenbook and what, what happened with that transition was I was building research companies and the world started to change.

Leonard: Right. The disruption of, of mobile, social media. Um, of course that was during the period of the great recession. So I was doing a lot of thinking as a CEO on what the future looks like in our industry specifically, and was trying to channel that into creating companies, had a stillborn startup, didn't know what the hell I was going to do.

Leonard: The folks at Greenbook I've been friends with said, Hey, why don't you come and consult with us, uh, and write about what you're going through. Now, realizing that to your point, there needs to be a mechanism to, because of the transformation, the industry was going through at that time, right?

Leonard: Different ways to engage consumers, different data collection methodologies, et cetera, et cetera. Um, we needed a mechanism to support startups. Um, and. There wasn't anything like Y Combinator or anything of that nature, uh, in our space that, well, you know, at the very least, uh, we're going to start this event, we're going to leverage our, our influence, and we'll create this competition, kind of a shark tank, for, uh, for early stage companies.

Leonard: And yes, uh, that first event was in Sao Paulo. Uh, Steve was there. He, uh, I think he had just written Zappi on a napkin at that point. But pitched it and the judges loved it and, uh, and they, they won and he got a check and yeah, not a big check, but I think it was 10 grand, 20 grand, something like that.

Leonard: But, uh, that started that process. And I, I, I am proud that we look at the history of that. Or the past 10 years, uh, that most of the companies that have won the competitions, Zappi's our poster child of that, I think have gone on to great success, right? They've been transformative, disruptive companies in the space.

Leonard: Um, and to greater or lesser degrees, but yeah, they've made an impact. And that's not the kind of thing that I did. I don't, my thumb's not on that scale at all. It's totally independent panel judges. But creating that ecosystem that allows entrepreneurs with new ideas to get them out into the market, uh, and then let the market do what the market does, which is, you know, pick winners and, uh, and help things change that I'm very proud of.

Ryan: Yeah, and that's I mean, I remember I was at the time working at GMI. We just cut off by Kantar and I'd remember this innovation exchange Zappi. Oh, that's cool And those of you who don't know this part of the story about five months later I get a text from Lenny looking for a new gig question mark and that new gig. So Lenny's competition at the time was just getting started but that led to Steve getting a million dollars in seed funding which then eventually allowed the company to capitalize a little bit further and hire the token American, i.e. me. Which was what was behind, um, behind that text. But you know, it's, it's an interesting perspective as, as new trends come up, it's easy to scoff at, oh, it'll never work. And particularly in a room full of intellectuals and academics, oh, it won't be credible or it couldn't be statistically significant.

Ryan: Um, And in 2013, that was true about Zappi. The system didn't stat test. You couldn't do multiple stim. It didn't really have great sampling capabilities. But that's the thing about innovation. Um, it's, it's exponential. And when you're focused on customers, you navigate markets to improve. Um, and to constantly evolve.

Ryan: And I think we're on the cusp of that now. I mean, I remember a year and a half ago, Oh, you know, we'll never use synthetic panels and then you see Stanford put out research that essentially suggests actually just as credible. Um, and that will keep improving. So if you fast forward to, we're nearing the end of 2024, which is crazy.

Ryan: What a year it's been. What are some of the trends you're seeing emerge in our sector that people should be paying attention to?

Leonard: Well, it's a loaded question. I mean, so it's the same trends really. You know, it's like, Oh, there's something new. No, it's not. We've been on this evolutionary, uh, journey for quite some time that it's how to use technology to leverage data, to inform better business decisions, that's the core.

Leonard: It's always been the core. You're not, you're not wrong to be the core, right? So, you know, you guys were a part of that process because then there's these economic factors of, you know, the iron triangle is my friend JD Deitch calls it, right? Cheaper, faster, better. Uh, and it's not pick two. It is all three.

Leonard: Um, and I think that the accelerator Of that fundamentally from a technology standpoint is a I, you were part of that early, uh, Zappi, you know, automation into, you know, machine learning and kind of leveraging that first wave, uh, generative AI, put that on steroids because fundamentally what it does is it allows you to synthesize information to get to an answer better than anything we've ever had before.

Leonard: So from a technology standpoint, that's just what it does. Uh, and that's an unlock in so many ways from an efficiency standpoint, and effective at a standpoint, but you know, garbage in garbage out still stands. So my view is that we've been talking about synthetic sample, but what I know to be true through lots of conversations that I have but the big AI companies recognize that the future of their technology is leveraging individualized data that answers business questions. That should come as no surprise to anybody so right they've already ingested the entire internet you know, there's walled gardens being put on the Things that are more opinion related to access that data was just like we went through with the social media era so now the drive is kind of to your example on, you know, how do we answer business questions with individual data, but we don't necessarily need to have a thousand completes.

Leonard: We can train it on a hundred and a hundred profiles of consumers, for example, uh, and it's pretty darn good. It's not going to, it's not going to answer everything, but I think of it as the great period of disruption that we are in right this moment, we can scale qualitative now and qualitative will be important in the way I think about it.

Leonard: Let me be clear. I'm not talking about qualitative from a sample size standpoint. I'm talking about qualitative in terms of the interaction. So the ability to, to engage and have a conversation. Um, at scale that will effectively replace the survey as a form factor is a mode, uh, except for when the survey, like, I'm not going to do a conjoint chat that would suck, right?

Ryan: Could you imagine what about this, right? If I did this, this and this, 

Leonard: So there, the survey, online survey is a form factor will continue and very specific. Uh, fit for purpose based on what you're trying to get to, right? But the bulk of what we do is going to look more like a chat, and it's going to be more about filling in gaps of information that are not already in existence within, profile information and data sets on behavior and buying habits, et cetera, et cetera, that's going to drive avatars, that's going to drive the, your agents, that's going to drive the creation of, I'm going to go there first and ask the existing data I already have first, then I'm going to go and engage people to fill in the gaps that they're not already there.

Leonard: And AI is going to power all of that. And that's just where we are. Um, sorry, you pressed my geek button, Ryan. I go off and on for forever, but I think that's the fundamental driver of the industry. Uh, as we now grapple with adapting our business models to that, it has huge implications, you know, for business models, our staffing, you know, our focus, that that disruption is just going to continue.

Leonard: And there's going to be winners or losers. It doesn't mean whether it's good or bad, it's just the way things change. 

Ryan: I like what you say. When I started my career, I got into like paint online panel when it was still, is this legitimate? And then the V1 of Zappi was agile research, which I've always found to be a bit of a bullshit connotation because.

Ryan: Agility is a mindset, not a methodology, but this industry slapped a sticker on quick turn testing, being agile, fine, whatever. And now this AI paradigm, but I love the constant that you draw. How do we use technology to make data driven decision making more accessible? And, and for me, that's always been my career thesis is like, I believe customer centric businesses win, and I believe technology enables businesses to be more customer centric.

Ryan: And so I love the way you, I love the way you kind of draw that parallel. I want to unpack the leverage what we know, to get at stuff that we don't know point that you make, but I want to start with the inputs. So, I've seen a change in the last year that I'm pleasantly surprised about Lenny, which is when we were trying to figure out automation in this industry, marketing moved the fuck on, right?

Ryan: They were testing ads programmatically. And we were like, wow, software could never do this. And it used to really drive me nuts. Like why, why are we, why can't we, why can't we get out of our own way? It's not really the case right this second. I'm seeing heads of insights, directors of insights, managers of insights at the biggest brands out there, co-chairing, leading, driving AI agendas.

Ryan: But it's, it's exactly why the big AI companies are looking at this because the consumer data is a dataset that is not as commoditized as what was happening on Reddit or what the web forms had or on Wikipedia or other places. Really presents to us a wonderful opportunity, regardless of who the winners and losers are.

Ryan: My question for you is, if most of our institutional knowledge is baked off of the data we have, and let's call it what it is. We went from telephone data collection methods to the same damn surveys on the internet. We exploited the world's population at a time where there wasn't a monetize your attention span.

Leonard: Yep. 

Ryan: And here we are today. I mean, you had JD Deitch on your podcast. He wrote this article that I thought was outstandingly sobering called the shitification of online sample. 

Leonard: It's such a great. Can I say it too? Because I just love saying it. The inshitification of an online sample. It's just. Yes. It's brilliant.

Leonard: You should check it out, guys. It really is. 

Ryan: You really should. It's one of those pieces of literature that you don't want to read because it's true. But I think we'll talk about the exciting part of the future next. How do we course correct this problem? Because I remember you might remember Susan Griffin.

Ryan: She was the first CMO at brain juicer. I still refuse to call them system one. I still call them brain juicer. And she used to always say to me, sample and data is the flower we bake our bread with. And I've never forgotten that sentence. And, you know, Melanie and her team are doing a good job of trying to police this and, and ESOMAR and MRS and all these like industry bodies, but this, this data set problem, this data quality problem seems to be chicken and egg for me, we can get the benefit, but this, so I guess, how do you view the data quality mess and what are some of the things you think we should be doing about it?

Leonard: Yeah. So this is a topic near and dear to my heart for a very long time. Fundamentally we have an engagement issue. And our engagement issue is that we, in my view, we didn't reverse engineer the problem. The problem was we needed to be able to talk to people, to get information from them in a scalable way to be able to drive business decisions.

Leonard: That's the problem. Instead, we got stuck with this. Well, we need a way to scale it. So we're going to create these, you know, uh, these form factors that were, it, it originally, it was. You know, pen and paper going door to door and, then telephone, woo, now we can do random digit dialing and, you know, la la la.

Leonard: And we, but you're right. We, the form factor didn't change. Uh, and our focus was on that. And our business models were built on data collection plus, right. It was built on the cost of data collection, um, rather than focusing on engagement mechanisms. Now, early on when, uh, GMI, right. The era of the early online panels, you know, e rewards, I I'll say, I thought your words had the best model at the time because it wasn't close.

Leonard: Yeah. I mean, they were, they were engaging with people where they were. It wasn't anything extra, right. They found a way to engage with populations off of their loyalty points. You know, your blockbuster, you get more points to rent movies. Right. I'm showing how old we are, you know, American airlines, whatever it was.

Leonard: Uh, it wasn't intrusive. It was a value add to the respondent. Oh, here's something that is important to me. I like my loyalty points because I can translate them into other pieces of value. Sure. I'll take a survey, get more loyalty points, right? Instead, we, we moved into, that's expensive and slow and, and, and hard to do in a variety of ways.

Leonard: All things move towards the lowest common denominator from a speed and cost standpoint, along came programmatic advertising, and then along came programmatic sampling. So, but unfortunately, both of those systems are designed for the first click. They are, it's optimized to get to the most likely first click period.

Leonard: That's what they're built to do. Um, they're, they're to drive volume and quality as a secondary consideration. That's why there's so much fraud in, you know, we think it's bad in research. It's really bad in advertising, you know?  So that's not a value judgment. Uh, you know, it, it made sense from a business standpoint to try as the industry progressed to drive volume, to find systems that allowed for scale.

Leonard: Um, but they had nothing to do with engagement. Oh, they had nothing to do with, you know, if we engage with people by default, we'll. Understand if they're real people or not right in those systems, but like, like the rewards again, use them as an example that they were connected to real people's loyalty cards.

Leonard: So that was thought through in a way that mitigated the fraud risk, created a better engagement opportunity. But we have ignored that fundamentally as an industry. Uh, and our business models, we drove the price for sample down into the basement. We trained clients to expect to pay a buck per, we treat a respondents like shit.

Leonard: So utterly. We ask them to do heinous, crappy, boring things with very little return. Um, while to your point, they're being pulled in a thousand different directions that are far more entertaining that I'd rather watch, you know, cat videos on TikTok. I mean, whatever, I mean, not me particularly, but the, uh, although it's probably there's days where I'd probably rather be doing that myself.

Leonard: But anyway, the, uh, so that's the, that's the issue we have to rethink. This process of we will address the quality issues, if we can address the engagement issue. And I, what I've always said is we need to think like marketers and instead we think like researchers. Um, but our solution I believe is if we're not gonna think like marketers, then we need to connect to marketers and we need to leverage the ecosystems that they are building, to create a conduit of access to consumers in a more effective way.

Leonard: Um, so quick, quick story, it's, it's relevant to this, uh, not getting into politics. I knew, that the polls were wrong going for the last election solely because it was readily apparent that there was a huge chunk of the population, uh, that were not engaged in our ecosystem. Now that you could look at the crosstabs and the public polling and see, but wait, where are young rural men, for example, 

Ryan: Right?

Leonard: They weren't there. Um, and. I saw where they were and where they were on X. Again, take away everything else. It doesn't, you know, your opinions about those things are irrelevant for this conversation. It's purely pragmatically this population we are not engaging with is on this platform. Uh, and perhaps we should pay attention to that.

Leonard: Even though it, for some people that may be distasteful, I understand, I get it. But we're talking about business here and the businesses, we need to go where our respondents are and we need to engage with them the way they want to be engaged with. So I was actually on a X spaces on election night with Elon Musk and he was talking about polling.

Leonard: His  sense was they were leveraging their internal data and they thought that the outcome, they knew exactly what the outcome is going to be from the internal data of X users. I raised my hand on this X spaces, uh, which is basically a big conference call, uh, cause I wanted to say was maybe we'd talk about X polling, Elon.

Leonard: Uh, I never got called on, but the point was everything else aside. We need, if there was a mechanism to engage with X users, somebody should do that while also doing the same thing with blue sky or any of these other platforms. Yeah, we need to, to branch out and think about ways to engage with Gauge in this very fragmented ecosystem that we have media ecosystem, social media ecosystem, the whole nine yards, right?

Leonard: Uh, people, you know, Oh, I hate Joe Rogan. Yeah. Well, you know what? He's got a hundred something million people that listen to him. Those people buy toilet paper like everybody else. And if we're not engaging with that population, we need to find mechanisms to engage with them though. And it doesn't, you don't have to like them.

Leonard: That's not our job. 

Ryan: Our job is not to sell your company. You're not like, that's not the point here. 

Leonard: Right. Right. And, the other people, the larger technology companies are not so burdened with, as some of us seem to be, with not thinking through this pragmatically and that's why they are eyeing these platforms as a source of data that can be monetized.

Leonard: And Bye. That should be our, we should own that we should, as an industry, uh, we have built the ecosystems and the know how to, to do that. And we are at severe risk of losing that fight. Just like we lost social media, just like we lost CX, just like we lost UX, right? Those things used to be part of research.

Leonard: They became separate entities, separate industries adjacent to us because we didn't think through appropriately from a business standpoint. Right. How to access that information, leverage our skills so that we would control the flow to answer the business question. So, uh, that's probably very heretical to anybody, but you know, I've been saying this for 15 years.

Leonard: So if this is your first time hearing me, uh, trust me, I've been saying this forever. Um, and I think it's just, that, that continues to be the issue that we must deal with as an industry. 

Ryan: It is. And I mean, like we, we go back to where we started with the, you know, the mode, how you can get information on how you can leverage information so you can ask for new information and a lot of a lot of where this comes down to for me is there's kind of two distinct paths.

Ryan: If you're in corporate America, who curates the consumer population data asset you think of? I mean, I heard this stat the other day. Coca Cola spends more money on research than Harvard or Stanford. Medical school. Wow. So think of the data that a business like Coca Cola, not to pick on Coca Cola, pick on anybody you want.

Ryan: Sure. Has. 

Leonard: I already say you use P&G as my example. 

Ryan: Let's pick on P&G. Yeah. P&G. Um, but, but, you know, like, so you have all this information. But even like the census or the way that we've always done net rep sampling doesn't account for acculturation batteries among Hispanics. As an example, it doesn't account for a political affiliation.

Ryan: I remember many years ago, back when I was at GMI, Mitch Eggers, who's a good friend of mine, came up with this, this, um, calibration vehicle to get at attitude, sentiment, geopolitical dynamics, so that the data was more representative and the, the, the industry now we're going back 12 years ago. The industry was not willing to spend an extra 70 cents an interview

Ryan: for that capability. So, you know, we've got a lot of work to do, but I think diversifying and the way like I'm personally using the technology in the sampling space is not just for exchange, but to say, Oh, I need this niche source because I need to engage them differently than this niche source than this niche source.

Ryan: And then you can sort of get at it. So the data asset management's one point. The flip of the switch to get at smarter ways to engage people is another point you make. And I think one of the things for me, Lenny, is like, okay, if I, if I'm curating the right data asset, how do I leverage it to ask you the three total questions I actually need to ask?

Ryan: Because I don't need to ask you 20. I already know how people like you respond to advertising. And so why don't I ask you about the latest celebrity endorsement. Um, and that is a really exciting funnel flip, but it requires us to have like the data cube and all the attributes in one clean place. Um, and then we can actually get at this.

Ryan: And one of the frustrating things is like, there are some niche companies who have great engaged panels. But this industry is, is supply challenged, not domain challenge. So it doesn't matter who you're doing business with. You're, you're going to sort of fight this fight. And I, and I do believe that you're right.

Ryan: We will get to the other side in terms of a curated data asset. We won't be asking radio button questions. We'll be doing WhatsApp conversational surveys, and that will look like quant data, that's super exciting. 

Leonard: Right. 

Ryan: Right. But what are some things you would do right now, if you're running an insights department, like to stop the bleeding for lack of a better word or to ask harder questions, 

Leonard: Well, I'll give you, I mean, I make you do this, but, uh, I would do what Walmart is doing if I can. right I mean, their launch of Walmart scintilla. You know, they're leveraging their internal data assets in a very effective way within their ecosystem.

Leonard: Uh, 8451 does the same thing with Kroger. I mean, not everybody obviously has that retail channel, but, but the, if you go to the register…

Ryan: You do have a first party data asset. You're sitting on it. 

Leonard: You do so this 8451 Kroger is a better example, right? And Dunhumby did this for Tesco, right?

Leonard: They're leveraging their loyalty program, which rewards consumers for shopping at their stores. You know, we're, we're a Kroger household. I love the Kroger fuel points, you know, that's great. We collect those, I get a buck off of a gallon of gas. That's fantastic. Do that all day long.

Leonard: That data, they're leveraging that data internally to help their entire supply chain be more effective while also having a distinct entity. Again, Kroger's, 8451 does it, Walmart does it, other companies are doing this as well to then find out where's the gaps in the information that we have, and filled that.

Leonard: Now a lot of that, to, to your point, that curated data set, more and more is going to be driven by behavioral data. Yes. So, because of this, they say to disconnect. And, You know, we think of that, well, that's a pain in the ass. People don't want to do that. Well, people do it. I have a Kroger app on my phone.

Leonard: I'm well aware that my Kroger app is capturing all types of information about my behavior all the time. And that's okay. Just like my X app or my Facebook app or whatever is capturing all types of information about me too. And they're leveraging it because I get some value out of it. So, so is that amasses more and more to your point?

Leonard: We're only asking the questions that are critically important. So to your question, what I should be focusing on is finding your partners that are focused on giving you the access to the best quality data relevant to your business or building that internally. Uh, and it's not hard to do now because of generated AI.

Leonard: The barriers to entry have just decreased across the board. It's just re it's a lot easier. It's not a year and 2 million, right? It's. So, uh, focus on that and then make sure that you're engaging from a methodological standpoint with the ways that will continue to boost the engagement with the consumer to get to the why.

Leonard: So, and that is going to increasingly look more qualitative than quantitative. And then the front end is probably going to be, you're, you're going to be engaging with, you know, uh, digital personas, you know, synthetic sample, shadow sample, whatever the hell we want to call it, agents do that for early stage ideation, you know, absolutely test, test your hypothesis there, see what's available, you know, then go test validate.

Leonard: With, real people with the, the important critical information that drives the business decision, which is now what, now, what are we going to do with this? Um, that's how I would be structuring my entire organization. The end result needs to be the now what everything else is tactical. That gives you the best information to inform the now what.

Ryan: It really resonates with me. I mean, we just did a survey with AMA. I want to say three months ago, 750 marketers. 

Leonard: Yeah. And it was a great, great survey, by the way. Great. 

Ryan: Oh, you saw that you saw the data. I love it. I thought it was super validating and insightful, right? So like we used to say, oh, we're not seen as strategic.

Ryan: And I can't, I can't remember the exact statistic, but insights people are no longer viewed as transactional order takers by and large. Good news. You know the terrible news? Less than 20 percent of the data gets collected with any intention beyond the initial question asked. So we're getting more strategic.

Ryan: We need to stop thinking whack a mole, whack a mole, whack a mole, whack a mole, whack a mole. We need to curate a data asset and then we'll be all right. Um, Lenny, it's a pleasure to have you on the podcast. I'm very sorry. It took me nine seasons to get you here. Well, I'm actually, I'm actually embarrassed, my friend.

Ryan: Um, but this was super fun. Where can people get at you if they want to learn more about what you're doing with Greenbook, about some of the projects you're working on, or if they need advisory help for their business or their team? 

Leonard: My presence is ubiquitous on, you know, LinkedIn and other platforms.

Leonard: It's lmurphy@greenbook.org. You can go to greenbook.org. I love chatting with people and, I guess I should just mention there's another piece of the business. Greenbook's also Gen2 Advisors. That is an advisory firm through our relationship with Zappi as well. Uh, And that's how we work with lots of companies, brands, and, and startups and having conversations like this and thinking through how to drive business impact.

Leonard: Um, but no, it's great, man. I wish that we had not waited so long either. Hopefully we'll have a chance to actually talk more, but I know we could go on for like hours, but neither one of us are Joe Rogan. So we probably don't need 45 minutes. 

Ryan: It would have been more interesting. Maybe. Yeah, but seriously, like I've had the benefit of partnering with Gen 2 as Zappi, but also I've seen some great work come out of Gen 2 in terms of helping insights leaders structure their data asset, their stack, their vendor ecosystem. So highly recommend to guys that are listening, engage Lenny and his team.

Ryan: Lenny, thank you so much for your time today. I really do appreciate it. Everybody, thank you for listening. We'll be back soon. 

Leonard: Take care. Bye bye.