AI's Impact on Research: Data Quality, Surveys, & Ad Testing Revolution

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In Episode 63, Karen Lynch and Lenny Murphy explore major advancements in data quality and AI that are reshaping research and advertising. Procter & Gamble’s ISO 20252 certification for online sample providers sets a new standard for data transparency, while EY’s Competitive Edge platform leverages AI to enhance market insights. They also discuss AI-powered active listening agents that improve survey engagement and a predictive advertising tool that refines ad testing. The episode wraps with a look at user-friendly AI tools and upcoming innovations, including Siri’s enhancements and the anticipated release of GPT-5.

Many thanks to our producer, Karley Dartouzos. 

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Transcript 

Karen Lynch: It's showtime. It's showtime. It's showtime.

Lenny Murphy: We are here. We are here. Happy November 1st. How did that happen? Oh, Lord, I know. It doesn't make any sense to me at all.

Karen Lynch: I'm looking at the calendar thinking, what? That's just crazy. Yes. And it's different. I don't know why.

Lenny Murphy: Like, yes, it just hit differently. Like, yes, there's Halloween. But it's November 1st. I know. There's only two months, you know, of us left in the year. You got all the holidays right around the corner. It should be Christmas. It's just like, damn, this year has flown by. It's crazy. I know.

Karen Lynch: It's funny because I have been seeing a lot of sort on social media about people who are like, okay, put away Halloween so we can, you know, we can move on to like Thanksgiving. I'm hosting Thanksgiving this year. So I'm like, I better get my head around that. Like that's kind of a big deal.

Lenny Murphy: And yeah, I, yes, I understand. My son-in-law's family is coming up here. For the first time. So we'll be doing the big, big Thanksgiving hosting. Yeah. Anyway, anyway. All right.

Karen Lynch: Well, let's get into stuff. Cause there's some really interesting stuff. And you know, when, when I'm, you know, again, like letting people under the hood about our process, you know, Lenny and I are sharing links back and forth. And you know, one of the things that Lenny was privy to, uh, saw on LinkedIn and probably has some insider scoop too, because of a solid working relationship with PNG. Is the idea that P&G set a deadline for quantitative online sample providers to be certified in its ISO or ISO 20252, which emphasizes data quality standards. And I just thought that's interesting because I know when I used to work for a full-time supplier, you know, those ISO certifications were a big deal. And I just think this one's worth talking about because of some of the things that are in this article. So why don't you kind of give high level like your point of view, but some, not in the article, in the, um, in the research, when you click on this LinkedIn post and you read a little bit more, like there's some things in here that I think everybody can learn from about like KPIs and stuff.

Lenny Murphy: So you go ahead and riff and then I'll, I'll share what I thought was interesting too. Sure. Well, I mean, background then P and G has always led the debate around, uh, data quality, you know, they've been very vocal about that. Um, uh, they've been on stage at IDX. Multiple times on this topic. So it is a big deal. There's not many mechanisms in place to force the issue except for ISO certification. So it's a point of leverage for them to try and force folks along because they take it really seriously. So there's the context, right? It's a good thing. That they're doing that, it raises visibility. Now, whether the ISO certification fixes the problem or not, I don't know. But it's certainly a step in the right direction.

Karen Lynch: Yeah  And what they're saying is, again, if you follow this link trail, P&G knows the need for researchers to easily be able to see data quality results at the study level. I thought that was really interesting because you and I have been talking about partners having to show that they are taking data quality measures, but also seeing it at the study level, I think is interesting. I don't know exactly what that looks like, but it's interesting. And then there's another thing in here that says, as proof of performance, the company will request standardized reporting of key KPIs, you know, and average time to complete the study, number of panelists who failed to complete the study. Like, by the way, also reporting on those metrics. And I just think that's also really important for anybody who is responding to an RFP from any of the large companies right now that have these procurement departments and some of these standards in place. Like, hey, if I were on that full service supplier side, I'd be making sure I show examples of this type of reporting against KPIs so that I'm ahead of it. And I show them, by the way, not only do I know this is a requirement, I know this will be something delivered against, here's what that looks like, Like I'd be way ahead of this if I were full service at this point.

Lenny Murphy: Absolutely. And this has a, so it's easy. Oh, okay. Well, they're doing this directly with the panel companies they work with. That is a mistaken assumption, right? This has a ripple effect on every supplier that they use. And although they are focusing on quant for the moment. Yeah. Absolutely. Pretty good authority. It's going to apply to qual as well. And actually I know, some of the suppliers they use already have ISA certification. And they're working more with them. So it's, yeah, it's not just shot across the bow. I mean, it's kind of a direct hit with a pretty good size cannonball to say, hey, it's time we work through this and it's going to have implications. So, uh, so hats off to, uh, to Kirti and Michael and core and, you know, uh, the whole team that are helping to push this through it, it, it needs to happen. And that is probably one of the coolest things about P and G just in general. Right. They, they, they are a participant in the industry and they always have been a very active participant. And, uh, and when. They lead, others will follow. And we need that. We have to have a buyer sometimes put their foot down. So thank you, P&G, for continuing to espouse the issue and for putting your money where your mouth is. Exactly, exactly. Raising the bar for everybody.

Karen Lynch: So speaking of leaders, I want to segue into this, one of these product launches from the week, which, you know, is not necessarily somebody that you're thinking about, you know, in the insights industry specifically, right? It's a different type of insight and intelligence that they're generating there. But EY launched this competitive edge platform, right? An AI-driven engine incorporating Microsoft's Gen-A technologies for market insights, okay? So, you know, a little bit different than maybe anything anybody's doing at the category or brand or product level. But I thought this was really cool. Because they are a leader showing how, you know, the goal is really, I shouldn't say the goal, that's not the right language. An objective right now is to get at this data querying, right? Like, let's have this system in place to interact with data, to chat with data, to explore, I think the words they used was to interrogate a rich set of data and insights. And I'm like, okay, they're doing it at EY and you know what that means. That means they're probably doing it certainly at the others in the big four there. And we need to, you know, follow what they're doing because that's business intelligence, competitive intelligence, and insights are feeding intelligence at a different level right now.

Lenny Murphy: That's my two cents. 100%. And I mean, you know, you dig in a little bit more. It's interesting. They're a consulting company. They're recognizing there's things they can't charge consultants for anymore, but they want to keep sticking this with their clients. So creating tools like this that create efficiencies, both internally for them, for their consultants and externally for their clients. You know, it really is, it's pretty clever. And it makes me think about what McKinsey did years ago with Periscope when they launched that research component that was kind of stuff McKinsey wouldn't normally mess with, you know, things like that. Those weren't McKinsey solutions, but it was an increased share of wallet, increased stickiness with the clients, you know, kind of a land and expansion, even though they were doing it at a different business model than the core. And I think we'll see a lot more of that and showing the power of connecting data and unlocking data across multiple business issues in AI is the tool that does it.

Karen Lynch: Yeah. It's pretty cool. So, all right, so again, everything is going to segue today. We're going to segue to segue to segue to segue. Why don't you share what's going on with Zappy? Because I know you have a longer history with Zappy, you know, as our first competition window and you talk about them a lot. And tell everyone about this system, this AI-driven innovation system.

Lenny Murphy: Well, you know, they've been very public about this, right? They just launched their book and they've talked, you know, on the podcast with Stephan and, you know, so nobody should be surprised by this. They've telegraphed it for years of where they were to be the platform for the data. You'd like the client, the customer data, um, how's that and use that to unlock more capabilities. And first they were doing it with creative, um, which was kind of their core. Now they're doing it with, uh, with innovation, um, uh, and it's powered by AI. I think that's just at a broad level, that is a, uh, Any company that houses a significant amount of client data, regardless of what it is, Qualtrics on the CX side, or Ipsos is doing brand trackers, or Zappy that's doing, sorry, innovation and creative, yada, yada, right? You put any company in there, Behaviorally does it with pack testing. You're housing all that data and you're using new capabilities to interrogate it, to profile, use synthetic samples, that idea, you know, you're, you're sitting on all that, Brocks is doing it. I mean, there's so many companies that are doing a variation on a theme. But the bet that the differentiator is, when you become the platform, when you become the default solution for a large chunk of business within a brand, and it is housed there, that makes that a whole lot easier. So we're going to see many, many, many companies. Actually, I would say probably every platform, every big data collection platform out there is doing some variation of this idea in some form or fashion right now.

Karen Lynch: What I liked about this one, and you dig into the press release, is that they're talking about an innovation process and how they're adding it to every step along the way. So rather than just say, we have this tool, this is really about creating, testing. Yeah, it's the whole thing, though. It's every step along the way of this design thinking-ish platform or process. And there's a quote in here. If you, the people, click on this link to the MRWeb press release, which is where this one was from, it talks about, determining in a matter of seconds how to strengthen its concepts and maximize their chance at success. And so how many concepts in the work I used to do did I see that we do some qualitative testing with to strengthen them and all that? And it was very laborious. I loved doing it. I enjoyed it. And there was always... Anyway, I just think this is a really cool possibility that... Anyway, good for Zappy.

Lenny Murphy: Good for Zappy. There's the general stuff and then there's the business issue specific. This is an example of that. Historically, bases, they're forecasting tools. It's like the next generation of those types of solutions that we've dealt with for a long time. They use normative databases that are predictive, but now just a hell of a lot more flexibility and depth that's available to them.

Karen Lynch: That they quoted a client right in the press release from Reckitt. I'm like, hmm, way to go. To me, I'm like, they know what they're doing there. There's no surprise that they know what they're doing because that was a very good testimonial for the process with a trusted, credible brand. I'm like, all right, I see you, Zappy. I see what you're doing there.

Lenny Murphy: Absolutely. And somebody besides PepsiCo.

Karen Lynch: I know, right? Right, exactly.

Lenny Murphy: More clients than PepsiCo. They do. They are pretty large.

Karen Lynch: They have others. They have others. Yeah.

Lenny Murphy: We're just going to see more and more of this. I know we have this too, but you, you, you called this out and I think it's important. We've been saying it and we see it in other data sources that have come out recently. Grit, Qualtrics report we talked about last week. The, um, we, we are, and we're seeing it in the financial performance of businesses of some of the public companies. We're the shift away from executing a primary research project that we would normally do to try it, like you said, the concept ideation process or optimization, that's happening, right? Because we can explore existing data to do that. Now, we still need to validate and do all of those things, and that's still gonna be a primary research component necessary for that, but that shift and how that changes the research process and budget allocation and in speed and efficiency, it is well underway. And we just have to recognize that, that it's just changing how we do things because we can do things certainly faster and cheaper and arguably better. Yeah, yeah.

Karen Lynch: Which is a great segue to this next kind of product launch, right? In Moment introducing AI. Powered active listening agents for surveys. It's all about real time feedback, increasing actionable responses. So I don't know much about it at the moment, but this press release here is all about tackling survey fatigue, right? So. Yep. Yeah.

Lenny Murphy: I don't know. Yes, but I would just characterize another example of the logical kind of love hanging fruit application of the technology, right? It is interesting that they're focused on the agent concept, right? It is an interactive component as if you're talking to a person. I don't even know it's not, we all know it's not. And that does have an effect on fatigue, right? It does, the engagement is different. After last week, I did the 10K human survey that we put together. Out there, right, the chat survey.

Karen Lynch: Yeah.

Lenny Murphy: And yeah, that was a much more engaging experience.

Karen Lynch: Yeah, it really was. I was having a conversation.

Lenny Murphy: And I knew that it was a robot. Yeah, it was kind of canned responses.

Karen Lynch: And I know that. Yeah. I know. Right. It felt different. Like, oh, this is okay.

Lenny Murphy: Yes. And just like if I'm using chat, I am polite. Yeah. And, and conversational in my requests. I know, we're going there.

Karen Lynch: It has to be nice to do these surveys now.

Lenny Murphy: It felt different.

Karen Lynch: It felt like, oh, wait a minute. I'm nicer to that than I've been to any quant survey, or I was nicer to that than any quant survey. I'm like, screw you. You don't need my answer. Not that I've done that, but you know what I mean. This was different. Well, and here's what's cool. What's cool about this, again, if you click on this particular release, for this in-moment survey, they talk about a study that they did, so appealing to researchers, right, where they kind of show some of the findings to the research they did on this new feature. AI-powered active listening led respondents to being 2.4 times more likely to provide actionable feedback. That makes me go, hmm, that's interesting. 24% were likely to share insights about their intent, making more opportunities for engagement. Interesting. I'd have to see what that looks like, but it increased word count by 70% per question. That was interesting. I'm like, oh, look at you, open ends. Your people are being a little interesting. That's all. And there's a few more in there. But I was like, oh, that is interesting. If our survey results get stronger by adding these agents when they're credible responses. Anyway, it allows us to explore more. Yeah. Right.

Lenny Murphy: So again, that blending of what we think of as quant and equal that that barrier between them is real thin now. Yeah.

Karen Lynch: Speaking of thin, every time you say something, I'm like, Oh, there's a good segue. Is it pronounced David or DAVID, D-A-I-V-I-D, is that the word?

Lenny Murphy: I'm not sure.

Karen Lynch: Who knows what they're doing with D-A-I-V-I-D? Right. But I like it because it's another case of AI speeding things up without sacrificing quality, right? So it's an AI powered solution predicting attention, emotions, memory, and impact, brand impact of advertising. And it's not the first time we've talked about a solution here. You, you, I know, following the ARF have gotten privy to a couple of these, but this is pretty cool, uh, in terms of predictive abilities and capabilities. It is.

Lenny Murphy: And with the focus on emotion, on conscious measurement, the, um, I mean, the underlying thesis is the same as, you know, facial coding. There's a database, a normative database there that, um, we're referencing or, I'm sorry, I'm going to sneeze.

Karen Lynch: Bless you in advance. No, because I thank you.

Lenny Murphy: I may still pop out my apologies in advance if it does, but Yeah, you have a lot of data. These things are all based on, you know, any of them, right? Whether it's EG or facial coding or text analytics or whatever, there's online data that has you built models off them to predict outcomes and match, you know, stimuli to that AI is really good at that stuff. And now applying it where it makes the most sense, which is around advertising testing.

Karen Lynch: They said this one is trained on tens of millions, tens of millions of human responses to ads. And I was wondering when I read that, like, you know, like the whole like Malcolm Gladwell outliers concept of 10,000 hours. There's a part of me that got to like, oh, sorry, big stretch. Stretch to the dog, right?

Lenny Murphy: You know that. She's beautiful.

Karen Lynch: Thank you. He's a good girl. Anyway, I wondered if the tens of millions of human responses to ads, if there is, if there is a point where it's like, you know, I don't know, I just thought that number stood out at me because of that idea, the tens of millions of human responses gives it a little bit more reliability as a tool. And, there's a quote from Tom Saunter, at Group M who is using this platform. They also mentioned somebody at Nike is using it. They didn't mention the name, though. But another kind of testimonial about Group M seeking a scalable solution for a while now, and they feel like they have one here. So an analysis of over 3,000 videos. Like, that's so interesting. And it's a predictor of actual performance. So again, like AI, when it comes to media and ad response, I think I've said it here.

Lenny Murphy: If not, I'll say it again. If you're in the business of creative testing, yeah. That's service driven. Yeah. You probably need to take a real hard look at the business model. Um, uh, cause that, that is the lowest hanging fruit of disruption here. Uh, so, uh, I think it's, it's, Yeah, it's cool stuff. One thing though, I am hearing more and more from the folks that are pioneers at that example. It does not take millions or even thousands that it looks like you can build really robust synthetic personas, agents off of like a hundred.

Karen Lynch: Interesting.

Lenny Murphy: So that's, you know, I'm sure our statisticians would, you know, gasp. And I'm sure there's limits of complexity to that. But we are not always talking about big data. Sometimes we really are talking about some relatively small and manageable data sets that fit right in the researcher wheelhouse to do something like that.

Karen Lynch: Interesting. Yeah, yeah. So interesting. So speaking of table stakes, Pete Gator, Pete Gator introducing AI Summary. When you look at this one, this to me looks like table stakes. This, click on that platform like, yes, it, it seems so much simpler from everything we've just been talking about. But like, yeah, pretty much you need to have this type of, um, yes, AI assist with summaries and conclusions and action points. This is all about, you know, having a tab offering on any kind of platform for summaries, conclusions and action points like yes, pretty much. I mean, this is sort of like what yabba was doing with their executive summaries a year ago. Like if you have an AI tool, it needs to be generated. It needs to be generatively summarizing what's there in a way that's workable and usable. So take a look at Peekator if you haven't broached this area yet, because yes, that to me is like, yes, table stakes. I looked at it and I already thought, well, duh. And it's funny because I didn't want to feel that way for them. I wanted to be happier for them, but I'm like, this is kind of basic. And I don't mean that in an insulting way, but it's like, yes, I already feel that this should have been happening a year ago, this type of launch.

Lenny Murphy: We still, in Greenbook and Grid, we still do an export from our friends at Forrester into a tab package, into Displayer, that's what we utilize, and export into Excel tabs. That is still what we do. Because that's what we're used to doing and then we take that and other platforms that process work. Yeah That means efficient. All right, and I will say this to peek toward anybody else out there, please for God's sake, please Make your system. So everything is exportable as an editable file into PowerPoint because regardless of what you think that is still the primary platform that 80% of all researchers and business people use to communicate information. So if you are just, if you make it some pain in the ass to export into a PDF or not, or just an object, just an image that sucks, you're, you're just missing something.

Karen Lynch: It's like we went right. We, we skipped, we, we skipped making sure everything was, you know, overtly like a usable in System when we when everything became AI we're like, let's not forget basic usability.

Lenny Murphy: Yes I would have I would drill on that for one second because I would tell you guys something right? So I've been using AI more correctly and I've taken I'm So I've developed this process, but it's a pain in the ass process, right? Well, I'll develop an outline for a deck utilizing chat GPT. Oh, that's great. Here's my outline. I'll load that up in the deck to push right? Oh great. Now. I've got my core chart Okay. But they're not editable. You know, if I export in a PowerPoint, it's just an image. So here's my, my hack. I have to export in a PDF. Then I convert the PDF into a PowerPoint to make it editable. And it's like, come on. So anyway, so guys that you're doing this stuff, just make it work. Make it work usable.

Karen Lynch: We need them. We need everything to be usable.

Lenny Murphy: So we got other stuff.

Karen Lynch: So yeah, we have other stuff. You know, I think these are two, there's really two big tech developments that I wanted to touch on because, you know, they're both, these are big ones. These are two big ones. So first of all, iPhone, Siri, right? Like, you know, hold everybody, you know, you have your phone. I'll hold mine up. It has my grandson on it. Like, come on.

Lenny Murphy: You planned that, didn't you? You had that.

Karen Lynch: Five bars into how cute my one month old grandson is. Anyway, Noah made his, I think there was another appearance for him, but anyway, sorry, friends. But your iPhone, Siri, with the new beta, it's in beta testing right now, it's in the hands of developers. They are adding full AI integration into Siri, which is going to be game changing for what you use it for, if you use that capability. Not everybody's on an iPhone, I know, It's going to be a big deal, because you might be able to go to Siri and still ask Siri a basic question, like what day of the week does Christmas fall on this year, or whatever, like whatever. But you can also say to Siri, hey, Siri, I think the example in the article was about cocktails. But for me, it would be recipes. What can I make with whatever's in my fridge? What can I make with a sweet potato? And rotisserie chicken. And it will go to ChatGPT. It won't just give me search results. It will go to ChatGPT and potentially even give me a recipe. So Siri is getting a makeover in that it will decide, hey, shall I check ChatGPT for this, or is this still a Google search kind of thing? And it's going to be really interesting. Nope, wasn't inviting you in, love. Sorry. Sorry. I bet.

Lenny Murphy: Well, it was factored into those, because today we didn't have a chance to put the link in. I don't think we need to. Everybody can see it.

Karen Lynch: Chachi Petit launched Chachi Petit Search today. Yeah. Yes, yes, yes, yes.

Lenny Murphy: I haven't really dug into that yet. And it's supposedly totally real time, just like Google or any other search engine. So as you mentioned that integration with Siri, that type of stuff, my bet is that that's where that's going to go. Yeah, it'll be, uh, it'll be integrated with GPT search, um, which boy, a whole new marketing game for that.

Karen Lynch: So, and let's just talk about the related kind of, you know, open AI news, because. We were talking about this happening before the end of the year, GPT five, whatever, um, whatever they do with it, their, their, the report says it's possibly code named Orion. Interesting. Orion, Orion, Orion. I'm picturing all of a sudden, like, is it Met in Black with Orion's belt on the cat collar?

Lenny Murphy: Right, right. Maybe, maybe. We'll see.

Karen Lynch: Anyway, so Orion, Orion, either way, the report says that it's a hundred times more powerful than GPT-4, although it's not clear what that means exactly. But supposedly it's coming out in December. It may not be available to everybody. Like we may not all be in this. This may be reserved for people that they have partnered with. You know, it might be in the Microsoft system specifically. Um, we don't know all of the details, but it is definitely coming. And again, we don't know if it'll be on Apple. We don't really know too many of the details, but it's going to be a big deal. Yeah.

Lenny Murphy: A hundred times more powerful if it's more powerful than no one, which is what I've been using in a beta. Yeah.

Karen Lynch: I'm not sure I can conceptualize. Yeah. Well, that's the thing is I think we've, we've gotten to this point where we know what it can do now. We know what's possible now, but this next, like every time it's like, what is possible next? I don't know what this is going to be. So pay attention. So we'll be talking about it obviously. Cause this will now, if they just said it's, you know, there's glimpses of it. Now there's, you know, talk, there's a report. Um, it's speculative. But Lenny and I will sure as heck be tracking it. It's what we will do.

Lenny Murphy: Well, that's probably a good way. Good segue. See, I'll use it too. Yeah, right. A little bit. The really our support for Morgan Stanley, that 40% of AI projects have exceeded ROI expectations. Yeah, yeah. He's leading engineering adoption due to better resources. And I think that was interesting. Because a few months ago, it's not what we were hearing. We're hearing these challenges of ROI. And I think we've earned the corner of practical, pragmatic and practical business applications. We talk about that every week here within the research space. And that's changing things, right? And as companies got a handle on this, okay, well now, unfortunately, I have been paying 100 people to do this. I only need 10 now. There's unfortunate impacts from this. Hopefully, it's more along the lines of, oh, wow, this has helped me unlock a whole other revenue stream or way to increase efficiencies without decrease in headcount, whatever. We've turned that corner, and that's why this report is really interesting to show that it's happening.

Karen Lynch: Yeah. Well, and yes, and I think as I was looking at the data, and again, admittedly, not necessarily data, a data, a data girl, but, but I have a great healthy appreciation for it all. One of the things in there was that it aligns with a report from Bain and Iconic Growth. So this is, this is now, you know, we have multiple data points confirming this, which is what I like to see. It's like, OK, that, you know, they are, they're all agreeing right now that this is a worthwhile investment that's paying off. So I think they're, they're shoring up some of the early challenges. So that's pretty cool. So yeah, that's a good read. And then also, this other piece that you found or Andrew, yeah, you know, it's exploring synthetic data, the concept that all research is synthetic data. It's very philosophical. It's interesting. And one of the things I liked in it was I'm looking at it and I'm like, all right, I don't know how I feel about it. But then I look in the comments, and there's, you know, Josh Seltzer at Next Intelligence, who's very versed in all of this and has spoken about AI at our events too. He's deep in our ecosystem and he's like, oh, I'm gonna debunk all of this. Now there's a healthy debate happening. And Finn has weighed in. I'm like, all right, this is a pretty interesting conversation. So I don't know how I feel about it all, because I haven't digested it all, but I'm like, I love when we have a healthy conversation and debate online, right?

Lenny Murphy: And Andrew Jevons, just shout out. Andrew's just, he's good at that. He's good at playing the curmudgeon and taking the oppositional role for that very purpose. And it makes you think. So he's great. I've never had the pleasure of talking to Andrew. He's really fun for that very reason. So yes, I agreed. I included it because I thought this is just an interesting discussion. Discussion to think about data in a different way. Because we've had this dichotomy, right? Live or synthetic. And synthetic is inferior to life. Well, I don't know. You step back and maybe not. So it's a fun read. And the debate's fun to read as well.

Karen Lynch: Yeah, pretty cool.

Lenny Murphy: So let's wrap up our content here.

Karen Lynch: But you want to shameless plug what you've got coming up next? Because it looks like a good webinar you're doing.

Lenny Murphy: It is. So our friends at Recollective, right? Every year, this time of year, we do a webinar. We've been doing it for years. It's just a thing that we do. We decided this year, a couple of years ago, we did Future of Insights Communities. I think it was 2019, before AI. And we thought, you know, let's revisit that. Let's see what we predicted then, where we are now. So it's a fireside chat between myself and Laura Polito. It will be fun and interesting. So tune in, excuse me, if you're focused on this side's communities. I am bullish on all things equal. And as is where we're very well documented and the community as the core of data, go back to our previous conversation, owning the data and doing all those things. Incredibly bullish on that as well. Doesn't matter whether it's whatever platform you work with, right? The enabling technology to do that, I think, is just really important. And, yeah, and we yield great benefits. So there's a sneak peek of my position.

Karen Lynch: It's funny, because in 2017, 2016, 2017, anyway, it was right, right as I was starting my stint at InsightsNow. And, and Greg Stuckey had said to me, like, Oh, let's do a webinar on the future of Qual. And he was like, you know, so you're coming out of out of know, a qualitative career, you know, what, what do you think the future of qual is? And I remember clear as day being like, well, duh, it's empathy, like the future qual is all about empathy. At the time, nobody was really talking about empathy. So we did this whole webinar on empathy. And every time I saw that over the last few years, I'm like, yep, nailed it. Yep. Yep. So anyway, so, so now I'm, I want to see what you guys are doing now. So that in the future, we can say, yep, nailed it. Yeah. Yeah.

Lenny Murphy: And lastly, before we say that guys, it's always, you know, yes, we nailed it. But the reality is if you just pay attention, it's really damn easy. The only difference is we pay attention because we live, read, eat and defecate this stuff. It's easy.

Karen Lynch: I wouldn't, I wouldn't, I wouldn't dismiss it. I don't know if it's easy for everybody.

Lenny Murphy: Sure.

Karen Lynch: There are some people who just need people to light the way and show them. And even if it's just a speculative idea, it helps them because this is why consumers can't predict the future, right? Because if you're not doing this, it's not always that easy for them. It's challenging to think about future intent or future happenings. That's why we can't predict what will happen in a day or two or five or seven.

Lenny Murphy: We just can't, we're not good at prediction. Fair enough, fair enough.

Karen Lynch: All right, well, on that note. On that note. Yes. On that note. Let's have a great weekend and wish you all well, and we will be back soon. Yes, enjoy the first weekend of November. Yeah. All right. Bye everybody. It's gonna be great. Take care Lenny. Bye. Bye

Links from the episode:

Procter & Gamble sets a deadline for quantitative online sample providers to be certified to ISO 20252 

EY launches Competitive Edge platform 

Zappi launches an AI-driven Innovation System 

InMoment introduces AI-powered Active Listening Agents for surveys 

DAIVID launches an AI-powered solution that predicts attention 

Peekator introduces ‘AI Summary’ 

Apple’s Siri gets a ChatGPT upgrade in iOS 18.2 developer beta 

OpenAI might release the next major ChatGPT upgrade 

Morgan Stanley’s report shows that 40% of AI projects have exceeded ROI expectations 

An article explores the concept that all research data is synthetic, linking market research with philosophical ideas from Plato and Nietzsche 

Recollective hosts a webinar on “The Future of Insight Communities: Trends & Predictions”