Standing Out In the Age of AI Sameness with Tina Tonielli

[00:00:00] Tina Tonielli: To me, efficiency was a really great opportunity with machine learning because you could automate things and you could in a lot of times take humans outta the process. For me, with generative ai, you actually need humans to steer. The fill in the blank truck, car, boat, whatever it is, because you have a tool to augment humans.

[00:00:22] Tina Tonielli: It’s not actually a tool to take over. Humans 

[00:00:26] MRII Announcer: welcome to MRII’s Insights and Innovators podcast, where we talk to top market research professionals to get their inside stories about innovative and enduring best practices. Now here’s your host for today’s episode. 

[00:00:39] Niels Schillewaert: Welcome everyone to today’s, uh, session, standing out in the age of AI sameness.

[00:00:46] Niels Schillewaert: I’m Niels Schillewaert, uh, your host for today. I’m a member of the MRII and I’m the head of research and methodologies at Conveo. Today we’re diving into a fascinating idea from our guest, Tina [00:01:00] Tonielli, who most recently led the consumer and bus business insights and analytics at Haleon North America. Uh, Tina believes that transforming that AI is transforming how, uh, insights are generated, but it’s also creating, uh, what she calls.

[00:01:16] Niels Schillewaert: The age of AI sameness. Uh, and so it’s a real opportunity for Prof, uh, for insights professionals if you, uh, if you think about it, uh, to really keep the human edge, um, which is creative thinking and critical thinking. So, um, Tina, welcome to our session. Uh, that sounds like music to my ears, but let’s just jump in.

[00:01:38] Niels Schillewaert: AI sameness, uh, it’s an intriguing phrase. Um, let’s start with that. What, what do you mean with wi with it? 

[00:01:46] Tina Tonielli: So it’s funny, I, I’ve been using the words AI sameness, AI slop, and AI brain rocked depending on the level of aggravation that I’m feeling at the time, honestly, and I would say [00:02:00] the best example that I could give for professionals especially is if you look at your LinkedIn feed.

[00:02:06] Tina Tonielli: And if you look at your LinkedIn feed and you just scroll for like a minute, you see a lot of times the same exact post from different people or a slight variation of that post from different people. And it’s like you feel like it’s the same thing over and over again. And what it is basically is people are going into generative AI and they’re saying like, I need a LinkedIn post about generative ai.

[00:02:30] Tina Tonielli: And they’re getting a LinkedIn post about generative AI from the same modeling, and it’s coming out and it’s looking the same, and they’re not making changes. And then it all looks, feels, sounds the same because it’s almost like they’re going to the same copywriter with the exact same brief and getting very similar, you know, results.

[00:02:49] Tina Tonielli: And so I think that to me is something that. I, I knew it was gonna come like, probably about six months ago. I, I was in a forum where we were talking about this stuff, and I, and that [00:03:00] was like the epiphany for me. It’s gonna happen and I feel like it’s happening. I’m starting to see it in, that’s sort of the early indicator for me, and I don’t think it’s caught up with corporations yet, or large companies yet, that they really see that.

[00:03:13] Tina Tonielli: But it’s going to, and so for me, that’s sort of the, the indicator. 

[00:03:18] Niels Schillewaert: Okay. Some form of incestuous spam news 

[00:03:21] Tina Tonielli: stuff. Yes. I love AI brain rot. That’s my, that’s true. That’s my, 

[00:03:27] Niels Schillewaert: that’s true. It, 

[00:03:28] Tina Tonielli: it, 

[00:03:28] Niels Schillewaert: it, yeah. Definitely. Uh, you know, covers it. Do you see it showing up or having an impact in terms of insights, uh, and or in terms of how insights are, are getting organized or, or doing their thing?

[00:03:40] Tina Tonielli: Yes. So. And I don’t, I hope I don’t offend anyone, so don’t come at me, anyone. But what I’m seeing, and it’s not just insights, it’s overall business. I would say there is a very strong focus on efficiency. And getting more with less. And that’s been [00:04:00] companies, by the way, in business for like the last fill in the blank, two decades.

[00:04:04] Tina Tonielli: How do we drive efficiency? How do we make people more efficient? How do we have less people? How do we spend less money, get more with less, and drive profitability of everything? And I think that when. Ai, generative AI first came out, there was a lot of promises made about taking humans out of the system.

[00:04:21] Tina Tonielli: We can take humans out of the system and so everybody’s been driving for efficiency. And to me, efficiency was a really great opportunity with, um, machine learning because you could automate things and you could in a lot of times take humans outta the process. For me, with generative ai, you actually need humans to steer.

[00:04:43] Tina Tonielli: The fill in the blank truck, car, boat, whatever it is, because you have a tool to augment humans. It’s not actually a tool to take over humans. And so what I’m seeing in, um, the insights community. Is some of those [00:05:00] efficiency plays, the way they manifest or the way I’m seeing them manifest is there’s a lot of digital twins and a lot of like, how do you augment your sample size, which is an efficiency play, or how do you do a digital moderator so you don’t need an actual moderator, which is an efficiency play.

[00:05:17] Tina Tonielli: I’m not seeing as many of the effectiveness plays, which is what I call it. Which is how do you take what you do as a company and augment that with generative ai, but maintain what you do and what makes you special, versus just trying to bolt generative AI tools onto what you do. 

[00:05:40] Niels Schillewaert: Yeah. Agree with that.

[00:05:41] Niels Schillewaert: Kind of, you know, it, it’s, it’s, we should be using AI to get to the added value quicker. Then on top of that, see where we can use it for adding value and, and creating, uh, distinctive insights if you want. Um, so, you know, you, you, you mentioned that it’s all about [00:06:00] critical thinking and, and creativity. Um, how do those human skills help us stand out as, as insights professionals?

[00:06:08] Tina Tonielli: So I think, I believe strongly that, um, the most critical things that we do as insights professionals, well the number one thing we do is to impact the business. So from my perspective, if you’re not driving business impact, you become very vulnerable in the age of efficiency. If you’re not driving top line, then you’re not seen as an effectiveness driver.

[00:06:34] Tina Tonielli: If you’re not seen as an effectiveness driver, you will be an efficiency driver. And so it has, you have to do one of those two things in business. I would rather be an effectiveness driver than an efficiency driver, as an insights professional. So if you wanna drive that top line and be an effectiveness driver, you have to start with that and say, okay, I need to solve a business problem.

[00:06:55] Tina Tonielli: And where do you start when you wanna solve a business problem at the beginning of your [00:07:00] research, and you say, what is the business problem I’m trying to solve? Not what is the research I’m trying to design? And I think a lot of times we’ll start with a curiosity question. Should this be green or red?

[00:07:12] Tina Tonielli: Should this be, you know, what should I name this? All that kind of stuff, versus. What is the business problem I’m trying to solve? And then ultimately, as you get the research back, and again, it could be research that you do with machine learning, generative ai, anything, when you get it back, you’re not done your research until that business decision is influenced.

[00:07:33] Tina Tonielli: That is to me, that business decision being influenced is your business impact. And then you can hang your hat on the fact that you have influenced the business and you can hold that up and say, I actually drove business impact. I am an effectiveness driver and not an efficiency driver. 

[00:07:51] Niels Schillewaert: Interesting. How, what does success, you know, uh.

[00:07:56] Niels Schillewaert: How does successful influence or being a successful business [00:08:00] driver look like for you in practice? Um, you know, especially because, well, a lot of insights people are, you know, using dashboards and, uh, data rather than anything else. Uh, what does it really mean for you? What, what does it look like in practice?

[00:08:15] Tina Tonielli: So, to me, I, I think we have to go from. The democratization of insights to the democratization of business impact or that maybe the delivery of business impact. Maybe I say it that way. So democratization of insights. What I saw there, and I’ve lived through a lot of these different eras, so Big Data era, the democratization of insights era to me was the dashboard era.

[00:08:40] Tina Tonielli: Where everybody said, I wanna get all of the data that you guys have over in the insights and analytics world. I want access to it. And what I saw was when the commercial leaders got access to all of the data, they said, I don’t want access to all of the data because now I’m overwhelmed and I don’t know what to do with all of the data.

[00:08:57] Tina Tonielli: So to me, the opportunity for the insights and [00:09:00] analytics professionals is to think like a commercial leader and interpret the dashboards and the data and pull the things together. It’s almost like we need to have commercial insights and think like a commercial leader and interpret the consumer insights and the business insights and pull that together and be that interpreter for the commercial leader to drive business impact.

[00:09:27] Niels Schillewaert: Interesting interpretation, that’s, how is that different from, from analyzing in your opinion? Uh, and why does it, you know, differ? Yeah. Why does that matter in the, in the era of ai, if you want. 

[00:09:40] Tina Tonielli: So, so I think there’s, there’s a couple of things. So you take what comes out of generative ai, what is, what is possible in generative AI is you could better synthesize multiple different, um, pieces of research, which was more difficult in previous years.

[00:09:58] Tina Tonielli: What you have to do is. [00:10:00] You have to be real careful with the output of generative ai and you need to know enough about those pieces of research that you, that you’re putting into that analysis to be able to check their work. So I think about generative ai. I have all kinds of analogies. To me, the analogy of generative AI is like they are the brand new intern who has PhDs in everything that you could possibly need to know, and they have access to all of the information you could possibly need to know.

[00:10:25] Tina Tonielli: But they’re a brand new intern, so you better be checking their work. And if you can’t check their work, you might not wanna just present that work. So that idea to me is like. If I understand the business insights research that they did and the consumer insights research they did, and they did that legwork for me to pull those two pieces together, then I can look at as a starting point what that interpretation was that they did.

[00:10:51] Tina Tonielli: And I can say, does that seem right? Is there something wrong with that? Let me check their work. But they can do some of that legwork for [00:11:00] me to pull those pieces together because the synthesis of multiple pieces of data. We always say it’s the most powerful and it takes time. So that whole idea of augmenting humans and like doing, helping humans do their job better.

[00:11:15] Tina Tonielli: To me, generative AI can help us, but you can’t just, you can’t just delegate it to generative ai. You have to check their work. Um, so that’s, that’s my interpretation there. 

[00:11:25] Niels Schillewaert: Yeah. We can’t delegate it. And we shouldn’t stop at the synthesis. We should. Exactly. 

[00:11:31] Tina Tonielli: Yes. Agreed. 

[00:11:32] Niels Schillewaert: Do you, do you see any, any, any space or potential for generative AI to help in that piece of adding value and and generating influence?

[00:11:42] Niels Schillewaert: Do you see any opportunities there? 

[00:11:44] Tina Tonielli: So it’s interesting. I think that what generative AI can help us with is the administrative aspects of storytelling and influence. I think that, interestingly. If you think about it, there’s the interpretation [00:12:00] of the research and then there’s the application of the research and part of the application of the research.

[00:12:06] Tina Tonielli: One of the biggest ones is change management in an organization, and that’s something that I think not a lot of researchers really take that to that mile. We kind of, there’s the. The, the early ones where people are like, here’s the research results, I’m done. The, the answer is blue and I’ve, I’ve done my job right.

[00:12:25] Tina Tonielli: And then the next, the next level is like, the answer is blue and therefore this package should drive X increases in your business. Right? That’s like an interpretation. And then the furthest one to me is like. And because it’s blue, your entire line is in the wrong color. And let me help you reshape the entire line.

[00:12:47] Tina Tonielli: And that’s like a whole business change situation. So what I’m seeing is most, I mean, obviously. Most researchers do the first one really, really well. Some do the second really, really [00:13:00] well. And then there’s very few that do that third one really, really well. But the ones who do the third one really, really well are the ones that truly are driving business impact at that higher level.

[00:13:11] Tina Tonielli: And they’re the ones who are getting less cuts and are getting more resources in this age when you can drive top line, um, and not be seen as a bottom line driver. 

[00:13:23] Niels Schillewaert: Okay. Interesting viewpoints. Uh, thank you for sharing those slightly different topic. Are you rather optimistic or, or pessimistic when it comes to the opportunity that, that, you know, insights can lead the way of how AI is used in, in, in business and, um.

[00:13:44] Niels Schillewaert: How would you explain whether you’re optimistic or, or pessimistic in that regard? 

[00:13:48] Tina Tonielli: Yeah. I’m really bullish, so I’m really bullish at the potential for insights. I think that insights professionals need to really embrace a growth [00:14:00] mindset, and that’s. The, the only thing standing in front of insights professionals are insights professionals themselves, I think, in whether they have a growth mindset or a fixed mindset.

[00:14:10] Tina Tonielli: So let me say why I think the potential is there and, and for insights professionals more than a lot of other professionals out there. So the way I look at generative ai, no matter how you’re applying generative ai, I don’t care if you’re, you’re trying to, you know. Automate something or you’re trying to do, you know, new science, right?

[00:14:33] Tina Tonielli: The idea of generative AI is you always have to give it a prompt. And a prompt, by the way, is. A business problem or a problem definition. A lot of people say like prompt engineering, all that kind of stuff. I think that’s total BS by the way. I think that’s people trying to get themselves jobs and and sell stuff to me, prompt engineering is just really good.

[00:14:57] Tina Tonielli: Definition and clarity around what I [00:15:00] need you to do and what exactly is the design of the experiment that I need you to run. And that to me, that is the sweet spot of really good insights professionals. And so we have been doing that for our entire careers. So to me that is like our sweet spot. We’re not writing Python, we’re not learning a new code.

[00:15:20] Tina Tonielli: You’re not learning prompt engineering. Like lose that and say, I need to be super clear on what is the problem I’m trying to solve here. That to me, insights professionals are outstanding at doing that. They’re also really good at when you’re doing research with someone or you’re doing a project, there’s stewardship in the middle, so you always check in in the middle and go, does this seem right, or is there something funky with it?

[00:15:43] Tina Tonielli: That’s the other piece of generative ai, which is like, you can’t just run it. And take it and go, yep. That’s just, that’s all good. You have to check in and say, is this doing what I thought it was going to do? Right. Again, insights professionals been doing it their whole [00:16:00] careers, and then at the end, like I said, there’s that one, two, and three that Insights Professionals can do.

[00:16:06] Tina Tonielli: That is 100%. Tied to what generative AI does. So if you really think about it, generative AI is almost like just doing research, but you’re doing research with the, with the assistant who is, remember I said the new intern who has. PhD is in everything that you need. So if you can get your data that you know as an insights professional, you just have an assistant who maybe makes some mistakes, but they can help you and you can show the rest of the organization how to do it.

[00:16:41] Niels Schillewaert: Yeah. Okay. Interesting. 

[00:16:43] Tina Tonielli: I feel passionately about this in case 

[00:16:45] Niels Schillewaert: that all be No, no, and that’s clear. And I share your passion by the way. I always think of AI gives you superpowers, uh, and, and a lot of, and takes out a lot of the ted tedious, so, so you can get to the interpretation and the business problem solving much [00:17:00] quicker.

[00:17:00] Niels Schillewaert: Yeah. And you, you, you alluded to change management, and that’s obviously created. Related to culture, what’s your piece of advice in terms of how organizations can create those cultures? And it seems to be as the way in your, uh, vision, it seems to be even, it’s always been important, but it seems to be more important even now with ai.

[00:17:21] Niels Schillewaert: What would be your, your advice there too towards, uh, organizations? 

[00:17:25] Tina Tonielli: So. Oh, gosh. I have a lot of advice, but let’s, let’s, let’s take a step back on this one. So I think there’s, there’s the advice I have for insights professionals and then there’s the advice I have for organizations. So let’s start with professionals.

[00:17:41] Tina Tonielli: I think for professionals, my advice is don’t be intimidated. You know how to do this, so just jump in and start playing with it and start playing with it in your personal life first. I saw some really interesting statistics recently that most people who are [00:18:00] doing generative AI are using it for lifestyle things.

[00:18:04] Tina Tonielli: So try it in your life first and figure out how it works, how it doesn’t work. It’s kind of lower risk, I think, to try it out in your life and get to know it and even just use it as Google. Like, try it out first. Jump in and figure it out. It’s not gonna kill you, I promise. So that to me would be like the insights professionals.

[00:18:25] Tina Tonielli: I would recommend that. And also I would recommend for Insights Professionals. This is not a project, this is not a roadmap, and this is not a capability that you build and you get your checkbox when you’re done. This is something like generative AI is like electricity. It is a foundational technology that you are going to need to keep learning.

[00:18:48] Tina Tonielli: As you go. So keep that curiosity and keep connected to how things are evolving. And if there’s somebody on your team who’s like really curious and loves learning, make them your [00:19:00] champion of this and stay really close to them. So that to me is like for the insights professionals, I think that also ties to functional groups in the insights industry.

[00:19:11] Tina Tonielli: You have to be. Always learning in this space. I made the mistake, like when it first came out, I was like, I’m gonna learn all of this. And then like I, I went super deep for like a year and then I was like, I got this. And, uh, and no. So I learned you have to keep, keep learning. Um, so that’s, and then the last thing for me on businesses.

[00:19:33] Tina Tonielli: I think they’re focused on the wrong KPIs, so I think businesses are focused on efficiency drivers and I think they actually need to expand their minds a little bit and think about effectiveness and not actually try to drive to what I’m gonna speak outta the other side of my mouth on business impact for a second.

[00:19:53] Tina Tonielli: I think business impact in this case, sometimes you need to play a little bit with things and have some faith. They’re gonna actually [00:20:00] do interesting things and experiment a little bit with it because we don’t know how it’s going to transform things in the future. So you need a little bit of resources and time to actually just play with it in business and figure out what the effectiveness things that it can do for you is, 

[00:20:18] Niels Schillewaert: yeah.

[00:20:18] Niels Schillewaert: Interesting. Um, on the organizations and the teams, do you have any example of teams or organizations that are, that, that you’ve seen are doing it particularly well? 

[00:20:29] Tina Tonielli: So there’s a company and then there’s, I’m gonna be biased and say that my team and Ion, but, um, so from a company perspective, there’s a company called One Strategy Studio.

[00:20:41] Tina Tonielli: I’m not sure if you’re familiar with them, but they. They jumped in very quickly and very early in the process, and actually built agents before anybody knew what an agent was. And so to me, they had incredible foresight in terms of like understanding it’s not just [00:21:00] about these or these, it’s about the jobs to be done.

[00:21:02] Tina Tonielli: And so they’ve created. Almost like a virtual agency with agents to do the different jobs. And then they have stewards to manage those agents and they work with the teams to do that. And that to me is an example of saying, how do I think differently about how to use generative ai? And I know that there’s a lot of people who are fast fascinated by it and using them to understand how to do that.

[00:21:29] Tina Tonielli: So I would say. That to me is a really interesting example of doing generative AI differently from a supplier perspective and then from a, from a business perspective, what I did at ion, and I mean I’m a little bit biased. Obvious. I’m a lot biased, obviously. What I did at ion, I brought a lot of suppliers together into ion, brought ’em into the, the building, and actually had almost like a town hall kind of convention and.

[00:21:58] Tina Tonielli: Introduced the organization to a [00:22:00] lot of different ways of thinking about it and forced my insights team to do a breakout themselves so that they had to get involved in it. And then what I saw was a lot of the insights team got interested in and intrigued in it, and about a year later we started something called the.

[00:22:15] Tina Tonielli: Generative AI Avengers team. So there were people who were really interested in it. And one particular person on my team, Theresa Carrera, she was like my champion. She became the champion of the Avengers and those Avengers were building that muscle and the flywheel that I was talking to you about, which was.

[00:22:34] Tina Tonielli: Keeping their ear tied to the outside learning and then teaching everybody on the inside about it and doing it in a continuous flywheel. And that to me was my biggest learning, I would say, was that’s what you need to do, I think, um, to really keep up with what’s going on with generative AI as an organization.

[00:22:53] Niels Schillewaert: Yeah, interesting. It’s all change management, but with, you know, technology on steroids, which is uh, which is kind [00:23:00] of what we aren’t maybe used to, that it’s going, uh, so far. 

[00:23:02] Tina Tonielli: Agreed. Agreed. 

[00:23:04] Niels Schillewaert: Tina, there’s one thing which we always look at from the MRII standpoint, but it’s also something, you know, based on my experience with ai and, you know, we’ve been around, we are quote unquote experts in research and insights.

[00:23:19] Niels Schillewaert: And for, for us, or for me, I always feel like, you know, uh, gen generative AI is helping me tremendously. But because I can sift out, uh, and I can look at, you know, judge, this is good, this is bad, this is not, I need to tweak this. But for young professionals, I’m wondering what is it that they need to do? How are they gonna, you know, learn the, the, the skills of insights and analytics.

[00:23:41] Niels Schillewaert: What would be your advice towards young professionals today with this technology? You know, on the rise? 

[00:23:48] Tina Tonielli: That’s a really, really good question. And Neil’s, I probably don’t have a great answer for that. I feel like I have great answers for all of them, and I’m like that one 

[00:23:54] Niels Schillewaert: very perspective really. 

[00:23:55] Tina Tonielli: Yeah. I think first of all, I guess what I would say is [00:24:00] I think we’ve lost the muscle a little bit of diving into data and swimming in data a little bit, and so I almost think that.

[00:24:10] Tina Tonielli: Young professionals need to earn their way into generative AI a little bit and do a little bit of the more practical and more sort of basic stuff first. Because I think what I’ve seen is when we jump immediately into dashboards and we don’t do the. Stuff before dashboards. When people get dashboards, they don’t know where they came from and they don’t understand the mechanics behind the math and the analytics that drove that.

[00:24:38] Tina Tonielli: So to me, there needs to be a step first where the folks who are coming in get some degree of basic, almost like basic training. So I think that basic training needs to happen. What generative AI maybe could help with is if folks who really understand it very well can teach a model. To teach the the [00:25:00] new folks about it and almost like have a coach to walk you through it.

[00:25:05] Tina Tonielli: Obviously you need to train the model and you need to make sure that the model really knows what it’s doing, but I actually think because people don’t have time to train people, how do you teach a model that then can coach the new folks their way through things because you kind of need that. Ask you a question, is this right?

[00:25:23] Tina Tonielli: What’s going on? All the time. And I think that could be something that could be amazing for some of the new folks as they sort of enter into the world of research. 

[00:25:32] Niels Schillewaert: Yeah. Excellent. And those new folks, those, you know, you, those new people, young professionals coming in, you mentioned critical thinking and creativity before.

[00:25:42] Niels Schillewaert: Uh, what advice would you give them? To encourage, you know, to, to strengthen those, those muscles. What, what would be your, uh, advice to them? 

[00:25:52] Tina Tonielli: So what I would say to them is, um, don’t declare a [00:26:00] major and stick to only the major. What I mean by that is a lot of folks in sort of my time declared they were an insights person or they were an analytics person and that’s what they were, and that’s all they were going to be.

[00:26:16] Tina Tonielli: And what I see now is there needs to be a degree of, you might have a major and a minor, so you might say, Hey, I’m an insights major. How do you understand the analytics in a minor level, or you’re an analytics major, but you need to be an insights minor and understand both sides. I think that nowadays, especially in insights leadership, there’s an expectation that you understand both pieces of research in order to be able to drive that business impact.

[00:26:46] Tina Tonielli: And I think as I talk to folks who are just coming up, I would say make sure. That you have a degree of both halves of that understanding, and don’t just close yourself off to one side because I think [00:27:00] that’s the biggest mistake that they could make as they come forward and as much diverse experience as they can get as possible.

[00:27:07] Tina Tonielli: If they can get commercial experience, I would absolutely recommend it. 

[00:27:12] Niels Schillewaert: So be fluid, be, be yes. Versatile really is what you’re saying. Uh, very well. Um, Tina, this has been a very, uh, thoughtful conversation. Um, but before we close, uh, we’d like to also end up, uh, you know, summing things up. So, question to you, what’s the single most important thing that Insights Professionals can do?

[00:27:34] Niels Schillewaert: To stand out in the age of AI sameness as you, uh, as you started out the conversation. 

[00:27:41] Tina Tonielli: Yes. So I would say, first of all, um. Don’t be afraid to use generative ai, so don’t be afraid. The second thing is really pay attention to what is the problem you’re trying to solve and know that you have an advantage in that [00:28:00] space, and then when generative AI results come in, you have.

[00:28:04] Tina Tonielli: Another advantage called interpretation, and you do that better than anyone else. So if you can channel your, what is the problem we’re trying to solve and the interpretation of results, and apply those two skills to generative ai, you guys are gonna be setting the pace for the rest of the organization.

[00:28:22] Tina Tonielli: And I’m voting for you. We’re rooting for you. 

[00:28:26] Niels Schillewaert: Very good. Thank you so much. Uh, Tina, it seems like we need to keep, uh, the eye on the ball. Uh, 

[00:28:31] Tina Tonielli: absolutely. 

[00:28:32] Niels Schillewaert: Thank you. The, uh, sports metaphor. Yeah. I want to thank you, Tina, and I want to thank you everyone for, for joining us. And, um, thanks for listening to another episode of Insights and Innovators, uh, POSCO podcast, brought to you by the Market Research Institute International.

[00:28:47] Niels Schillewaert: See you next time. Cheers. 

[00:28:50] MRII Announcer: Thanks for joining the Insights and Innovators podcast for Market Research Institute International. Click subscribe to never miss an episode and visit [00:29:00] us@rii.org for more market research insights.