Beyond Demographics: Redefining Flavor Segmentation with Kim Duncan

Transcript

Stephanie 0:00 Yeah.

Matt 0:02 Today on the Curiosity Current, we're joined by Kim Duncan, head of consumer insights at Givaudan, the global leader in flavor start over. It's not flavor. It's not. Okay. Today on the Curiosity Current, we're joined by Kim Duncan, Head of Consumer Insights at Givaudan, the global leader in flavor and fragrance innovation. Over the past decade, Kim has been instrumental in developing Flavor Finders, a groundbreaking segmentation framework that overlays traditional insights with behavioral, emotional, and even psychological dimensions of flavor adoption.

Stephanie 0:39 Think about the Rogers innovation curve, but for your taste buds From the cautious hesitators to the bold trailblazers, flavor finders helps brands understand not just what people like, but why they like it, and how to meet them where they are in their flavor journey.

Matt 0:55 Kim's work isn't just redefining how we segment consumers. It's redefining how we innovate, message, and even name products in today's crowded, fast moving food and beverage market. So let's dive in. Welcome, Kim.

Kim 1:07 Thank you for having me.

Stephanie 1:09 Thank you for being here. Uh, Stephanie and I are are both, uh, research nerds, so we're excited to jump right in and learn all about this fantastic, um, research endeavor you've undertaken with FlavorFinders. It's such a fresh take on segmentation. Uh, we know, you know, it's rooted in a a decade of behavioral research. Can you take us back to the moment? You know, what really made you realize, uh, that looking at flavor preferences through traditional demographics just wasn't really telling you the whole story that you wanted?

Kim 1:46 I think it probably went back all the way back to 02/2011, if I remember right. I think that's when the idea of, like, foodie versus non foodie was really becoming a thing and things that were talked about all the time. And as I was thinking about that, I was also thinking about, well, that has to apply to flavor depending on how adventurous you are if you're a foodie or a non foodie. Well, that's gonna translate into how you are experiencing and choosing different flavors of products. So I started thinking there, and, um, we have a long term research partner, Bellamy, that we do a lot of our primary proactive research with. And in that point, we started adding in a series of 18 questions to all of our surveys and had no idea what we were doing with it, um, other than we were gonna try to get at this foodie versus non foodie idea. So at the end of the the the research and the projects and the surveys and things like that, we would loosely be like, oh, if they're eight, nine, and 10 to these questions, they're a foodie. If they're a one, two, and three, they're a non foodie. So that was kind of the start of it was that idea of foodie versus non foodie.

Stephanie 0:00 Yeah.

Kim 1:07 Thank you for having me.

Stephanie 2:48 Interesting. And you it sounds like you didn't necessarily have a a concrete idea of, you know, what the segmentation model was going to be or or what your exact outputs were gonna look like. You you knew there were some data points you wanted to collect, and you figured that would just be a good place to start.

Kim 3:04 Yeah. You know, it was funny. So, like, I don't even know if segmentation necessarily was on the radar at that point.

Matt 3:10 Yeah.

Kim 3:11 But as we you know, as the years went on, you know, we're like, oh, everybody has segmentation. We want one too. But I was also really cautious about that because we work with our customers who know everything there is to know about their consumers. I didn't want to I wanted to make sure it was not something that, um, conflicted, maybe what that they had decided about their consumers and what they knew about their consumers. So kinda how could we have something that was more complimentary to what they had? And Bellamy and I had lots of discussions over the years from 2011 on. And finally, in 02/2018, they were like, we have a lot of data we've been collecting. We might be able to just analyze this. So that was it took a while to get to that point. Um, it was on our radar, but like I said, it was a big thing. We wanted to make sure that we were not our customers know who their consumers are. We didn't need to tell them that. So we were trying to approach it from a little bit of a different angle.

Stephanie 4:05 Were they looking for something that was complimentary? Mhmm.

Kim 4:07 Yep.

Matt 4:09 That's so interesting that it's something that came out of then having these years of data that you could mine to build this segmentation rather than starting with this a priori idea that you're gonna build a segmentation. You you don't see that very often. That's really cool.

Kim 4:23 Yeah. I mean, it was definitely exciting after we'd already spent all the money on this other research. It's like, oh, there's an additional, um, value we can get out of this research. So, you know, everybody always wants that crystal ball. I would like to say that, like, I had that crystal ball in 02/2011, but I didn't. I just knew that, you know, we wanted to do something, and then we were kinda able to go back and and repurpose that and and get to where we are today. the end of the the the research and the projects and the surveys and things like that, we would loosely be like, oh, if they're eight, nine, and 10 to these questions, they're a foodie. If they're a one, two, and three, they're a non foodie. So that was kind of the start of it was that idea of foodie versus non foodie.

Kim 3:04 Yeah. You know, it was funny. So, like, I don't even know if segmentation necessarily was on the radar at that point.

Matt 3:10 Yeah.

Stephanie 4:05 Were they looking for something that was complimentary? Mhmm.

Kim 4:07 Yep.

Kim 4:23 Yeah. I mean, it was definitely exciting after we'd already spent all the money on this other research. It's like, oh, there's an additional, um, value we can get out of this research. So, you know, everybody always wants that crystal ball. I would like to say that, like, I had that crystal ball in 02/2011, but I didn't. I just knew that, you know, we wanted to do something, and then we were kinda able to go back and and repurpose that and and get to where we are today. can get out of this research. So, you know, everybody always wants that crystal ball. I would like to say that, like, I had that crystal ball in 02/2011, but I didn't. I just knew that, you know, we wanted to do something, and then we were kinda able to go back and and repurpose that and and get to where we are today.

Matt 4:48 Very cool. So one of the things that that's super interesting to me about FlavorFinders is that it doesn't just categorize consumers, you know, certainly not by demographics, not even by what they consume. It it looks at how they approach consumption overall. Right? Both emotionally, behaviorally. What led your team to consider this sort of notion of a flavor adoption curve as a viable segmentation model when you got to that point? And and then kind of related to that, how did you end up validating it, uh, as something that was meaningful and and ultimately scalable for your partners?

Kim 5:23 So, you know, we kind of we've always kind of looked at the, um, product adoption curve. Right? And then flavor kinda mirrors that. So we've we have always looked at that for inspiration. We share that with our customers, but, you know, things that are, like, at inception and fall on the flavor curve are way different than things that fall on the other end of the spectrum. And, you know, we knew that there was something there to be able to try to understand. Not everybody is going to be playing at inception. Um, you know, there are some people that aren't gonna join it until it gets to mainstream. So we wanted to kinda we had that in mind as we were creating this as well. And, you know, we didn't wanna just ask I'm all about trying to figure out how to ask questions to get true answers from consumers. You know? We all know in the research industry, aspirational and socially acceptable answers that everybody gives.

Stephanie 6:13 Yeah.

Kim 6:14 So whenever we're designing questions, including for this, you know, we're always trying to figure out, like, how how easy is it for them to answer aspirationally. So we really try to make them be questions that it would be very tough for them to answer that way. And then by the fact that we had these 18 questions that ranged on all those different things, kinda like how they approached, um, flavor, how they approached shopping, how they approached going to restaurants, and all of those things. It'd be very hard to answer all of those questions socially aspirational or, you know, socially acceptable or whatever. So that was kind of the way we wanted to make sure that we approached it so that we weren't just focused just on flavor. It was kind of taking into that broader that broader context. can get out of this research. So, you know, everybody always wants that crystal ball. I would like to say that, like, I had that crystal ball in 02/2011, but I didn't. I just knew that, you know, we wanted to do something, and then we were kinda able to go back and and repurpose that and and get to where we are today.

Stephanie 6:13 Yeah.

Kim 6:14 So whenever we're designing questions, including for this, you know, we're always trying to figure out, like, how how easy is it for them to answer aspirationally. So we really try to make them be questions that it would be very tough for them to answer that way. And then by the fact that we had these 18 questions that ranged on all those different things, kinda like how they approached, um, flavor, how they approached shopping, how they approached going to restaurants, and all of those things. It'd be very hard to answer all of those questions socially aspirational or, you know, socially acceptable or whatever. So that was kind of the way we wanted to make sure that we approached it so that we weren't just focused just on flavor. It was kind of taking into that broader that broader context. of those things. It'd be very hard to answer all of those questions socially aspirational or, you know, socially acceptable or whatever. So that was kind of the way we wanted to make sure that we approached it so that we weren't just focused just on flavor. It was kind of taking into that broader that broader context.

Stephanie 6:58 So it sounds like, you know, a lot of strategic thought was was put in right, uh, at the jump for, um, how to mitigate some of the the challenges that that appear when when you're asking respondents to report their behavior or or, um, report how open they are to to trying something new and new and exciting. Um, you know, from the research side, I think all that makes a lot of sense. Can can you walk us through how this, uh, this this change, um, how this actually changes the business decisions that companies were making, uh, like, in their innovation departments or or to their go to market strategy. You know, this this flavor finders sort of represented a a shift from, you know, thinking about basic demographics to adoption behaviors. How did that go over with with the business units themselves?

Kim 7:54 I think it's still, um, it's still, uh, it's still something hard for people to grasp. So you you mentioned demographics, and that was one really big moment for us at the end of doing all the analytics of this data and creating the segmentation is that the flavor finder segments are truly demographic agnostic. Um, you know, we get asked all the time, what are flavors for females? What are flavors for Gen z?

Matt 8:18 What are of those things. It'd be very hard to answer all of those questions socially aspirational or, you know, socially acceptable or whatever. So that was kind of the way we wanted to make sure that we approached it so that we weren't just focused just on flavor. It was kind of taking into that broader that broader context.

Matt 8:18 What are ics, and that was one really big moment for us at the end of doing all the analytics of this data and creating the segmentation is that the flavor finder segments are truly demographic agnostic. Um, you know, we get asked all the time, what are flavors for females? What are flavors for Gen z? What are

Kim 8:18 flavors for millennials? You know, all of the traditional asks that we get, and the assumption is is that everybody's the same. Well, at the end of our at the end of our research and when we created the segmentation, we discovered that we'll use millennials because at the time, that was the big generation that everybody cared about. Everybody thought millennials were adventurous. Right? We need adventurous flavors for millennials. And what we found out at the end of our research were there are just as many adventurous millennials, so flavor trailblazers, as there are nonadventurous millennials, so flavor hesitators. So this assumption that a demographic is all the same is very misleading and probably leads to that, you know, eighty percent of new product failure. It probably plays into that a little bit. Um, so, you know, we wanted to make sure that we kind of understood that. And then when you start looking at the data and you overlay flavor finders with that, you know, like, I think about all the years I've looked at data, and you look at the total column, and you're like, oh, what flavors come to the top? What flavors don't do well? And then when I started overlaying flavor finders with that, it was a completely different story. Sure. There were some flavors that if they did well overall, all of the segments like that, But of 40 flavors, there might be three flavors that are like that, and then you really start seeing some nuances. So if you're really trying to target that trailblazer and it didn't show up in the total, you're probably missing your opportunity Or vice versa, it showed up in the total, but it really leaned towards a trailblazer, not a hesitator. So you're really gonna miss your target on that. So I think it's really tried to help bring in that, um, that perspective early on in the product development process about, like, where flavor plays in and what direction you should be going.

Stephanie 10:02 Was it challenging? Was it was it a sort of a a challenging approach, or or, um, uh, was it difficult to kind of affect that mindset change in in, you know, your clients or or your business partners? Or did you find, um, you know, it was it was received with open arms, like a fresh take, you know, were were people really looking for something new in the in this innovation space to get excited about?

Kim 10:28 Um, I would say yes, but it still is, you know, an uphill I don't wanna say battle, but an uphill journey. We'll say an uphill journey. Um, everybody, like, it's kinda like an moment for a lot of people when they hear it. Yeah. Yet it's still they wanna default to what what what you've always done. Right?

Stephanie 10:46 It will Easy to quantify and ics, and that was one really big moment for us at the end of doing all the analytics of this data and creating the segmentation is that the flavor finder segments are truly demographic agnostic. Um, you know, we get asked all the time, what are flavors for females? What are flavors for Gen z?

Matt 8:18 What are

Stephanie 10:46 It will Easy to quantify and you know, an uphill I don't wanna say battle, but an uphill journey. We'll say an uphill journey. Um, everybody, like, it's kinda like an moment for a lot of people when they hear it. Yeah. Yet it's still they wanna default to what what what you've always done. Right? It will Easy to quantify and

Kim 10:48 Yeah. And, um, um, you know, and and kind of biases and all of the things and all of the history that people have thinking about flavor and that sort of thing. It's it's really a mind shift. So, you know, we just keep kind of saying the same thing over and over and over again. You know? All of our research that we do, um, you know, that we we partner with you guys for a lot of our research, and we have our segments are identified. You know? We have a panel that we specifically created so that we could know who our flavor finders were, and we can go out and know who they are straight out of the gate versus, like, screening for them every single time. And by having that, we we bring that element into everything that we do from a primary perspective, um, and share that with our customers to kind of start continue you know, like, always say there's more to the story. There's more to the story. There's more to the story. Um, every time we can, we incorporate that.

Stephanie 11:44 That's great.

Matt 11:46 So that kinda makes me that, you know, you were just talking about, you know, being able to, uh, kinda target these segments, um, in the research that you do, like, on our platform. And I'm curious, like, I I just wanna use, like, a very hypothetical example. But, like, let's say that there is a company that is, uh, making lab grown meat. That's just the thing that's popping me into my mind, but that's something that's new. Right? Like, that's very new. So would your, like, sort of suggestion or orientation be, like, on the flavor adoption curve, that's, you know or and maybe that's not flavor per se, but, like, this is a new this is something where our Jujuvenant's perspective would be that you need to be targeting, uh, these trails trailblazers in this case. And that's sort of the audience that you're gonna wanna focus your early innovations on and your early sort of marketing and the way that you frame the value proposition and all that because that's the audience that's most open to that this category, this sort of new innovation in this new category of food. Is that how you kinda think about it in practice? you know, an uphill I don't wanna say battle, but an uphill journey. We'll say an uphill journey. Um, everybody, like, it's kinda like an moment for a lot of people when they hear it. Yeah. Yet it's still they wanna default to what what what you've always done. Right?

Stephanie 10:46 It will Easy to quantify and

Stephanie 11:44 That's great.

Matt 11:46 So that kinda makes me that, you know, you were just talking about, you know, being able to, uh, kinda target these segments, um, in the research that you do, like, on our platform. And I'm curious, like, I I just wanna use, like, a very hypothetical example. But, like, let's say that there is a company that is, uh, making lab grown meat. That's just the thing that's popping me into my mind, but that's something that's new. Right? Like, that's very new. So would your, like, sort of suggestion or orientation be, like, on the flavor adoption curve, that's, you know or and maybe that's not flavor per se, but, like, this is a new this is something where our Jujuvenant's perspective would be that you need to be targeting, uh, these trails trailblazers in this case. And that's sort of the audience that you're gonna wanna focus your early innovations on and your early sort of marketing and the way that you frame the value proposition and all that because that's the audience that's most open to that this category, this sort of new innovation in this new category of food. Is that how you kinda think about it in practice? ou're gonna wanna focus your early innovations on and your early sort of marketing and the way that you frame the value proposition and all that because that's the audience that's most open to that this category, this sort of new innovation in this new category of food. Is that how you kinda think about it in practice?

Kim 12:54 Yeah. A 100%. You know, one of the like, kind of the tagline that we say here, it's it's about creating the right flavor or product for everyone, not everyone. And, you know, everybody wants to create something for everyone because, gosh, that's gonna get 85% of the population to buy your product or whatever. In my opinion, you know, those products already exist. Those flavors already exist, and they do great. As, you know, choices become more abundant to consumers Yeah. You're never going to have everyone again. Like, it's going to be very, very hard to get 85% of the population straight out of the gate, especially with a new product because they're getting further and further and further out there. Right? Right. Like, your lab based meat, um, is a good example. Think back to plant based. Like, when Impossible Burger and Beyond and the plant based burgers came out. People that were buying those to start were your trailblazers and your investigators, which is, like, 29% of the population. And they took off like crazy because they were marketed and targeted to the right consumer. Right. Hesitators, they may still have not tried any of that stuff yet. Yeah. But if they did, it was years down the road when, like, Burger King brought the brought the Impossible Whopper to the table. Right? And they could, like, alright. I'll try it. It's low risk. You know? That's the thing you have to think about too is risk means something different to every single person, every single segment. And, you know, what's risky to a trailblazer is a lot different than what's risky to a a hesitator. And kind of along those, something else we've learned over the years too is the idea of adventurous is different to a trailblazer versus a follower. You know? They both are adventurous people, but, oh my gosh, the way a trailblazer thinks of adventure versus the way a follower thinks of adventure are two completely different things. So understanding all of those nuances is really, really important. And having just these broad buckets and how we thought about it for all these years is it's it's very misleading, I think.

Matt 14:52 That makes a lot of sense. You so you kinda spoke to this a little bit earlier, but you've described FlavorFinders as complimentary, uh, to, you know, other segmentation models specifically because of what your business model is. Right? You you, uh, work with brands who often have their own segmentation models, uh, for their products. How does the overlay of FlavorFinders to an existing segmentation that a client's using for their, you know, product road map and product development, how does it work in practice? ou're gonna wanna focus your early innovations on and your early sort of marketing and the way that you frame the value proposition and all that because that's the audience that's most open to that this category, this sort of new innovation in this new category of food. Is that how you kinda think about it in practice?

Matt 14:52 That makes a lot of sense. You so you kinda spoke to this a little bit earlier, but you've described FlavorFinders as complimentary, uh, to, you know, other segmentation models specifically because of what your business model is. Right? You you, uh, work with brands who often have their own segmentation models, uh, for their products. How does the overlay of FlavorFinders to an existing segmentation that a client's using for their, you know, product road map and product development, how does it work in practice? of what your business model is. Right? You you, uh, work with brands who often have their own segmentation models, uh, for their products. How does the overlay of FlavorFinders to an existing segmentation that a client's using for their, you know, product road map and product development, how does it work in practice?

Kim 15:23 So, you know, when our customers have shared their segmentations with us before and when we see how they've defined their segmentations, we can kinda say there are sometimes where it's almost verbatim about how they will describe one of their segments and how we've described one of the flavor finders.

Matt 15:37 That's cool.

Kim 15:38 So we can easily say, like, you know, we think that this segment would be more trailblazers investigators, where this one over here would be more hesitators. And then when we're working with them on a project and we're, you know, bringing flavor recommendations and talking about flavor with our customers, we can then kind of steer them down the right direction. So we can say, like, okay. This segment is trailblazers and investigators. If that's truly your target, then these are the flavor profiles and the flavor directions you can go down versus a hesitator. You would wanna go this way. So it really kinda brings in that that complimentary aspect of it and can really give them another layer that they don't have access to.

Matt 16:18 Yeah. Like a a very rich data append almost. Right? It's like you have a ton more data that you can attach to their existing segmentation when they kinda line

Stephanie 16:28 up.

Kim 16:28 Yeah. And, you know, and we are a flavor company, so we sell flavors. Right. So we focus all of our research is focused on flavor. And I know our customers, gosh, when they're doing their research, flavor's a very small part of it. They have to worry about so many other things, pricing, branding, packaging, messaging, all of the things. Flavor's just this, like, little little dot on the radar of all the things they have to consider. So we can you know, we wanna be their partners and bring in that flavor perspective early on to where kinda like they can have confidence in the flavor, then by the time they get to really digging into their research and validating their products and all the things with their customers or their consumers, It's not so much about their the flavor's good, but, you know, they're trying to figure out everything else. of what your business model is. Right? You you, uh, work with brands who often have their own segmentation models, uh, for their products. How does the overlay of FlavorFinders to an existing segmentation that a client's using for their, you know, product road map and product development, how does it work in practice? So, you know, when our customers have shared their segmentations with us before and when we see how they've defined their segmentations, we can kinda say there are sometimes where it's almost verbatim about how they will describe one of their segments and how we've described one of the flavor finders.

Matt 15:37 That's cool.

Matt 16:18 Yeah. Like a a very rich data append almost. Right? It's like you have a ton more data that you can attach to their existing segmentation when they kinda line

Stephanie 16:28 up.

Kim 16:28 Yeah. And, you know, and we are a flavor company, so we sell flavors. Right. So we focus all of our research is focused on flavor. And I know our customers, gosh, when they're doing their research, flavor's a very small part of it. They have to worry about so many other things, pricing, branding, packaging, messaging, all of the things. Flavor's just this, like, little little dot on the radar of all the things they have to consider. So we can you know, we wanna be their partners and bring in that flavor perspective early on to where kinda like they can have confidence in the flavor, then by the time they get to really digging into their research and validating their products and all the things with their customers or their consumers, It's not so much about their the flavor's good, but, you know, they're trying to figure out everything else. where kinda like they can have confidence in the flavor, then by the time they get to really digging into their research and validating their products and all the things with their customers or their consumers, It's not so much about their the flavor's good, but, you know, they're trying to figure out everything else.

Stephanie 17:15 Such great thought all along by way of ensuring that the results are, um, that the outputs can actually drive decision making. And and and, you know, the implications from the work are are meaningful. There's another success story that I wanna shine a light on with flavor finders as it pertains to just the sheer scale of of what you pulled off. Right? So so some folks, some listeners might not know. Jimmy Don, as you said, it's a flavor company, massive flavor company. Right? So I have some hard numbers, uh, just to explain what we're talking about here. The the initial study was something like 9,000 plus, uh, respondents across eighty six eighty six distinct food and beverage categories. How the heck did you pull that off? Because, like, those of us who have done segmentation, you know, we it's a struggle just to get alignment on how to approach the work when you're talking about a single product in a, um, a single somewhat narrowly defined market. Um, you've really I mean, there there's a lot more ocean to boil, I'm sure, but you've you with this work, you've you've really captured a you've boiled a significant portion of the ocean already. How did you achieve that from, like, a alignment standpoint and an analytical standpoint? How did you pull all that all of that together? where kinda like they can have confidence in the flavor, then by the time they get to really digging into their research and validating their products and all the things with their customers or their consumers, It's not so much about their the flavor's good, but, you know, they're trying to figure out everything else. Such great thought all along by way of ensuring that the results are, um, that the outputs can actually drive decision making. And and and, you know, the implications from the work are are meaningful. There's another success story that I wanna shine a light on with flavor finders as it pertains to just the sheer scale of of what you pulled off. Right? So so some folks, some listeners might not know. Jimmy Don, as you said, it's a flavor company, massive flavor company. Right? So I have some hard numbers, uh, just to explain what we're talking about here. The the initial study was something like 9,000 plus, uh, respondents across eighty six eighty six distinct food and beverage categories. How the heck did you pull that off? Because, like, those of us who have done segmentation, you know, we it's a struggle just to get alignment on how to approach the work when you're talking about a single product in a, um, a single somewhat narrowly defined market. Um, you've really I mean, there there's a lot more ocean to boil, I'm sure, but you've you with this work, you've you've really captured a you've boiled a significant portion of the ocean already. How did you achieve that from, like, a alignment standpoint and an analytical standpoint? How did you pull all that all of that together? lly I mean, there there's a lot more ocean to boil, I'm sure, but you've you with this work, you've you've really captured a you've boiled a significant portion of the ocean already. How did you achieve that from, like, a alignment standpoint and an analytical standpoint? How did you pull all that all of that together?

Kim 18:42 So for this, for sure, is where our our partner Bellamy comes into play. You know? They they they work the magic behind the scenes kind of thing. Right? And by you know, we got our 9,000 respondents by looking at seven studies over the course of from 2011 to 02/2018. Um, we had one study in there that was a little bit 3,000 consumers, around 3,000 consumers. So that was kinda where our basis was for them to start with the analysis. And, you know, they were able to work their magic and do all the things and be able to create a 100 hundreds of potential different datasets using different approaches to kind of see what would work. And then where the other about 6,000 respondents came into play was then you were they were able to kinda go back and kinda validate that a little bit because we already had all of that data. Um, and then at the end of that, you know, the the larger sample size actually was, like, a good thing for them because they could have a lot more confidence and a lot more direction in in what they had. And then at the end of all of that that we were able to do, we actually had a seven segment solution to start. There were four segments that were pretty similar, so we were able to lump those into one to get us down to a more reasonable four segment solution, you know, not as fragmented. That that segment is is pretty similar, so we didn't need to split them out quite that much. Um, but then at the end of the day, really, what we were able to to get, you know, was something that was differentiated. It was meaningful. It was actionable, and more, you know, importantly, it was reproducible. We knew that it didn't matter what we did, and, you know, it's very consistent with what I see since I've been looking at the data since we've started implementing this layer. Um, it's very consistent no matter what study I look at, um, no matter what category I look at. So, like, in those 9,000 respondents, they were users of lots of different foods and beverages and brands and all of the things. So we had a really good robust representation of all of that in this. Um, so it doesn't really matter what it is. It's it it's always very consistent with the results that we see.

Stephanie 20:45 That's like the holy grail of segmentation. Right? It's like you're you're chasing this this, um, this this framework, this this set of heuristics that drives the ability to distinguish and differentiate between groups, but at the same time, it has to have this, like, universal applicability that that that makes it work. Right? And I feel like that's it's it's kinda rare that you hit something successfully with at that scale.

Kim 21:12 Yeah. I think, you know and it goes back to the fact that we are a different kind of, um, company. Right? Yeah. We have to know lots of things about a lot of categories. lly I mean, there there's a lot more ocean to boil, I'm sure, but you've you with this work, you've you've really captured a you've boiled a significant portion of the ocean already. How did you achieve that from, like, a alignment standpoint and an analytical standpoint? How did you pull all that all of that together? So for this, for sure, is where our our partner Bellamy comes into play. You know? They they they work the magic behind the scenes kind of thing. Right? And by you know, we got our 9,000 respondents by looking at seven studies over the course of from 2011 to 02/2018. Um, we had one study in there that was a little bit 3,000 consumers, around 3,000 consumers. So that was kinda where our basis was for them to start with the analysis. And, you know, they were able to work their magic and do all the things and be able to create a 100 hundreds of potential different datasets using different approaches to kind of see what would work. And then where the other about 6,000 respondents came into play was then you were they were able to kinda go back and kinda validate that a little bit because we already had all of that data. Um, and then at the end of that, you know, the the larger sample size actually was, like, a good thing for them because they could have a lot more confidence and a lot more direction in in what they had. And then at the end of all of that that we were able to do, we actually had a seven segment solution to start. There were four segments that were pretty similar, so we were able to lump those into one to get us down to a more reasonable four segment solution, you know, not as fragmented. That that segment is is pretty similar, so we didn't need to split them out quite that much. Um, but then at the end of the day, really, what we were able to to get, you know, was something that was differentiated. It was meaningful. It was actionable, and more, you know, importantly, it was reproducible. We knew that it didn't matter what we did, and, you know, it's very consistent with what I see since I've been looking at the data since we've started implementing this layer. Um, it's very consistent no matter what study I look at, um, no matter what category I look at. So, like, in those 9,000 respondents, they were users of lots of different foods and beverages and brands and all of the things. So we had a really good robust representation of all of that in this. Um, so it doesn't really matter what it is. It's it it's always very consistent with the results that we see.

Kim 21:12 Yeah. I think, you know and it goes back to the fact that we are a different kind of, um, company. Right? Yeah. We have to know lots of things about a lot of categories. t that that makes it work. Right? And I feel like that's it's it's kinda rare that you hit something successfully with at that scale. Yeah. I think, you know and it goes back to the fact that we are a different kind of, um, company. Right? Yeah. We have to know lots of things about a lot of categories.

Stephanie 21:21 Right.

Kim 21:22 Um, and we don't have just one thing to focus on. So we're able to kinda look at it this higher level kind of thing. We don't have to get into the nitty gritty for the consumers because that's not what we do. We were not selling anything any one thing specifically. So it really allows us to be able to kinda have that broader approach, um, and be able to just think about it differently compared to what our customers have to think about just based on, you know, the way their companies work and and what their goals are.

Matt 21:52 Super What I like about your description of your process too is sorry. There was a gnat in my room. I'm, like, trying to grab it. Um, but what I like about that, the your, uh, your process and the way that you talk about it, it really also just speaks to the fact that, uh, segmentation truly is both a a science and an art. You know what I mean? Like, it's both. And when you talk about, like, you know, we started with seven, you know, that's what the the data gave you. That's the science part. Right? But your business brain got in there and was like, yeah. But there's similarity here, and there's a lot of value in kinda reducing this down to something a little bit more consumable, right, and and, um, actionable. And so I always love it when people can talk about the segmentation process and really highlight how much of it is just bringing both of those aspects of things to the table, the the sort of the business acumen or just more the art side and then just the hard data and science of it as well. Yeah.

Kim 22:49 That's one thing I love about my role here is that it's truly about the it's truly about the insights. We don't get hung up on data because we're not making those important decisions as to whether to go or no go on a product. Yeah. Um, so it's really it allows me to have that flexibility and that fun, I guess. I mean, I still think it's fun after all these years.

Matt 23:09 Yeah.

Kim 23:09 Um, but it allows me to be able to really, you know, really see what the data is saying and what story it's creating versus getting all hung up in the numbers. Um, and I think that allows us to have, like, a little bit of a different perspective about how we approach research in general, but then how we were able to kinda get to this point with the with the segmentation.

Matt 23:28 For sure. Well, to switch gears a little bit, we talk a lot on the show about the evolution of agile approaches and research. I'm curious, how does Slavery Finder's model, um, enable faster iteration and product development cycles? And where do you see opportunities to kind of further accelerate this process using either AI or predictive analytics? Do you have thoughts in those sort of realms? t that that makes it work. Right? And I feel like that's it's it's kinda rare that you hit something successfully with at that scale.

Kim 21:12 Yeah. I think, you know and it goes back to the fact that we are a different kind of, um, company. Right? Yeah. We have to know lots of things about a lot of categories.

Stephanie 21:21 Right.

Matt 23:09 Yeah.

Matt 23:28 For sure. Well, to switch gears a little bit, we talk a lot on the show about the evolution of agile approaches and research. I'm curious, how does Slavery Finder's model, um, enable faster iteration and product development cycles? And where do you see opportunities to kind of further accelerate this process using either AI or predictive analytics? Do you have thoughts in those sort of realms? t the evolution of agile approaches and research. I'm curious, how does Slavery Finder's model, um, enable faster iteration and product development cycles? And where do you see opportunities to kind of further accelerate this process using either AI or predictive analytics? Do you have thoughts in those sort of realms?

Kim 23:52 Yeah. I mean, I think, you know, going back to what I said earlier about, like, bringing us early on into the innovation or the product process and the the innovation process, um, helps kind of have a better direction on that flavor. So that's always been the case, but then with the addition of flavor finders, like, you can hopefully zero in even more on what flavor is gonna be most relevant to your consumer. So, you know, hopefully, that again, like, at the end of the at the end of the process and when you decide whether you're gonna launch a product or not and the feedback that our customers get when they do their research, you know, hopefully, it's not the flavor that's holding them up. So you don't have to go back to the drawing board there. There might have to be something else that has to be changed, but, hopefully, it's not flavor. So, hopefully, that can kinda, like, give a lot more confidence early on in the in the product development process to help speed that part up at least, at least that one piece of the product. Um, oh gosh. Predictive analytics and AI. Um, you know, I wish I had a crystal ball. If I had a crystal ball, I probably would not be sitting here with you today. Right. I don't know how it plays into the predictive part. I think where I think the AI part could be interesting is how can flavor finders, um, overlay with things that we already know or already exist. There's lots of information out in the world that you can get. Um, you know, there's secondary research

Matt 25:10 Yeah.

Kim 25:11 Suppliers. There's articles that are written. There's all the things. There's so much information. And when you see those things, there's always great insights in there and some really good foundation, but there's always more to the story. And it'd be interesting to know, like, some stat is published in an article somewhere. And it's like, well, okay. Is it 65% of the general population, or is it 65% of trailblazers, which is a lot of diff much different story. Right? So I think that would be that's one way I think, um, could be interesting with AI is kinda how can you incorporate flavor finders into what you're doing with, um, with AI and when you're looking at all of all of your insights together or when you're trying to pull in secondary and primary, like, how does that all work together? t the evolution of agile approaches and research. I'm curious, how does Slavery Finder's model, um, enable faster iteration and product development cycles? And where do you see opportunities to kind of further accelerate this process using either AI or predictive analytics? Do you have thoughts in those sort of realms? Yeah. I mean, I think, you know, going back to what I said earlier about, like, bringing us early on into the innovation or the product process and the the innovation process, um, helps kind of have a better direction on that flavor. So that's always been the case, but then with the addition of flavor finders, like, you can hopefully zero in even more on what flavor is gonna be most relevant to your consumer. So, you know, hopefully, that again, like, at the end of the at the end of the process and when you decide whether you're gonna launch a product or not and the feedback that our customers get when they do their research, you know, hopefully, it's not the flavor that's holding them up. So you don't have to go back to the drawing board there. There might have to be something else that has to be changed, but, hopefully, it's not flavor. So, hopefully, that can kinda, like, give a lot more confidence early on in the in the product development process to help speed that part up at least, at least that one piece of the product. Um, oh gosh. Predictive analytics and AI. Um, you know, I wish I had a crystal ball. If I had a crystal ball, I probably would not be sitting here with you today. Right. I don't know how it plays into the predictive part. I think where I think the AI part could be interesting is how can flavor finders, um, overlay with things that we already know or already exist. There's lots of information out in the world that you can get. Um, you know, there's secondary research

Matt 25:10 Yeah.

Stephanie 26:00 Super interesting. Wanted to offer a quick plug. You have a great white paper all about, uh, flavor finders available on your website. Um, we were, uh, geeking out over that, uh, before the call. And, um, in there, it it sorta it talks about a lot of the things that we've already covered, um, you know, the, uh, the importance of of driving the segmentation from a, um, a behavioral perspective versus a demographic perspective. Um, they got us thinking, okay, behaviors well, behaviors shift over time, of course. Right? Um, depending on, you know, the, uh, person in question's lifestyle, uh, their their life phase, um, people change. How does how do you see flavor finders um, capturing that dynamic, that variable? Or or what or if if it's more of a point in time type of thing, what is the next iteration of flavor finders going to do, um, to sorta capture this dynamic of of changing behaviors? k that would be that's one way I think, um, could be interesting with AI is kinda how can you incorporate flavor finders into what you're doing with, um, with AI and when you're looking at all of all of your insights together or when you're trying to pull in secondary and primary, like, how does that all work together? Super interesting. Wanted to offer a quick plug. You have a great white paper all about, uh, flavor finders available on your website. Um, we were, uh, geeking out over that, uh, before the call. And, um, in there, it it sorta it talks about a lot of the things that we've already covered, um, you know, the, uh, the importance of of driving the segmentation from a, um, a behavioral perspective versus a demographic perspective. Um, they got us thinking, okay, behaviors well, behaviors shift over time, of course. Right? Um, depending on, you know, the, uh, person in question's lifestyle, uh, their their life phase, um, people change. How does how do you see flavor finders um, capturing that dynamic, that variable? Or or what or if if it's more of a point in time type of thing, what is the next iteration of flavor finders going to do, um, to sorta capture this dynamic of of changing behaviors? ifestyle, uh, their their life phase, um, people change. How does how do you see flavor finders um, capturing that dynamic, that variable? Or or what or if if it's more of a point in time type of thing, what is the next iteration of flavor finders going to do, um, to sorta capture this dynamic of of changing behaviors?

Kim 27:09 I don't absolutely. Somebody's, um, preference can change. Like, a good example would be somebody whose, you know, parents are hesitators. They go off to college. They meet new friends, and they get out, and they do their thing, and they find this whole new world that they've never been exposed to. Right? But there's still gonna be plenty of people that still stay in that hesitator. So I think you might have people that shift here and there, but a hesitator is not all this like, my colleague, I'm a trailblazer. My colleague is a hesitator. She is never gonna move from a hesitator to a trailblazer. Never gonna happen. Like, she is just never going to change her behaviors that much that it's going to, um, make her jump from one segment to another. Um, I think I think the younger generations, you know, possibly could change, but at the end of the day, you're still going to have people that fall into each one of those segments for all of the demographics. I don't think, like, you know, our our percentages, like our, you know, 14% is is trailblazers. I don't think that's going to change. You know? And I think the only time you really run into the issue is, like, with the panel, you know, that we've profiled, but we'll refresh that and we'll update that. Um, but when you're doing your research and you think about applying this to every single thing that goes down the road, I think that break is still gonna always gonna be the same. I see. I don't think all of a sudden you're gonna have 50% trailblazers one day and 14% hesitators. Like, it's never gonna change like that. So that is a little bit different from a lot of other segmentations where life stage and all those things really do come into play. Um, be a little bit of that, but I don't I don't think people will change a whole lot once they've kind of established their adult life, uh, which this is just based on 18 at this point. Right. Um, once they've established their adult life, they're probably not gonna change a whole lot.

Stephanie 28:56 So it's more about identifying these, um, longer term positions on on, um, adventurousness and and innovation and less about sort of evaluating where people are today specific to their their, um, openness to new flavors, more about really identifying, um, what type of person they are when it comes to trying new things because those those larger scale truths are are less likely to change over time.

Kim 29:26 Correct. ifestyle, uh, their their life phase, um, people change. How does how do you see flavor finders um, capturing that dynamic, that variable? Or or what or if if it's more of a point in time type of thing, what is the next iteration of flavor finders going to do, um, to sorta capture this dynamic of of changing behaviors? I don't absolutely. Somebody's, um, preference can change. Like, a good example would be somebody whose, you know, parents are hesitators. They go off to college. They meet new friends, and they get out, and they do their thing, and they find this whole new world that they've never been exposed to. Right? But there's still gonna be plenty of people that still stay in that hesitator. So I think you might have people that shift here and there, but a hesitator is not all this like, my colleague, I'm a trailblazer. My colleague is a hesitator. She is never gonna move from a hesitator to a trailblazer. Never gonna happen. Like, she is just never going to change her behaviors that much that it's going to, um, make her jump from one segment to another. Um, I think I think the younger generations, you know, possibly could change, but at the end of the day, you're still going to have people that fall into each one of those segments for all of the demographics. I don't think, like, you know, our our percentages, like our, you know, 14% is is trailblazers. I don't think that's going to change. You know? And I think the only time you really run into the issue is, like, with the panel, you know, that we've profiled, but we'll refresh that and we'll update that. Um, but when you're doing your research and you think about applying this to every single thing that goes down the road, I think that break is still gonna always gonna be the same. I see. I don't think all of a sudden you're gonna have 50% trailblazers one day and 14% hesitators. Like, it's never gonna change like that. So that is a little bit different from a lot of other segmentations where life stage and all those things really do come into play. Um, be a little bit of that, but I don't I don't think people will change a whole lot once they've kind of established their adult life, uh, which this is just based on 18 at this point. Right. Um, once they've established their adult life, they're probably not gonna change a whole lot.

Kim 29:26 Correct. ion and less about sort of evaluating where people are today specific to their their, um, openness to new flavors, more about really identifying, um, what type of person they are when it comes to trying new things because those those larger scale truths are are less likely to change over time. Correct.

Matt 29:26 Correct. Yeah. That makes a lot of sense. I think the one area where, you know, as as you know, if we were all it just had infinite time and resources where I was like, oh, it would be so cool, and we were academic researchers, that the sort of developmental aspect of it with kids would be so interesting because kid kid flavor tolerance is quite different, right? Like, they start with Matt and I are both parents. We're thinking about our kids right now. And I'm, you know, I'm absolutely in the chicken nuggets and pizza phase, which I feel like lasts, like, ten years, Right? But, like, but those kids grow up to be many of them, like trailblazers and and, uh, investigators and and followers and such. And I very curious how they get there. Do you guys think about that kind of stuff, or do you really try to stick to sort of adult flavor programs?

Kim 30:15 I mean, we have talked about it, but, you know, there's always that lovely budget that falls

Matt 30:20 into play. Yeah.

Kim 30:21 Um, you know, and yeah. We for sure. Like, obviously, if parents are hesitators, they're not introducing their kids to anything new. Where, like, you know, I'm a trailblazer. And with with our daughter, we've always been like, here, try this. And she has always like, we've always, like, forced her to take a bite of something. She's now 18, so I will I will add that in. So those few years were very painful. And, you know, she would take a bite of something and immediately take a drink, and then I'm mean, and I would be like, no. No. No. Chew it up. Swallow it. Then you can have a drink. And that was it. Like, we didn't make her continue to eat it. We never made her order anything weird, but it was only you just have to try it. It. And at eighteen and probably no. Not even at eighteen. Like, twelve, we're going to a restaurant, and she's ordering the chargrilled oysters for her Andre.

Matt 31:08 Love it.

Kim 31:08 Oh. Because, you know, like but that was because we exposed her to that. Like, Debbie's kids would never do that because they're just they've never grown up doing any of that kind of stuff. Right. So, yes, for sure. I kinda went off on a tangent, but it just kinda, like, kind of illustrates it a little bit better. Um, Yeah. I would love we would love to get into that to where we can kind of understand, like, kids' preferences and then how that overlays with what their parents are and the things that you expose them to and and all you know, parents have only have so much influence. I mean, you could be a trailblazer, and your kid could be hard fast chicken nuggets only. Who knows? Totally. But, um, but, you know, I did that is definitely an avenue that would be very interesting to explore further. It's just time and, you know, time and money, always the the thing. Right? ion and less about sort of evaluating where people are today specific to their their, um, openness to new flavors, more about really identifying, um, what type of person they are when it comes to trying new things because those those larger scale truths are are less likely to change over time. Correct.

Kim 30:15 I mean, we have talked about it, but, you know, there's always that lovely budget that falls

Matt 30:20 into play. Yeah.

Matt 31:08 Love it.

Kim 31:08 Oh. Because, you know, like but that was because we exposed her to that. Like, Debbie's kids would never do that because they're just they've never grown up doing any of that kind of stuff. Right. So, yes, for sure. I kinda went off on a tangent, but it just kinda, like, kind of illustrates it a little bit better. Um, Yeah. I would love we would love to get into that to where we can kind of understand, like, kids' preferences and then how that overlays with what their parents are and the things that you expose them to and and all you know, parents have only have so much influence. I mean, you could be a trailblazer, and your kid could be hard fast chicken nuggets only. Who knows? Totally. But, um, but, you know, I did that is definitely an avenue that would be very interesting to explore further. It's just time and, you know, time and money, always the the thing. Right? y have so much influence. I mean, you could be a trailblazer, and your kid could be hard fast chicken nuggets only. Who knows? Totally. But, um, but, you know, I did that is definitely an avenue that would be very interesting to explore further. It's just time and, you know, time and money, always the the thing. Right?

Matt 31:53 Always the limiters. Absolutely. Your point about exposure is so well taken, though. I've read that it takes an average of 30 exposures before a child is accepting of a food, which is a lot of exposure. So And

Kim 32:05 I think the other thing my my daughter told me is I think it's every seven years, I think your taste buds change.

Matt 32:11 I sure think.

Kim 32:12 So, like, that's Yeah. You know, that's a that's another factor. So it's it's all of these things why

Matt 32:17 Mhmm. You

Kim 32:17 know, looking at these high level generalizations just really start to derail the success of a product, whether it's from flavor perspective or just willingness to embark on something new.

Matt 32:29 Yeah. You talked a little bit earlier about risk, and I I wanted to dive into that a little bit more because I think that's really interesting. Um, one of the most compelling insights from FlavorFinders is how segments differ and their willingness to take risks, uh, with flavor and what risk means to them like you said. In a market where novelty is both an asset for product companies and and a liability, how should brands be thinking about and calibrating risk when they're launching to different flavor finder profiles?

Kim 33:00 I think it is understanding that. Right? Like the trailblazer, their risk level's extremely low. If they go to the grocery store and find see something and it's $10, they're gonna and it's brand new or it intrigues them or whatever, they're gonna buy it. They don't care how much it is. They're gonna go try the new restaurant. Um, and and, you know, and it that doesn't really matter on income. Like, somebody could

Matt 33:20 be more income y have so much influence. I mean, you could be a trailblazer, and your kid could be hard fast chicken nuggets only. Who knows? Totally. But, um, but, you know, I did that is definitely an avenue that would be very interesting to explore further. It's just time and, you know, time and money, always the the thing. Right? Always the limiters. Absolutely. Your point about exposure is so well taken, though. I've read that it takes an average of 30 exposures before a child is accepting of a food, which is a lot of exposure. So And

Kim 32:05 I think the other thing my my daughter told me is I think it's every seven years, I think your taste buds change.

Matt 32:11 I sure think.

Kim 32:12 So, like, that's Yeah. You know, that's a that's another factor. So it's it's all of these things why

Matt 32:17 Mhmm. You

Matt 33:20 be more income and find see something and it's $10, they're gonna and it's brand new or it intrigues them or whatever, they're gonna buy it. They don't care how much it is. They're gonna go try the new restaurant. Um, and and, you know, and it that doesn't really matter on income. Like, somebody could be more income

Kim 33:21 and still be willing to do that because it's not a risk to them. It's worth spending that money on it. Where a hesitator is like, oh, if I buy this and I don't like it, I've lost this money, and I could have used it someplace else or I could have done something else with it. And it doesn't mean that hesitators are cheap or that they're low income. It's just where they place their value on something. Right. Um, you know, like like I said, I go out and I'm, like, always buying the new things, and I'm trying the new restaurants and trying the new cocktails. And it's like, if you hate something, I mean, I'm pretty much gonna consume whatever it is. It's I'm just not gonna turn me off that much. But, like, a hesitator would be like, oh my gosh. I just spent $10 on this drink, and I don't like it. And I am not going to drink it because it tastes so bad or or whatever. Right. So it's really it's, you know, it's just one more element of understanding about flavor finders and where their risk tolerance is. Um, you know, very low risk for a hesitator, uh, or a sorry. Very low risk for a trailblazer. Hesitators have a very high risk tolerance. Gotcha. And and that's also could be something from a food perspective, but someplace else, they might be willing to risk. You know? Like, maybe they will go buy the new technology thing Right. Even though they're a hesitator when it comes to food.

Matt 34:40 The box very specific to food. Yeah. Yeah. Yeah. Yeah.

Stephanie 34:44 That actually, um, brings up one of the questions we had for you, which was, like, we, you know, we we talked to folks from a lot of different industries. Uh, we love when we can find ideas or concepts that have legs for cross pollination. Right? So what when you think about flavor finders, uh, do you see potential for this framework to be applied beyond food and beverage? Say, in, uh, wellness or supplements or, um, you know, beauty, uh, anything. Uh, are there are there universal truths here that might apply outside of the category?

Kim 35:24 I think so. I mean, you know, we'll ask in our surveys, we will ask different usage and attitude questions, and you can see differences and that sort of thing. We actually do have another segmentation that we put together, um, around health and wellness finders. So it's kind of exploring that health and wellness space. Interesting. Um, and we've overlaid flavor finders with it, and there's a lot of similarities. You know? Um, there's a lot of similarities with the people who are really active and engaged in the space versus people who are not. Um, you know, I think the one thing is I think the difference is in some of these things where you kinda have all you have all of the people represented in flavor finders. Right? You every single one of them are eating food and beverages. and find see something and it's $10, they're gonna and it's brand new or it intrigues them or whatever, they're gonna buy it. They don't care how much it is. They're gonna go try the new restaurant. Um, and and, you know, and it that doesn't really matter on income. Like, somebody could

Matt 33:20 be more income

Matt 34:40 The box very specific to food. Yeah. Yeah. Yeah. Yeah.

Kim 35:24 I think so. I mean, you know, we'll ask in our surveys, we will ask different usage and attitude questions, and you can see differences and that sort of thing. We actually do have another segmentation that we put together, um, around health and wellness finders. So it's kind of exploring that health and wellness space. Interesting. Um, and we've overlaid flavor finders with it, and there's a lot of similarities. You know? Um, there's a lot of similarities with the people who are really active and engaged in the space versus people who are not. Um, you know, I think the one thing is I think the difference is in some of these things where you kinda have all you have all of the people represented in flavor finders. Right? You every single one of them are eating food and beverages. people who are really active and engaged in the space versus people who are not. Um, you know, I think the one thing is I think the difference is in some of these things where you kinda have all you have all of the people represented in flavor finders. Right? You every single one of them are eating food and beverages.

Stephanie 36:06 Right.

Kim 36:06 And, um, but the way they interact with flavor is different. Where with something like the health and wellness space, we found out, like, 50% of the population does not care about any of that stuff. I see. Right? Right. So that is a big that was like, okay. That's a big moment because, again, everybody thinks like, oh, everybody wants it. No. 50% is like they don't even care. Right. But within those other segments, sure, like, our, you know, our more, um, active people are definitely falling in more into that trailblazers and that investigators spot. But then, like, the the one, which is more of our, like, our reactors, that could be trailblazers and investigators just because they just it's not as much on their radar or whatever. So I think you you see a lot of tendencies that kinda mirror what FlavorFinders is, but there can definitely be some exceptions to it. Um, but, you know, I definitely think there are overlaps, um, with other segments and kinda how they interact with other things in their lives. Not everything, but definitely some some categories you could see some similarities.

Stephanie 37:10 Yeah. Interesting.

Matt 37:15 Um, something that I was wondering about, um, and it's especially in the context of kind of reading I love that you guys do this, but reading the blog posts of people from within your org who identify as a segment and then they write a a blog post that's like a a day in the life of a hesitator or whatever really brings it to life in such a compelling way. And it made me, you know, really think about the role of storytelling in, um, in Flavor Finders. Um, do you have, like, an example where, uh, the segmentation uncovered maybe a counterintuitive insight that reframed a story a client was telling themselves or a brand that you work with about their target audience? people who are really active and engaged in the space versus people who are not. Um, you know, I think the one thing is I think the difference is in some of these things where you kinda have all you have all of the people represented in flavor finders. Right? You every single one of them are eating food and beverages.

Stephanie 36:06 Right.

Stephanie 37:10 Yeah. Interesting.

Matt 37:15 Um, something that I was wondering about, um, and it's especially in the context of kind of reading I love that you guys do this, but reading the blog posts of people from within your org who identify as a segment and then they write a a blog post that's like a a day in the life of a hesitator or whatever really brings it to life in such a compelling way. And it made me, you know, really think about the role of storytelling in, um, in Flavor Finders. Um, do you have, like, an example where, uh, the segmentation uncovered maybe a counterintuitive insight that reframed a story a client was telling themselves or a brand that you work with about their target audience? way. And it made me, you know, really think about the role of storytelling in, um, in Flavor Finders. Um, do you have, like, an example where, uh, the segmentation uncovered maybe a counterintuitive insight that reframed a story a client was telling themselves or a brand that you work with about their target audience?

Kim 37:58 I can't think of any you know, I I can't think of anything super specific. Like I said, they've shared segmentation before, and I think being able to overlay some of what we know about flavor finders and how that really, um, overlaps and really complements what they have can really, like, at least get them focused in. Like, they can sit here and say, like, oh, this consumer is really adventurous, but they still think that the consumer is adventurous. They think it's a broader, you know, reach or whatever. And then we could say, okay. But, yes, this is adventurous, but that is also what that means. It's only 14% of the population when it comes to flavor kind of thing. Um, I really think the you know, where we've seen, like, such the shift is when you look at like, for instance, we do a a fit and appeal study. We have a fit and appeal program that we've been running since 02/2011. We refresh it every couple of years, and we do it in the nonalcohol and the alcohol space. And for years, like, I've looked at the data, and it's like, oh, we kinda bucket the flavors, and we kinda have our mainstream flavors. We have our emerging flavors, and we have our niche flavors. And that's looking at how appealing it is and how well it fits with the category. And that's kinda what we've, you know, used a lot of our foundational, um, flavor inspiration for. And then when we overlaid flavor finders with that, the story just completely started to change. So when you looked at, like, those that mainstream bucket, for instance, and there might have been 20 flavors in that, when you overlay flavor finders, there were only, like, six flavors that showed up for hesitators. And if you just stopped at that mainstream, you and your target's a hesitator and it wasn't one of those six that you launched and it failed, that's probably why. Because you think it's mainstream, but it's not mainstream for hesitators. So I think that has been you know, we've seen that happen a lot where a flavor or a flavor doesn't do well, maybe it falls into niche, and your target's a trailblazer, but it shows up as, like, one of their top flavors. And you would miss that if you were targeting a trailblazer. Like, that would be how you would pull that trailblazer in. So I really think that that that's probably the best example that I have of kind of how we've seen just how the data has shifted over the years by incorporating flavor finders into our surveys.

Matt 40:05 Yeah. That makes a lot of sense.

Stephanie 40:08 Do you have any plans for a flavor finders two point o? You know, is there are there any, uh, planned iterations, bringing in different data sources, um, changing the approach up at all, or or is the next rendition of FlavorFinders just gonna be, you know, kinda staying steady, Eddie, and and keep getting that longitudinal view. way. And it made me, you know, really think about the role of storytelling in, um, in Flavor Finders. Um, do you have, like, an example where, uh, the segmentation uncovered maybe a counterintuitive insight that reframed a story a client was telling themselves or a brand that you work with about their target audience?

Matt 40:05 Yeah. That makes a lot of sense.

Stephanie 40:08 Do you have any plans for a flavor finders two point o? You know, is there are there any, uh, planned iterations, bringing in different data sources, um, changing the approach up at all, or or is the next rendition of FlavorFinders just gonna be, you know, kinda staying steady, Eddie, and and keep getting that longitudinal view. e any plans for a flavor finders two point o? You know, is there are there any, uh, planned iterations, bringing in different data sources, um, changing the approach up at all, or or is the next rendition of FlavorFinders just gonna be, you know, kinda staying steady, Eddie, and and keep getting that longitudinal view.

Kim 40:32 I think it's still continuing to figure out how where else we can incorporate it. Um, I think that, um, as far as the segmentation itself goes, you know, I think we're we're pretty confident in that, and that's kinda kinda be, like, our our framework that we're gonna continue to use. I think continuing to explore the segments definitely would be, um, interesting. Like, we'd, uh, did a deep dive into followers because I considered them very noisy when I was looking at the data. They're, you know, they're right they're just a little bit more risk or less risk adverse than hesitators, but they're kinda along that line. And I would start seeing, like, they liked every they either liked everything or they were neutral to everything. And I'm like, this is just weird. Like, they would like a weird flavor that a trailblazer wouldn't like. And I'm like, yeah. I don't think that's gonna line up. So we actually did deep dive into them a little bit more with some journaling and IDIs, and we, you know, we found out that they're noisy. That is that noisy group that everybody worries about, and they're the ones that say one thing on paper, but what they actually do is completely different. So by digging deeper into them, like, that really helped us get a little bit more perspective on that. You know? I'd love to do that with all of our segments. Yeah. Um, I'd love to explore the kids thing. Um, I'd love to explore the you know, how can we incorporate this into other data points and and secondary pieces of information that we find and that sort of thing? So I think there's definitely ways to make it bigger and broader and, you know, more, um, more ways that it can apply. Um, but I think the framework, I think we're pretty we're pretty we're pretty confident in that framework, and I think that'll kinda continue to stay as is.

Stephanie 42:06 I love that. That that's like the beauty when you get a a good segmentation framework to stick, right, is that then you can just focus on that deep learning and really developing your understanding. I think that's really, really exciting.

Kim 42:18 Thanks. Absolutely.

Matt 42:20 Well, uh, Kim, to kinda wrap us up here, uh, there are a couple questions that we always like to ask our guests. Um, the uh, is around sharing some of your wisdom with the next generation. Do have a piece of advice that you'd offer to someone who's kinda just starting out in the world of consumer insights? e any plans for a flavor finders two point o? You know, is there are there any, uh, planned iterations, bringing in different data sources, um, changing the approach up at all, or or is the next rendition of FlavorFinders just gonna be, you know, kinda staying steady, Eddie, and and keep getting that longitudinal view.

Kim 42:18 Thanks. Absolutely.

Matt 42:20 Well, uh, Kim, to kinda wrap us up here, uh, there are a couple questions that we always like to ask our guests. Um, the uh, is around sharing some of your wisdom with the next generation. Do have a piece of advice that you'd offer to someone who's kinda just starting out in the world of consumer insights? 2:20] Matt: Well, uh, Kim, to kinda wrap us up here, uh, there are a couple questions that we always like to ask our guests. Um, the uh, is around sharing some of your wisdom with the next generation. Do have a piece of advice that you'd offer to someone who's kinda just starting out in the world of consumer insights?

Kim 42:39 Um, keep the curiosity and and always be critically thinking, I think. You know, I I mentioned earlier about how I'm constantly trying to figure out how to ask a question that is not just and being able to somebody give a socially acceptable or an aspirational answer. I also always try to think, like, what is this going to give me? Like, if I ask this question, how do I use this? How is this actionable? You know? So that is always how what I keep in mind when I'm designing questions and almost more importantly, answers. Um, you know, if you use a scale for your answer or it's a yes or no or whatever, like, how does that what story can you tell with that data? If you can't tell a compelling story with whatever result you're gonna get back, then that probably is not the right scale or question or comedy you know, whatever. Yeah. Um, but that's always, like, kind of the lens that I'm looking at is to really try to and I my marketing team is constantly, like I'm always pushing back on that. I'm like, well, what are you gonna do with the data?

Stephanie 43:37 Mhmm.

Kim 43:37 Like, so you find out that, um, you know, that nobody's consuming, um, a certain category because they don't like the flavors. Well, okay. But alright. But what specifically you know, what's specific about that? Um, so it's just I think that would be my biggest piece of advice.

Matt 43:56 Yeah. Love it.

Stephanie 43:57 I think it's great advice. Like, yeah, starting starting with the end in mind and driving towards, you know, a particular goal in your data structure is just, like, spot on. I think that that nails it. Um, my my question the question I always ask you've mentioned a couple times that you do not have a crystal ball. I'm sorry. Um, but, uh, I need to ask you to look into your crystal ball and see, you know, from your perspective, you know and and it's interesting too from from your your, uh, perspective at Givaudan in this very, um, front end innovation, uh, space. What do you see, uh, changing in the consumer insights world over the next few years and how brands are using insights to drive strategy in the face of of everything that's that's changing in our industry right now. What, um, what concrete things do you think are are are coming our way? 2:20] Matt: Well, uh, Kim, to kinda wrap us up here, uh, there are a couple questions that we always like to ask our guests. Um, the uh, is around sharing some of your wisdom with the next generation. Do have a piece of advice that you'd offer to someone who's kinda just starting out in the world of consumer insights?

Stephanie 43:37 Mhmm.

Matt 43:56 Yeah. Love it.

Stephanie 43:57 I think it's great advice. Like, yeah, starting starting with the end in mind and driving towards, you know, a particular goal in your data structure is just, like, spot on. I think that that nails it. Um, my my question the question I always ask you've mentioned a couple times that you do not have a crystal ball. I'm sorry. Um, but, uh, I need to ask you to look into your crystal ball and see, you know, from your perspective, you know and and it's interesting too from from your your, uh, perspective at Givaudan in this very, um, front end innovation, uh, space. What do you see, uh, changing in the consumer insights world over the next few years and how brands are using insights to drive strategy in the face of of everything that's that's changing in our industry right now. What, um, what concrete things do you think are are are coming our way? front end innovation, uh, space. What do you see, uh, changing in the consumer insights world over the next few years and how brands are using insights to drive strategy in the face of of everything that's that's changing in our industry right now. What, um, what concrete things do you think are are are coming our way?

Kim 44:49 I I mean, it's all we it it just keeps getting faster and faster and faster. Right? We need to be able to make decisions quicker. We need to be able to get insights quicker and all of the things. And I think with AI and and and that sort of thing, that's gonna be even more of the expectation. I think that AI is going to maybe help us be able to come up with ideas, whether it's survey ideas or question ideas or concepts, you know, in the case of our customers, to kind of start getting, like, a framework and, like, a good foundation, but I think you're always gonna still need to figure out how to incorporate the actual consumer plug for research so that it doesn't go away.

Stephanie 45:27 Um,

Kim 45:27 but, you know, there's still something to be said for having somebody to, like, think of all of the things. Like, when we're looking at data, we're not just looking at this data point. We're considering multitude of data points and how they interact with each other. So I think having that human element is still gonna be there, but I think the the AI is just gonna be able to help that happen quicker and also find themes that you just don't you can't find because there's just no way that you can look at the data and all the different options. But you still have to have somebody thinking about how do I wanna look at the data and typing in that question, and then it spits out the answer really quick versus you looking at all the tables and creating new tables and all of that.

Matt 46:06 Synthesis. Yeah. Yep. Yep.

Kim 46:08 But yeah.

Stephanie 46:09 Yeah. No. I think that's a that's a great call out. We've we've noticed, uh, sort of a theme of, um, more of an openness to embracing, you know, what we're calling today AI tools for that generative thinking, like, using it as as inspiration for, um, uh, coming up with new ideas, new concepts, um, answer options as long as, you know, you you are using your critical brain in the background. Like, there's I definitely see things moving in that direction.

Kim 46:39 Yeah. I don't think it's going anywhere, so we gotta fig we gotta figure it out. Right. I'm a little slower on that adoption curve, but I'm getting there.

Matt 46:47 It's a journey for sure. Well, Kim, this has been an excellent conversation. I have to tell you, I'm gonna go, uh, type myself using the Flavor Finder styling tool and find out what I am. So I will I will let you know. Um, Yeah. Thanks so much.

Kim 47:06 Thank you for having me.

Stephanie 47:08 Thank you. front end innovation, uh, space. What do you see, uh, changing in the consumer insights world over the next few years and how brands are using insights to drive strategy in the face of of everything that's that's changing in our industry right now. What, um, what concrete things do you think are are are coming our way?

Stephanie 45:27 Um,

Matt 46:06 Synthesis. Yeah. Yep. Yep.

Kim 46:08 But yeah.

Kim 46:39 Yeah. I don't think it's going anywhere, so we gotta fig we gotta figure it out. Right. I'm a little slower on that adoption curve, but I'm getting there.

Kim 47:06 Thank you for having me.

Stephanie 47:08 Thank you.

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