In this episode of the Greenbook Podcast, host Karen Lynch engages in a compelling conversation with Nick Graham, the SVP Global Head of Insights and Analytics at Mondelez International. They dive into Nick’s extensive career journey and his pivotal role in leveraging data to guide strategic decisions at Mondelez. Nick discusses the delicate balance between technology and human expertise in generating actionable consumer insights. He also shares innovative ways generative AI is being used to boost efficiency and creativity within the insights team. For professionals in the field, Nick offers invaluable advice on networking, career growth, and adapting to the ever-evolving landscape of insights and analytics. This episode is packed with practical tips and forward-thinking perspectives that promise to enrich your understanding of the industry’s future.

You can reach out to Nick on LinkedIn.

Many thanks to Nick for being our guest. Thanks also to our producer, Natalie Pusch; and our editor, Big Bad Audio.

Transcript

Karen: Hello, everybody. Welcome to another episode of the Greenbook Podcast. It’s Karen Lynch. I’m happy to be hosting today, and I’m happy to be talking to our guest, Nick Graham, who is the SVP global head of insights and analytics at Mondelez. I’ve actually known Nick for a few years because for a while there I was privileged to facilitate a few meetings at PepsiCo when he was there. I recently saw him at the insights event at Yale, a fellow resident of my state of Connecticut. Nick, welcome to the Greenbook Podcast.

Nick: Great to see you again. Thanks so much for having me on.

Karen: My pleasure. My pleasure. So, you know, I could introduce you and talk a little bit about your bio, but I always find that our guests can introduce themselves better than I could [laugh] possibly introduce you.

Nick: [laugh].

Karen: Tell everybody who’s listening a little bit about your role.

Nick: Sure, absolutely. And it’s always better for me introducing myself because people always overinflate it when they introduce me. And then I feel like I’m always gonna underdeliver and un-impress people so I can do my own British version of my introduction.

Karen: Excellent. Excellent. Please go ahead.

Nick: So, as you said, so Nick Graham, I’m the global head of insights and analytics for Mondelez International. So, for anybody listening who doesn’t know the name—because we’ve been around ten years, but some people might not be familiar with the company itself—but we’re the people who own Oreo, Cadbury, Milka, Toblerone, a lot of your sort of favorite cookies, crackers, chocolates, candy brands. I’m originally from the UK, started my career in advertising planning, brand innovation consulting, and then I spent the last, I guess, more than a decade in corporate insights and analytics roles. So I led insights and analytics for PepsiCo, for their global beverages business and then for the US business. And then the last three years, I’ve been leading insights and analytics for Mondelez. And two big things that I do in my job—because people often ask me, like, what do you do every day. It’s a good question. Not just my boss, but other people will ask me, what are you doing every day. But I think—I mean, there are two big things that I’m doing in a global insights role. One is, obviously, you know, I have the privilege of sitting at the nexus of all of our teams. There are 350 people in our business who are working in the markets. So big part of my job is sort of aggregating, bringing all of that together for our global enterprise team, for our CEO, and our leadership team, to help them understand what’s happening with the consumer and the shopper across the world in our categories, and what does it mean for us in terms of how we grow? And then, just as importantly, the other part of my role is how do we continue to advance and transform the function? So it’s understanding the right tools, the right capabilities, the right talent, the right skills that we need to continue to be the very best that we can be as a function. So it’s a good—it’s a good balance. A good balance of, like, the what and the how. Right? And so, yeah, it’s a good combination.

Karen: Yeah. You know, one of the things you said in that is, you know, kind of bringing it all together.

Nick: Mm-hm.

Karen: Talk to me a little bit about, about that part of the job. How do you bring it all together? What are some of the practices that you have in place to wrangle all of the insights and analytics you’re taking in or your team is taking in?

Nick: Yeah, I mean, it’s obviously, it’s an art and a science. Right? Because obviously, we have, from a—from an analytical perspective, we have a lot of models that we’re putting on top of everything that the markets are doing to synthesize it up and to say what are the biggest opportunities, either in terms of dollar value or in terms of incremental for the organization. And obviously, you know, we have a really great data infrastructure now that we have built over the last few years to really make sure that we have as much of the data as possible in one place. Right? I can go into Marco, which is our in-house data platform, and I can find at a glance what’s happening with our categories, what’s happening with penetration, what’s happening from a social listening perspective across all of our different markets, and as much as possible, aggregate it up. Where I said it’s an art and a science is, of course, because it’s—particularly when it comes to things like what’s happening with consumer behavior, what’s happening with the categories, a lot of that is not harmonizable because it’s lots of different perspectives. It’s lots of unstructured data. And so that’s where, you know, I really rely on the great leaders in our insights teams in the markets to help me synthesize it and help our team synthesize it to say, “Okay, so from all of this, what are we actually seeing in terms of patterns?” So there’s a certain amount that the technology and the automation and the analytics can do. But then, you know—and we’ll talk a lot about this—doesn’t replace the human intelligence. I really believe in the power of the two together. Right? So the human intelligence is what can really surface up the patterns, the picture, the narrative that, as of now, no machine can really deliver in the same way.

Karen: So much to unpack. So I just want to stay with that thought for a minute about the human intelligence and what it can do. Because I think part of what you’re talking about, you know, wrangling all the information, synthesize it, taking in all of the different data points, ties into a question I have about both the insights and analytics aspect of your team. Those are different mindsets. Right? And I think we—I talked about this with some folks at the event at Yale that we were both at. You know, there’s something about the insights function, the traditional consumer insights, the waiting for the ah-ha moment that happens through, you know, the type of work that you do, whether it’s qualitative work or even quantitative, you know, survey work. Like, you get this feeling. Right? But the analytics side of things, whether it’s looking at shopper data or any other kind of trend data, it’s not quite the same. So how do you manage the fact that the people on your team must have different skill sets and different ways of looking at the data—all human beings, but different definitions of the type of human thinking that’s going into that work? To me, that feels like a huge challenge on the job.

Nick: It’s interesting. I don’t see it as a challenge. I really see it as—well, first of all, I think the whole insights versus analytics and qualitative versus quantitative division, I think is a little overstated. Let me explain what I mean by that. Because what I have across the team, regardless of your skill set, is I have people who are really good at unlocking or unpacking what’s happening in data. It’s just the data is different, and how they go about unpacking that is different. Some people are, as you say, using quantitative data, using modeling. Some people are using qualitative data, and they’re applying. So they’re applying different structures and models on top of that. But we’re all essentially trying to hunt through data at different times to try and find out what that ah-ha is. So it’s interesting, you know, some of the best insights I see are not from what you would classically call the consumer insights function. It’s actually from our analytics team, because they’re hunting through the data and like, “Hey... there’s a really big opportunity here that we’re seeing or a really big disconnect that we’re seeing.” That’s a great insight. It may not be what we classically think of as an insight, but to me that’s just as insightful. And likewise, my insights team use—I’m going to use a lot of air quotes when we talk about this.

Karen: [laugh].

Nick: My insights team is hunting through data, structured, unstructured data, to look for patterns, which to me is really just—it’s a different type of analytic. So, I think stepping back for a second, I’m not sure the division is as clear as we sort of have thought it was. And then what I’ve really, really come to appreciate, I think particularly over the last, you know, decade or so, incorporates—in this corporate role, is you really need that blend of skills. Yes, there’s tension for sure, but there should be. You should always have good creative tension. You should always have people with a different set of skills. I think what I found really helpful to think about, though, is—I was talking about this the other day. You need to think of insights and analytics as a team sport, and you need people with different skills. Right? Just if you’re running a relay race, right, you’ll need—you need someone who’s really good for the first hundred meters. You need someone who’s a brilliant closer. And just like that, you need people with different skills and capabilities who can pick up different pieces, but who are really good at working together. Now, that’s obviously a mandatory. They’ve got to be able to connect with one another. And so, you know, I need people on the team who are deep technical experts at advanced analytics and can find patterns in data and can work with data scientists to, you know, unlock what’s in our structured data sets of all different kinds. But I also need people who are behavioral scientists. I need people who are really attuned to what’s happening with consumer behavior. And then I also need people who can translate it into the business. Very rarely, you might find someone who’s really good at all of those. But in reality, what you need is you need a SWAT team of people who can bring all of that together. And then I just need to be clear, and I think, you know, I’ve talked about this a lot recently. I think as long as you’re clear on roles and responsibilities so—and not everyone will always lead, and it won’t always be the same person who leads. But as long as you’re clear on your job here is to be the translator. Your job here is to be the brilliant expert at the data. Your job here is to be, you know, the translator of human behavior. If that team can really work brilliantly together, that to me, is magic. Like, that can unlock all of this—all of the hidden insight that is there. You can really get to the heart of it and then translate it into action, which is the other part of—to me, with insight is it’s all very well for, you know, you and me, Karen, to sit and go, “Hey... we’ve got a great insight,” but if the business doesn’t see it and act on it, then it’s like the tree fell in the forest, but nobody heard it. It was an insight, but nobody used it. Was it really an insight? So really translating that into action is a whole other skill set that we need to help bring part as team.

Karen: I do want to get into that in just a minute, but I want to hover on the team for a little bit because what’s interesting is I—you know, you know, full disclosure, I kind of, kind of grew up in Westchester. PepsiCo headquarters were a big deal in my, again, my, my orbit, right as I was, you know, going through school and then going to college and, and it was like in the area, there was this feeling of Pepsi being like a gold standard company in that they hired very sharp individuals.

Nick: Mm-hm.

Karen: There’s a sharpness on teams at PepsiCo that-you know, it’s almost like ‘first in class’ type of employees, which, you know, that would be the way I would summarize the type of individual that works at PepsiCo.

Nick: Yeah, yeah, yeah.

Karen: Is there a similar kind of comparison or vibe for the people, I’m sure equally talented, but with a different type of panache, if you will, that, that you have at Mondelez?

Nick: Yeah, I would say similar level of sharpness, right, in terms of talent and capability. One difference, I think—and I can’t—I’ve never—I haven’t quite worked out why this is, but the culture of Mondelez is a little bit more communitarian or collective than PepsiCo was. And, you know, it’s not a stereotype either company.

Karen: Yeah.

Nick: But PepsiCo, you get a lot of very A-type people who are like, “I am going to be the very best at what I can do and I can work with other people. I am going to solve this problem.”

Karen: Yeah.

Nick: What I also like, though, about Mondelez is because you’ve got a bit more of that—whether it’s because it’s a bit more global in scope, and so you’ve got, like a lot more kind of cultural diversity. PepsiCo obviously was very—again, it’s a global company, but as you say, very—it was a very New York, Texas company, right, in terms of its profile, in terms of where the weight of the business was. So maybe there’s a US dynamic to it. I’m not sure. But what’s interesting with Mondelez culture is you just get this sense of a more sort of collective sense of we all have to work together to solve this. And I do think this recognition that—and we’ve talked about this a lot as a team in the last year of recognizing that you’re not on your own; you’re actually part of this ecosystem. So I don’t need everybody in the team to be the best at everything. What I need is you to be really, really good at a couple of things and really, really good at then connecting and working with people. We were talking about this actually with our Europe team last October. We had a big off-site together with our Europe based insights team. And it was really exciting then—that sort of light bulb moment. You talked about the light bulb insight moment, that light bulb moment where people are like, “Oh, actually, I don’t have to know everything about AI. I don’t have to know everything about the category, I don’t have to know everything about the latest advances in behavioral sciences. I need to know the thing that’s really important to my job, and I need to know and trust,” and obviously trust is a big part of it, “that I can rely on my extended team, whether they sit in my direct team, whether they sit in the global team, wherever they sit, but that’s my support network. Right? Those are the people I need to go to.” And it’s okay for you to say, “Actually, I don’t know about AI, but I know who to go to. I know who is the person who’s going to help me answer that question.” And I’ve been thinking about this more and more because it is interesting—just it partly came out of that event in Europe, but it just struck me how much we are asking of insights and analytics professionals these days because of the huge transformations in the industry, because of first party data, because of—like it’s just, and, and, and, and. and. And it’s just not possible anymore, I think, for you to know everything. You, you really do need to think about this as a—again, like an ecosystem of really fantastic experts and specialists who can work together as opposed to, you still need—there are still some generalist roles, but again, those generalist roles tend to be translators into the business. But really reframing, I think, insights and analytics and recognizing it can be, again, like a collective of specialisms I think might help us rethink about how we can continue to advance the functions; opposed to, you know, even when I was at Pepsi—it’s part of the reason I took this job to transform the function. But a lot of the questions I was getting was, “Should I take a specialist track, which is going to be slow and probably going to get stuck, or do I take a generalist path, which may not be what I want to do, but is probably the path to progression?” And I kind of feel more and more we’ve gotten that wrong, because actually, you need people who can continue on a career path as a really fantastic specialist, and you need people who can be—you know, generalist business interpreters may not know everything, but they know how to connect the team together to get something done, and that those are just as valid and just as important career paths. So that’s, I think, my view on the skill sets you need and how they work together has changed enormously in the last few years.

Karen: Yeah. By the way, I love this. Everything you’re saying, you know, I just find myself nodding and saying, “Yes. That’s very astute.” So thank you for sharing all that. You know, a question that I have is, you know, you mentioned kind of within an organization, whether it’s PepsiCo or Mondelez or any of the other kind of large organizations that might have tangible career paths, where people can pick and choose from within an organization. A lot of our audience, they may work at smaller firms. They may not have as many clear options to choose generalist or specialist, or, you know, they’re just kind of in a broad function. And I’m wondering, you know, thinking about what you’re saying, what would be some sort of career advice for them in an era where we are expecting a lot of people? What kind of wisdom might you impart to somebody who doesn’t have the support of an organization like yours?

Nick: [laugh] Completely. Completely. No. And I think—I think one thing—I’ll come and answer your question in a second, but I think one thing that really struck me—and, you know, it came out of things like, you know, our conversation at Yale as well, is even if you’re in a small team, even if it’s, you know, I know, some insights and analytics functions with literally a team of one or two. Right? I think just remembering that even in that situation, you’re not on your own even in the organization because there are other people in the organization who are either great data scientists, great insightful thinkers. So thinking about your network of people you can rely on beyond just the people who are called insights and analytics, whatever organization you’re in, I think is a good start point. I mean, I will say, just as an example, you know, even within my team, obviously, I’m very lucky that we have a big insights and analytics organization. But also some of the people we really rely on are our IT team because they obviously bring—they may not bring the same expertise, but they bring a deep understanding of how we can execute some of the changes. And also, yeah, some of my most insightful people are not—they’re not even in marketing. They might be in sales. They might be in finance. You know, some of my finance and supply chain friends are some of the most insightful people and actually some of the most consumer centric people. So, again, just thinking about your support network as broader than just the, the direct people that you work with day to day. And then of course, that’s why we have industry forums. That’s why we have conferences. That’s why we have things like, you know, SMR and ARF and IIEX and all of these things is to build your broader network out. So a lot of the time that I have spent over the last couple of years has also been, you know, helping people in, you know, noncompetitive companies, but sharing advice about how this is what you’re doing with GenAI. Oh, that’s really interesting. Like, what can we learn from that? And vice versa. So just knowing that your network isn’t just your box on the screen or the people you work with every day but thinking about how you can expand that.

Karen: Yeah, mindfully expand it. It has to be—

Nick: Exactly. Exactly.

Karen: It has to be deliberate, mindful action. You’re not just going to an event to make a sale. You’re not just going to event—

Nick: Correct.

Karen: —you know, to learn.

Nick: To learn. Right? Yeah.

Karen: You are there to nurture your own, you know, posse, your own community of people—

Nick: Correct.

Karen: —that will support you for the entirety of your career. I feel like one of the things that shifted largely during COVID is just networking in general.

Nick: Hmm...

Karen: People don’t necessarily know how to do that anymore. And I age myself when I say that was, you know, a big part of all the career advice when you were younger.

Nick: Yeah, yeah. You’re right.

Karen: When I was younger, you know, it was network, network, network. And, you know, there was a lot of training around that. Whereas now I think it needs to be much more organic. We’re not trying to network. We are trying to just build relationships with other human beings who are on the same journeys as we are or parallel journeys. 

Nick: Exactly. And I think even more so post COVID is there’s a need on all—everybody’s side. And so I think recognizing that this isn’t a one way street. I think everybody is looking for that network that’s even harder than ever to create. But also that, you know, regardless of where you’re coming from or what your experiences are, you have—you have something to give as well as something to ask for. So, you know, some of the—I was talking to somebody a while ago, you know, more junior in the industry but has really great experience in generative AI. And it’s interesting. They came to me saying, “I’d like some advice on my career and, you know, where I should go,” and, and felt bad that they were asking me and, like, you know, for my time. And I said, “Well, no, this is a two-way street.” Like, I think most good mentoring is, actually, I’ll be selfish in return and ask for your help and advice on the topic area that they’re really passionate about and have a lot of experience in. So it can really become this two-way street. But it does mean, as you say, sort of going into networking, thinking, I don’t—not just I need to make a sale or I just need to further my career but thinking about it as a long-term investment in a relationship and knowing that that means giving as well as receiving from that person as well.

Karen: Yeah.

Nick: And, you know, you asked me about a piece of—a piece of advice as well, and I think the best advice that I ever got from a mentor was—it’s funny. This is decades ago now, but it was such a revelation—was around really focusing on what you’re good at and what can become your real superpower. And I remember thinking, “God, that’s a real”—because first of all, I spent our entire lives, right? Saying, like, what are we really bad at? Oh, my God. Like, you know, when you’re doing, like, your, you know, your personal development. It’s always the stuff that you’re bad at that you focus on. You’re like, great, great, great. Tell me what I’m bad at. Like, thanks.

Karen: Yeah. Yeah. Yeah.

Nick: But to me, the pivot was, right, to say, okay, you need to know that. You need to be humble about that. You need to know what of that you need to fix or, or surround yourself with your network to help you compensate for that. But what are you really darn good at? Like, what are you passionate about? And it’s funny, whenever I do career conversations with my team or with mentees, the first thing that we talk about is, like, what actually excites you? What do you enjoy doing? What do you really—what gets you out of bed in the morning? What do you think you’re absolutely outstanding at? And it could be—it could be managing people. It could be influencing. It could be a technical skill. But, like, what is it that really motivates you? And how do you turn that from being a thing you do into a thing that you’re famous for that people come to you for? Like, that, to me, is a really powerful opportunity, I think, for people. Now, again, you need to know how to solve for things that you’re not so good, so it’s not a—a neglecting that, but it’s also not neglecting the fact that there are things that you can do that probably nobody else can do, and that’s just as important as the stuff you’re not good at.

Karen: Yeah. And probably when you’re—if you’re on a career journey, you know, those, those first ten years in the work world and you’re figuring a lot of that stuff out, trust that someday you will perhaps be on a team where—for example, if you’re lousy at Excel spreadsheets but your team is actually quite good at them, there is no shame in saying, “Can you help me with this task” for—

Nick: Completely. Exactly.

Karen: That’s a small—a small task and a bit of an inside joke because I am queen at messing up a good excel spreadsheet if I’m not careful. So it is a much [laugh] easier thing for me to say to somebody else, “Could you just—let’s just not even like beat around the bush here. Not my strength, but it is yours. So, you know, let’s, let’s account for that.” And there is nothing wrong with being vulnerable and admitting something, in my opinion, and admitting something that somebody else can do better.

Nick: Completely, completely agree. And, you know, obviously there are things—sometimes things we just have to do because that’s what we have to do as part of our job. But, as you say, there’s no shame in saying I’ve really tried it, that I’m just not good at it. Therefore, I need to find someone who can either help me or I can delegate it to or can be my support person. Like, that’s absolutely fine. You know, and I was talking recently with somebody on my team about presentation skills and, you know, how you tell a really compelling story. And we’re using this example to say, well, you don’t have to do it all on your own. Like, go and ask for help. Go and ask for somebody to give you feedback on something outside the meeting. Like, it’s not like just click your fingers and be a better presenter. But, you know, what we were talking about is if in time you discover that’s really, really, you hate doing it and it’s really, really not what you’re good at, then you need to think about, then, what are you good at that you then can then focus on instead? Because we don’t need everyone to be the same cookie cutter sort of like profile, but again, it’s going to lead you down to a different potential career path.

Karen: Yeah. Yeah. Well, I love that. I love all that advice. Thank you. I want to talk a little bit about something else that you had shared—a little bit of advice that you shared in your panel at the Yale Insights event, which was around a changing definition of work. I think it was actually you that had said part of the new role of insights and analytics is really to act as strategic advisors.

Nick: Yeah.

Karen: Driving change in an organization. I think your quote had to do with business comes first. Insights and analytics is second to that. I don’t know if you remember the spirit of that conversation. Can you expand on that for our audience? Because it was one of the most important things that I walked away from that entire event focused on and really thinking about. I’d love for our audience to learn from you on that.

Nick: Sure. I mean, I think that—and, you know, I don’t think I’m the only person saying this. I think you saw this in sort of Kantar’s Insights 2030 as well. What seems clear to me, and certainly the way we’ve tried to reshape our insights and analytics team over the last few years, is I think the ask from the organization is for insights and analytics truly to be the strategic business partners, the strategic thought leaders about where the category and consumer are going, and most importantly, what does that mean for the organization? What are the implications? And I think that really does shift the role and expectations then of the team. Because I can’t remember exactly what the quote was, but you’re absolutely right in terms of the spirit of it. But the spirit of it is we need to think of ourselves as strategic business partners first. Like, our primary job is to help the business grow, however big, small our business is. That’s our primary job. It’s to—now how we do it is we use—we use research. We use analytics. We use AI. We use—like, we use all of these. But they’re tools. They don’t define us. They just help us or deliver what we ultimately deliver, which is insightful strategic direction to the business on how to grow. And that sort of shift from us being defined by what we do to being defined by what we deliver and how we impact and influence the organization, I think that is—that’s a big mental shift. It’s a big mental shift for our teams. It’s also a big mental shift for the organization because—and you know, I still fight this. Not every day, but every other day, someone will come and say, “Oh, I need to do a piece of research, or I need to do a piece of analysis or whatever. I need to do segmentation.” I’m like, “I’m just going to stop you. No, you don’t. But we’ll tell you how we do it, if we—if indeed if we need to do any work.” The thing when you talk about is the business problem, is the business question. Right? And that’s a big mental shift is insights and analytics not thinking of itself as a research and analytics supplier to an organization or support to an organization but actually thinking about itself as an independent, objective, strategic voice. But ultimately its job is to drive growth in the organization. And then we just happen to have within our team people who are expert using those tools and capabilities to bring the right answers to the organization. And I remember somebody—it was a couple of months ago. Somebody said to me about—I can’t remember what the context was about the role of insights and analytics, and somebody had said, “Oh, our role is just to be the consumer voice.” And I was like, “Yes and no.” Yes, but yes, in order to drive growth. Just saying we know what the consumers thinks isn’t the an-, that’s—again, that’s just a delivery. That’s what we just have provided. But our added value is we then turn that into a meaning for the organization. We turn it into direction. Like, that’s our delivery. The consumer voice is just our unique way to do that, but it’s not our ultimate—the ultimate delivery.

Karen: Yeah. And I think this—the reason why I’m really glad we’re having this conversation here is that—that to me, it changes the conversation of the role because, yes, representing the voice of the consumer in C-level conversations is important, but it’s not just that. It is so much more than that. And that has become very clear. You know, we talk about transformation. We talk about, you know, driving growth. We talk about driving change. But the things that you really need to solve for at a very high level in an organization, it’s so much more than just representing the voice of the customer in those conversations.

Nick: Right.

Karen: It’s so much more than that. So that seems like a big mandate for insights industry professionals who may not feel that they have the chops to do that. Right? That might feel much bigger than what they’re used to and uncomfortable for some.

Nick: Yes. And I think going back to what we talked about before, I think it won’t be right for everybody. And that’s okay. I don’t need—and we don’t need everybody to play that role. Because again, I think within the ecosystem of roles that can exist in insights and analytics. Again, because we’re going—because we’ve got increasing need for specialism and expertise, if your passion is the research and your passion is really deeply understanding consumer behavior and what’s driving it—and maybe you’re not. You don’t feel comfortable that you’re the person who can then represent that and drive that into the organization. That’s okay, because this should still be a role for people in our organizations, in our agency partners, who are deep experts at the analytics, deep experts at the research because, of course, those are the foundation stones. We have to be brilliant at that, and we have to be even better than ever given dynamic consumer behavior, given digitization, given all of these things. It’s not that that problem or that opportunity has gone away. It’s just that I think on top of that, there need to be people in our team who really do feel comfortable being the translators, the influencers, the storytellers. And maybe they’re different. Maybe—I think where I’ve changed my mind is thinking that people always have to evolve to that. To say some people will just remain a really brilliant analytics or research expert, and that’s okay. That’s—that is they can evolve and develop in a different way. And maybe some of these other people, maybe they’ll evolve from insights and analytics professionals. Maybe they’ll come in from outside of the function or outside of the industry. And that’s—that is also okay because it’s not—again, I think we have to think about there are—just as with any function, there are lots of different roles and skills for everybody. And I think as long as you think about—it’s not a hierarchy, it’s a collective. Right? And just because you’re the one who’s the translator to the organization, driving the strategic thinking, doesn’t mean you’re any better than the person driving incredible research or the person doing the analytics. Because you know what? That person being the translator relies very heavily on the people who are doing the really powerful research and analytics as well. So, to me, it’s about thinking about the skills you need and putting them together in the right teams.

Karen: I love the concept that it’s not a hierarchy; it’s a collective. Just thinking about that just makes me just say yes. Absolutely. Isn’t that a great model for not just teams within an organization but all businesses? Right? Now, you are a collective of employees with diversity of thought in different skill sets, and that’s how you build a strong organization. So I love that. Thank you. I definitely want to make sure, before we end our talk, to talk about generative AI, because it’s come up a few times in this conversation [laugh] and I don’t want to not get there. You know, you’ve mentioned you’re using some of it. Talk to me a little bit about either initiatives that you are able to share, how you’re dabbling in it at Mondelez, how you’re fully, not even just dabbling, but all in. Like, what’s the—what’s the current, you know, current vibe there when it comes to—

Nick: Yeah. Well, first of all, kudos to us that we’ve managed to get through at least 30 minutes of conversation or whatever it is by the time we’ve cut it down without actually talking, really, about generative AI, although I prompted you a few times.

Karen: [laugh] I will pat ourselves on the back.

Nick: Exactly. Exactly. That’s progress, right?

Karen: Yes.

Nick: No, I mean, I think I’m really excited about the opportunity for generative AI for a couple of—couple of reasons. So we’ve sort of—we’ve said that there are three big ways of thinking about it. One is about efficiency and productivity. So how can it help us, both AI and generative AI? How can they help us be much more effective and efficient of what we’re doing today? So a big example of that is, you know, we have this big knowledge management system that we built over the last five or ten years. It’s where we house all of our agency reports. It’s links to our data ecosystem. So we’ve got this huge amount of data now. Up until now, it’s been a fairly simple search where I can search for something and try and find something tagging. This is transforming. You know, we’ve basically been doing a pilot the last six, nine months on the generative AI solution that now sits on top of it. And it is truly transformative because from ‘I can find things,’ it now gives you answers to things. It can write up a simple report that says, this is now what we know about what are the trends in dark chocolate; what’s happening. So just think about the amount of time and energy that somebody is putting in to try to find sometimes very basic information or what’s, you know, what we know about a particular topic. So the efficiency and productivity that that can bring is just incredible. Same goes on business performance and business reporting. Right? I mean, any of any of your listeners who are working in a, you know, a big organization, whatever type, the amount of time and energy that often goes into business reporting, describing what’s happening, you know, diagnosing what’s happening—again, we’re looking at some simple solutions that can sit on top of our data to say very quickly, here’s what’s happening; here’s why it’s happening; here’s where to go looking. So all of that time and energy that gets wasted writing a report, like, updating an excel spreadsheet, you know, explaining where to go look, like, we’ve just dramatically reduced that. So instead we can focus our time on what really matters, which is okay, so what now? Right? Where do I go from here? So that’s the productivity side. The second one is around generating insight. You know, we talked about this at the very beginning. So we’re piloting a few systems at the moment, AI systems, where we’re putting it on top of a ton of both structured and unstructured data that we have, just to see what patterns it can see, what opportunities it can unlock that a human being, well, couldn’t easily find because we’re, you know, combining a ton of different datasets together. So, for example, we’re looking at it in product development. So putting in a lot of our product development research that we’ve done over the last few years. We’re giving it, you know, here’s how everything scored; here’s how everything tested. We’re giving it our quality data, and we’re just asking it to try and find opportunity areas that we might not have seen. And already it’s coming up with some—now, there’s a lot of—there’s still a lot of chaff within the middle of all of it all, but we’re still finding things that are really interesting. And again, this is before we’ve spent time really training and nurturing the models. Which, by the way, back to career paths, there’s going to be a whole career path in AI in future, which is going to be around LLM, model training, interpretation.

Karen: Specific for our industry yeah.

Nick: Specific for our industry, absolutely.

Karen: Yeah. Yeah.

Nick: And then the last one is creativity. So we are doing a lot of pilots at the moment on generating particularly innovation, innovation ideas. So we’re feeding a model based on everything we know about all of the innovation ideas that we’ve tested. What’s tested well? What hasn’t? We’re feeding in some other information that we have, and we’re just asking it to start to come up with potential innovation ideas. Now, again, it doesn’t necessarily—it’s not the answer, and it’s not the end point, but it does give you, suddenly, 20 start points that you hadn’t even considered. And so it’s helping our teams not only be much more efficient, but it’s helping us, like, springboard to, oh, I wonder why it’s coming up with that idea. Like, maybe there’s something in that we can—that we can push forward on. So those sort of productivity, insight and creativity, I’m really excited about. I mean, you know, it’s still super early days, but I think there’s real potential and opportunity here for us.

Karen: I love this, this last part, this creative angle so much. You know, my—everything in my head that has to do with creative problem solving and ideation facilitation is, like, going crazy because what you’re doing is by, by leveraging these platforms that way is you are developing stimuli for even stronger ideation.

Nick: Absolutely. Correct.

Karen: You know, there’s a pool of thirds in the world of ideation. It’s like you have to push through the first third. Well, if you kind of push through the first third and then let AI take the second third, then your human intelligence can come up with a third third of these ideas, which are really where genius can lie. So I love that you are training models in that way. I think it’s a fantastic point.

Nick: And my dream on this is—on the creative side and on the innovation side is if we can train the LLMs smartly and safely, of course, to generate start points, like generate a diversity of start points, could we then, either through classic, you know, quantitative testing—we can then do screening very early on. And we were looking at something a while ago where with an external partner, where the system they built generated—automatically generated innovation ideas and then immediately put them into testing so that you could start to screen them. And there was no human intervention. It just generated them, put them into testing. And what I love about—it’s scary in a way, but what I loved about it is it really helped drive diversity of thinking. Now, of course, they’re only as good as the models you build them on. Which, again, goes back to why that will be a critical skill set and critical piece of competitive advantage. What I really liked is it forced you to challenge a lot of your orthodoxy. So, for example, it was coming up with chocolate ideas, which I don’t think any of us would ever have come up with, but by putting them into testing, you were getting back—and you’re like, oh, actually, maybe there’s something that’s not so crazy about this idea or something in this idea that we need to go and explore. So, you know, we’ve talked about bias in AI, and of course that’s something we need to be careful of. But it also actually just sometimes challenge our biases and our assumptions that we think, oh, that can’t work. No one will ever want that. But actually, sometimes it’s just pushing you to be creative. And as you say, to take you about two thirds to say, actually there is maybe something in this idea. And now, again, the human intelligence needs to come and sit on top and say, what is it?

Karen: Sure.

Nick: How should we evolve it, et cetera, et cetera. But I think there’s something really powerful in what this could potentially unlock.

Karen: I know, and I just thought of—and I do have to be mindful of time. Tell my producer Natalie that I’m watching the clock. But one of the exercises, when I used to facilitate, that I loved would be to say to people, okay, now—like, well, at Pepsi, we would fill the room with people from different pillars of business. So there, there were legal team representatives in some of those ideations, or there were procurement representatives in some of those ideations. So, you know, you know, score for that model. But what if we, you know, in building in those AI platforms to generate ideas, you said, okay, assume the role of a procurement professional. What ideas might they bring to the table?

Nick: Yes.

Karen: Or assume the role of my, you know, most stringent legal team. What ideas would they bring to the table? You know, because you can train it to take on roles, it would be very interesting to see the output if you were to say to it put on—you know, how would Oprah solve this challenge? Like, some of these very, very clearly facilitated techniques in an in-person ideation, but train the machine in them. I just—it’s—

Nick: Absolutely, no, I think that’s right. The bit that I’m not sure of yet is the sort of audience brain part. So the idea of creating an LLM that could—I’m going to use my [unintelligible 00:38:36]—it could replace testing.

Karen: Yeah. Synthetic. All of the whole—

Nick: The whole synthetic audiences, et cetera.

Karen: Yeah [laugh].

Nick: I mean, I guess I have a philosophical issue with it because I just think human beings are so much more complex. And particularly, again, when you are asking to respond to a piece of stimulus, I really worry that you’ll—you are inherently creating a system which is going to bias you—aid to familiarity. Right? So, you know, we were talking about this. When we were talking about creative testing, we were exploring the possibility of creating a synthetic audience for some creative testing. But I said my biggest concern is because you’re going to feed it on everything we know already. So, if you put unfamiliar content, two things can happen. Either you cannot get a reaction when potentially there is a reaction there, right. Because, you know, we’ve all seen advertising, right, that has a really negative reaction because there’s something in it that we didn’t see; we didn’t know to look for. And so I do think that’s a risk. But also though, that then we talked about some examples of some more purpose driven advertising we’ve done. And I think the risk is, again, that might come back like failure, when actually, again, you’re not building the model in such a way that it can allow for the diversity of content and reactions that you might get. But again, let’s see. I mean, I’m open to testing it. I’m just—that’s the one I’m not convinced about yet.

Karen: Yeah. Yeah. Yeah, well, as the technology evolves and as we evolve, I will be watching for that as well.

Nick: Yeah.

Karen: And we’ll be talking about it, you know, down the road probably at many, many of our events to come. Because, you know, as you know—and this will be, this will be a wrap kind of question for us, Nick, because I’m watching the time. You know, we are all about the future of insights, and we’re all about kind of keeping our, our finger on the pulse of what’s to come.

Nick: Mm-hm.

Karen: And a lot of the guests that we have on our show, I tend to think, are also aligned to that future-forward looking lens that we share. Any other thoughts about what’s to come that you think people might want to have their eyes open for?

Nick: I would say that—I guess I’ll start with my reassurance to us all because I feel like every two years we go through the cycle of “Oh, my God, insights, analytics. It’s the end of the road. You know, there isn’t a future in this industry.” And I guess I’m here to say I don’t think that’s true at all. I think, if anything, what we’ve seen through, obviously the dynamic changes COVID, society, economics of the last few years is there is so much dynamism and change and that—and so much dynamism and change in consumer and shopper behavior that you will always need a team. It might look different, but you will always need a team of people who are understanding that and translating that into what to do for the organization. Because, again, we are the voice of the consumer and shopper. But what is unique about us is we can bring that to better help guide the organization on what to do differently and how to respond to and how to shape consumer and shopper behavior in the way that, you know, we’re looking to grow. So number one is there’s huge potential still in our industry. I think my second point, though, is, but we are probably going to have to rethink back to everything we’ve talked about today. We’re going to have to rethink what our role is and what each of us can contribute to that in future and how we operate together. Because again, I think the, the old model, the old hierarchies, the old structures probably won’t help us. And I think what we’ll need to do, again, just is to rethink this idea of roles and skills that you need within this sort of—this SWAT team. So thinking instead less about hierarchies and structures and person A report to person B but actually thinking about what are the skills and capabilities that I need. You’re obviously going to have to have a structure, but what are the skills and capabilities that I need? And then how do I more, in a more fluid way, bring together the right people for the right problem, as opposed to just thinking about this person works on this problem or this analytical solution the whole time and really thinking about it more as a collective of really smart people that you can draw on to solve problems. And I think that that should, first of all, be—make us much more effective and allow us to have different career paths for people. But I think also ultimately will be more rewarding for us as insights professionals. Because again, rather than trying to force everybody to be one thing will allow people to be diverse and comfortable from different perspectives and give them the reward of actually working on things that they’re really passionate about and that they can really add value to. So, yeah, I think the why we exist will definitely be there, but the how we show up is going to look different.

Karen: Yeah, I love that. Total transformation and kind of paradigm shifts in our brain. So, Nick, thank you so much for this conversation. It was such a pleasure to be talking to you in these past few minutes.

Nick: You too, Karen.

Karen: I have no further questions, sir.

Nick: [laugh].

Karen: Anything you wish we had talked about, or shall we end on that, you know, stellar summary of the call?

Nick: So somebody asked me a while ago, like, what piece of advice do you have when somebody’s doing a piece of work to, you know, ensure that it lands in the organization, ensure that the business—you know, the business takes account of it? My piece of advice is always start with the end in mind. Like, what’s the business problem? Who is the decision maker? What’s the decision actually that somebody’s trying to make? Do they already have a bias about it? It’s all the stuff that actually is the beginning and ends of the process. I think as insights and analytics professionals, we often rush into the—because we’re passionate about it, we rush into the—what’s the methodology going to be? You know, how many people we’re going to talk to? What’s the analytic plan? Like, we rush into that bit, and that’s where we get excited and engage. But just spending the time to say—like, all of the internal stuff, like how is it going to land? Who is it going to land with? What decision is the decision? It’s what’s the deadline? Like, all of that stuff to tighten the brief. Your brief is razor sharp, and you really know what business problem you’re trying to solve for makes your work better. But more fundamentally than ever, that’s the thing that unlocks whether it lands in the organization is if you really understood what the problem was to begin with. So that would be my one thing —that one, hopefully practical piece of advice that you can do every single day, every single project, every single request.

Karen: Yeah, I love that. It’s actually begin with the end of mind is one of my favorites. I think it stemmed from Stephen Covey’s 7 Habits of Highly Effective People.

Nick: Yes.

Karen: That particular one has stayed with me. So excellent advice. Nick, thank you again so much for your time today.

Nick: Thanks, Karen.

Karen: And everybody, I also want to thank you for giving us the time to listen today to this conversation. We appreciate you showing up and tuning into the Greenbook Podcast. I want to thank Natalie, our producer, for all you do; and Big Bad Audio, who is our sound editor. Thank you for doing what you do. Until next week, friends, we will talk to you soon. Have a great one and take care. Bye-bye.