Description
In this episode of The Curiosity Current, hosts Stephanie and Glenn Fleischman talk with Michael Nevski, Director of Global Insights at Visa, about how AI, affluent consumers, and the gig economy are reshaping payment trends and brand strategy. Drawing on his career spanning roles at Danaher, IRI, and Charles Schwab, Michael explains how curiosity guided his path into insights and why blending transactional and behavioral data is essential for building a true 360° view of the consumer. He shares perspectives on affluent households sustaining spend, the diverse motivations of gig workers, and the growing need for brands to address long-term consumer anxieties around trust, security, and opportunity. The discussion also explores how agentic AI enables real-time insights and hyper-personalization, why synthetic data must prove its reliability, and what leaders can do to position insights teams as strategic partners rather than order takers. Michael underscores the value of mentorship, cross-functional collaboration, and intellectual curiosity as the foundation for driving foresight and growth.
Michael -00:00:00:
We need to acknowledge that in the last five to six years, the concerns are shifting from shorter-term to longer-term.
Michael - 00:00:08:
Will I be able to buy a house as a young family? Will I be able to raise a child? Will I be able to get my education and achieve longer-term career goals? I remember that in 2020, 2021, the concern was, "How can I survive now? How can I keep my family safe and live through these tumultuous times?" And now with geopolitical events, inflation, although inflation subsided, but the tariff situation, the concern is shifting to long-term issues. That's what we see right now: those concerns are shifting.
Stephanie - 00:00:44:
Welcome to the Curiosity Current, the podcast where we dive deep into what's shaping today's trends and tomorrow's consumers. I'm your host, Stephanie, and I'm so glad you're joining me. Each episode, we tap into the minds of researchers, innovators, and insights professionals to explore how curiosity drives discovery and how discovery drives better decisions in an ever-changing market landscape. Whether you're a data enthusiast, a strategy pro, or, like me, just endlessly fascinated by human behavior, this is the place for you. So, get ready to challenge your assumptions, spark some fresh thinking, and have some fun along the way. Let's see where curiosity takes us next with this brand new episode.
[Stephanie - 00:01:29:
Welcome back to the Curiosity Current. Today on the hosting side, I am joined by AYTM's Chief Revenue Officer, Glenn Fleischman. I'm so excited to have you hosting with me today, Glenn.
Glenn - 00:01:41:
Thanks for having me.
Stephanie - 00:01:42:
And today's guest is Michael Nevsky, Director of Global Insights at Visa, where he leads thought leadership that informs the company's global economic forecast and strategic planning. Michael has spent his career at the intersection of data, emerging technology, and human behavior, turning insights into strategies that drive revenue growth and enhance the consumer experience. Michael has also had leadership roles at Charles Schwab, IRI, and Danaher Corporation, designing campaigns and research programs that have consistently generated measurable business impact. More recently, Michael has been exploring the evolving gig economy, agentic AI, and the ways affluent and digitally savvy consumers are shaping the future of payments and financial services. In today's episode, we'll dive into how AI is transforming consumer behavior, what the rise of the gig economy means for brands and payments, and how strategists, marketers, and insights professionals can harness data, creativity, and tech to anticipate trends and drive growth. Michael, we are genuinely, genuinely excited to have you join us today.
Michael - 00:02:54:
Thank you, Stephanie. It's an honor to be here. I'm very excited to have a conversation today and be on your show. Thank you so much for having me.
Stephanie - 00:03:02:
Thank you. Well, we want to just rip the Band-Aid off and jump right in. And, just to kind of go back to the intro that I just did, you have built this truly fascinating, high-impact career at the nexus of consumer insights, emerging technologies, and data-driven strategy. Looking back, how did your career journey evolve in this way to focus on the intersection of all of these things? Can you take us through how you got here in your career?
Michael - 00:03:31:
Absolutely. I guess the short answer is natural curiosity. For a researcher and marketer, you always need to have that. But I started my career in marketing and rose through the ranks, and was a VP of marketing for a DVD distribution company early in my career. I loved the strategy. I loved the consulting piece. But through my career, I always was interested in what we call semantics or details—why people do what they do. And at a certain stage, when I was with Danaher Corporation and handling the water quality division in terms of direct marketing and supporting the medical devices and dental devices divisions, I was trained in conducting a Voice of the Customer (VOC) and that's what really triggered my interest because I realized that insights, research, us, we all represent the research industry. We truly identify the white spaces for our businesses and our clients and consumers, and we're helping with the direction where corporations, services, and products need to move. But not only that, we're helping them to connect to that and get from point A to point B to point C. And I love this analogy - we are the neck in marketing, and the rest of the company is the head. So, we turn “the head” in the right direction and point, "Go there, go here." And that's how I started transitioning from marketing to marketing research. That was the first stage.
Michael - 00:05:05:
And the second one, I got lucky in my career because, look, I worked in manufacturing like Danaher, and then I went to the CPG world, syndicated data, and handled the global consumer panel for IRI, now they are Circana, who was in charge of delivering products and services to all the major retailers in the United States and beyond, as well as CPG clients. What we call the annual restatements is where you pretty much update the platform and deliver services. And then I went to Charles Schwab, where I led marketing research for wealth management, and now I am in payments. Many people might say I'm just lucky, probably, but it was always focused on that natural curiosity. Learn beyond what is needed to do your job. And even in my current role, I try to learn about new products and services and why we do what we do in terms of the business. So it's, kind of, understanding and always looking above and beyond, not because I'm an overachiever, but it's just a curiosity again, learning something you don't know. Sometimes, even though I look up this new product we're rolling out or doing something, I try to understand the definitions and how it works and what it's for. So, that's how I, kind of, started moving in that direction.
Michael - 00:06:23:
And the last piece, always focus on, like, you guys—full disclosure, as you know, we have a working relationship with you guys, and I know my corporate team is enjoying the relationship, and you help us and I use the fruits of your labor guys myself internally by partnering with my business partners. It's my little commercial, right? So, it's like, you know, when you read those articles by analysts and they say, "Full disclosure, we have those stocks and we have those stocks," that only represents the same thing. So for our audience, yes, we have that relationship, but at the same time, like I said, we're focusing on the trends. Right? So, like, what you guys help us sometimes to identify, I guess some people can say it's DIY, some people can say it's quick and dirty but you can really conduct a quick research to truly understand once you see the new trend. And you mentioned, like, the gig economy or the rise of single-person households, generative AI in terms of shopping and retail. And now we're talking about agentic AI, which is a very big topic for my company, both of them, actually. Right? So, again, focusing on those trends or identifying what else, predicting what else is going on.
Michael - 00:07:31:
To give you an example, even right now, last week, we heard a lot of talk by big CEOs, large company CEOs, saying that maybe generative AI was a little bit over-exaggerated. Right? Maybe we need a pullback in terms of the investment in generative AI. And right away, what comes up for me is, "What does it mean for us? What does it mean for my clients?" Right? Because I operate in a B2B environment, pretty much a B2B2C environment. So, I have started thinking proactively already. “So what if we start pulling back, not my company, but other business partners? What does it mean for the industry? What does it mean for consumers? What's the consumer perspective of that?" Right? So that's how you start thinking about something and predicting what else or the minions. Right? Like my economics team because I'm a part of the economics team, they already published the report on that. They already started thinking, with the tariff situation, "What might happen?" So, again, to answer your question, that's how I'm probably getting there, but again, I'm not perfect. I don't know all the answers, but I'm also learning from my peers. I'm learning from the industry. I'm learning from you guys. Right? So, what other companies are doing, going to major events. I'm lucky to present at many conferences and also, of course, I'm on the board of advisors for the marketing research event conference, one of the largest events in our industry in the United States, and you always, kind of, like a sponge getting all that information and observing. So I probably gave you a very long answer. I don't want to ramble, but that's a big, big question, and we can probably talk for hours. Right? So, and I'm sure you guys have a lot of stories to share as well, so if I were to be asking you that question.
Stephanie - 00:09:13:
I really love that answer because I think it's one of those, to talk about curiosity. It's been this real touchstone in so many of the interviews that we do with people who are really, change agents within their companies. You know? It always starts with that sort of kernel of curiosity. That said, I had never heard anybody use that neck-head analogy, and I will not be able to stop thinking about it. I love it. I think that's so smart. So, thanks for that.
Glenn - 00:09:42:
Yeah, Michael, a really fascinating career arc. I want to circle back to something you said. You were talking about speaking at conferences, and in fact, I'm really looking forward to attending your session at TMRE next month. I know you're planning to talk about AI and market research, so I thought maybe we could start there. As agentic AI gains traction through the industry, how do you see market researchers leveraging AI, not just as a tool, but really as an agent that's informing strategy, decision-making, and anticipating consumer needs without compromising human judgment or ethical considerations?
Michael - 00:10:17:
Oh, absolutely. So the top benefit, I'm going to start from in reverse because you touched on a very important point, ethical and privacy, but the top benefit probably for us in terms of the research is real-time insights. Because once the technology is there, once consumers build the trust, and once we put all the policy protections in terms of privacy and do it in an ethical way and not in a creepy way, and for consumers to feel that they get the benefit, we will be able to get that data in real time as consumers are browsing or asking technology to act on their behalf and select products and services. In my effect, probably the most impact is probably going to be on the marketing industry, search engine optimizations, and search engine marketing and stuff, because in a sense, it's going to be technology marketing to technology, but this is technology that knows us as consumers. Right? So that's the main aspect, real-time insights. It's not like, "We're going to do this survey. We're going to conduct this campaign and measure." We will be able to adjust our research pretty much on a daily basis, or what we're learning, because we're going to get a much fuller picture of the consumer path to purchase. That 360, let's put it this way, we're going to understand much better cross-channel preferences, not within, let's say, consumer packages because grocery, right, like Circana Nielsen, spins kind of a situation, but understanding where consumers are staying, where they're flying, what airlines they're using, right, what they're reading, and stuff like that. So agentic AI with consumer permission will give us a full picture because consumers will need to also in a sense adapt in terms of, they need to build the trust with the generative AI. And once they do that, we as researchers might ask for permission to use some of their information. Again, not maybe per se, because there are, to me, different types of PII. Some companies consider PII as everything. Right? But for me, demographic attributes, for example, when they syndicate the form, it's not really PII (personally identifiable information) because if I'm a male between this age group and with such-and-such income, you're not going to find me that, you know, needle in a haystack. Not my email. It's not my Social Security. It's not my phone number. It's not my address. Right? But some companies feel that it's much stricter. But anyway, that's number one.
Michael - 00:12:42:
Number two, in terms of building the profiles, dynamic profiles, and datasets, right? So because once consumers do it for this type of personalization for me, then we can actually specifically be very tailored for that. If a consumer says, "You know what?"—and it's already true, a recent survey, I don't remember who was doing that—and the survey was saying that 46% of consumers are saying, "I trust agentic AI to provide the advice on fashion and lifestyle if I'm shopping for clothing," and the reason why is because it's an unbiased opinion. If I'm your friend, Glenn, and you're saying, "Oh, what do you think about this shirt?" so I'm, like, buying, and I'm, like, even if I feel that maybe it's not so good, I might scale back my response, saying, "Yeah, maybe it's good, you know," because it's our personal relationship, and I don't want to you to take it the wrong way. But the AI might say, "Glenn, this is awful. Don't buy this color. Don't buy this style." Right? And consumers feel that, actually, when they deal with this technology, it's a much more unbiased reply or unbiased advice.
Glenn - 00:13:48:
I mean, if you think of the evolution, right, we went from brochureware and companies providing the feedback, and then social media became the more trusted source. And now you're saying that AI is really the next step in that evolement and is going to deliver even more trust during the consumer journey. It's really fascinating.
Michael - 00:14:05:
Yeah, absolutely. The thing where we need to differentiate, for example, some simpler, I would say, tasks like acquiring products and services, definitely, consumers are probably going to do that way more on the technology. When it comes to the emotional part, consumers are still going to have that human presence to say, "This is mine." So you handle this part of the journey or shopper journey. If it's something more emotional, I will actually decide on my own as a human being, and that human interaction is still important for consumers. Right? It's going to be a mix of that journey. So that's why I think, again, to answer your question about generative AI, we're going to become much more efficient, delivering in real time, have a bigger seat at the table influencing marketing and product and brand and strategy. But at the same time, we're going to still have some base of us being core researchers because when it comes to emotional, when it comes to, “let's put this as the deep dive, right, with emotional”, we're going to have to have that presence of ideas, for example, in-depth interviews, focus groups, or something. We're still going to do research; we're still going to have a human conversation with our consumers or clients. So it's going to be a hybrid. That's how I see our future, and it will allow us to do much more digital stuff. But, again, once it's offline, it's going to be more with that emotional and human-being connection.
Glenn - 00:15:32:
Yeah, it's fascinating. The thing I love about market research is that, especially in the digital world, is that we have so much of the "what" already. Right? All this syndicated data, all the quick stream data tells us what's happening. I love that market research addresses the "why" it's happening. And it sounds like what you're explaining is that agentic AI in general is going to really advance that and really do more of answering the "why" in real time. So that's really amazing.
Stephanie - 00:15:59:
I think you also brought up something that I don't think we talk about enough, which is that, you know, there's the consumer, kind of, hesitancy around AI and companies using their data. But when you can tie it to these very specific benefits like personalization, unbiased personalization, there starts to be a real benefit for them. And so the idea of sharing information to get a better experience is far more attractive than, "We're using this for efficiency." Very compelling.
Michael - 00:16:27:
Yeah, absolutely. Because right now, for example, another data point, similar to that, and I think statistics just published, 46% of consumers do not want their info shared with any type of AI. And the reason why is because we're still in a, I would say, early majority, because through my quarterly consumer survey, which replicates the Census Bureau survey and it predates me, we have, like, a 12, 13-year-old tracking study. I measured the adoption of generative AI. I started doing that two years ago, I think, or a year and a half when we started getting into that. And now, my latest July survey was indicating that over 30% of U.S. consumers are using it on a daily basis, but statistics just published a study that 46% of consumers do not want to share that information. Why? Because they still don't trust the technology, whether it's a hallucination or they're afraid of being breached for data. So it's going to take time and adaptation for consumers to actually start trusting the technology. At the same time, there's another data point that about 50 to 60% of U.S. consumers are already saying, "I use one way or the other, generative AI, let's put it this way, not specifically, but generative AI to shop, doing reviews, pricing, comparison." There are some providers already that are connected to the internet in a sense to give you the real-life search results with all the analytics and all the summary. So we already see those two trends. We need to understand and separate them, because one is the adoption and another is the issue of trust. So, but again, yes, we already start getting into that area, and agentic AI is the next step of evolution of generative AI for personalization. Right? So, to your point, Stephanie. Yes.
Glenn - 00:18:16:
Let's dig into that trend. I know you've done some work with affluent consumers. They're often thought of as the more digitally connected and the more influential of the different cohorts of consumers. Have you seen their purchase journey expectations change with AI in terms of personalization, convenience, trust, those types of things?
Michael - 00:18:35:
Yes. So you look at the overall generation, it would be younger millennials and Gen Z who are much more adapted to technologies. So, because they're digital, especially Gen Z, digitally native and younger millennials are pretty much up there as well. So, but at the same time, we see the trend that high-net-worth consumers, number one, they pull their weight. They continue spending. Because in our internal data, we already see that retail sales, although they're still growing, but they're actually being supported by a smaller number of consumers because I have to launch the Spending Momentum Index with my economics team. I have 2 brilliant economists who actually launched the Spending Momentum Index, and we actually, I acted as more of a product person on this data product, which indicates how consumers spend by several categories like discretionary and non-discretionary, restaurant, gas, and also by geographies. And it's an index. A 100 is flat. If it goes above 100, it shows that spending on credit or debit is expanding. If it drops below 100, that means it's contracting. And so by doing that analysis, we were able to see the retail spend versus the Spending Momentum Index by Visa. Right? So, those two indicators, and that's what we see. What it means, it's like in my previous world with Circana IRI. Right? How heavy, medium, light buyers. So when light and medium buyers leave your category, then your average ring at the checkout or transaction goes up. Why? Because you stay with the heavy buyers who actually buy from your category, buy your products more, right? That means your average transaction goes up. Same idea here, what we see is that low-income and mid-income consumers are already pulling back in terms of their spending, but high-net-worth or high-income consumers, they continue to spend.
Michael - 00:20:27:
That's number one, Glenn, to your point. Number two, it's still very similar to the rest of the consumers. They're still looking for convenience, security, and efficiency. Right? So at the same time, they continue to spend because they can afford to spend, because it's important for them to focus on what is important to them. And with this particular case, yes, they're much more prone to use the technology because affluent consumers, they're much more familiar with the technology, more accustomed to utilizing the technology. They're much more often would be early adapters of the technology, like we know from Crossing the Chasm. Right? So from our business school programs and stuff that, you know, early adopters are moving to, innovators or adoptions in the early majority, and so that's where the bulk of the high-income consumer is sitting. But at the same time, they're much more, I would say, fluent in terms of their decision-making process. Because when we're comparing them to the rest of the population, it's a little bit easier for them to make that purchase decision because, I guess, they're not being challenged by the price situation. Right? There is a little bit more fluency there. Right? "Okay, I'll buy that because I can afford it. It's not like a huge deal. I'm not losing, or I'm not investing too much into this product, right, because it's not, much less a share of my income.” Let's put it this way, affluence is influencing that decision-making process, which takes a little bit less time, in comparison to the rest of the consumers. And so technology comes in actually as a tool which they perceive to help them to make that decision even faster because they're much busier, I guess, in their lives. They're running families, businesses, they're traveling, and that's where the technology comes in and says, "Hey, I'm going to help you to organize your day. I'm going to help you to make those decisions much faster so you can focus on what is important to you." So, that's where I see that bifurcation between the rest of us consumers and high-net-worth or affluent consumers.
Stephanie - 00:22:36:
To turn to some of your other recent work that I think Glenn and I are both pretty fascinated by, touching on the gig economy and its evolving workforce. It sounds like from what I read, and then I listened to your waves of thinking interview that you did at Quirks, that, surprise, gig workers are not a monolithic group with a common set of motivators and preferences. I would love it if you could talk a little bit about what nuance patterns that you have observed in that population. And in particular, what are the implications for financial providers and other brands that are engaging with different segments within the gig-working population?
Michael - 00:23:13:
One of the interesting facts is that some of the gig workers, yes, there is a money aspect of it. Right? Especially side hustles. Those are actually who are doing it because they want extra income. But a key component is that the majority of them are doing it out of either passion or flexibility because they want to do what they want to do, not to have a 9-to-5 job and having a boss, following their passion, that's the primary motivator. But also interesting is that, yes, definitely, a side hustle. And a side hustle, how we define that, is when you have a primary income, whether you're working full time or part time for somebody, or you're doing a gig economy full time, but you're also doing the side hustle. That's where we see that people say, "Yes, I'm doing it because I need the extra income." That's number one. So we need to differentiate that. So for many people, a gig is more of an expression of themselves.
Michael - 00:24:04
: Second one, actually, younger consumers, of course, are driving that. Right? So, that means millennials, Gen Zs. So for financial institutions, to your point or implications, it's more focused on planning their future and what they want to achieve. And the third one, the gig economy allows people to be more like digital nomads, unless you know, like, delivery services. Right? So, much more flexibility in terms of the locations, places they work from, because some of them can travel, let's say, if you're a computer programmer or doing something via a digital platform. Right? So, access to that. And I would say the number four point, actually, if you look at how to get involved in a digital economy, very often the bias is that it's an Uber driver. Right? It's a Fiverr client, but actually, it's not the case. 60% of them are doing it directly, almost 60% in the United States. So they're not using the platform. You're tutoring, let's say, a language, or you have your own website, so you don't have to, like, people think that actually it's going to be a digital platform. It's not the case. About 20% utilize Uber or Fiverr or TaskRabbit or any other platform. And another interesting fact, because I consider those also as a gig economy, and I have a friend like that who is an excellent IT architect, but he prefers 1099 as an independent contractor. He might work for one big company, I'm sure you know those names, for six months or a year, then he moves and works for somebody else, and he looks always for that hourly rate, very well paid, because he says, "I'd rather buy my own insurance and taxes, but I want to achieve as much as possible." But at the same time, "I want to have that flexibility. I want to have pretty much that opportunity to follow what I want to do with my career." So it's another about 19, 20% of gig workers who are actually doing the 1099, one employer at a time. Right? So not having multiple ones. That's what we need to consider as well, and we consider those gig workers too.
Stephanie - 00:26:14:
Do you consider, like, I've noticed that there's a lot of, over the past few years, a lot of fractional folks, and these tend to be higher up in corporations, but fractional CMOs, I'll give you an example. Does that get bucketed into the world of gigs, or is that different?
Michael - 00:26:30:
No, you're absolutely right. So, definitely. And those actually, again, could be independent 1099 contractors because, say, you're a smaller business, right, you cannot afford a full-time CMO, and that's where the fractional comes in. Either the contract or working 10 or 20 hours for that business and helping them to get off the ground and then moving on to the next one or maybe in the future come back and do more work for them. Definitely, that's what I call pretty much 1099 in a sense because they can sign a contract for so many hours. Yes. That's a very good example, Stephanie. Thank you for asking.
Stephanie - 00:27:08:
Well, it makes you realize how truly big the gig economy is becoming.
Michael - 00:27:13:
Yeah, absolutely. So that's why, again, back to your original question, companies need to tailor products and services based on those needs. And it's not only generational, but it's also based on what they're trying to achieve in terms of their lives and careers because they value independence. They value experiences, by the way. So, like affluent consumers, actually, circling back to Glenn's question, that they're looking for those memorable experiences, personalized experiences. So that's where there is actually an overlap, a little bit in similarity, between two segments, I would say. So because that independence is very important to all of them.
Glenn - 00:27:56:
It's the "live to work" crowd versus the "work to live" crowd.
Michael - 00:28:00:
That is correct. Like Europeans, right? Just kidding. Versus us in America. Right? So they always keep telling us. Right? So yes.
Stephanie - 00:28:11:
We’re on to something. I wanted to go back to something you were talking about a little bit earlier, which is, we were talking about the difference between the high-affluent folks and those who are not in that category and some of their concerns and anxieties, and I wanted to focus on them for this question. We definitely know, I think, that consumer concerns more broadly are shifting rapidly in the current landscape of, you know, economic uncertainty, political uncertainty, technological uncertainty. Right? It's like hitting us from all sides. Are there particular consumer anxieties—privacy, job security, anything like that, that you see as having the most impact on consumer confidence, spending behavior, and even things like brand loyalty? And what I'm really trying to get to is, what can brands do to assuage, alleviate, or address those kinds of concerns head-on?
Michael - 00:29:04:
It's a very good question, Stephanie. Number one, we need to acknowledge that in the last five to six years, the concerns are shifting from shorter-term to longer-term. Why? We lived through the COVID situation. Hopefully, COVID is, kind of, behind us, although sometimes I hear about vaccination still and some people are still affected. But in a sense, I remember back in 2020, 2021, the concern was, "How can I survive now? How can I keep my family safe and live through these tumultuous times?" And now with geopolitical events, inflation, although inflation has subsided, but the tariff situation, the concern is shifting to longer-term concerns. "Will I be able to buy a house as a young family? Will I be able to raise a child? Will I be able to get my education and achieve longer-term career goals?" And that's what we see right now, that those concerns are shifting. And to your point, what brands can do, companies can do, whether it's financial or any other, is to start building that trust in terms of helping consumers to realize that we as brands are here. We understand your situation, and we're here to support you, whether it's a flexible return policy or helping them with access to additional capital, although our rates are still high, let's say, with the mortgages. Right? So, having a special program. So, like, for example, some builders when it comes to a new housing situation, sometimes have extra discounts, give you extra closing cash. If you finance with us, we give you some discounted points to lower your interest rate, for example. Right? So, education.
Michael - 00:30:47:
Again, that messaging, right, of trust, of being present, of walking their talk, is a very important factor. And helping consumers to say that, "Yes, we're going to help you to realize your dreams because we thought about it." Right? "We're creating products or services which help you to achieve that." And that's a very important thing to understand. Again, shorter-term concerns are shifting to longer-term concerns. "How am I going to live in this situation?" and not just "living for this situation," "How can I achieve my goals?" And I was presenting at one of the conferences, and we put that deck together on that shift back in the spring. And nowadays, to achieve the American dream, you need almost $5,000,000 to do that. Right? Because it's so expensive: buying a house, raising your kids, going to college, buying a car. And that's what it is, because sometimes brands don't realize that consumers are being very much stressed. "Is this the American dream? Was it ever that dream achievable?" So, we see more and more negative trends among consumers that they're thinking more and more negatively towards or about achieving the American dream.
Glenn - 00:32:03:
Michael, one of the superpowers you bring to research is how you bring together different data streams. I've heard you talk about this a couple of times when we spoke directly on other podcasts. You bring together traditional and behavioral economic data, quantitative and qualitative research signals, social sentiment, and AI-driven predictive analytics to build this holistic view of consumers. Can you share a little bit about your process and how you go about doing it?
Michael - 00:32:29:
Oh, absolutely. And again, back to that original question Stephanie asked me, "How did you get here?" I got lucky, guys, because I worked in marketing, then marketing research, more traditional. But I also, and Glenn, you probably were part of that conversation when you, me, and the founder of your company had a laugh. We had a conversation. Because I was hired by Circana and trained and was lucky to manage that platform, I got access to syndicated data and understanding transactional data. And very often, my colleagues would stay there in one area or the other. I was lucky enough to cross that bridge in terms of working on both sides of the equation. Right? So that's why I feel that I'm lucky with understanding, and the reason why is I'm kind of setting the stage here for you. So because, definitely, the traditional insights or research is very, very important, whether it's quant, qual, or social listening. But once you learn about the transactional area, it gives you so much information about consumer behavior. And the way I see it, it needs to be a synergy or a total equation of everything: regular research, syndicated research, other sources of research, transactional, and panel research. Nowadays, it's all expanded. It needs to all come together to really create that 360. And by the way, again, agentic AI will help us to cross that chasm, I would say, to cover that bridge.
Michael - 00:34:09:
That's what I'm excited about because it's not going to be just, "Yes, we're going to do the brand tracking," but then we're separately looking at the transactional data if you're working with CPG companies. "Hey, we did this, right, this campaign. Did we have a lift on ourselves?" Right? So, yeah, I guess, yeah, we can build a look-alike. But when we're going to have deterministic data, like, for example, my Spending Momentum Index, we built, it's a deterministic data. It's not look-alike. It's not modeled. It's really based on our transactional data, what we call VisaNet data. Right? So that's how it's done. And that's what we need to do, and that's what I'm excited about when you say, "How do you do it?" So you look at the insights portion and say, "What do I know currently, right, about my consumer, about my clients?" Then, "What is missing? Can we enrich that data with transactional data, with demographic data, right, with the social graphic data, like some profiles again?" We know, like, I don't want to advertise, but Mosaic or PersonicX data, right, and lifestyles or some other data. Right? So that's how you start bringing this whole thing together. Right? So if consumers opt in to saying, "Track my purchases," then in return, "Get me that personalization done," which actually my company is already doing. Right? So consumers can opt in or certain retailers, right, what they call merchants. So, again, bringing that data, all that data together, and creating that mix. And, again, because I've been exposed to that side of our industry, so that's how I always proactively think. "I have a qual. I have a quant. I have some other sources. What about transactional? What about the panel? What about the demographics data and any other data in between?" Right? "So can I build that whole equation, right, with all that data and make it more deterministic versus modeled data?" So that's, kind of, a very simplified answer to your very complicated question. I'll be honest with you, because what you ask is much, much bigger. That's a great, great, great, great question, actually. Right? So, but, and again, we can talk much more in-depth about that, but it's a, yeah. I feel like I'm giving you a very simplified answer, not going into the depth of that.
Glenn - 00:36:26:
I think it's a really good answer, and, like, every good answer, it creates more questions. I'm curious how you're leveraging AI specifically to make that exercise easier or more valuable, to improve the signal-to-noise ratio?
Michael - 00:36:38:
For now, especially in our case, there's a big push to adapt generative AI tools at work. And, definitely, you can actually bring different datasets to really analyze and look for those trends, and there are already opportunities to do that internally. But I think, although some tools are already there to enable agentic AI, for example, to your point, but, the precedent, the cases, still need to be developed. And the reason why, I'll be honest with you, is because it's an ecosystem. Right? Think of it this way. I work for a company which represents a true network. We don't sell anything direct to consumers. Right? In a sense, we definitely support consumers. But what it creates, it creates the ecosystem where you have a merchant, you have an issuer, you have a processor, and you have a network. And all of those players need to adapt and enable the technology to be able to really start building that equation or building that, kind of, a customer 360. So I think we're not there yet, but it's coming. Once the ecosystem is being enabled through the ecosystem, then we as researchers will be able to come in and support that because we will be able to connect to that. Right now, you kind of bring separate datasets and try to use the technology to, kind of, connect that as a look-alike, sometimes indirectly. So, there is already some progress there, but we need to take that beyond and make it more efficient and provide real-time insights. That's what we talked about earlier.
Stephanie - 00:38:17:
I think too, something that you're, it just kind of hearkens back to, you know, at the beginning of the conversation, we were like, "How did you get here doing all of these various things?" But you're, you're kind of bringing up something that I think is going to need to change for a lot of insights professionals, which is that a lot of us think of ourselves as, like, primary researchers or people who work with syndicated transactional data. And I can tell you, I've said a handful of times in my career, "I don't work with that kind of data. I don't work with transactional data. I work with, I do data modeling, but, I mean, I need to work with a contained dataset." You know? And I think that the more tools we have, especially in the generative AI world, the more it breaks down those walls for us and allows people like me to be able to say, "No, I can work with lots of different kinds of data." I didn't do it the way that Michael did through natural curiosity, but I have been facilitated. Right? Like, this time of facilitation is coming where, and along with that, the expectation that we need to be able to work with all of these kinds of data to build a holistic picture.
Michael - 00:39:17:
Absolutely. And nowadays, it's much more democratized. When I was at IRI, there was a duopoly industry in a sense. Right? Two and a half companies, three and a half companies total, probably, in the industry which had access to syndicated data and consumer panels or worldwide, I would say. Right? Nowadays, you have more players. Again, we're not going to promote those names who use the receipt data. Right? So, scan the data where they have some SKU level data. But, again, it's very niche focused, it's a grocery, it's apparel, but we want to go beyond and above that. Right? So, high involvement versus small involvement. Right? So, durable goods and stuff like that. We want to get that information to our manufacturers. I want to learn about that. We want to have more understanding of airline industries or hospitality industries and everything in between. And I see slowly but surely, there are more providers which start covering those areas. That's why you can start bringing all of those datasets together, and our industry is evolving.
Michael - 00:40:19:
And to your point, Stephanie, like right now, there is a conversation, "Oh, yeah, I'm going to be a generative AI prompt engineer." That means asking the right questions. So, but I think it needs to be much more comprehensive, to your point. So we need to expand our skill sets and understand the new technology, but not just saying, "I only know this or I know only that," like, for example. And definitely, there's a space for excellent facilitators, and I worked with some of those even as a contractor during my time at Schwab or other companies. Right? So, people who really just conduct in-depth interviews. Right? And you need to be a very good facilitator. Definitely, focus groups, especially, even more control and avoid advice. But at the same time, for regular researchers, we need to cover all of those areas. Start learning about if you are missing that piece of, say, syndicated data. Right? Start understanding better the syndicated data. Maybe you're not going to be an expert who is going to run some leak history analysis from a consumer panel or category analysis on the point-of-sales side, but really understanding how that data works, what major attributes, and how I can actually get it all together with the primary research versus secondary research versus third-party data, because we need to be well-rounded experts, because we're being perceived more and more as internal consultants, again, that neck to our organizations to help them navigate. Right? And I bet within your organization, there are probably some people who consult on products and services or whatever, not only those who are client-facing people, right, but internal people saying, "Where are we going to take our company next?" So what's the next phase of our development in terms of the products and services we provide?" So those factors we need to keep in mind, and that's the future of the research, that we're going to expand our skill sets and be those internal consultants. Let's put it this way. "What do you do? I do internal strategy insights consulting for my company." That's how I would answer my question if somebody asked me that question. "So what do you do for your company?"
Stephanie - 00:42:21:
I feel like you're teeing me up for this question in a similar vein, but one of the things that I love about the work that you do is your focus on the future and really developing these data-driven strategies that ensure that Visa is ready to meet the consumer of the future on firm footing. And so with the convergence of all of these things that we were just talking about, I won't go through all of them, but, you know, we've got a lot of things at our fingertips. What advice would you give to leaders to help them foster this culture where teams aren't just analyzing today's state and reporting the news, let's say, but acting as proactive strategists who are anticipating trends before they even emerge in the market?
Michael - 00:43:02:
That's a great question. So a few points here. Number one, provide for your teams. So be open to innovation. Be open to new things. What it means by saying that is not only focusing on the task at hand, like how we can help our marketing to add to the bottom line and learn about the consumers. What are the outside-my-industry trends? Learning about that. That's number one. So what is changing with the consumer mindset regardless of my product and services or what I represent? Number two, identify skill sets which your team is either low on or lacking, what needs to be added, what skill sets we need, or technology to add to be much more efficient, to be much more productive. And number three, provide opportunities for people to work together as a cohesive team. What I mean by that is when you have multiple departments working in silos, especially with larger corporations, as you know. Right? When a business is a small business, it's much easier to communicate. But with a larger business, a larger structure, sometimes you might have several departments working on similar initiatives. Have that synergy, a line of communication open, maybe like an intranet site or some, kind of, a hub where people can exchange ideas, express those ideas, and share the projects you're working on, so not having those silos. Right? So, that's the third component. I think coming with that, again, outside-your-industry trends, skill sets I need to be successful, and also open up for those silos. Right? So information flows. I think it creates that competitive advantage for any insights department to really build those relationships with your business partners. Because, again, we're there to support our business partners. We're not sellers in a sense, although we enable sales, we enable marketing, but building those relationships are very, very important. So not being order takers, being strategic consultants, subject matter experts, not saying, "Okay, you came to me and said, I want to achieve this. I want Michael, I want you to conduct this research. I need these types of consumers. I want a story or whatever that is." No. “What are you trying to do, guys?” or "how can I help you”, but I need to understand how to be most efficient, maybe lacking a supplier who has that capability, maybe we're lacking some skill sets, then I'm going to train my team, maybe I need to acquire this platform and technology like yours, right, to do that DIY research because you have access to certain panels. You can provide me with an efficient way to save my budgets because this year there is a challenge. Many other factors, I'm just kind of giving you an example, right, so that's where the competitive advantage comes in.
Stephanie - 00:45:54:
That is incredibly practical advice, I feel like, that you just gave.
Glenn - 00:45:58:
Yeah. And on keeping on the theme of practical advice, one of the things our podcast listeners are always eager to hear are, like, real-world applications they can take back to their own work. You've just given us some great ones. Maybe from your perspective, you can share what you think are the most promising or even surprising applications for agentic AI in market research, whether that's something that's already having measurable impact today or something that you see untapped potential that's coming, like, just around the bend?
Michael - 00:46:27:
Oh, you got me. That's a very, very good question. Again, because we're talking about an absent technology. So I think hyper-personalization, and I explained it because consumers, especially affluent consumers, are expecting more and more hyper-personalized experiences when they deal with us as brands and agentic AI, I mentioned that we call it tokenization token, when consumers say, not from a security perspective because there is a tokenization for each of your transactions which replaces your actual PIN number, personal account number, the 16 digits number whether it's a debit or credit card. But tokenization is when consumers are proactively opting in saying, "Track my history, but in return, I want you to provide very hyper-personalized experiences specifically for me," whether it's product recommendations or services or exclusive opportunities, like in the loyalty programs, to buy two tickets to Taylor Swift's canceled show, for example, which is not easily attainable. And that's where agentic AI can easily come in because you can turn on. In a sense, a consumer gives you opt-in permission, and then you start feeding those recommendations very easily and efficiently based on technology capabilities and consumer history. But also, you can start feeding some primary research as well. If you know this is the type of a consumer in terms of age, demographics, whatever, so you can actually predict their taste because consumers want technology, not in a creepy way, but they want predictive decisions. Say, "Hey, did you think or have you thought about this?" Right? So, "Oh, yeah, actually, that product would be perfect for me. I never thought about it." So and "This is the right price." So that's where we start moving, and I think that's the practical application that we're already starting to see with the opt-in transactional history programs that we already have right now.
Glenn - 00:48:22:
Interesting. Hyper-personalization is definitely something everyone can be thinking about today. Looking at their broader implications for AI, how do you see it reshaping the relationship between brands and consumers, especially in the high-value segments? We talked about hyper-personalization. Do you see any other places where brands and consumers are going to evolve due to AI?
Michael - 00:48:45:
I think services. But I think again, if you're asking me from the service perspective in general, right, we're going to see more automation even in the physical world. I was at another conference in LA, and I saw this little cart on wheels calling a person's name in a certain area and delivering food, actually. Right? It's like a lockbox. Yes. I have a video I could show where it has, like, a lockbox, and it says, "Michael, Michael," like, "Michael Nevsky." And then I came in, and I punched the code they gave me. And I opened the box, and the lunch was delivered. So I pulled it out and closed the lid and walked away. So we're already in LA, on the streets of LA. And some people even treat that as a pet, and start kind of feeding that box on wheels. So, yes, on wheels. It's a very interesting experience. We're probably going to see more of that in services where more services like that will be provided. We already see some of the trash or tray, dirty dishes collection, like, at the United Club lounges. You've probably seen those moving around. And I think we're getting into that area of robotization and generative AI or agentic AI where the offline world is going to fuse together with the online and offline world. So we're still far away from there, but I think that's another application of what I can see is coming our way.
Glenn - 00:50:06:
That's a great example. And I do wonder how far away it is. It depends on what you read, but it does feel like agentic AI and generative AI is going to be the thing that untethers the robots and really lets them act a little more autonomously and more independently.
Michael - 00:50:21:
Absolutely.
Glenn - 00:50:22:
One of the biggest topics in AI related to market research today is synthetic data. I'm curious if that's anything you've thought about, you've looked at, or have any hot takes for us.
Michael - 00:50:33:
So I think there are a couple of aspects. I see some of the providers or some of the players in our industry start doing a mix of synthetic data versus actual real-person data and creating this symbiosis together to have that more reliability. And I think that's an important aspect again, like with regular consumers. We as researchers, we need to make sure that synthetic data is a reliable source of information. Yes. Once again, we build our trust, and it's there. Definitely, more researchers like me are going to say, "Yes, I'm going to rely on synthetic because I can run it, and with 95% confidence, we can say that the results of this synthetic study will replicate an actual physical study with real human beings," then it would be much less expensive, then it will be much more efficient, and us researchers will adapt to that. But before we get there, we need to have more proof, and the proof is in the eating and using it. So we need to have more proof on synthetic data that it is not a black box, not like, "Trust me, it's going to work." So we as researchers, we want to really understand the details, semantics, and dive into that. And once again, we have that proof to say, "Okay, maybe at the beginning, it would be a bit more expensive for research companies because you would probably provide a real-life study versus synthetic data to say, 'Hey, we did that independently, and look. The results are very similar, and we needed you several times to really prove that.'" Then, again, us as human beings will say, "Oh, okay. I can trust that technology. I can trust what you guys are doing." Right? So that's what it is. Not just saying, "This is a shiny tool. We use synthetic data because of generative AI, we can now create that, but how reliable is it?" Adoption period.
Glenn - 00:52:20:
I think a lot of us on the supplier side are going to have a new business line and synthetic data side-by-side for a long time, you know, it's really replacing a human panel. Thank you for that, Michael. I appreciate the answer. I appreciate the freewheeling.
Michael - 00:52:33:
Yeah, absolutely.
Stephanie - 00:52:35:
Well, Michael, this has been an absolutely fascinating conversation. I had a feeling it would be, and it definitely exceeded my expectations. So thank you so much for joining us. To close us out, there's a question we just always like to ask our guests on behalf of the folks who are coming into the industry. What is one piece of advice that you would offer to someone who's just starting out, whether that's in the world of insights or economic analysis or strategy, just sort of across the areas you work?
Michael - 00:53:05:
Besides what I already said about natural curiosity and going beyond and above in terms of learning and supporting people and building a relationship, have a good mentor. I got lucky in my career, whether it's Iraib Octame, who was leading our practice as an EVP, or former CEO of Visa, Lynn Bigger, who mentored me through the National Advertising Week mentorship program, introduced me to Visa. Have a good mentor who helps you to develop the skill sets, to understand the business, to learn how you can progress, not only in terms of the regular hardcore skills, but your soft skills. So, being a professional, as an executive, learning those skills are also very important. Again, having maybe not one or two or three mentors, or I would say people who can support you, is very, very important. And I always strive to have people from different functions, different areas of life and careers and the corporate world because I love to learn, and it's very, very important to me to continue that development.
Glenn - 00:54:12:
That's such a great message, especially in market research, because so many market researchers can be introverted and maybe a little less comfortable with actively seeking out mentors and developing that relationship. So, to hear that from someone who spent a career in market research, it's really valuable advice. Thanks for sharing that.
Stephanie - 00:54:30:
Well, Michael, thanks again for joining us on the Curiosity Current. It's been great to get to hear from you.
Michael - 00:54:36:
Thank you so much. It was an honor, and I truly enjoyed our conversation. You guys are very good at asking tough questions, but those are great questions. So thank you so much.
Glenn - 00:54:45:
See you at TMRE in Las Vegas.
Michael - 00:54:48:
Yes. Hopefully, I will see you guys at the TMRE in Las Vegas and many other places. Looking forward to continuing our partnership. Thank you.
Stephanie - 00:54:55:
Absolutely. Bye, Michael.
Michael - 00:54:57:
Bye-bye.
Stephanie - 00:54:59:
The Curiosity Current is brought to you by AYTM. To find out how
AYTM helps brands connect with consumers and bring insights to life, visit aytm.com and to make sure you never miss an episode, subscribe to the Curiosity Current on Apple, Spotify, YouTube, or wherever you get your podcasts. Thanks for joining us, and we'll see you next time.
.jpeg)
