NIQ Global Intelligence plc ($NIQ)

All right. Thanks to everyone for joining. Appreciate it. My name is Andrew Nicholas, and I'm the business services analyst here at William Blair. Before getting started, I'm required to inform you that for a complete list of research disclosures and potential conflicts of interest, please visit our website at williamblair.com. With that out of the way, very pleased to welcome NIQ to the 46th Annual William Blair Growth Stock Conference. NIQ is a global consumer intelligence company that synthesizes retail, e-com and consumer panel data to provide a comprehensive view of how consumers shop and what drives purchasing behavior. I have CFO, Mike Burwell, Chief Product Officer, Troy Treangen and here with us today. And we're going to just give you an overview of the business, walk through a few key topics and hopefully give you a better appreciation for the company.

So maybe I'll start with you, Mike, or maybe you could help me with that and provide a brief company overview and walk through the 2 main segments, the Intelligence and Activation segment, and what you're doing for customers there?

Yes. Glad to do it and glad to be here. Look, NIQ is a global system of record for consumer commerce. We deliver the full view of consumer shopping globally overall. We do that for 23,000 brands. We work with over 9,000 retailers. And look, our top 5 clients have been with us literally over 75 years. We cover $7.4 trillion of consumer spending around the world. We operate in 90-plus countries. And we have almost 5.5 million panelists that are giving information as to why they're making those decisions or purchase decisions. And we have 253 million items that we track. I think interestingly enough, we're processing 4 trillion transactions a week in terms of consumer transactions, and that's up from 3.4 trillion a year ago. And our AI is helping us process that in a much more expeditious fashion each and every day. When we break our business down really in 2 segments: our Intelligence segment, which is really a measurement-based solution as to what where, how much is really a recurring revenue stream. Think about it as 3- to 5-year contracts with annual escalators that are included into it. So that's 80% of our business. The other 20% we call Activation, analytics-based solutions that really feeds off that data to answer the questions as to what's -- why did you -- first is, what was purchased and then why did you purchase it? What else was in your basket? So you can help make different decisions and analytics associated with it. So look, we're deeply embedded in clients' workflows. When you look at us from a NDR standpoint, we're 104%. But if you look at us from a GDR standpoint, we're 99%. We have very little client churn of our 23,000 clients. So we're very deeply embedded into our clients' workflows and decision processes.

Perfect. Troy, thank you for joining us today. Can you talk about the client value proposition, how you're evolving that, and maybe more simply, what makes your data mission-critical, important to clients and why you have 75-year plus client relationships all over the place?

So like we're 100% rooted in a trusted decision grade data set. If you think about the industries that we play in, you need detailed transaction information for a brand to actually work with a retailer to get it on shelf and actually get it in consumers' hands. So that's the key around the topic there. But then when you get into the actions, there's pricing, there's promotions, there's assortment, there's understanding incrementality of items if you have this item versus that item, all of those analytics are built on top of that trusted coordinated asset. And then how things are evolving in the world of AI is important. The whole objective here is to -- everybody wants to use AI to do things faster and make decisions faster. And in order to do that, you need to have trusted data that is more granular than it ever has been because you don't want to make decisions on a price, what -- where -- which consumer you want to actually target, you need more granularity. That's how the businesses have -- that's how the world is evolving. And that's where we're putting our energy from just a broader value proposition kind of trend and action list. We are doing many POCs right now with clients that actually tackle that in more granularity and detail. So that's ultimately the value prop.

And maybe just to give a little bit more context for the audience, like maybe walk through some examples of what the decisions that NIQ data is helping clients with? I mean, you mentioned promotions, pricing, but maybe some examples just to hit it home.

Yes. So you got to think of a manufacturer and a retail in a couple of different lenses. The first lens is they need the data to understand how they're performing and where they want to make their strategic investments? So that's the first bucket of items. It's kind of called internal operations. But more importantly is what I kind of referenced in the last answer, which is how do you actually make decisions with a retailer or where you want to target your advertising to know where consumer shopping behaviors are shifting. That's what's very, very important to kind of go do. So you asked for a specific example, I'll give you one. More and more data -- or more and more shoppers are actually buying things on TikTok shop or there's an emerging trend of people doing discovery within ChatGPT or Claude or whatever to find which products they want to buy. In order to actually get that content and make the right recommendations, you need to have really good product reference data. You need to have availability of those products where they can pick those things up. Those are all parts of that algorithm to make sure when you get that answer, it's a realistic answer and an answer that you can action on to meet that expectation.

Very helpful. And so taking all this together, you come up with a business model. Can you walk through the revenue growth algorithm? Mike, I think there's multiple components of that, but I'll let you go through it.

Sure. So when I think about our overall revenue algorithm, we've been in mid-single digits for the last 9 quarters. When we look at it, it's roughly 2 to 3 points are coming from price. As I said, 80% of the business is subscription-based with annual escalators that are in there in terms of pricing that's negotiated not over the life of that contract, but each and every year. Another 2 to 3 points are really coming from cross-sell and upsell. When we look at our innovative products that we have to be in place, those innovative products we're bringing them into our ecosystem and driving it. And then lastly is coming from new end products or end markets in terms of what it is that we're doing. So when we look at what we're doing in packaging, what we're doing in government, what we're doing in financial services, each of those are new end markets, and we're getting about 1 point there. So that's really the -- we're kind of looking at a 5% to 6% kind of revenue growth range, and that's really the driver of that algorithm.

And can you talk a little bit more about kind of product innovation within that growth algorithm? Or how important is that to sustaining growth? What are the areas of the business that are fastest growing now?

So e-commerce, we had acquired 8 companies in the e-commerce space. Our e-commerce, as we reported on our first quarter, our earnings was greater than 30% growth. We're also seeing panels and panels are growing greater than 15%. We're -- two areas that we've invested in, and we're continuing to see that. Troy is leading all the efforts that's going on in -- more and more in the AI world in terms of revenue. And I think that's really what we're seeing the next supercharge that's coming. We've launched our AI BASES Screener in the marketplace, which is really looking at synthetic consumers. I've got a new concept idea, we're working with over 70 clients and 2,300 concepts to bring that to life and evaluate that, where before it was taken months, now we're doing it in a matter of minutes. In fact, one of our clients has referenced that it's cut their cycle time in half to be able to evaluate new product concepts overall. So that's just the beginning in terms of where we are, our journey of innovative new products.

I think during the first quarter, on the call, you also highlighted some competitive win backs, which, at least during the IPO process, you kind of hadn't included in your expectations, so really good momentum there. Can you speak to what's driving that, what brings the customer back after having left maybe a couple of years ago?

I think it's -- they're continuing to see the value proposition that we're bringing to the marketplace. And based on our value prop, I look at Americas, which is we kind of split that market with our competitor, and our market in the United States grew 9.3%. I look at what's -- as I mentioned, e-com and panels growing at 30%-plus and 15%, respectively. And I would say in Europe, EMEA, overall, we have brought together both panel information as well as measurement information. We're the only ones have it. It sits in our product Discover and being able to evaluate it. And clients are saying, "I don't want to deal with 2 suppliers. What you're delivering to me, it really differentiates that decision process, and therefore, it's differentiated." And ultimately, we delivered 17 7-figure wins in the first quarter alone. So I feel very good about the momentum that we're seeing overall. And as I say, with what Troy is building and leading, the AI world here is really only going to supercharge where we're going to go going forward.

That's a great segue. So it wouldn't be a conference Q&A in 2026 without some AI discussions. So Troy, maybe to level set, and we've written this several times. I mean a big part of the investor community is focused on how proprietary the data is, how the data is accumulated, how difficult it would be to replicate? So can you spend some time talking about how it's sourced, how it's differentiated, what's proprietary, what's analytics and maybe we'll go from there?

Yes. Look, -- so over 90% of our data is actually proprietary. And it's all built on 3 foundational layers. Number one, it's on governed retailer relationships. There's a trust factor in our industry, like there's over 160 retailers in the U.S. All those retailers share that data with us and expect us to treat it in a way where we protect certain aspects of that, right? So that governed relationship is very, very important. And I just talked about 1 market, think of doing that in 90 markets. So that's a lot. The second thing is we have consumer panels that are all over the world, where you have a trusted relationship with the consumers that are actually keeping track of all the things that they buy with their receipts and all that, that we then collect on top of it. And that's the second part. And then the third part, which is kind of under -- it's not talked about as much because it's not a [indiscernible] is all of the glue and the decoders that make all that data talk together. We call that our product Content a lot of the times. So when we say that this sparkling water is in this category of water, like that all of that matching and mapping across all the difference retailers, all the consumer receipts that come in makes it so we can get a trend and data within that. So that's ultimately our moat in all the markets that we play. That's the core capability. Now how do you actually activate that with AI is what's -- is the next phase. You either do core analytics to make -- to determine some of the things we've already talked about, which is what's the right price, what's the right place that you put this product? All of those things are analytics that help manufacturers and brands figure out what to do. The second layer is AI enablers, which is all about, like I mentioned already, speed to those decisions. how much faster can you make these decisions? And how often can you pick up what new trends are showing up in the marketplace to be able to react to those. I was -- we were having a meeting just a little bit ago, and I gave the example that we're now actually monitoring trends, especially from the Asia part of the world. We actually just had a thought leadership study that came out about East Meets West, what beauty trends are happening in Asia, what other trends are happening in Korea? And can you adapt those and bring those products to the U.S. to capitalize on that same type of thing, which is a very common thing these days. So that's where AI helps understand the trend, but also then react to the trend and adjust it in our products so we can deliver faster insights.

Are there any kind of regulatory considerations to kind of keep in mind when it comes to managing data, selling this data that could be supplemental to the moat?

So I think there's nothing really regulatory that we have to worry about within our business. We do have certain things we obviously got to protect. And that -- when I'm saying protect, that's more of a relationship protection than a broader government kind of issue there. So it's actually a positive, I think, for us that we have this trust with these relationships, we can use that to our advantage.

Great. So maybe I'll ask Troy. Mike highlighted some of the product innovation that's already happened on the AI front, but maybe you could spend a little bit more time talking about product innovation tied to AI to date? It sounds like those are driving some competitive wins, some win backs, but maybe a little bit more on what products are out there that are new and maybe even what's in the pipeline?

Yes. So from -- we break up our products into 2 big buckets like we've already said. So we have our Activation set of products, which are AI native products like he already mentioned like AI Screener that was where you actually can go out and understand what innovation you can bring to market. You understand it from synthetic respondents, those are basically AI digital twins, and you can actually make innovation faster and bring it to market. He already gave some stats on that. So that's one example. We also got these things called product developers. They are different things. it's -- think of this space, it's all around how do you actually work with the manufacturer and how do they create innovation faster in the market, whether that's a new twist on flavor or whether that's a completely new item that you want to bring in the category. There's a series of products that we're launching, and we'll continue to build in that space, primarily on the things we talked about, which is how do you make decisions faster, how do you bring innovation faster, how do you get some insight that you need to need to uncover. And the other thing to just say in that space that's very, very important, granularity is key to making those decisions. Think about [indiscernible] in your lives today everything is becoming very micro for a brand and a consumer of what they want to buy. 10 years ago or so, like a P&G or some company can mass market products. You just go out and say, I want to have just this one water. That's it. All consumers, that's what they want. I'm going to make it. That's not how it works anymore. Everything is getting micro targeted to specific demographics, specific areas of the country. Most retailers in the U.S. now allow a percentage of their store to be local assortment. So store managers now have the ability just to bring in their own products based on the demographic that sits around that store. So all of those things, when you talk about bringing some like AI capabilities to determine those, that's Bucket 1. The other part, though, which is in our core business, which is on our measurement assets, which is, he already mentioned Discover, we actually have 3 different buckets of products that we're working on that spot. We are saying, how are we building better AI in our tools to deliver those insights? So we have this thing we call OPTIC. We have -- which is our AI chat interface. We had built that on top of our components so people can navigate our assets easier and faster. That's Bucket 1. We'll have some more announcements right around the corner about some innovations that we're launching. And the second bucket in that space is all around what we talk about delivering AI capabilities with our clients and in our clients' environments. So we work with them and have products where we allow a manufacturer, retailer partner of ours to actually connect their environment to our environment so they can activate those AI components faster. So maybe they have an AI set of models or they have their own chat interface that they want to use, so we plug and connect those as another way to consume those assets. And then third is all around working with the leading, call it, LLM tools so you can actually connect to our assets within those. So next week, we've actually already launched it, so it's not anything super private, but we're going to highlight it next week in our conference. Like right within Claude, you can actually connect to our environment to actually use Claude capabilities to understand what's happening in NIQ trends, the data that we provide and do some level of analysis. So the point I'm just trying to make there is, we have a 3-pronged approach, our tools, our clients' tools and then also leading market tools, all 3 of them we're innovating and building capabilities within.

And maybe this that final bucket is where you find agentic commerce. It seems like that's an area that you're really excited about. You guys spoke to it quite a bit on the last earnings call. Maybe you could flesh out that opportunity. Why is it important? Why do you see the market going that way? And why is NIQ?

So agentic commerce is just another channel that's evolving. So again, if you go back 50 years ago or 20 years ago, almost all the groceries and anything that you would buy was obviously through a brick-and-mortar store. That's obviously evolved to be more e-comm, which is a big general bucket, which says you go to a website or use something to go get that and get it delivered to you. But e-comm is evolving. If you go back to -- social commerce was an emerging trend within the last couple of years. That's the definition of like TikTok shop, right? So you're in a social media kind of base platform. It's got a store. You're doing live shopping or whatever the new trend is and then those are sold. That's just an evolution of kind of an e-com type thing. Agentic commerce is that next evolution, which is on top of that, when I'm in an LLM and I'm just doing product discovery, and I want to buy it, that's just another channel to us. So when we talk about agentic commerce is our whole value proposition as a company is to measure where people are buying things and informing with our partners what's happening and what decisions to make like we've kind of talked about. That channel is shifting. So we will innovate and create products in that space. So that's the first part. The second thing that's really important in agentic commerce is it changes the way -- the data -- the granularity of data that's needed to transact in that environment is way more granular than all the other channels have been. Because if you think about what you do is you go in there and say, I want to go buy, we use this example, a high-protein granola bar. It's easy. Usually a product says those things, right? So you could filter out, you can do a web search that's already collected through various pages. I know it. They say high protein on the package, so you can kind of filter it. But the next set of questions that people usually ask these days are not specific about how many calories is in this. They want something that is derived, which is what we call derived on top of it. So I want something that's heart healthy. I want something that's a clean label. I want something like that. That question is not an easy question to answer. You have to understand what are all the ingredients of this product, what other trends are in the marketplace that you got to connect that data with. Then in order to filter that question to what you're actually asking for, you need a new set of data capabilities to be able to do that. Now in the old world, again, you would make that conclusion and just do it on your own. You would say, what is clean label, you do the research. The LLMs are allowing to interpret that and do that for you, which, again, it needs those assets to do it. So that granularity is very, very important. So when we think about agentic commerce from a product standpoint, we have 4 buckets that we're going after. There is the discovery part. So as you as -- I'm sure -- there was just a recent study that we put out. Over 50% of shoppers are already using an LLM to do product discovery. I mean that's a pretty big number already how quick this has been. But it's like how do you actually report on that? What's the share of discovery? How many people went out and searched for the sparkling water last week within the various LLMs? So that's product development Number 1 is share of discovery, share of components. Then there's a quality metrics in there. When the results that they got back, did they actually meet that requirement and expectation of what they were asking for? So going back to my clean label example, if they ask for products with a clean label, did -- what come back, whether they're they actually clean labels, was it a good or a positive or negative? So that's Bucket 2. Bucket 3 is where did they leave from that LLM? Did they actually go to a website to buy it? Or did they just drop and then they may be bought it in a store. That's still some path to purchase stuff to figure out, but that's Bucket 3 of product innovation. And then Bucket 4, which is what we've always done, which is the measurement of that channel. And the measurement of that channel is important because when I was talking earlier about it's just a new channel that's emerging, people want to understand where that volume is shifting from. You, as a consumer, are not going to buy everything on an agentic commerce incremental, right? It's like, yes, I used to buy 100 units of this water at Costco or pick a store, now I'm not. I'm only buying 80 units from Costco, but I'm now buying 20 through an LLM based service [indiscernible] do it, right? There's a shift that happens. There will be some incrementality, don't get me wrong, but it's not 100%. So you got to understand that in there. So long answer, but the big 4 points are the whole thing about measuring discovery on what's happening, but also the quality of it, where they're going from that discovery, so where did they actually go click, and then fourth is you got to measure the sales, do they actually get that conversion. All those things are very, very important for a brand or a retailer to understand, to actually know how to talk to the right consumers, market to them in the right way and all that. And that's -- at a high level, that's what our business is. It's just this emerging channel is just changing dynamics a little bit.

And there's a lot of different ways for people to interact with the data. Customers are now interacting in a bunch of different ways, whether it's in your tools, in their own environments. We're talking about all that. So like at a bigger picture level, how do you see kind of data usage evolving? And does that impact the way that you price for that or the pricing models...

Yes. I mean the commercial model is starting to shift, right? It's not just us doing that. The industry is shifting, which is we call it a hybrid model, which is there's a base set of fees that to get going, and then there's consumption-based models on top of it, exact same way that you would buy Claude license today, right? You have a certain number that you have and then you get a certain amount of consumption. And when you run out of those credits or tokens, you got to buy more. Same type of way that our business will evolve and has already started to evolve. So that's kind of the trend on the commercial side of things or the commercialization and the pricing side. But the second part of that question really is around how are we ensuring that -- demand is increasing with the data asset, right? We already talked about that in the data usage overall. But we're not going to be requiring people to always use our tools to go do that. We allow, like I said earlier, all 3 different options. So however you want to consume it, we are completely happy. We'll fit into your supply chains. We'll do our own. We'll work with partners, and like I said, the Claudes and all those things out there to do it. And then our monetization will be consistent, and we can pick up transactions and revenue from all 3 of those buckets.

Great. Thank you. We have about 5 minutes or so. I want to make sure we hit margins because that's a huge part of the story, I think. So Mike, can you talk about -- I mean, there's been quite a bit of progress on the margin front over the past several years or since the carve-out. Can you kind of just talk about that and the runway?

Sure. So at the time of the carve-out, this was March 2021, the margins in this business were looking roughly around 13%. We reported at the end of the first quarter at 21%, which is a 150-basis-point improvement through the fourth quarter of last year. We've given guidance that our margins will be 23.5% to 23.8% by the end of this year and that our midterm guidance will be at 25%. And we see a path to ultimately 30% margins in the business. What's happened in the first quarter has really been the GfK integration that we've had going on as well as our productivity actions that we've done, and that drove about 100 basis points of margin improvement. And with our fixed cost base, at 80% fixed cost base and the 9 quarters of mid-single-digit revenue growth, we're driving roughly about 50 basis points of margin improvement overall. So we continue to see very good opportunities within margin, and we've only gotten started really on the AI side. We are coding more and more being done through AI. As I look at our back office actions and opportunities, they're real, and we're going to continue to see more drive as it relates to margins going forward.

Great. How about capital allocation? I think it would be helpful for a newly public company to kind of just walk through your framework there, how it's evolved since going public, where you sit in terms of the balance sheet and how you expect to use cash going forward?

Sure. So we were -- we had put at the IPO date that we would get our leverage down to 3.5 by the end of 2025. We were at 3.4 in terms of where we ended the end of 2025. We put the target to be below 3 by the end of this year. We're on track to get there. That's Number 1 on our mindset. Our TTM was $130 million positive through Q1. Q1 is a low point for us. We pay bonuses. More of our IT payments and more of our data costs are actually happened in the first quarter and then the cash flow ramps up through the rest of the year. When we look at share buybacks, we only have about 15% float in the business right now. So the opportunity to do share buybacks, we just don't see it as a great opportunity. We put things in place to be able to do that at some point, but we're just not executing on it until we get our debt paid down. In terms of acquisitions, we will continue to look at deals that make sense. We did 2 deals last year, Gastrograph and M-Trix. One was an ingredients business and the other one was really a supply chain business. Think about them in the $25 million to $50 million kind of range and equally in that kind of revenue range. But when you bring them into our distribution channel, they're accretive really fast. So we don't see a big transformational deals. We see -- we don't see that we have a product gap. We don't see we got a geographic gap, but we see these opportunities that are really going to accelerate our business, and those things are in place. So if I kind of recap back -- going back saying, debt paydown is Number 1 focus right now. We're going to continue to look at acquisitions that make sense in terms of what's happening. We'll always keep opportunities open to think about when would we do a share buyback? When might we think about dividends? Those things are on the table for, us. But right now, Number 1 is focused on getting that debt paid down.

Great. Maybe 1 more I'll squeeze in just on kind of guidance and guidance philosophy. As a public company, to this point, you've been very successful in outperforming your expectation, Street consensus. Can you speak to that philosophy? What you're going to have out there in your guidance now? And maybe upside or downside to what you have out there for the full year?

Yes. So both Jim and I, and this isn't our first rodeo in terms of being senior executives at a public company. So our philosophy is to make sure we put expectations out there that we can meet or exceed and make sure we're consistent about that, and we think that's the right way to set it up. Having said that, we don't want to be overly cautious either. So we're trying to make sure we set that bar at the right level. So for the last 4 quarters, we've met our -- the objectives that we've put out there, we've done that. Going forward, when we looked at the first quarter, for ourselves, we had a couple of wars going on. We're looking at our competitors that were almost a lot of them were a beat and hold, if you will, in terms of thinking about it. And so we evaluated that ourselves and said, that's probably where we need to be at this point in time. But we're not giving up in terms of what the year is going to look like. I said on the first quarter earnings call, April look very good, better than what we saw in the first quarter. So we'll reflect our guidance going forward based on what we see happening overall. And we know it's important to be able to make sure we give the right guidance as we think about our future.

Perfect. With that, we'll wrap it up. Thank you to both of you for being here and engaging with me. Thanks to everyone in the audience. We're going to be moving to Richardson for the breakout for anyone who's interested. Thank you.