All right. Thanks everyone for joining. Appreciate it. My name is Andrew Nicholas. 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 here with us today, 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.
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 two main segments, the Intelligence and Activation segment, and what you're doing for customers there.
Yeah. 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. Look, our top five clients have been with us literally over 75 years. We cover $7.4 trillion of consumer spending around the world. We operate in +90 countries. We have almost five and a half million panelists that are giving information as to why they're making those decisions or purchase decisions. 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. Our AI is helping us process that in a much more expeditious fashion each and every day.
When we break our business down really into two segments, our intelligence segment, which is really a measurement-based solution as to what, where, and how much, is really a recurring revenue stream. Think about it as three- to five-year contracts with annual escalators that are included in them. That's 80% of our business. The other 20% we call activation. Analytics-based solutions that really feed off that data to answer the questions as to first what was purchased and then why you purchased it and what else was in your basket so you can help make different decisions and analytics associated with it. Look, we're deeply embedded in clients' workflows. When you look at us from an 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.
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 and important to clients, and why you have +75-year client relationships all over the place?
Yeah. Look, 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 the shelf and actually get it in consumers' hands. That's the key around the topic there. When you get into the actions, there's pricing, there's promotions, there's assortment, and there's understanding the incrementality of items if you have this item versus that item. All of those analytics are built on top of that trusted core data asset. How things are evolving in the world of AI is important. The whole objective here is everybody wants to use AI to do things faster and make decisions faster. 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, which consumer you want to actually target. You need more granularity. 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. That's ultimately the value prop.
Maybe just to give a little bit more context for the audience, maybe walk through some examples of what the decisions that NIQ data is helping clients with. You mentioned promotions and pricing; maybe some examples just to hit it home.
You have to think of a manufacturer and a retailer in a couple of different lenses. The first lens is you need the data to understand how they're performing and where they want to make their strategic investments. That's the first bucket of items. It's kind of called internal operations. 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 go do. If you ask for a specific example, I'll give you one. 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 act on to meet that expectation.
Very helpful. Taking all this together, you come up with a business model. Can you walk through the revenue growth algorithm? Mike, I think there are multiple components to that, but I'll let you go through it.
Sure. When I think about our overall revenue algorithm, we've been in mid-single- digits for the last nine quarters. When we look at it, roughly two to three 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 two to three 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. Lastly, it is coming from new -end products or end markets in terms of what it is that we're doing.
When we look at what we're doing in packaging, what we're doing in government, and what we're doing in financial services, each of those are new end markets, and we get about a point there. We're looking at a 5%-6% kind of revenue growth range, and that's really the driver of that algorithm.
Can you talk a little bit more about the kind of product-based 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?
E-commerce: we had acquired eight companies in the e-commerce space. Our e-commerce, as we reported on our first quarter earnings, was greater than 30% growth. We're also seeing panels, and panels are growing greater than 15%. Were two areas that we've invested in, and we're continuing to see that. Troy is leading all the efforts that's going on more and more in the AI world in terms of revenue. I think that's really what we're seeing, the next supercharge that's coming. We've launched our BASES AI 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 taking 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. 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. Really good momentum there. Can you speak to what's driving that? What brings a customer back after having left maybe a couple of years ago?
I think they're continuing to see the value proposition that we're bringing to the marketplace. Based on our value prop, I look at the Americas, which we kind of split that market with our competitor, our market in the United States grew 9.3%. I look at what's, as I mentioned, e-com and panels growing at 30% and less than 15%, respectively. I would say in Europe, EMEA overall, we have brought together both panel information and measurement information. We're the only ones that have it. It sits in our NIQ Discover and is able to be evaluated. Clients are saying, I don't want to deal with two suppliers. What you're delivering to me, it really differentiates that decision process. Therefore, it's differentiated. Ultimately, we delivered 17 seven-figure wins in the first quarter alone.
I feel very good about the momentum that we're seeing overall. As I say, with what Troy's 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. It wouldn't be a conference Q&A in 2026 without some AI discussion. 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, and how difficult it would be to replicate. Can you spend some time talking about how it's sourced, how it's differentiated, what's proprietary, and what's analytics, and maybe we'll go from there?
Yeah. Over 90% of our data is actually proprietary, and it's all built on three foundational layers. Number one, it's on governed retailer relationships. There's a trust factor in our industry. There are 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? That governed relationship is very, very important. I just talked about one market. Think of doing that in 90 markets. That's a lot. The second thing is we have consumer panels that are all over the world, which you have a trusted relationship with consumers that are actually keeping track of all those other things that they buy with their receipts and all that we then collect on top of it. That's the second part.
The third part, which is kind of under, is not talked about as much because, like I said, it 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. When we say that this sparkling water is in this category of water, all of that matching and mapping across all the different retailers and all the consumer receipts that come in makes it so we can get a trend and data within that. That's ultimately our moat in all the markets that we play. That's the core capability. How do you actually activate that with AI is what is the next phase.
You either do core analytics to basically 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? 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. That's where AI helps understand the trend but also then reacts to the trend and adjusts it in our products so we can deliver faster insights.
Are there any kinds of regulatory considerations to keep in mind when it comes to managing data, selling this data, that could be supplemental to the moat?
I think there's nothing really regulatory that we have to worry about within our business. We do have certain things we obviously have to protect, and when I'm saying protect, that's more of a relationship protection than a broader government kind of issue there. 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. 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.
Yeah.
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.
Yeah. We break up our products into two big buckets, like we've already said. We have our activation set of products, which are AI native products. Like he already mentioned, like AI Screener. That was where you actually could go out and understand what innovation could 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. That's one example. We've also got these things called product developers. Think of this space, it's all around how do you actually work with a 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, we'll continue to build in that space, primarily on the things we talked about, which are how do you make decisions faster, how do you bring innovation faster, and how do you get some insight that you need to uncover. The other thing to just say in that space that's very important is that granularity is key to making those decisions. Think about it: even 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, a P&G or some company could 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 and specific areas of the country. Most retailers in the U.S. now allow a percentage of their store to be local assortment. Store managers now have the ability just to bring in their own products based on the demographic that sits around that store. All of those things, when you talk about bringing some AI capability to determine those, that's bucket one. The other part, though, which is in our core business, which is on our measurement assets, is already mentioned in Discover. We actually have three different buckets of products that we're working on in that spot. We are saying, how are we building better AI in our tools to deliver those insights? We have this thing we call OPTIC, which is our AI chat interface.
We build that on top of our components so people can navigate our assets easier and faster. That's bucket one. We'll have some more announcements right around the corner about some innovations that we're launching. The second bucket in that space is all around what we talk about delivering AI capabilities with our clients and in our clients' environments. 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. Maybe they have an AI set of models, or they have their own chat interface that they want to use. We plug and connect those as another way to consume those assets.
Third is all around working with the leading, call it LLM tools, so you can actually connect to our assets within those. 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. 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. The point I'm just trying to make there is we have a three-pronged approach. Our tools, our clients' tools, and then also leading market tools. All three of them are innovating and building capabilities with that.
Maybe that final bucket is where you find agentic commerce. It seems like that is an area that you are really excited about. You guys spoke to it quite a bit on the last earnings call.
Yeah.
Maybe you could flesh out that opportunity. Why is it important? Why do you see the market going that way, and why is it NIQ?
Agentic commerce is just another channel that's evolving. Again, if I go back 50 years ago or 20 years ago, almost all the groceries and anything that you would buy was always through a brick-and-mortar store. That's obviously evolved to be more e-com, which is a big general bucket that says you go to a website or use something to go get that and get it delivered to you. E-commerce is evolving. If you go back , social commerce was an emerging trend within the last couple of years. That's the definition of TikTok Shop. Right? You're in a social media -based kind of 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 big 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. 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. We will innovate and create products in that space. That's the first part. The second thing that's really important in agentic commerce is that it changes the way that 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.
If you think about what you do , you go in there and say, I want to go buy a, we use this example a lot, a high-protein granola bar. It's easy. Usually, a product says those things, right? You could filter it out, you can do a web search. It's already collected through various pages. I know it. They say high protein on the package, you can kind of filter it. 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. 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 all the ingredients of this product are and what other trends are in the marketplace that you have to connect that data with. 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. 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 allowed to interpret that and do that for you, which again, needs those assets to do it. That granularity is very important. When we think about agentic commerce from a product standpoint, we have four buckets that we're going after. There is the discovery part. There was just a recent study that we put out.
Over 50% of shoppers are already using an LLM to do product discovery. Sorry. That's a pretty big number already at how quick this has been. It's like, how do you actually report on that? What's the share of discovery? How many people went out and searched for this sparkling water last week, within the various LLMs? That's product development number one : share of discovery, share of components. There's the quality metrics when the results that they got back, did they actually meet that requirement and expectation of what they were asking for? Going back to my clean label example, if they asked for products with a clean label, what came back? were they actually clean labels? Was it a good or a positive or negative? That's bucket two. Bucket three is where did they leave from that LLM?
Did they actually go to a website to buy it? Did they just drop it, and then maybe they bought it in a store? There's still some path to purchase stuff to figure out, but that's bucket three of product innovation. Then bucket four, which is what we've always done, which is the measurement of that channel. 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 incrementally in agentic commerce . Right? It's like, yeah, 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 to kind of do it, right?
There's a shift that happens. There will be some incrementality, don't get me wrong, but it's not 100%. You have to understand that in there. Long answer, but the big four 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. Where did they actually go click, and then fourth, did you have a measurement of sale? Did they actually get that conversion? All those things are very important for a brand or a retailer to understand, to actually know how to talk to the right consumers, market to them the right way, and all that. At a high level, that's what our business is. It's just this emerging channel is just changing the dynamics a little bit.
There are 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 or in their own environments. We're talking about all that. At a bigger picture level, how do you see data usage evolving, and does that impact the way that you price for that or the pricing model?
Yeah. The commercial model is starting to shift, right? It's not just us doing it. The industry is shifting, we call it a hybrid model, there's a base set of fees to get going, then there's consumption-based models on top of it. Exact same way that you would buy a Claude license today, right? You have a certain number that you have, and then you get a certain amount of consumption, when you run out of those credits or tokens, you have to buy more. Same type of way that our business will evolve and has already started to evolve. That's kind of the trend on the commercial side of things or the commercialization and the pricing side. The second part of that question really is around how we are ensuring that demand is increasing with the data asset, right?
We already talked about 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 three different options. 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 three of those models.
Great. Thank you. We have about five minutes or so. I want to make sure we hit margins because that's a huge part of the story, I think. Mike, 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?
At the time of the carve-out, this is March 2021, the margins in this business were looking roughly around 13%. Reported 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%-23.8% by the end of this year and that our midterm guidance will be at 25%. We see a path to ultimately 30% margins in the business. What's happened in the first quarter has really been 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. With our fixed cost base at 80% and the nine quarters of mid-single -digit revenue growth, we're driving roughly about 50 basis points of margin improvement overall.
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 just walk through your framework there, how it has evolved since going public, where you sit in terms of the balance sheet, and how you expect to use cash going forward.
Sure. 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 at the end of 2025. We put the target to be below three by the end of this year. We're on track to get there. That's number one on our mindset. Our TPM 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 happening in the first quarter. 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. 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 two deals last year, Gastrograph and Mtrix. One was an ingredients business, and the other one was really a supply chain business. Think about them in the $25 million-$50 million range and equally in that kind of revenue range. When you bring them into our distribution channel, they're accretive really fast. We don't see big transformational deals. We don't see that we have a product gap. We don't see we got a geographic gap. We see these opportunities that are really going to accelerate our business, and those things are in place.
If I recap back, going back, saying debt paydown is the number one 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 we would do a share buyback. When might we think about dividends? Those things are on the table for us right now, number one is focused on getting that debt paid down.
Great. Maybe one more I'll squeeze in just on guidance and guidance philosophy. As a public company, to this point, you've been very successful in outperforming your expectations and street consensus. Can you speak to that philosophy? What kind of guidance do you have out there now, and maybe an upside or downside to what you have out there for the full year?
Yeah. Both Jim and I, this isn't our first rodeo in terms of being senior executives at a public company. 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. We think that's the right way to set it up. Having said that, we don't want to be overly cautious either. We're trying to make sure we set that bar at the right level. For the last four quarters, we've met 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 , and we're almost a lot of them are beat and hold, if you will, in terms of thinking about it.
We evaluated that ourselves and said, That's probably where we need to be at this point in time. 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 looked very good, better than what we saw in the first quarter. We'll reflect our guidance going forward based on what we see happening overall. 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.