NIQ Global Intelligence plc ($NIQ)

I am part of the business and information services team here at BofA. This session will be on NIQ Global Intelligence, and I'm pleased to have the Chief Financial Officer, Mike Burwell with us . We're going to construct this session as a fireside conversation, and we'll [indiscernible] go the sort of questions if time permits. So thanks, Mike, for joining us.

Glad to be here.

So it's been almost 8 months since NIQ has gone public. It seems like the first year of a company going public is really the time that sets the building block for the years to come. Now with several quarters of public performance, what has been the biggest misconception and how the investment community perceives NIQ full view compared to your internal reality?

Yes. I think the biggest misconception is that NIQ is a static data provider. Internally, we see the business is becoming more embedded more differentiated and frankly, more valuable as our clients continue to operationalize AI. In fact, our client behavior tells really -- to me, a real compelling story and that is that a 105% of our net -- we have 105% net dollar retention. We have 98% gross dollar retention, over 30% increase year-over-year in terms of our data being used by our clients. So when I think about that, that's not about commoditization. Really, frankly, that's deeper embedment that we're seeing actually in our clients. We're also seeing roughly 2/3 of our top clients, or top 50 clients using one of our AI native products. We've launched 3 in the marketplace today, and that's not AI-enabled, this is just in terms of AI being used as stand-alone products. I guess I just -- when I think about our AI BASES Screener, our AI developer and now AI analysts, which we launched last weekend, these are AI native products that we are seeing adopted already in the marketplace. And also, when I think about our full view, it's more than just data. It's more than just data and that it's governed, it's permissioned, it's harmonized systems of records across 90 markets. So as we see AI move in more and more into high state decisions, we cannot have hallucinations. It's important for us to be in that role to be able to help our clients be successful. So when I think about it, what AI is doing for us is continue to drive durable revenue growth and ultimate value for all our stakeholders, including our investors.

Perfect. And then the IPO and the subsequent debt refinancing has fundamentally transformed the capital structure. From a CFO's perspective, how do you balance the need for short-term public market consistency with the long-term technology investments to required to maintain your leading market share?

Yes. I think it's not an either/or question that you're putting forth, which is how do you make the short-term consistency commitments that we've made to our investors but equally make sure we're investing for the long term. And the good news is we spent well over $400 million investing in our platform, and we did that before we went public in terms of really bringing that platform to life. But equally, when I look at the fourth quarter of last year, we did 410 basis points improvement in our margins. At the same time, we continue to invest and we continue to invest in AI. We continue to expand our panels. We continue to invest in our full view capabilities. So look, we set and looked to set guidance that we can hit. So that's what we set up in terms of the short-term view. But over the long-term view, making sure we will continue to invest in this platform for the future. And when you look at just on a CapEx standpoint, we're looking to spend 6.5% to 7% consistently going forward, of which 70% of that's growth capital, the other 30% really being maintenance capital in terms of what it is that we're looking to spend on. So I think we're balancing both that short term and long term, and that's why I use that word and as opposed to or.

Got it. Great to clarify. Another AI question. A common theme across the Intel services space has been AI and the replicability of data. For those of us in the room who are a bit newer to the story, can you walk us through how you ensure NIQ's data remains proprietary and for parts of the data asset that may not be as proprietary? How is the embedded list of the data, make it difficult for customers to switch?

So our advantage is scale, breadth and granularity of our data engine. And the complexity and governance of unifying thousands of data sources into a permission system clients can rely on daily for making major operational decisions and run their business. Now said another way, what we're teeing up is we cannot have hallucinations. People are making multimillion-dollar decisions based on our data, and we can't be wrong. So it's something we take very seriously. So when we think about scale, we're operating in 90 countries in terms of what we're operating in. When I think about breadth, we're bringing in retail data, consumer panel data, e-receipt data, e-commerce data, we're bringing that all together and harmonizing it in a proprietary product that really builds a reference data later that people are able to access. And that's what creates this, what I call decision-grade intelligence for our clients. Look, we have strong governance. So it's our data. Now you just can't use it in any way you want. And we've made those commitments to the various data sources that we've entered into to be able to get that information. And look, we see our clients have embedded it operationally into their business practices, whether it's pricing, whether it's promotions, whether it's supply chain, all those workflows are absolutely what we're seeing happen across our client base. So look, we're very well embedded on changing us out or switching us or simply saying you're going to book an LLM model to this is not an easy feat and not something that we see happening.

And building on that prior question, in an era where data scraping is common, why is direct from retailer data still the gold standard for enterprise demand?

Yes. Look, when I think about high-price decisions or very important decisions, call them high-stakes decisions, if you will, look, it requires trusted, permissioned and reconciled data and not just scraping information. Now we do web scraping, too, just to be clear, but it's in the context of putting it all together. It's not just taking some fragmented information, you're going to summarize that and project it to the future and have some client making decisions on it. We just don't see that to be the case. So when you said that gold standard around retail data, I think it's a gold standard around all the data that we bring into the house and how it is that we look at and make sure it's accurate, how can you harmonize it, how can I put it into metadata. It's not just data, it's how do I take that data and turn it into advice and help our clients make better decisions. So look, as AI increases, the cost of being wrong continues to raise. And look, the value of what I would call enterprise-grade data only goes up. and it plays directly to our strengths overall.

Got it. And earlier, you mentioned that of the top 50 clients that you have, I think 2/3 you said was adopting AI native products. Does this suggest the moat is widening as your data becomes more deeply embedded in your clients' own proprietary AI models?

Yes. Yes, it does. I mean if AI widens the moat by increasing embedment as we see it being embedded and usage intensity. As you quoted those numbers, as we look at the clients, our top 50 clients are adopting over 2/3 of our AI native products. We also see those clients then are the same clients that are increasing their spend with us as opposed to non-adopters. Data consumption is up 30% on a year-over-year basis. And just to put it in context, we process 4 trillion transactions a week in terms of data sources that we bring into our platform, up from $3.4 trillion 12 months earlier. So a tremendous amount of data. But the role that we play is an important role in terms of being able to harmonize that data, bring that data to life, and it's only increasing that moat when you think about people using that 30%, and you're seeing that increase happening with the AI native products.

Great. And then I just want to switch over to your medium-term outlook of mid-single-digit organic revenue growth. It's built on a signs of retention, cross-sell, upsell, innovation, pricing and penetration to new markets. Can you walk us through what parts you are most excited about in terms of upside opportunity? And where in the algorithm can you see sustained level of outperformance?

Yes. I'm excited about our business overall, but I'll break it down in a few points. So one, when I look at our intelligence business, it's remained durable. And with our pricing that we have to be in place and our renewals, we've seen that for the last almost 7 quarters at greater than 6% revenue growth. So we feel good about what we're seeing as it relates to our core intelligence business. Our activation demand is intact. We've seen certain clients defer a little bit of their timing of those projects, but we don't see any difference in terms of demand in the marketplace overall. As I said earlier, as you look at our top 50 clients, 2/3 of them are adopting our AI native products. And so we feel good about our activation business and continue to see that grow into the future. And what I'm most excited about, and you asked that in your question, is our new capabilities. When I think about AI BASES Screener and how I can develop new products and get them to market, one of our clients in the market has touted it that they can develop new products 65% faster by using AI BASES Screener because they're able to look at synthetic consumers, where before it would take you weeks or months to be able to evaluate that product in the marketplace, now you can do it in a matter of minutes. When I look at our AI development in terms of being able to develop that new product and bring it to market and managing those supply chains is something that we're able to do even faster for our clients and helping them. And equally, the new product offering that we have coming out as it relates to AI analyst is really looking at personas, over 40 personas that are out there. And say you're a brand manager, we're going to help you ask the right questions to be able to do this in a democratized way where before you may need to be an NIQ specialist to be able to do it, now you're able to ask natural language questions and get responses based on your persona. Now we haven't launched all those -- all 40 personas yet, but we've just launched a few in the marketplace, but we're going to see those overall. So look, I'm not counting on upside in our model, just to be clear. So as you think about it, but I think the building blocks are in place when I look back and say, look at our intelligence business, look at our activation business and look what I really see in terms of our new capabilities overall and probably last but not least, to think about our new verticals, I feel good about what our growth algorithm looks like going forward.

Awesome. A lot to be excited about. I think while intelligence growth has been robust, activation, like you said, has been fairly flat over the year. You're taking decisive actions to return activation growth in 2026. I know you mentioned that you're not seeing -- you're seeing a deferment, not really a delay in customers there. So can you just walk us through what is needed internally in client behavior for them to see confidence and for you guys to see confidence in this?

Yes. I mean, look, activation softness to date has been driven by project timing and uneven client conditions, not demand erosion or some people have put forth to us that it's AI disintermediation. Again, I go back to that 60% of our top 50 clients using an AI native product today. I mean, so the demand is there. It's increasing engagement and repeat usage. But to be fair, we have sharpened our focus on a few -- number of offerings we've had. We've had over 64 offerings in this space. We're looking at narrowing down that. We're also improving our discipline in terms of how we convert those leads into revenue and wins, which I think directly deals with some of the past friction points. So look, in a nutshell, I look at it, our demand is intact, execution is improving. Activation is well positioned to grow in 2026, and that's our expectation.

Great. Let's switch really quickly to margins. It seems like during the era where NIQ was private among the transformation steps was cash data costs, which is now about 15% of revenue from 21% back in 2021. From a CFO's perspective, is there a theoretical floor for these data costs? Or will continued AI scaling drive that percentage evenly?

Yes. As a CFO, I probably should never say there's a floor, to be fair. But I mean, I just go back to the fact that you quoted on our data costs back in 2021 were 21% of revenue. Today, they're -- for the last year-end, they were 15%. The biggest impact of that has been the value proposition that we're bringing back to clients. So when you think about data, we get data roughly 4 ways. We buy the data, we rev share the data, we get the data for free or we trade and barter the data. So many of our retailers want feedback back on their operations. And so we enter into those arrangements. Because of the value proposition that we're giving to them with our platform and their ability to be able to leverage that capability that we have, we're able to negotiate different deals than we've had in the past. So to me, it speaks to that data going down is something that's just enhanced capabilities, the quality of the product and what our clients are thinking about it from a value standpoint overall. So to me, it continues to support our margin expansion. Some people have asked me and maybe just how do you compare yourself to other clients? And what's the right margin for the business overall? We've looked at it and said, if you look at many of our peers, if you will, in a broader case that, that margin looks more like 40%. And we said in the midterm that we would get to the mid-20s, and we're spending 15% on data cost that's stitching you right in that bucket, but we're not willing to stop there. We're looking to continue to drive with our revenue growth algorithm and 80% fixed cost base to continue to drive margin improvement. And in fact, we've put forth a 200 basis point improvement in margins in our guidance for 2026, of which half is going to come from our AI productivity actions and the other half is going to come from just revenue growth and with that fixed cost base to continue to see that improve.

It just leads into my next question about your 2026 margin. You just touched on it briefly, but just as you think about not just 2026 margin, but the longer-term cadence in '27 and '28, how should investors think about the cadence there? And what are some of the factors that will unlock margin expansion?

Yes. So we look at back to '25, we improved margins by 320 basis points for '25. We improved them by 410 basis points in the fourth quarter of '25. And we put forth a 200 basis point improvement for 2026. And then beyond that, what I think is we ought to generate 50 to 100 basis points improvement with our revenue growth algorithm and our fixed cost base. So right now, that's what we continue to see. I mean, I won't say forever. But nonetheless, in our foreseeable future in the years that you talked about, we should continue to see that kind of margin improvement.

Got it. Part of the IPO story has been to use the proceeds to reduce your leverage, with the target set to sub 3x by the end of 2026, once you hit those targets, how do you see the appetite for capital allocation adjust, if any? How would you manage interesting data assets that come up on the M&A block during that debt paydown period?

Yes. So we had set a target to get below 3.5x by the end of '25. We ended the year at 3.25x. And as you said, we've put forth to get below 3x by the end of '26. And we were free cash flow positive at the end of '25, and then we'll see the further inflection as it relates to 2026 and beyond. We were looking probably 2 to 4 M&A deals a year, but we don't see them as big deals. We don't see any place that we've got a void in terms of geographic point we don't have or data that we don't have to be in place. But we are seeing opportunistic deals coming our way. We did 2 deals last year. And when we look at these deals that they're going to be accretive in year 1. They're going to be tuck-ins when you bring them into our distribution channel that we're going to be able to really drive them pretty quickly overall. And so when we look at that, we'll look to continue to generate that free cash flow, use that cash flow to pay down debt and continue to drive it below 3. We will continue to look at that capital or cash flow to look at these tuck-in M&A deals overall. And look, the goal is flexibility at the end of the day, is to allocate capital where we see the most value, and we'll continue to do that going forward. But that's kind of our philosophy and thinking about it at this point.

Got it. 2025 is described as an inflection point. And like you mentioned, you achieved positive free cash flow ahead of schedule. How does that early inflection change your day-to-day strategic flexibility and ability to pursue opportunistic growth in the current environment?

Yes. I mean positive cash flow gives me more optionality for sure. So I'm not looking to change our discipline around making sure we're continuing to drive free cash flow into the future. As I looked at the second half of '25, we put forth what kind of free cash flow we're going to drive, and we were going to be positive over the second half of '25, and we delivered $315 million of free cash flow over the second half of 2025. And as a result of that, what's that position us? It's lower interest expense significantly, both from the deleveraging of the IPO as well as the refinancings that we had done. And equally, the onetime items that we've had, we're structurally continue to drive those down. We know that, that's important for our investors, and it's important for us going forward to make sure the earnings line up with cash, and there's not a lot of adjustments associated with that. But at the end of the day, this free cash flow gives us the ultimate ability to invest, to delever and respond opportunistically to the things that are actually happening in the marketplace. So look, I don't think we want to stretch the balance sheet, but we want to put the balance sheet in the right shape, and I think we're headed in that direction overall.

Great. And before we end our conversation here and open up for questions, I'd like to do a quick word association to something we're doing with the presenters this year at our conference. So tell me the first thing that comes to your mind, I'm going to say the following, going public.

A lot of work.

Data costs.

Going down.

AI.

Misconstrued by the market.

Got it. And the last one, your CEO, Jim Peck.

Awesome. Best CEO I've worked with, and I've worked with several.

Perfect. Let's open the floor up for questions, if any.

I have a question. Everyone is talking about AI risk. What -- have you seen anybody even try to use whatever tool? And then if they were to try to do that, could you charge like some sort of access fee because they're not going to just go cold turkey, like completely shut you and start -- it's going to be -- because I'm hearing some of the firms -- some of the software companies, that's what you're doing. They're like, okay, fine, you want to go start using AI tools, but you want to get our data and you're going to have to pay for the data then.

Yes. So what I see is kind of a bit back, people view that they can get access to our data. It's our data. It's permissioned only by us. And we spent a lot of time cultivating those various data sources over time. So we're not just going to give them to someone's LLM model and let them go roll with these things. But we have looked at segmenting our clients into kind of 3 buckets. I call it AI builders, which are the more sophisticated people that want to use it. We call it AI buyers and then AI beginners. And so you've got different flavors of clients and how they're using AI. But ultimately, it's a permissioned data. It's not just about data, it's also about advice that we're bringing into it. And what I mean by that is you're looking at what was purchased, but why was it purchased? What else was in the basket? How am I giving you those insights to make those decision points? So look, we're continuing to work with our clients and want to be adaptable, but we're not just giving our data away. And frankly, without our permission, you can't use it. And so we have no one client that's greater than 3% of our revenue. So that's the reality of what's happening. And so how are you going to get the data? You just can't get it without going through us. And then how we get that data is, we've got Bodegas where we're going in India and beginning inventory, ending inventory, give me your purchases and I back into your sales, and we have a whole field force that's going out in terms of doing that stuff or how you compare e-commerce receipts between various suppliers to match them up is not easy work, that's been going on for years. So this proprietary data we have is unique. And so we're not giving it up easily without making sure we're getting paid for it.

Yes. And the next question is if you have [indiscernible] but then you're telling me your data costs are going down. So like how are you getting it cheaper if -- I was just trying, if it's proprietary, then people should be asking more for.

Yes. Well, it's going down because we're giving a greater value proposition to it. They're able to use it in an easier way. So assume you're Walmart just as an example. And we're getting those detailer POS reads every single day from them, but also they want impacts of what's going on in their particular stores. So we're giving them access to our Discover platform, and they're able to ask in a natural language process that allows that value creation for that store manager or that individual that's responsible for that area to be able to get that value creation a lot higher. So that trade-off is changing every single day. So I'm not sure that impacts the proprietary data as much as it's just a value exchange that's going on.

I think we have time for one more question.

[indiscernible] CPG marketing spending environment kind of weak [indiscernible]?

Yes. So the CPG environment has been weak for a period of time. But what we've seen is you need more data to be able to -- if you're going to reduce your advertising spend or your trade promotion spend, you need our data, you're not going to fly blind to make those decisions. So we're a small portion in the grand scheme of that spend. And as a result of it, we're seeing that demand happen as they're thinking about cuts or alternatives or can I delay this for a year. They're not flying blind. They want our insights to be able to make those decisions.

All right. That's all the time we have. So thank you so much, Mike for joining.

Thanks.