Dun & Bradstreet Holdings, Inc. (DNB) Earnings Call Transcript & Summary

So you mentioned EBITDA moving higher because of these investments that you're making and the cash that is generating. Much of management's long-term compensation is tied to EBITDA. Why is EBITDA the right metric for your compensation or for investors to really focus on in a somewhat capital-intensive business?

Yes. I think it's interesting because capital intensive, I think, is tough because, in the short term with the catch-up of where we're at, I think longer term is not right. It's in the kind of normalized 5%, 6%. But when we think about a couple of things, one, the business was pretty highly levered when we took it over and so obviously, when we're thinking about from a peer comparison and overall comparison, it takes out the interest side of the equation. And then think about taxes, right? I mean you're a Notre Dame guy. We love the Irish. We do a great job in Dublin. But pillar 2 obviously cost us, what, almost $0.06 of EPS this year because it's gone long from 15% to 9%. And so as you're going through those types of things, we're thinking about what is kind of the organic growth acceleration, what is the margin flow through from that side. And then we're managing the debt stack down. We're managing the overall, obviously, tax rate as much as we can from that perspective. And so when I think about the incentive compensation structure, I mean, on our side, management incentive comp is organic revenue growth, new sales. There's some on EBITDA, and then there's on EPS, and we have 10% on a risk metric. When we think about the vesting, right, from a stock-based comp perspective, which I think is what you were referring to as the majority, there's a 3-year vesting cycle, right, and there's an EBITDA hurdle from that perspective. In the end, my compensation, my earnings in the majority of our sections -- all of our Section 16(b)s and the management team is in stock, right? And so really what we're predicated on is what everybody else in this room is predicated on, which is driving shareholder value because, again, it's obviously -- if you looked at kind of the math of like cash comp versus stock-based comp, I mean, we're like 25th percentile when you're talking about cash comp. And that's the method, right? We want our people motivated by driving shareholder value, and that's why it takes into account the organic revenue growth, right, the EBITDA growth and how we handle OpEx, how we think about cash investment because all of these things are not just important to investors, they're important to us and they're important to the long-term sustainability of the business.

Got it. That makes sense. So maybe coming back to the business a little bit. You guys have spoken about the value of the D-U-N-S number quite a bit, particularly in terms of it being a competitive differentiator for Dun & Bradstreet. But -- and you kind of touched on this earlier. The company has had the D-U-N-S number for a long time, and that in and of itself wasn't enough to turn that asset into a revenue growth driver for the company. So what are you guys doing differently leveraging the D-U-N-S number that hadn't been done historically?

Yes. So I think the D-U-N-S numbers, interestingly enough in the criticality of what -- not just the D-U-N-S but the data that was attached to what it was doing, allowed the business to stay flat in such an underinvested -- I mean, most businesses -- I mean, it was one of the things that attracted me to the role, into the opportunity. It was like there were a lot of room for improvement, yet it still stayed where it was at. And so really, what it is, is about use cases, right? And so the D-U-N-S and the hierarchy in the entity resolution sits at the middle from a Master Data Management perspective. So clearly, taking that from 300 million to 550 million is big because you've expanded the aperture of the businesses that you're covering throughout the world. Then it's about the different data elements that you're ultimately adding on and the use cases that are driven out of it. Traditionally, we've been great at like horizontal use cases, right, trade credit extension, business-to-business lending and then on sales acceleration, right, lead generation and prospecting and propensity to transact, right? What's cool now and one of the great, I think, examples is like capital markets, right? So in the end, you'd say, well, the data that goes into the capital markets, hasn't that existed in that same time period, too. And the answer is generally yes. Right now, things like shipping data of some of the alternative assets have made it that much more interesting. But you got to have some subject matter expertise when you create that product line and when you launch that into from a GTM perspective. So we hired Brian Filanowski, who's the former President of Fitch Ratings. Gary Kotovets came in, who was the #2 guy in data at Bloomberg and is our CDO. And you put them together with our product team, and all of a sudden, we landed a top 10 private equity company with a pretty significant deal in the fourth quarter last year. So the interesting piece, I think, in there is we're supposed to do a press release with them, right? And then their CEO said, no, right, this is too unique to us, too proprietary. And so that's a good sign, right, from our perspective.

So taking a step back, what do you think investors who aren't very familiar with Dun & Bradstreet don't understand about the company in the way that your biggest, longest-term shareholders do?

Yes. I think, one, what we talked about, what the company was and what it is today are light years apart, right? And you can see ultimately in the difference in financial results where we're performing now versus where they performed for 20, 30 years, right? How we're thinking about the data, how we're innovating, how we're investing the vitality index, right, the organic growth acceleration, all those things, right, are indicators of where it was and where it is now. And so that's -- I think that's a big thing for folks to take a look at and say this is not your, whatever, grandfather's, grandmother's, whatever, Dun & Bradstreet. The second is, I think, that when you're looking at all of these metrics, you have to take into account, right, the fact of while it is a relatively seasoned business, right, it's, in terms of maturity of its investment cycle, has only been going on for the last 3 or 4 years. So when you ask questions like, okay, the perfect info services businesses out there are growing revenue at X, have EBITDA of Y and free cash flow conversion of this, you've got to do things like invest upfront, which is why the free cash flow conversion is a matter of time, right, because there's a mathematical differential between those 2. When I think about things like where the debt stack is, right, there's sensitivity around, hey, it's at 3.8x. I mean difference between 3.8x and 3x, right, is $500 million, right? And it's 6%, it's $30 million of interest expense. And this is a business that's been around for decades and decades and decades, right, and has very deep relationships. And so from that perspective, we can handle, you know what I mean, that much more. But of course, we're looking to get down to that 3 and frankly, could migrate lower from that perspective. So I think it's one of those that, as you're picking it up, right, you kind of have to see the transformation and how it's evolved and where we're at and certainly the opportunity of where we're going. And so the folks that have been willing to kind of do a little bit of work and see through some of those early things, that's, I think, the opportunity that's presented because any which way you cut it, right, I think there's a lot of value to be had in where we're trading at today.

And maybe just to follow up on the topic of your debt burden at this point. What is the right level of debt for this business in terms of kind of maximizing the debt structure but also kind of providing comfort to your shareholders?

Yes. Look, I mean, you've seen it. Regardless, we're at below 4x, but if there's a day of inflation or there's a day of PPI or CPI print that goes one way or the other, it seems to trade from that side. And so I think the feeling is that as we migrate -- and we talked about being around like 3.25. You migrate into that 3, 3.25 range, and I think at that point, if you got a mid-single-digit grower, you got the margins of 39%, 40%. And expanding free cash flow is kind of catching up from that tail that we discussed before. And then the only other thing that's out there is buybacks, right? And so that's where you're kind of looking at the balance between those things. But right now, we're focused on investing in the business organically. The dividend's maintained where it's at. And then it's deleveraging through EBIT expansion but also gross repayment. M&A, look, we don't want to bury our heads in the sand, right? But the level of quality of that acquisition has to be very high right now for it to be overly interesting from where those other components are at.

I'm going to take a pause here and see if there's any questions in the room. All right. I don't see any hands being raised, so I will keep going.

All right.

You touched on GenAI earlier, and I suspect that's been a very popular topic of conversation at this conference this week. What are you guys doing in the GenAI space right now that's really interesting to you?

Yes. I think there's 3 things. And so what's great is like -- you look at our customer base, right? And I mean, it's everybody and anybody, I mean, the 90-plus percent of the Fortune 500, all the major players that are in that space, whether it's the Microsofts, the Googles, the IBMs, right, from that side. And I think what's interesting is they all have different strategies in terms of like how they want to use the GPT versus watsonx versus the Gemini or the different models, and what all underlies it, though, is the data. And so if I think about the first step from our side, Master Data Management, Identity Resolution and the overall data that we can provide from a commercial use base perspective is unparalleled from our perspective. And so I think there's a really unique opportunity. And one of the things that they're seeing is this theory that all the data can just get shoved into these large language models and it will all get processed and organized and the correct answer is going to get spit out is a fallacy. And so what you're seeing is that data needs to be mastered. It needs to be curated, and it needs to be repeatable over time to keep the hallucinations down, to keep the creep, the drift from occurring, right? And so that's where I think, as this thing is rapidly evolving, it's one thing on the consumer side, right? But if you're going to make true business and commercial decisions, lending decisions, operational decisions, operational changes, you got to make sure that it's sustainable from that perspective. And so the data underlying it has to be strong. It has to be auditable. It has to be consistent, right? It's got to be trusted. Like that's where it plays into our wheelhouse really, really well. Then you go into -- and we mentioned Ask Procurement. We mentioned aid for Hoovers. The assistance were kind of early on, right, in terms of, if you look at the overall kind of landscape, I mean, most of the work out there that's being done is like customer service, HR. There's a little bit in finance and operations. But take a spot like vendor management and sourcing and procurement, huge space, right, very manual. In large corporations, if you take large software companies, that could have 1,000 people in there. You take a large hardware provider. You can talk 3,000, 4,000, right? And you have someone primarily going in and hey, Googling in who's who, right? And then some -- but financials aren't there. Risk profile's not there. Sustainability is not there. And again, even if you're working inside of a software application like RA, it still takes a level of filtering and searching through a relatively large dataset versus Ask Procurement, which is trained on our models in the background with the support of an LLM and you're asking it a very simple question. Like, for instance, if I was to use if I were a pizza manufacturer, right, and I needed all the pepperoni companies in the Tri-State area, I can ask that question. Who are the pepperoni manufacturers in the Tri-State area? Great. By the way, who are financially stable with the Paydex of over 90? Okay. Great. Who are ones that have a high sustainability score? Okay. All of a sudden, I took this universe of 1,000 and I shrunk it down. And that's a pretty simple method. But imagine you're a hardware or you're making a phone or you're making large-scale industrial equipment and that is now a global question to ask from that perspective, like that's the kind of stuff that, I think, is super powerful, and we're really excited about.

Got it. Very interesting. I think that's a good spot to end on. So thank you, everybody, for attending, and thank you very much, Bryan.

Great. Thanks, Patrick.