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

Good morning. I'm Alex Hess, a member of the Business and Information Services Equity Research team led by Andrew Steinerman at JPMorgan. We're pleased to have with us today Dun & Bradstreet's CFO, Bryan Hipsher. Bryan has been CFO at D&B since February 2019. Bryan, this is the fourth year that you and I have had this conversation. So welcome back.

I want to start with -- we sort of have to start with any updates to the ongoing exploration of strategic options that you guys are undertaking. You're clearly exploring your options. You provided an update to the investment community about 2 weeks ago, a lot can change in a deal process in 2 weeks. What might you be able to share about where D&B stand today? We're going to let us be an open-ended question.

Yes. So first, thanks for having me back 4 years in a row. It's been quite the journey, I think, over that time period from back in February of 2019 to where we are today. So I appreciate the support and I have already had a really good start to the day today. In terms of the -- our response to the inbound interest, I think, as you know, this was something that we weren't going out exploring a process from that side. But for several reasons, including what we think is clearly a disconnect between kind of where we thought the value was and where we were trading, we got some of that inbound interest. And as you said, we spoke to it in our prepared remarks. And so from that perspective, the team is doing what you would expect us to do, which is take the interest in, make sure that we're handling it appropriately from that side and making sure to fulfill our fiduciary responsibilities. And so the core team is obviously focused on continuing to execute, grow the business, fulfill our kind of medium-term ranges from that side and close the year out. But at the same time, like I said, handle everything with, I would say, focus and clarity. And just make sure that obviously, the broader community, the investment community, any of this interest understands where the business is today, right? What we've really achieved from that perspective. And frankly, some of the opportunities, I'm sure we're going to talk about in terms of what we could do on a go-forward basis. So that's really where it's at from that perspective. And again, this is a leadership team, a management team that is, again, really focused on the creation of shareholder value and maximizing that is really our priority.

Got it. So we're going to -- since we're asking foundational questions, let's walk through the core data asset for a second. You have the D-U-N-S Number, widely used identifier in industry, and then the D&B Data Cloud, which has data on 557 million, this is the last I think public number company is.

It's actually a little higher now. I think we've crept over like 580 million. Yes, it's continuing to grow.

Okay. So it's little over 580 million. Good. So can you give investors a high-level view of maybe some where you source your data, what's proprietary, how many contributors data feeds into D&B and into that Data Cloud product, just sort of high-level thoughts on dimensioning that?

Yes, it's really clearly one of the exciting things. And I think a little bit serendipitous, right, from that perspective, too, in terms of what the construct of that Data Cloud is. And frankly, when you're talking about 580 million entities, clearly, the vast, vast, vast majority of those are private companies, right? And so when we think about U.S. number of sources, it's tens of thousands of sources, right? So I think even within kind of the contributory aspect, and I'm not including the ones that come in as small businesses who want to be a vendor to Walmart, who want to be in the Google Play Store or who want to be in the App Store, who then are contributing right some financial statements or other documents from that side. Just in terms of like the receivables and the payables. So kind of the trade credit we received that in and of itself is over 10,000 sources within the contributory network. We then kind of have, I would say, two other branches, right? One is, in essence, data that we go out and have kind of exclusive relationships with providers. So there's a big software provider on the shipping side of the equation that we have a long-term exclusive relationship with for shipping data. And so that's an example of like where we're purchasing, right, a data asset but it's exclusive to us over our long-term arrangement. And then there's, of course, going out to the thousands and thousands of registries and other publicly available sources where we're procuring the data from that perspective. And all of that, to your point, gets tied under the D-U-N-S hierarchy, right, which is a unique identifier and has linkage characteristics up through a parent-child database -- parent-child hierarchy. And so there's probably about 4,000 data elements per D-U-N-S. So we don't have 4,000 on every single D-U-N-S. But the more of those elements we have that are connected to the D-U-N-S, then the more use cases and insights really can solve from that perspective. So traditionally, that's been on a Finance and Risk or a Sales and Marketing use case. If you think about where we're headed in terms of some of the opportunities in the private and capital markets, you're kind of turning that Rubik's cube a little bit and pointing to that more specific use case from that perspective in a more I would say, verticalized approach. So again, very early stages in that realm, but really exciting in terms of, you said, proprietary private company data. This is something that if it's about vendors, third parties, if it's about financial risk and then certainly, if you're starting to think about it from some of these different applications, really, really unique and powerful asset.

One way I sort of think about Dun & Bradstreet is you have that core set of receivables data contributors, then you have some -- for natural reasons, a little confidential on who the counterparties are, but then you have additional value-add data that comes from other parties. You then do the hard work of stitching that together into something that is, has proprietary inflow, bought inflow, partner inflow, whatever, but the sum total is a truly proprietary and unique data asset.

Absolutely. And look, when you think about in the background, the matching algorithms, in the linkage capabilities that we have, those in themselves are extremely proprietary. And so I look back sometimes in my career when I was at, call it, Black Knight, and even in the United States, there were only like two large-scale real estate databases that were literally called public record databases, and there were two. Because even that in itself the cleansing and kind of matching across the different MSAs in the U.S., that in and itself is challenging and expensive. Let alone every country in the world on 580 million entities and the amount of data, it is truly proprietary from that perspective.

So when we think about how do you take this proprietary data asset and turn it into faster revenue growth. We ask ourselves a lot, can D&B grow faster? What would drive it? How do we get there? And I want to ask about maybe the two things we hone in on a little bit are new business, new logo growth and pricing. We'll start with maybe the new business side. Do you have the internal sales capabilities at you're scale? And you are on the smaller side of the Infoservices sector, to deliver on that growth, you've sort of outlined 5% to 7%? Or are partnerships going to be a vital aspect of getting the new business contribution?

Yes. I mean, first, I think you've seen the acceleration of growth. Since the 4 years we've been on this conversation continuing to progress into that kind of mid-single digits. In terms of how we're really addressing the landscape, obviously, we have the core North American sales force. And then we have some sales forces that are individual to, for instance, UKI to DACH region. And so from that perspective, that's kind of how it's organized. What I would say is in terms of the use cases that we have today, Finance and Risk, Master Data Management, Sales and Marketing, we have the coverage in terms of getting to strategic corporates, getting to solving the use cases we have today. And from that perspective, again, that's where I think you've seen the continued progress in terms of cross-sell, upsell. The ability to take price, obviously, is something that we've shown the ability from less than 1% of growth to now this year, it's about 2.5%. So really good progression from that perspective. Where I would say that the next turn of the crank in terms of the GTM evolution is going to be a little bit more verticalized, and a little bit more industry specific. And so where we thought about it before is like these solutions really play very well horizontally. So if you're extending trade credit, if you extend in business-to-business lending, if you have Master Data Management within a large organization, Marketing and Sales, those are things that are relatively, I would say, similar across the board, but there are uniqueness in terms of if you go deep into insurance, if you go deeper into health care, if you go deeper into FI, where the data -- and then just the selling expertise needs to be tweaked a little bit to really understand, okay, I get the use case, but like how does it really affect, a financial institution versus a software player versus an automotive player? Those things are different in their nuance. The other side of the equation in that is the solution sets, that then become more nuanced, become more specific, and then need that industry expertise to really drive it home. So cap markets. I can sit here and tell you, we have hundreds of thousands of financial statements on private companies. Nobody else has that. And so this data that really is sitting and underlying within the asset has a lot of application in cap markets and in private markets. Do we have the product team right now? Do we have the sales team right now, to really maximize that value right this moment? No. I mean it would be disingenuous to say. But we've started to bring in and build that asset out. But what we've also done in the short term is we've picked some partners, to do some, I would say, limited use cases from that side that are very strong within the space that start to kind of seed our data and start to bring that knowledge forward in terms of what our abilities are in that spot. And I think we mentioned two of them right on the call, which were LSEG and ICE. And again, it makes a lot of sense, ICE, that has to do with a climate solution, from that perspective, where our data, their data and some of their distribution mechanisms made a lot of sense. But again, we try to do these things in a relatively controlled environment because there's a much broader opportunity for us over the longer term, and we wouldn't want to, I mean, give away too much from that perspective in any partnership. Same thing, LSEG Refinitiv, a really great partner has great distribution platforms. And that's one thing to, Alex, that over time, thinking about how do you evolve from a platform perspective. So we have platforms in Finance Solutions. We have platforms in MDM. We have platforms in supply chain and third-party risk management, but you need to kind of turn that again to have platforms that deliver through when you're thinking about these use cases and the users within those types of institutions.

Yes. I think those are really what I think about the three core businesses at D&B, the three core ways, you're going to move the ship forward. It's going to be Finance Solutions that we've always been the Third Party Risk and Compliance business, which has grown very nicely. And then MDM, which has been -- which was growing and now it's growing. There's been some acceleration there. So maybe we'll pivot to -- obviously, you guys have embraced Gen AI, but there is a specific application, an important application that you envision the MDM business having within a Gen AI world, can you elaborate a bit on what that is?

Yes. We think that while Gen AI is a really exciting opportunity, and there's a very broad base from that perspective. In a commercial environment, having organized, curated, transparent, really responsibly source data upfront is going to be key in really leveraging Gen AI for a long-term business outcome. So when we're talking about things like doing functions within our organization and making significant changes from that perspective, you're really going to have to rely upon the data set. And again, is it trusted? Is it responsibly source? Is it legally sourced? Because once you make those shifts, if you're going to make a shift in terms of your workforce through automation and efficiency gains, because that's what Gen AI is ultimately going to do. It's either reducing expenses or it's increasing revenues or reducing your risk profile. And the only way you generally do those things is either less software and/or less FTEs, in resources on the expense side. And then on the revenue side, it's just a more efficient sales force and a more efficient GTM from that perspective, along with, of course, the ability to generate new products. But from that side of it, when you start to layer those in and you start to institute those, it can't be something that works for 6 months or works for a year, then you figure out, oops, I didn't actually have the data rights or I didn't have the proper data set that was trusted and built upon. And therefore, I have to kind of unwind this and try to wheel back like that's a very challenging position for a business to be in. And so we think that, obviously, having a core MDM strategy upfront is foundational. And the other side of it, and you'll see this a little bit, I'm sure, from a product that we just released, which is Chat D&B. And Chat D&B is interesting because more and more of these kind of solutions and platforms are going to start to cross right organizations. And so Chat D&B actually sits off of our foundational data set. It's connected to Data Blocks right now and allows you in a more layman's terms, natural language, conversational to be able to query in question and see responses back on the data. Now could you ask it a Finance and Risk question? Of course, could you ask it a Sales and Marketing question? Of course. Could you ask it data management question? Of course. Now what responds and what ultimately comes back is going to be predicated on the entitling, and the commercial aspect of the equation. But it's a much more intuitive and a much more, I would say, cross-functional way to start to think about how data is utilized within an organization.

And I know Chat D&B is a new offering. And I know you guys have moved at a deliberate pace in your Gen AI product rollout, but I've seen them and I've been like that's pretty nifty.

Yes. Sean loves it. I know that.

Can you maybe explain to investors a little bit what traction -- how have you commercialized things thus far to the extent that you have? And then what traction are you seeing?

Yes. I think initially, if we think about commercialization, clearly, the first step was the kind of data management and starting to think about it from a data perspective. Now we don't allow our data to be utilized in training of large language models. But obviously, it's a complementary component of the utilization of a large language model. And there's a really important differentiation there between those two. As we're starting to roll out products. So Chat D&B went from internal use, we exposed with then to a select set of first-mover customers, and now it's rolling into general acceptance. So from a commercial perspective, we'll start to see that impact flow through. And then we just rolled out a little bit more specific use case on what's called Ask Procurement. And so again, we're trying to take these agents and models and utilize them in a little bit more specific use case perspective, too. And so on that one, you can imagine it's tuned to vendor management, monitoring and procurement practices. So really, I think, powerful applications early on. But again, in terms of monetization, it will be a mix of something like Ask Procurement stand-alone, sell it as an independent solution. Chat D&B will be a mix of it being built into the platforms eventually in terms of like a Risk Analytics or Finance Analytics that will help drive usage and volume. And then you'll see it as a complementary asset, which may be an upcharge that sits on top of like our API delivery mechanisms. And so that's how we're thinking about it from a monetization perspective.

That makes sense. And just for anybody in the room who's not familiar with, when they say Finance Analytics, Risk Analytics, those are the two sort of core platforms on the Finance.

On to Finance. And then again, like a Hoovers is a great example, on the Sales and Marketing side. And so before where you're using the user interface to kind of drop down and inquiry to create a market analysis, or a plan of if I'm going to sell a new green server, which types of companies should I go out and go after. Now you're, again, a more natural way asking it, hey, I'm looking for customers that look like X, Y and Z. Oh, and by the way, I want to see if they have a high sustainability practice, and then it can ping off our ESG data, bring that back. And all of a sudden, your funnel of 40 million businesses has become 10,000, and that's where you're going to focus your time and attention.