Afternoon, my name is, Brent Bracelin, Co-Head of Technology here at Piper Sandler. Thank you all for joining us this afternoon. Really pleased to have, Cameron, CFO of, ZoomInfo, here with us. Cameron, welcome to Nashville, and thanks for coming.
Thank you very much.
We're between two ferns.
Okay.
Should we start out with some jokes here or no?
Well, that's really up to you.
Well, how—
I'm not good with the jokes, so.
Well, let's not focus on that and focus a little bit about the software market and the trends that we've seen over the last, you know, couple of years. If I look at the Cloud 100, it's been tough. Valuations were high two years ago, they're a lot lower now. We've seen industry growth rates basically cut in half over the last, you know, two years. But there is some signs we're getting closer to some stabilization. There's some slight evidence of maybe we can even talk, God forbid, about accelerating growth a year from now. So let's take a step back and think about your business, the slowdown in your business. How do we get here, and what's the path forward?
Yeah. So, you know, our business is obviously an interesting one because we, you know, serve such a, like, massive market, and, you know, we're still very lightly penetrated. But it also means that, you know, our early adopters became a very big part of our business very early. So, you know, software and technology firms were really a big part of the early adoption curve. I know many of you have heard the story of ZoomInfo, but, like, that was really where DiscoverOrg got its start and was focused on high-quality data. When we merged with ZoomInfo, or acquired ZoomInfo in 2019, we really exploded the TAM and were able to serve customers outside of software. But, you know, coming into, you know, this year, software is still, you know, close to 40% of our revenue.
You know, certainly where you see a lot of firms that are deprioritizing new sales and focusing on efficiency, where they have, you know, really to examine their operating model and, you know, think about a lot more profitability, that's been a tough road for us. You know, we're kind of the tail end of that, that starting point where a lot of companies are-- their growth rates have come in a lot. So, you know, I think that, that dynamic is one that we're continuing to deal with. I think, you know, we're focusing on getting through this next set of renewals. I kind of think of peak negativity for, you know, a lot of our customers as being kind of February, March of this year, of 2023.
So, you know, as we're kind of moving through and coming up on lapping peak negativity, I think that gives us a more solid base, where we've cleared out a lot of the underbrush, potentially overbuying historically and so forth, and cleared that out, where we can have a more solid foundation to grow with those customers going forward.
So as you think about the layoffs, we started to see some of the software folks, you know, given it was 40% of the business, less now. Layoffs started, you know, a year ago. You started to see those layoffs, and they really picked up momentum in Q4, and then obviously into, let's say, maybe peak layoffs, hitting in Q1. How long does it take for those renewals? There's a little bit of a lag process-
Yes.
so you have to really kind of get through the renewal before-
So-
You start to see the business stabilize?
So yeah, most of our, most of our contracts are annual contracts. We do have a pretty solid amount of two- and three-year contracts as well. So, you know, really lapping that, that set of peak negativity for our annual contracts is a big piece. This year, for whatever reason, is also a bigger year for multi-annual contracts, so, you know, we'll have a number of the two- and three-year contracts also renew in the third and fourth quarter this year as well. And so between all of the annual contracts obviously lapping and then having a good reset point for a good portion of our multi-annual contracts, I think that gives us a much better spot.
Obviously, there's still a small tail of contracts that'll, you know, renew after that, but getting through that big bulk this year is the, you know, primary hurdle that we need to get through. So outside the exposure to software, going through some pains now, we'll get through those renewals. So maybe a year from now, we'll be talking about a little bit of a different opportunity set. Let's take a step back, though. That's kind of where we're at today. Maybe walk me through, how did you become the gold standard in software? Like, what, what, what was it unique to the product that you became the gold standard in the software? And more importantly, can you replicate that gold standard outside of software?
And the real value that we're providing to customers is really high-quality data and insights about their customers, their prospects, and the, and the triggers or signals about those companies. With those high-quality, you know, data and insights, you can really drive a better motion. And so I think that's really what kind of propelled us to be that standard for, you know, software companies. And by the way, we're still not totally penetrated into the software universe. I think there's still a lot of opportunity there. Realistically, that same dynamic is true outside of software. If you can get good quality insights about your customers and prospects, you'll be able to drive a better motion.
I think one of the things that's a little different about non-software versus software is, you know, there's more inertia in non-software. I think if you think about your average software company, you know, maybe it was founded in 2013 or 2005 or, you know, sometime in the last 20 years, the internet existed. You know, there's a lot more tools and capabilities, and frankly, you're probably a younger company, so when you're developing your go-to-market motions, you're looking for some of the tools like ours.
If you are a, I don't know, box manufacturer in Cleveland, like, you very well could have founded your company in 1953, and you're 70 years old, that means you have had some level of success. You're developing the motions that you're kind of going to market with. In the 1950s and 1960s and 1970s, the internet didn't exist. You came up with, you know, new and novel ways, whether it's hiring people that already had a book, maybe you joined, you know, the country club to meet people, maybe you're going to a particular conference or set of conferences.
Like, those things are all well and good and have worked for you, but when we get in front of folks, particularly when they're going through some level of change, maybe you're a family-run business and the father's handing the business over to his daughter, or you're hiring a new set of salespeople, or you're moving into an adjacency, like, all of a sudden, that inertia that you've been, you know, kind of working with historically, there's some impetus for change. And when people see our system, the common refrain is, "Wow, I never knew something like this existed." And it's really obvious how you're going to be able to generate more sales, justify the cost, and really drive value. And that's why, you know, our sales cycles for new customers continue to be less than 30 days. They continue to
Even outside of software?
Even outside of software.
Okay.
They continue to be really strong. And, you know, frankly, our non-software business, you know, it's almost two-thirds of our business now. It's an $800 million business. I think when I was here, you know, before, it was less than that. So it's a real software business by itself. And, you know, it continues to grow pretty solidly. If you look at our, like, June results, you know, outside of software, it was a 20%+ growth rate, whereas software is obviously feeling a lot more pressure and is, you know, actually down year-over-year.
So non-software, $800 million business, what is a little bit different? And I think about different in that, like, not all of those non-software companies have inside sales teams.
Yeah.
Like, do you have to kind of have an inside sales team, or are you convincing them to build the inside sales team? Like, walk me through that.
You don't need to have an inside sales team. What you need to have is a focus on growth and finding new customers oftentimes, 'cause while the system can help people, you know, in any number of sales kind of use cases, the really obvious sales use case is prospecting. You know, every business has to do some prospecting. In some businesses, it's much more of a technology thing or maybe a really big company thing. You have SDRs or, you know, you specifically carved off the prospecting motion to a, you know, set of people. In a lot of businesses, the sales guy needs to prospect and generate their own pipeline.
It's actually great for us because, you know, ultimately, that means that those salespeople need tools to help them prospect, and that's where, you know, a lot of those customers end up being, is that it's a, you know, a salesperson that needs to generate their own pipeline or a team of people that need to generate their own pipeline. Yeah, that's a really obvious use case for us to help them.
Data system, empowering sales, any sales environment is kind of the way to think about the-
Yeah, I mean, I, I think it's any sales where it's a business selling to another business. Obviously, our value is, you know, really on the high-quality data about businesses themselves. But that B2B, you know, economy is a really massive economy with a lot of opportunity for us.
Software, clearly the biggest vertical you had success with, got to 40% of the mix.
It's actually a lot more than that.
It was more than that? What was it two years ago? What were the peaks?
two years ago, I think it was 46.
40-
I actually think before that, there was a point at which it was well over 50.
50?
Yeah.
Wow! Okay. So you've diversified the business, but as you think about maybe that success and momentum, building critical mass, becoming the gold standard in software, does the go-to-market functions need to change in these different verticals? Are you trying to say, "Hey, let's really build critical mass in a specific vertical and change your own go-to-market approach with a vertical focus, or is the horizontal approach working in non-tech?
Yeah. So the horizontal approach continues to work realistically in our account management teams, so those are the teams that are servicing existing customers and helping them to grow. We have started to specialize folks.
Okay.
So, you know, and it's just easier, particularly when we're up market and trying to have a conversation with the C-suite or, you know, higher-level decision makers, to be able to talk financial services or to be able to talk telecom. You know, there's just slightly different nomenclature, and, you know, frankly, gonna have a lot more success if you walk into AT&T and say, "Yeah, I was talking to T-Mobile the other day, and here's how they use the system." Same for financial services, same, frankly, for software.
So, like, that's where we've started to specialize teams to, you know, verticalize them so that they can have those higher-level strategic conversations in a more natural way. Realistically, on the new side, for new customers, most of our new sales come from the non-software vertical at this point. It used to be that, and this was as late as, like, Q1 of 2022, software was probably 30%+ contribution to new sales. Now it's below 15. So, like, most of our sales, new sales are coming from non-software companies. And, you know, in that environment, we actually have more generalist sales, and we're routing leads to people based on who's the best person to close a particular deal at the kind of best value for us.
So that means that, you know, if you're really good at financial services, you're probably gonna get more financial services like leads, but that doesn't mean that's all we're gonna feed you. If, you know, you're good at financial services and telecom, then we're gonna feed you both of those. It's all based on the history and their performance, so that ultimately, our best salespeople are getting the best leads for them to convert, and it actually allows our salespeople to kind of move up and motivate them as they go through as well.
Sounds like you're using data to drive your own sales go-to-market.
And that, that has been-
That's a recurring theme here.
That has been a long time theme that, you know, frankly, we do use our own system. In my mind, that helps us generate, you know, far better sales efficiency. Even at the lower levels that we're at today, where, you know, it's been impacted by the economic environment, still one of the most efficient, like, sales motions that you're gonna find out there, and a lot of that comes from, frankly, being able to use the system in an even more aggressive way than our customers could.
Totally makes sense. So let's pivot to where some of the investor debates are right now, and, and that's this idea that, yes, your growth rate's been hampered by outsized exposure to software you're addressing that, but what about competition? I think about competition in two vectors: one, this was a pretty fragmented market on the data side five years ago, and you consolidated the market. But it's been three, four years. Are there new data competitors that you're kind of watching that present existential risk to that potential recovery a year from now, one vector, and then the second vector is large language models. Does that change the ability, and change the competitor, that you worry about, competing against you in two years from now?
Yeah. So on the data side, yeah, I don't know that I would say we consolidated the market. Obviously, we made a couple acquisitions, but there's always been-
A lot of data.
lower quality, smaller companies that are offering a, you know, in our mind, what's a lower quality solution at a much lower price. And, you know, realistically, there's always been that kind of noise at the low end of the market. I feel like it's a merry-go-round of who's the number two player, whether it's, you know, Seamless.AI or Rocket Reach or Apollo or Lusha or Cognism. Like, you know, some level, they're like, you know, fighting among themselves and taking like, you know, from each other. But there's just always this, like, set of number two players that are, that are down there. They grow to a certain size, and then they start to fizzle out. And so that's been around forever.
Yeah.
Realistically, we focus on, you know, our win rates at that low end of the market and where we see people coming up. Frankly, our win rates are marginally up from where they were, you know, a year or two ago, so that's a positive in terms of how that's running. And so, you know, I think we continue to be focused on that and want to make sure that we're, you know, taking as much oxygen out of that low end of the market as we can. But I don't think that... I don't think it would be realistic to assume that it's just gonna, that those companies are all gonna go away and never be there. Like, there's always gonna be some noise at that low end of the market.
That's interesting that the win rates are improving in the last year at that low end, so that's encouraging. What about the large language models, the art of what potentially could be possible if I want to think about pulling data from different things and using large language models to substitute or augment or replace the data that you're providing?
Yeah. I mean, I, I think that that's a real opportunity for us because we do provide such a vast array of data, and it's actually not always easy to get to everything that you need. So the large language models, to the extent that you're pulling data in, you know, even just from our system, are gonna make make the usage even better over time. And so I think we're really excited about that. Obviously, there are certain data points that, you know, you could use a large language model to generate. For instance, if you pointed an LLM at a website, you could probably figure out what the- what industry that company does, right? That's a really small part of the value that we're providing to, you know, customers.
In reality, all of the proprietary data that we're providing, whether that's contact information, you know, org charts about companies, all the signals and intent that we're providing, that's really why, you know, companies are, you know, using ZoomInfo and the real value that they get around it, with some of that, I'll call it, publicly available information around the edges. So, you know, I don't think that your phone number is gonna be in a large language model ever. I don't think it's ever-
I hope not.
Gonna be able to, like, figure it out.
Enough people have that already.
Yeah. Enough people have it.
The warranty exchange calls I still get.
But that, that's not something that a large language model is, like, gonna be able to figure out by trolling the internet. You know, the signals about companies and what they're researching online is not something that a large language model can, can figure out by trolling the internet. The IP addresses for companies that we're using in the back end to drive our, you know, anonymous website identification or intent, like, all of those proprietary data sets are things that a large language model could leverage if we provide it to them, and obviously, we're gonna get paid to provide that to them so that people can drive, you know, better go-to-market motion based on those. So, like, that actual opportunity for us is really the biggest one in our mind, where people are gonna wanna deploy these things....
are going to want proprietary, highly accurate and timely information in order to drive better outcomes with those. And that's the opportunity that, you know, we can frankly, sell more data as a result of that.
So maybe the LLM existential threat, at least on the near-term horizon, doesn't seem like a substitute or alternative. And if that's the case, what can you do with a large language model internally to start to, I don't know, build new things, leverage-
Yeah
this large data set? Are there new products that you could potentially start to think about?
Certainly, we've started with, you know, using the down the middle of the fairway, use case, which is summarizing transcripts that we're already, you know-
Yeah
helping people with. That's gotten a lot of, you know, positive feedback from our customers. We're, you know, currently working on areas that we could use natural language search to basically get to deeper parts of the system with less, our product people like to call it, cognitive load. So, you know, if you had to go and, whatever, take 25 clicks to get to a complex search and kick off a motion around that, being able to do that with, you know, natural language, you know, modeling in order to get there is, like, really interesting. I sometimes think about it as like, you know, people only use 10% of their brains. I think a lot of people only use 10% of the value of the software that they're using, and that's-
Right
probably not that, that different for ZoomInfo as well. So if you can leverage an LLM to actually give people the capability to get 30%, 40%, 50%, 80% of the value out of the system, like, that's a huge win, and obviously will create, you know, more value for our customers and more stickiness as well. And then, you know, ultimately, I feel like copilot is an overused term, but you could see-
Yeah, copilot
you know, a virtual SDR capability particularly for a lot of those salespeople that are responsible for generating their own pipeline. You know, having that sort of capability where you have someone that's running a motion to reach out to prospects, setting up meetings for you, you know, figuring out, like, key data points for your demo or whatever you're doing, like, that's the longer term vision that we could ultimately provide for customers as well.
As we think about the unit economics, as you think about a new world where data's kind of becoming more important, maybe the sales rep becomes less important over time or more productive over time. What are the unit economics, as you just think about the solution set you can provide when you're kind of more data-as-a-service versus that's the traditional seat-based model?
Yeah.
What's the trade-off there?
The interesting example would probably be if, if we have a customer today that, you know, might be a $100,000 customer.
Sure.
So say it's 50-100 seats, whatever. If that same customer was coming in to buy a similar level of data in the Operation OS, that might be a $200,000-$400,000 ticket for us. So, you know, I think we actually like those deals where we're actually providing data into the back end of something, 'cause ultimately, it's actually more sticky than having a bunch of people log in, because it's really getting routed into the core of workflow for whatever that company's doing. And, you know, frankly, a lot of companies see more value out of that, and so we're able to generate more revenue. So if I can do that-
Yeah
for-
All day.
I'll do that all day.
Where are we at in that appetite and those customer engagements as they start to move away from engaging on a seat-based model to these, this data pull model?
Yeah.
Is that early?
I'd say we're still pretty early, and-
Okay
you know, interestingly, in the, you know, couple examples that, like, come to the top of my head, like, a bunch of people will come to us and say, "You know," and sometimes bigger examples, like, "I spend $600,000 with you. I wanna, like, consume the data, and I'm gonna get rid of all the seats." And we'll say: Yeah, great. That's a $1.2 million deal. So they end up paying us $1.2 million or whatever the number is, and then they, like, never really get around to getting rid of the $600,000 of seats. Partially because, like, the users still want that interaction. They wanna be able to use it. Sometimes it's because, like, building something that the users will actually live in is hard, so if they have these other tools.
But, you know, every once in a while you do have someone who can, you know, take out those seats, but it ends up being, you know, a happier customer for us at a higher level, and, you know, there's continued growth there. So it's still a deal that we're gonna do, but the number of times that I've seen that conversation, like, more often than not, they end up keeping most of the seats anyways, even if the plan is to, you know, ultimately, build something internally to kind of fully replace.
Clearly, this data-as-a-service thing I need to do more work on. It sounds pretty interesting. My last question really is around next year. What's the one thing that you're most excited about as you think about what investors should think about next year? Product, go-to market?
Yeah. You know, I think, I think we're continuing to build capacity on the go-to-market side-
Sure
so that, you know, like, I, I don't know when the economic situation is ultimately gonna stabilize and improve, but we wanna be in a position to take advantage of that when it does. Certainly on the product side, there are a lot of exciting things, some of the AI stuff we've talked about, but also, you know, the real focus on the enterprise, 'cause I do feel like, particularly at the really high end of the market, is an area where we're continuing to do really well. And, you know, we're focused on permeating those those motions down into the, you know, the rest of the enterprise and upper mid-market. So as we do that, I think we're excited about the product capabilities that we're integrating in and the go-to-market infrastructure that we're building to, you know, be able to improve that high end of the market for us.
Cameron, thank you so much for your time. Really appreciate it, and enjoy Nashville.
Absolutely. Thank you very much.
Take care.