Zeta Global Holdings Corp. (ZETA) Earnings Call Transcript & Summary

Chris, Winnie, thank you for joining us. Good morning, everyone. Let's get started. I'm Ron Josey. I lead coverage here of the Internet sector at Citi. And look, always excited to have Zeta, the Zeta team with us today. Chris and Winnie represents Zeta. Zeta is, I think everybody, for those who don't know Zeta. Zeta is sort of at the crossroads of a lot of different enterprise and advertising areas, so to speak, right? So when it comes to data cloud, when it comes to MarTech, when it comes to marketing tech, when it comes to advertising tech, when it comes to AI infrastructure, Zeta is there. So anyway, thank you for coming, both of you today. And what I wanted to do is maybe we'll kick off really quickly, just Chris, tell us about what Zeta is in your words and introduce yourself. And then Winnie, I'm going to come to you next because Winnie, by the way, is the Head of the Data Cloud, I believe, at Zeta. So a treat to have you with us today. We're going to get the nitty gritty on what is the data cloud. Chris, you know everything about the business. So...

And we give her an IR badge for far too much of her time that she wants to do, she's amazing. Yes. So I'll start off with where we pioneered our footprint. But as you said, that footprint now and really the application of what we can do, we're being pulled in directions that we didn't anticipate, but now kind of going their full throttle. But initially, our primary buyer was the Chief Marketing Officer. And if you put yourself in the Chief Marketing Officer shoes, and these are with the biggest brands in the world in every industry vertical, they are trying to figure out who they want to reach, how they're going to reach them. And once they do, how do I get that individual to engage with my brand? And they have done that through the use cases of I want to keep my existing customers for longer. I want to further grow my wallet with them, and then I want to acquire new ones. If you are a marketer or a CMO, you have to apply dozens and dozens of technologies against those 3 use cases. Whereas with Zeta, as you mentioned earlier, not only do you get as part of the out-of-the-box solution of using Zeta, access to our proprietary data cloud that you can't get anywhere else outside of the walled gardens, but you also have the ability to consolidate many, many, many different point solutions and technologies that can consolidate all those use cases on a single platform, market through every single digital channel without exclusions, even through social. And as I'm sure we're going to get into today, not just solve those marketing use cases, but we're now being pulled into the office of the CFO, into the Chief Strategy Officer, into customer success and service and starting to see the application of our data with use cases well beyond just marketing.

That's great. I mean I want to stick with this for a few seconds because, Chris, you mentioned the primary buyer was the CMO. We ended this conversation on now pulled in the office of the CFO, pulled in the office of the Chief Strategy Officer and several others. This has been a massive year for Zeta, maybe 12, 18 months in the making, probably more, just given the partnerships with Palantir, of course, with OpenAI, with Snowflake. So when we emerge or call it, expand from the office of the CMO into the office of others within the organization, how do we do that?

No better example than Palantir, right? So if you think about what Palantir does better than anyone else in the world is they create a perfect digital twin of every single piece of information that sits within an enterprise, whether it's their customers, whether it's their vendors, whether it's their own internal policies and procedures, their own people-based data, they create a perfect digital twin that is hyper machine readable that then allows an enterprise to make far more effective decisions and also makes them hyper efficient. Now bring in Zeta. Zeta has the ability to do the exact same thing for you, but everything is happening outside your 4 walls. So when you think about the applications of Zeta and Palantir together, and we've talked about having several pilots underway and working on many, many more beyond that. These are the biggest brands that are -- that love Palantir and are now proactively going to Zeta and saying, what more can we do as an extension of what we're doing with Palantir. Those could be certainly marketing use cases, and that's where we're beginning. but I think we could be additive beyond marketing. Maybe you can give some examples of that.

Absolutely. And we actually just had a question about Ford in our previous meeting.

Ford, okay.

And so I was giving some examples of how we can create more intelligence for them outside of their 4 walls. So one, from the consumer perspective, from a marketing use case is we would know when their lease date is ending. We know what competitor dealerships those people are going to. But we even know more things about the overall market. So imagine for certain locations, maybe there's a predominant Hispanic community there and perhaps those individuals prefer Spanish language. So we can give that information over to Ford to say, in these locations, you need to make sure to staff up on Spanish language speakers to be able to speak to those individuals. So simple things like that, but it can be also more complex where, where are the next best locations to open up because we know where is their demand in terms of new automobiles, the type of automobiles, all the information around it, life events that are happening, population changes where are people moving to and out of to help inform them along with their competitor information, where there are a lot of competitors in those same areas that we would then be able to give them data points that would help them make decisions around where do they open new locations, where might they want to divest from in certain locations.

Yes. So we're doing that in a dealership franchise model. We're doing that in quick-serve restaurants. We're doing that with retail chains. We're doing that with hotels, this kind of location-based intelligence now.

And how -- so that's a great example and great to hear the other verticals as well because the question we often get is how do you operationalize the entire Zeta that's coming with you? And so Winnie, I want to get into your background in a second, but let's continue with this idea of Ford. A lot of what you mentioned were demographic information, things that might be available otherwise. Talk to us about what Zeta brings within the broader platform that the dealers and/or Ford Motor might really benefit from specifically.

Yes, absolutely. Primarily our real-time signals. So decisions are made quickly, although automotive probably taken over a more extended period of time. But we know what people are reading online. What do they care about? Is it the features of that automobile? Is it the security? We know their financial and household profile. Are they going to need financing opportunities from you? Are they growing their family? And that's the reason why they're choosing to up level into a larger automobile. So we really create the context around the consumer and the market that helps give better understanding and glean intelligence around who those users are by enriching those profiles with more than like 5,000 attributes and signals per individual within our data cloud.

So let's dive a little deeper on that. That's very helpful. Winnie, I think you've been with Zeta for 17 years.

Yes, you got your math right.

And Chris, how old is Zeta?

Barely over 17 years old.

Barely over 17 -- going on --. So one of the pioneers of Zeta, which is impressive, and congratulations on that. Let's dive and you're now in charge of the data cloud within Zeta, which frankly holds that 535 million consumer profile, if you will. And I know it's not just a profile. What I wanted to hear more is that specific living, breathing data cloud and the application? And how do you keep the data fresh? Where do you get the data from? And maybe most importantly, why is this data proprietary to you? What does Zeta bring that...

Yes, absolutely. So we think about data in 3 core buckets: identities, which are a representation of the individual identifiers, so those identifiers associated with you. Your personal e-mail connected to your business e-mail to your digital ID. And then signals, both the data at rest, but more importantly, the data in motion, things that are frequently changing about you. So across those 3 vectors, we have a multitude of sources. We have our own demand side platform, where -- DSP essentially, where the bid auction is happening across our programmatic channels. We own our own SSP, the own supply side, that's the publisher network, while we own our own, we also plug into all the major SSPs as well. We have our own message transfer agent that enables us to send e-mails across our IPs and our domains where we send more than 6 billion acquisition e-mail campaigns per month, and that's only on the acquisition side. That's not even inclusive of the CRM side. We own Disqus. Disqus is on more than 6 million publisher websites. So imagine there's a publisher site that has a discussion board. Most likely, Disqus is powering that. We also own LiveIntent. Think about premium newsletters like New York Times or Washington Post. You get an advertisement within that newsletter. We are powering the bid option that happens -- that goes in there as well. We have some of our owned and operated properties, and then we also round out our identity graph with some third-party partnerships where we are really trying to create more intelligence for our customers. And to your point about how do you keep it fresh and what are you adding to it? This year, we've added a couple of different sources. So one, we've added into the information voter history data. This political cycle that's happening right now. We have which elections you're voting in, what party are you registered with donation signals, where now we're able to glean intelligence. I just did a report for Georgia looking at persuadables. What issues are starting to increase over time that the constituents care deeply about. Those persuadables really care about education right now. They're carrying a lot more about disaster preparedness as well as racial inequality. Those are the key issues that need to be spoken to, to really influence those persuadables. We're also really fortifying our international identity graph where we are creating more connections to those identities, bringing in more signal information as well. We're working with a big international airline brand where now we can tell them for their U.S. consumers, what are their destinations of interest, for their Canadian ones, where are the differences. For their French consumers, how do we better market to these different areas. And we've also really fortified our business-to-person capabilities where we've now brought in technographic data. So now we know which companies has your competitor technologies that we can help you better conquest against. And all of these signals as well as the connections of those identifiers are continuously broken and created based on what we see. So if we don't see that connection for a while, we'll break that connection because we see that, that identifier associated with this other identifier is no longer prevalent for that individual where oftentimes, we also see new connections happening that we can add those signals in as well.

And one last one for you, Winnie, and Chris, I'm coming back to you in a second. But over the last, call it, I was going to say 3 years, let's say, 18 months, talk to us about where you have all these signals, how these signals have really evolved to sort of build this marketing platform where you can bring it all together? And did things change significantly over these last couple of months now that you partner and have all these partnerships to unearth the data to help drive business results.

Yes, absolutely. I'll say that one of our major changes was around 2024, actually. That was like one of our main pivotal moments where AI had really advanced where it could interpret images and data a lot better before it was a little bit hallucinatory. So once that change happened, we actually pivoted our road map. We paused in developing net new and our focus was integrating AI into all of our applications. We have dozens of them. So we actually created a challenge for ourselves because it was almost too much data for our customers. So by incorporating AI, then we were able to then make it simpler for individuals to understand what are the insights and opportunities for me. But to your point, the last couple of months ago, we introduced Athena to be able to talk to our data cloud applications. So you don't have to look at each application individually and get the insights around them. Athena will help you navigate to the right applications that will help answer the business questions that you have, be able to interpret the data, combine those different data sets and give you actionable insights and recommendations all within minutes.

That's super insightful just because everything is changing here and to hear how it's changing on the ground is key. Maybe, Chris, we're going to bring it back up a notch to a certain extent. And we hear some pretty incredible numbers. Multi-use case adoption rose 90%. I think in 2025, NRR reached 120%. So once you're on the platform, you typically stay on the platform, right? More customers are allocating greater spend to the platform. One of the questions we often get is just about the visibility of the model. And we kicked off the session talking about how Zeta sits within so many different clouds, whether it be the ops of the CMO or the Marketing Cloud or acquisition. Now we're going to more. But just talk to us about the visibility that you see in the model and as a CFO, what allows you to sleep at night.

Yes, it's highly visible. So if we first kind of decompose the revenue model itself, call it, 60% of the revenue model is what customers pay Zeta to license the data cloud, perform analysis, create campaigns, build audiences. And that's the recurring part of the revenue. It's obviously highly visible, highly predictable. The other 40% is when the meter begins to run, the consumption-based model for when our customers take those audiences and campaigns they created and then put them through activation strategies. Activation strategies, meaning Zeta's AI knows which individuals in that audience are most responsive to which curriculum of activation, whether it's CTV, mobile, display, video, social. We have now 5 years' worth of pattern data with customers. As you mentioned, the last 3 years of our net revenue retention has been 112, 114 and the last year, 120. And we're through the first half of the year above our model of 110 to 115 above the high end of that. So that's good, and we like that. But what makes it predictable is we set for the most part, our customers through pilots and then they become scaled and they become super scaled and beyond. And we have data in our supplemental earnings release materials, but I'll kind of walk you through it. Within the first 12 months, an average pilot is called $100,000. In their first 12 months, our super scaled -- our customers in general gets around $700,000 in spend. In that next 1 to 3 years, that $700,000 on average goes to $1.1 million. In years 3 through 5, it goes to 2.1 million. And in years 5 plus on the platform, it goes to almost $4 million, $3.9 million. And there's years and years and years of this data, this pattern where once we start someone on the platform, we can reliably see how their spend is going to grow. And that spend happens, as you mentioned. It's up to this point, mostly happened through adding channels. We talked about 5 or more channels being up 50% year-to-year. But now in the last, call it, 1.5 years, once the One Zeta strategy was coming into play, which is the selling of multiple use cases, retain, grow or acquire any combination, which was up 90% year-over-year multi-use case, we're starting to see bigger deals happen. So one of the data points we shared in the last earnings call was deals that were won in the quarter. The average size of those deals was up 40% year-over-year. It's one thing to look at kind of what are the deals in the pipeline. Sellers can be aspirational at times in terms of what they loan to the pipe. But at the end of the day, what closes is what matters, and those deals were up on average 40%.

So we should expect this cohort data, the 5 years just gets better and better as more years go on.

Yes. I mean you talked about customers who will leave the platform. The reality is the longer you're with us, the more you spend, the more you use the platform.

Right. And why do you think that is the case? I know we're going to get into Athena, and we're going to get all the different tools or whatever. But what exactly is changing in Zeta's approach to market that's allowing that ramp from $100 to $700 to $4 million or what have you over 5 years?

We start by lowering total cost of ownership. So the initial value proposition is test us for $50,000 to $150,000 on something you're spending millions and millions on. Let us show you how we can lower your spend by starting your marketing strategy much lower in the funnel. We can tell you deterministically who is in market at this moment, not just who you'd be wasting spend on for someone that maybe isn't credit approved or not even in market. So we'll make you hyper efficient and will allow you to reduce technology spend. But what allows us to get bigger over time is provable value, right? So within our platform, we create a visibility mechanism to where they can measure the efficacy of their marketing spend, not just of Zeta, but handled against every other vendor they're spending with. And it's one of the best ways we go in as we say, let's put pixels down, challenge your other vendors to do the same, prove out the spend. We allow them to measure that efficacy, not just on Zeta scoring the homework, but on their own methodology. So this last touch attribution, multitouch attribution. If they want to take the data and move it to a third party like a Verizon would do and have that vendor really test out and prove the ROI, it's ultimately the return on spend that we're helping them generate that allows us the permission to go back and ask for more and to do more with them.

Measurement is one of the hardest things to prove out within marketing. And so talk to us about how you're able to prove out that measurement so you can build the ramp.

Yes, absolutely. So we basically first ask to pixel the website to be able to then track all of the traffic that's coming in. Not only traffic that we're driving in to Chris' point, be able to measure everyone else as well. By placing that pixel on the website, we can see all that referral across those different types of providers, the combination of the channels that we see people coming in under. And then we also ask for all the conversion information. So for e-commerce, would have a conversion pixel on that page, and we'd also ask for all their offline conversions to all go into the same omnichannel attribution dashboard. From there, we can then allow them to compare and contrast different types of attribution models because there's different preferences from one client to another. We allow them to then be able to see each of the channels broken out, the combination of those channels. I was even looking and analyzing one of the reports that we have for brands where we were looking at the combination of the channels. And weirdly, we saw that if you had a combination of display CTV and online video for this brand in particular, your performance was not as good as if you just had display and CTV or display and online video. And that indicated to us, you didn't need multiple brand awareness strategies around online video and CTV. You just needed one of them because then you're creating inefficiencies when you started to introduce more. But that's very unique to that brand. That might not be true for another brand. So to be able to see that holistic picture across their entire ecosystem and how we can connect them, I saw another brand, a huge amount invested into direct mail. Well, imagine the people that aren't engaging with you in direct mail. Let's find them in another channel where they're actually engaged, try to reengage them based on all the signals that we see here.

So we've spoken a lot about we just got through visibility and measurement, the data cloud and all the attributes. Let's move forward a little bit in talking about something that happened this year, which was the launch of Athena. And I think that's the intelligence layer, if you will. So maybe, Chris, I'll start with you specifically on Athena, we were testing it for some time. We went live in March. Just maybe bigger picture, tell us what it is. And then I won't say early, but relatively early results thus far. And Winnie, I'm going to come to you and ask you how Athena changed sort of the go-to-market opportunity when working with clients. So Chris, first to you on what it is.

We've been -- we've provided our customers with a sixth-generation fighter jet, where most are equipped to fly a Cessna. And in marketing parlay, that is an incredibly powerful platform where there is a vast amount of data. That feedback to us was it's almost overwhelming. Athena was initially created to be this facilitation tool. So how do I get our customers to use more of what's in the corners of the platform, right, really kind of take advantage of finding efficiencies and creating value. And in those early days of adoption, we're roughly, call it, 40% of our superscaled customers are now monthly active users, not just access to it. All customers now have access to the platform across enterprises and agencies. But who is actually using it every day and what patterns are emerging and what can we glean from that in terms of our revenue funnel. And what we're seeing is that of those 40% superscaled customers that are in the platform daily performing work, over 80% of how they're interacting is conversational, whereas prior to Athena, it was all keystrokes, it was all discovered on your own. Sometimes there was our hand helping them along the way. But for the most part, it was their hands on keyboard, really doing their discovery and their own analysis and planning and building of workflows. They're now engaging conversationally, which is driving more automations. And in our world of how we monetize, if you think about back to our revenue model, what customers pay us that subscription fee for is the creation of audiences and the analysis inside the platform. We're seeing thousands of more audiences and campaigns being created because they're now engaging in a conversational way rather than having to do it manually. Downstream from that should be the usage component of our revenue. So once you create an audience, put it through workflow, the next logical step in working with Zeta is now I want to go reach those individuals. Now I want to go acquire them. So the early optimistic signs are that it's performing as it's intended. It's making the platform easier to use. They're doing more output as a result of that, which should down the line lead to even further activation, more consumption-based revenue.

Got it.