Zeta Global Holdings Corp. (ZETA) September 9, 2026 Conference Transcript

Chris Winnie, thank you for joining us.

Thank you, Ron.

Morning, everyone. Let's get started. I'm Ron Josie. 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 represent 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 the 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 us, 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 it's a treat to have you with us today. We're going to get a nitty-gritty on what is the data cloud. Of course, you know everything about the business.

And we give her an IR badge for far too much of her time that she wants to do, but she's Yeah, 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 there full throttle. But initially, our primary buyer was the chief marketing officer. And if you put yourself in the chief marketing officer's 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 three 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 a primary buyer was the CFO. 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. So 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 that 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 that's happening outside your four walls so when you think about the applications of Zeta and a Palantir together. And we've talked about, you know, having several pilots underway and working on many, many more beyond that. These are the biggest brands 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 can be certainly marketing use cases. That's where we're beginning. But I think we could be added to be beyond marketing. Maybe you can give some examples of that.

And we actually just had a question about Ford in our previous meeting as an example. And so I was giving some examples of how we can create more intelligence for them outside of their for all. 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 mean to make sure to staff up on Spanish language speakers to be able to speak to those individuals. So it's 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, we're doing that in the dealership franchise model, we're doing that with 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 forward. 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?

Absolutely. Primarily are 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, but 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, I don't think you can do the math, but yes.

Chris, how old is Zeta?

Barely over 17 years old.

Barely over 17. Going on 20. One of the pioneers of Zeta, which is impressive, and congratulations on that. Let's die, and you're now in charge of the Zeta, 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 in 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 data bring this?

Yeah, absolutely. So we think about data in three core buckets. Identities, which are a representation of the individual. Identifiers, so those identifiers associate with you. Your personal email connected to your business email, 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 three vectors, we have a multitude of sources. We have our own demand side platform, a DSP essentially, where the bid auction is happening across our programmatic channels. We own our own SSP, the own supply side. That's a publisher network. While we own our own, we also plug in to all the major SSPs as well. We have our own message transfer agent. That enables us to send emails across RIPs in our domains, where we send more than 6 billion acquisition email 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 Live Intent. Think about premium newsletters like New York Times or Washington Post. You get an advertisement within that newsletter. We are powering the bid auction 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 big political cycle that's happening right now. We have which elections you're voting in, what party are you registered with, donation signals. We're now 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 caring 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 graphs, where we are creating more connections of 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 the Canadian ones, where are the differences? For their French consumers, how do we better market 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, Wendy and Chris, I'm coming back to you in a second. But, you know, over the last call it, I was gonna say three years, let's say 18 months. um 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 did things change significantly over these last couple months now that your partner have all these partnerships to unearth the data to help drive you know business results yeah 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 roadmap. 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 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's 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 um more customers are allocating greater spend to the platform one of the questions we often get is just about the visibility of the model sure and we kicked off the session talking about how zeta sits within so many different clouds whether it be the opposite 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 yeah you know what allows you to sleep at night yeah it's it's it's highly visible so if we first kind of decompose the revenue model itself you You know, call it 60% of the revenue model is what customers pay Zeta to license a 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 responsible, most responsive to which curriculum of activation, whether it's CTV, mobile, display, video, social. We have now five years worth of pattern data with customers. As you mentioned, the last three 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, you know, 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 then become super scaled and beyond. And we have data in our supplementary earnings release materials, but I'll kind of walk you through it. within the first 12 months, an average pilot's called 100K. In their first 12 months, our super scaled, our customers in general get to around 700K in spend. In that next one to three years, that 700K on average goes to 1.1 million. In years three through five, it goes to 2.1 million. And in years five plus on the platform, it goes to almost 4 million, 3.9 million. And there's years and years and years of this data of 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 five or more channels being up 50% year to year. But now in the last year and a half, once the one Zeta strategy was coming to 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 one 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 five years, just gets better and better as more years go on.

Yeah. I mean, you talked about customers don't leave the platform. The reality is the longer you're with us, the more you spend, the more you use the platform.

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 five years?

We start by lowering total cost of ownership. So the initial value proposition is test us for 50 to 150K 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 we'll 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 candled against every other vendor they're spending with and it's it's one of the best ways we go in is we say let's you know put pixels down um 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 methodologies, whether it's last-touch attribution, multi-touch 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 that you can build the round.

Yeah, absolutely. So we basically first asked 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's 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 omni-channel 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 a brand 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 our 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 re-engage 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 then, 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, you know, a sixth generation fighter jet, where most are equipped to fly a Cessna. And, and in marketing parlay, that is a incredibly powerful platform, where there is, you know, vast amount of data, that feedback to us was, it's almost overwhelming. yeah um 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 of of finding efficiencies and creating value and in those early days of adoption we're roughly caught 40 of our super scaled 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's 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% super skilled 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 discovery 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 i want to go acquire them so the 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 yep got it cool yeah and I'll add on and I'll give a real example of a meeting I recently had and how Athena played a huge part in it, but also address to some of the points earlier around like selling to the CTO,

the CFO, the other heads of the organization as well. We were with an agency where the head of their board was on the call, their CTO, their CFO. Funny enough, mostly not marketing people were on that call and they wanted to understand our platform and the value that we can bring to them. And one of the first things I talk about is we can white label our platform, give you the technology that you typically wouldn't have as an independent agency to be able to provide services like a CDP capability, to be able to onboard your first party and to glean all the intelligence and actually monetize the platform. Once I said monetize the platform, their CFO's eyes lit up and they're like, wait, let me play that back to you. We would be able to charge for it. They could have a subscription fee for it. It could be if you spend X amount, we'll be able to then monetize in this way. So very simple things that our platform was able to help support across those different types of decision makers. But then the next thing I wanted to show them is they're in tons of pitches. They send us a lot of different RFPs, very quick turns in helping respond to those RFPs. I show them how quickly and easy it is to do with Athena. So they were pitching a news media brand. And so we happen to have that data in our universe. They specifically were interested in their product line around cooking. I looked at our intenders. So in our intenders, people who are reading about recipes, who likes to cook and who likes to entertain. And for that persona, we have all the information around them. What types of entertainment do they like? What kind of cuisines do they like? What types of food delivery services are they typically using? And I basically asked Athena to help me create a persona based on all those attributes and signals. It took all that massive amount of information, things that would probably take you days to actually analyze and put together. It gave me this very long output. Thank you, Athena. But then it's also hard to digest just in a chat window, right? Then I had to create a PowerPoint slide for me. Clear, concise, executive ready that we could then share with them, that they could then share with the end client that they're pitching. So not only creating that first persona, but even sub-personas based on the signals that we're seeing and even giving them recommendations across their different product lines which one should you feature when based on the trends that we're seeing all of this Athena did within minutes this call was only maybe like 45 minutes long I could show them all these things that we could do that you could then show that that end client that you're pitching within minutes so removes a lot of friction makes things more smooth and you're seeing significant adoption yeah um and we're only a quarter in a quarter ish in yeah called a couple quarters yeah and then how does palantir fit into this equation so we're now on foundry we're seeing more wins i think chris just talk

to us about the palantir integration that we've been seeing and how do you how does that bridge us with the benefits and the uh the use cases or the capabilities of it yeah it's multifaceted so i think first off integrating zeta's data or i should say you know having having zeta's data the cloud move from AWS onto Foundry makes the implementation process with Palantir's customers that much more seamless, but it also makes our process, our speeds at which we can deliver analysis much faster. Palantir is interesting from other partnerships that you mentioned earlier in that. So of the ones that, you know, were kind of newly minted this year, there's, you know, Snowflake, there's OpenAI. Palantir is unique in that we also, in the course of delivering this seven-year contract, created incentives around joint selling that escalate over the years. It could be number of wins that we have, but then the ACV tied to wins. So when we were able to put into the press release that we would envision this being at least $100 million business on a per year basis, it was actually tied to goals and incentives. So even to little old Zeta, it's obviously pretty meaningful, but it's meaningful even to Palantir at those levels. the go-to-market engagements one-to-one so it's not just zeta sellers going in it is uh palantir sellers with our our senior sellers in the company and and you know david's leaving a lot of these right now himself which is awesome um he's our best salesperson um and you know i think it's got a lot of promise it's early days too though so walk me through the 100 100 million dollars again because that's a number that was thrown out of the press release we've talked about it quite a bit the the goals and incentives side of it incentive side of it all yeah talk to us about what break that down a little bit more yep so it's a rev share model um uh their their take if you will comes off the top so it's not a margin impact um they're meaning counter correct uh it is a higher margin product for zeta uh it tends to be more leveraging our data analysis which is very limited cost to us um i think it could lead to activation down the road but right now it's you know more on the analysis side uh and the business intelligence side. But it is, you know, we didn't disclose the tiers that have to happen over the years, but it's an escalating amount of client wins per year. And it's an escalating total value of contracts per year.

Since we're talking escalating wins and contracts and everything, talk to us about, I think we saw two pilots and we have several others in the hopper.

Yeah, we're working on, we've ring fenced 20 specific opportunities with their largest customers with the largest marketing spend. So these are all brands spending over a billion a year. They are emerging to be bigger than our normal pilots. I mentioned earlier, 50 to 150k. These are kind of, you know, add a zero to them, which is great.

Okay. Every quarter, we'll hear more about this. Yeah.

I think in every earnings call, I would be surprised if there wasn't a mention for a while.

Okay. And Winnie, with the integration of Foundry, How does it amplify the cloud, the marketing cloud? How does it amplify Athena's capabilities? And ultimately, so that's two questions. Chris, we're going to come back to you. With Palantir plus Athena, maybe I should talk about Snowflake and OpenAI, but with Palantir and Athena, how does it allow you to get into other areas in the organization? So if you remember the question, Whitney, talk to us about how this amplifies the data cloud.

Yeah, so oftentimes our Athena right now, as it is with all of our data cloud applications, can interpret and make recommendations based on what it sees in our data and what it can find across the open web. Now, you can imagine how much powerful it gets with the context of the organization applied to it, knowing what they care about, their business rules, their compliance, their rules, their regulations, what are their KPIs and their goals. Having that context will make the recommendations even more powerful and insightful for those organizations.

Great. And Chris, how do we expand to other parts of organization?

Yeah.

Meaning, you know, outside the Office of CMO and the BFO, Chief Strategy Officer.

I think it's, you know, I think it's, you know, we put in our supplemental deck, we wanted to show what we're further use cases we're being pulled into. And these were the applications of, I mentioned earlier, you know, location-based analysis, customer service analysis, what new lines of business should I be moving into? All of those are natural extensions that we could also be doing with Palantir's capabilities.

How easy is that conversation? So, in other words, the CMO calls up his or her counterpart in the tech organization. Give us a little bit more there. because it's not as, I mean, I know everyone is on the same executive committee and we're talking about the opportunities, but it's a little bit more than that.

Specific to Palantir. And if Dave were to hear what he would tell you is that these sales engagements are going faster than they normally would. I think there's immense credibility. There is a passion, positive passion for how customers love Palantir and what they're doing for them. That is breaking down some of the speed bumps that normally exist when you're pitching a big ticket item. So deals are moving faster. Deals are moving wider into the organization as a result of this partnership. We were seeing this, by the way, even within our own sales cycles. I think a lot of times we'll get the question is, given the macro environment, are you seeing extended deal cycles? And our deal cycles have been consistent because most of our deals close in two to five months because they're pilots. They're not subject to RFPs. That could be five to 15 months. So that's not out of the ordinary for us, but even moving faster than what we would typically see.

Got it. We have about three minutes left here. I want to open up for anyone in the audience, any questions, and we can think about the questions. I keep going as well. So, okay, we'll keep going here. So I want to get to margins specifically, but before I do, we've talked about your other partnerships that were announced, the Snowflake and OpenAI. How do those fit into Palantir's Foundry and Athena's launch?

Yep. So for OpenAI, the voice that you hear of Athena is powered by OpenAI. We are also accessing some of their newer models that we can test out earlier. And then we're also actually one of their clients on the advertiser side. So we're actually testing out their advertising within their ChatGPT model. On the Snowflake side, a clean room environment enables us to get customer data faster into our platform, since they're already on Subflake, we are as well, that we can then bring in that data very quickly and glean intelligence faster, a path to intelligence there. We also enable clients to run their own BI models in that clean room environment because they'll typically have their own analytics team that they want to be able to run anything that they think is going to be most powerful for them. And then we also provide in their marketplace a lightweight version of our customer pulse that basically gleans intelligence on brands at first party data as well. So really trying to create more opportunities for us across their network of clients, too.

So as we as we wrap this up and we think about the core platform with the data, the new go to market approach, the ability for these not RFPs, but pilots feed a larger level. Chris, talk to us just about the margin side, because when I see this and the ability to upsell and I hear larger numbers, it would suggest margins could start to expand. So how do you think about that?

Yeah, look, I think it's all additive for sure. So if you kind of start with the top javelin throw, which was by 2030, we wanted to get to at least 30% adjusted EBITDA margins, which is call it pretty darn close to 20% free cash margins. You know, everything we've talked through today is part of that roadmap to get to those levels. We have consistently grown free cash flow margins even faster than even margins and even margins have been expanding every quarter and every year while we've been growing 20%. So we haven't had kind of that make that trade off for growth versus profitability. I think we can over the longer term, we can get anywhere from 100 to 300 basis points of gross margin improvement. I think we can continue to be very efficient in G&A. I think we can continue to be very efficient in sales and marketing, and as well as R&D. We talked about last quarter, almost 90% of new code that was generated was done in a fully automated way. So each one of those OPEX areas as it relates to expense to revenue, like you saw last quarter, should continue to see strong efficiency gains that get us from, call it this year, a little north of 22% of just even margins on our path to 30. Okay.

Look forward to continued updates on that market structure because there's all these new changes in the market to the business. So I think we're at time. Thank you, Chris and Winnie, for joining us today. And I look forward to next time.

Awesome. Thank you.