Title: Guest Thesis: Zeta, The Next Global Marketing Platform
Show: Joseph Carlson - Qualtrim Studio (Investor Exchange)
Guest: "Nick" (Qualtrim / Joseph Carlson Show community member; last name withheld; robotics / electro-mechanical engineering grad; 42% of his portfolio in ZETA), hosted by Joseph Carlson
Date: 2026-AUG-23
URL: https://www.qualtrim.com/app/studio/watch/cfe059aa-e3b9-4734-8cd8-089ea6c4ed52
Length: 1:48:25
Note: Transcript from the in-page English subtitle (WebVTT) track of the self-hosted, login-gated Qualtrim Studio video (no youtu.be deep-links); (mm:ss) cues real, grouped ~15s. Fillers (um/uh) and immediate word repeats removed; wording, numbers and names otherwise verbatim (auto-caption mis-hearings left as captioned).

(00:00) I won't say your last name, but welcome, man. We have Nick here and we're going to be going over the stock Zeta Global. Now this is one that I'm not familiar with. So I haven't done any deep dives on this one. But I've seen what feels like a little bit of a it's like a small community, Phil, that's starting to just grow a little bit.
(00:18) You know, when you fill that with stocks, when you see ones online that I'm like, I hear the chatter on the horizon. There's like this new stock, interesting ticker, pretty unfamiliar. But it's starting to grow a little bit of a I don't want to call it a cult because that's a bit demeaning, but
(00:33) I think a very active, enthused investor base that is doing deep research on a so far a stock that is on the fringe. And I see that with Zeta Global, I see the name around. I see a couple people very excited about it.
(00:48) There are some people I see that are super bullish, a couple that are not believers in it, and I've seen that happen. I think over maybe the past year the stock is getting a little bit more popular. And so we're going to be going over Zeta. That's the stock that I want you to kind of look at this presentation.
(01:04) And we'll be we'll be looking at let's see if I have it right here, this presentation right here, what you've built for us. So we'll be going through that. But before we get in. I first want to just get a little bit of background about you because you have all of this research.
(01:20) You've done a lot of work on this, and I think it'd be good for people to know how you got into the Joseph Carlson Show community, how you found the channel, what your background is as an investor, and we're so excited about this company. So if you want to go into any of that, that that'd be a good start. Sure. Yeah.
(01:35) No worries. So I approach investing from more of a technological perspective. I went to school for robotics. So this to me is sort of just natural. I love technology and I kind of affiliate with technology in all kinds of different realms. So you said you're doing a school for robotics?
(01:50) No, I graduated from school from robotics. Oh, okay. Yeah. Technically electro mechanical engineering okay. That's amazing. Not a lot of people, you know, go into the abbreviated term they came up with is robotics because, you know, it is this very long title
(02:08) and they don't want to just paste it everywhere. No, it clicked anyway. That's cool. Yeah. Yeah. So that being said, I kind of gravitate to some of these more complicated thesis sometimes and or complicated companies and I like to pick them apart, just sort of a thought experiment.
(02:23) And I stumbled across data not specifically because it was just, you know, technologically different company. I didn't know that at the time. But I have this screener and I've had it for quite some time that kind of flags companies that have a fundamental drop in their stock price. But the fundamentals of the company haven't changed.
(02:40) Yeah. And it flagged roughly I think it was like January 2025. This was after they reported there. I think it was their Q4 2024 earnings. And a short report came out against the company. At that point in time, I didn't really think much of it.
(02:56) I just had on my watch list. And then it was later in 2025, I believe it was May, April, May when I really started digging the company. I listened to their Q1 earnings, the Q1 earnings call, and, you know, I started peel back that veil a little bit and dig into what is it, a global.
(03:12) So that's kind of, you know, some of my background where I started and when I started to look at Zeta. Okay. So you said what year was that 2021. No, that was 2024 okay. Early. So new relatively new couple a couple of years old.
(03:29) When did to IPO. It was back in 2021 okay. So the IPO in 2021 you found out about it in 2024. And you said that it was like a short report that drew you to it. Yeah. Which obviously caused the stock price to plummet. And it triggered some.
(03:45) It was like a notification in my stock screeners that sort of said, hey look at this is a stock that, you know, has quite strong fundamentals. But the price all of a sudden dropped suddenly. What were the shorts like accusing the company of. They were essentially accusing them of round tripping. And then since then Zeta Global has come out and they've had Deloitte
(04:03) do an audit. And Deloitte has found that there is no round tripping. So essentially now what is round tripping? Round tripping is essentially somebody saying like, hey, you buy my product and then I'll turn around by your product and you buy my product again. So at Nvidia and you know, all of them are accused of doing the circular financing.
(04:22) Yes. Yes. Yeah. That's it's a little bit different, I guess, when you're just making agreements to buy each other's product to boost your revenue. And so you looked at that. Did you think there was any truth to it where you're like, oh, this is actually there's some validity to it, or was it just totally bogus?
(04:37) Well, at first I was, you know, speculative because obviously that's a huge claim. So that's why I kind of waited I went into their Q1 call. By that time they had Deloitte do they're on it. And they found out, okay, the they essentially debunked the vast majority of the short report. The only thing that is currently in limbo right now is a debate
(04:54) over opt in data the company doesn't technically have. At least this is what the plaintiffs are arguing, a definition of what opt in means, or what is the standard to opt in, right? Zeta says, hey, you can opt in all kinds of different forms.
(05:10) So when you accept cookies, that's a form of opting in. When you sign up on using your email on an account, or if you sign up for our newsletter. These are all different forms of opting in. And because they don't actually have that written down somewhere, they've made a case and they're going after Zeta to say, hey, they've misled investors.
(05:27) That sounds like lawyer exploitation. Okay. So yeah, interesting. Interesting short case. That's not a fundamental short case though. That's not like a it's a bad product that will do poorly. So we think well the actual the original short case
(05:45) was that they said, hey by the way, they're doing some sort of round tripping and they're wrongfully taking people's data because people aren't opted in. Therefore, a vast majority of their revenue is just not legible. Right.
(06:00) So when Deloitte came out, they audited the numbers and they said, okay, there's absolutely no round tripping happening here. This is just completely wrong. Then the case kind of evolves to this lawsuit specifically about the definition of opt in data.
(06:16) Okay. Good. Well let's go ahead and jump in. I first want to go over. We can go into the presentation. And I think it would be good. You know, from the perspective at least starting off to just give an overview of what the company is on a super rudimentary level, what it does, how it makes money.
(06:33) Sure. Yeah. So Zeta Global, essentially this marketing company, but a marketing company that focuses on proprietary first party data, this is their sort of unique proprietary advantage, what you could call their moat. From this data, Zeta's been able to then activate and launch
(06:51) into all kinds of different verticals. And we can get that get to that those topics here shortly. The cool thing is that when I built this slide deck, I just kind of compiled all kinds of different bits of information that I've posted over the last year or two.
(07:06) That being said, I start off at a really good place. So if you want to bring up the slide show, I kind of touch on what is a marketing cloud, and then we get into the competitors and why Zeta is a little different. So if we go to the second slide okay, let's let's go down. I like how this is like this is cool I like this presentation.
(07:22) It's fun. That's awesome. Yeah. So just so everybody knows full transparency you may look a little AI. Well definitely does look a little AI because I dumped all of my solid posts or solid content and research into cloud and said, hey, spit me out a wonderful slide deck ready to go.
(07:39) And this is what a this what it produced. No, that's for sure. To use AI. If you're making it come up with the content then that's a problem because you're just outsourcing your thinking to it. But I use it the same thing. I say, hey, here's my all my thoughts on this subject. Can you make like a flow chart that represents this?
(07:55) And it will be like I'm designing a flow chart. So, you know, I use it for that as well. I use a ChatGPT or whatever for that as well. I don't see any issues with that. What are we looking at here with this first one? Okay, so what I Zeta falls into this category of marketing clouds, right?
(08:12) Jim Cramer recently came out and says, hey, Zeta is difficult to figure out because it's just another marketing cloud. Well, technically, yeah, it's another marketing cloud, but it's quite different than the other marketing clouds. And I'm going to explain to you why. But first step, what is a marketing cloud? A marketing cloud is essentially this place where an enterprise
(08:28) or large medium sized businesses can amalgamate all their customer data or their customer record, transaction history, profiles, loyalty programs, but also in the exact same environment bring in things like their creative studio so other, you know, creative assets, videos, pictures, media campaigns,
(08:46) and then in theory, in this marketing cloud, they'd be able to build a campaign and then activate it. What activating means it's just a fancy marketing lingo term to say, hey, I'm actually going to launch an ad campaign on some sort of surface or some sort of channel.
(09:02) A channel or surface is also another fancy term to mean, hey, I'm going to launch a campaign on meta Google via email newsletter in the mail. You know, a TV ad on Netflix, Spotify, those are all different channels. Those are all different surfaces.
(09:17) So that kind of breaks down some. Okay, what is a marketing cloud? What is the intent of a marketing cloud? How do companies use it. And then you know, again, just normalizing here just so that I can prime this conversation as we kind of get deeper and deeper into this thesis, what is a channel, what does activation mean, etc., etc..
(09:34) So the marketing clouds where you basically put all your company's assets and information into the system so it can kind of understand everything it has to work with, all the assets has to work with. And then you activate a campaign where it advertises in different channels.
(09:50) Does that sound correct? Okay, I think I got it now where things are a little different is that some marketing clouds have a vast number of channels they can activate on, some only activate for a few channels, some activate
(10:07) for some of the competition, or refuse to activate on other channels. So it's quite a competitive domain, but the idea is that you have one platform, one cloud where you put all of your customer data. You dump everything there, including your marketing sort of creatives and whatnot,
(10:25) so that you could build these campaigns and activate hidden behind your screen. Right now, Joseph, is the purpose of why somebody would use a marketing cloud. The intention of using a marketing cloud is to usually do one or a combination
(10:41) of three things, that is, retain your current audience as an enterprise. Meaning okay, how do I keep people spending? How do I keep people using the subscription, etc. etc.? How do I grow my audience to make them the exact same audience? Spend more money incrementally okay with my company?
(10:58) And then finally, this is a much more difficult thing. How do I acquire a brand new individual to buy my product? Yeah, it's part of the like as part of the value proposition of a Zeta marketing cloud rather than like.
(11:15) So I'm looking at this and I'm thinking like, well, you know, you can do some of this stuff on like meta, some of it on Salesforce, some of it on Shopify. Is it to have a more autonomy is it's one thing and you can launch it on any system. It's kind of divorced from any single platform or tied.
(11:31) It's not tied to any single platform. Is that why people would prefer something like this? Yeah. So you pretty much hit the nail on the head and you're kind of thinking ahead here, which is awesome because I think you're starting to understand this thesis already, Zeta. And we'll get to this in a couple slides.
(11:46) One is Zeta's core value propositions is it's a agnostic hub. So you get to upload all of your data. And that data can compound over time. And all the intelligence that you derive from that data stays on one platform that you can agnostic integrate into any activation channel.
(12:05) So Zeta can activate on meta, Google, Reddit, you know, let's say Spotify, Netflix, email, SMS, CTV, the list goes on and on and on and on. That's just one place where you can upload all of your data, continue
(12:21) to grow that data and compound that information, learn from that information, and then implement that into campaigns, and then deploy those campaigns in how this iterative process again and again and again. There's another piece of this puzzle. We'll touch on this in a little bit. I don't want to get too far ahead of the slide deck,
(12:37) but you can go to the next one if you want. Sure. Let's go. All right. So we're looking at this legacy. MarTech loses value between systems. Correct. Okay. So what I wanted to capture here is what is that core
(12:52) difference between Zeta and sort of highlighting Zeta's value proposition versus some of these legacy systems? The marketing cloud industry is quite old actually. It's like in some aspects about 20 years old, you have legacy players like Adobe, Salesforce, Oracle, SAP, so on and so forth.
(13:10) More recently, you have companies like the Trade Desk spawning or is it a global spawning or Braes or Clavijo, these type of companies that are coming up and offering competitive, you know, solutions that are leaps and bounds better than what some of your legacy players would do now
(13:26) looking at that architecture back on screen, on the left hand side in the red, these legacy marketing clouds have been built over this ten, 20 year period where essentially it's just a bunch of acquisitions that were kind of plugged on to the core
(13:42) marketing cloud, and that may be a CRM platform. Let's let's think about Salesforce, for example. That is hard to CRM platform. The marketing cloud was something they acquired as an adjacent product that they could kind of use as a flywheel to sell their customer, their enterprise audience into.
(14:00) So what happens here is that these are separate silos. And because they're separate silos, every time you communicate with them, there's latency. And because we're moving into a world where marketing and campaigns are required to be at such a high frequency in order to reach an individual,
(14:18) or if an individual engages something online or completes a Google search, or a search in Chatbot or Gemini or whatever it may be within milliseconds, Zeta as an alternative, can deploy an ad where these companies struggle because there's so much latency.
(14:34) For example, you have to go from that CRM platform into the marketing cloud. Marketing cloud goes to the LM pings back to the marketing cloud. They then process that result reference to third party data, which then feeds back into the marketing cloud. And then they can activate.
(14:49) On the flip side of the equation, you have a zeta global. Like I said, they can deploy an ad within milliseconds of getting some sort of signal. The signals essentially, you know, kind of what I alluded to, if somebody searched something on Google or search something on, that's a signal that says, hey,
(15:04) this individual is highly likely to be interested in X product. Let's deploy an ad. What is the best service or what is the best channel for us to activate that ad and reach that individual? Each individual is different, and we could touch on how Zeta targets an individual in a moment.
(15:21) But going back to that graphic, I just want to kind of highlight just before I conclude this one point is eight on the right hand side. As you can see in those dashed lines, this is a single platform, a single ecosystem. See it as a unified ecosystem. And within a unified ecosystem you can have data.
(15:38) Customer data and reasoning all happen in one platform, one ecosystem, in order to activate rapidly. That's the architectural sort of difference between some of these legacy players. That gives them a competitive advantage. Sorry, I know that's long winded. So I'll kind of in there for a minute.
(15:54) Yeah. So from a okay. So if you're basically how would this be represented from the company's perspective. Like if you're going to say, hey it's going to work this different, you know, this different architecture, it's going to work this different way. How does that manifest to the company itself?
(16:09) Are they going to see like quicker turnaround times with effectiveness of ads or like what are they expecting to see as the benefit of this type of advancement? Okay, so Zeta essentially has a return on ad spend guarantee or a threshold that is much higher than the competitor.
(16:27) So in today's world, a lot of the marketing agencies will sort of market to two sort of benchmarks, which is cost per purchaser CPP. And then you have Roas, which is return on AD spend, ROA s return on AD spend essentially.
(16:43) Okay, how much money do I put up and then how much revenue do I generate on the back end? Okay. That's what data is measuring to. And customers are actually seeing a much greater return on ad spend on average. So management cites a third party report which I believe was done
(16:59) by it's escaping me anyways. But they've cited on average data returns, roughly 600% return on ad spend for their enterprise class. Huge. You don't want to pile more money into ads when you're getting that.
(17:14) Yeah, 100%. It almost becomes an arbitrage, right? It's like the more you spend, the more revenue you produce. One of the concerns I think about is with the marketing cloud system. I know a lot of companies that are also very well capitalized and have lots of talent.
(17:29) They're also like more vertically integrating their marketing efforts. So like if you look at like meta, right, they're like, hey, we're going to make it way easier to market on our system. We're going to give you like AI tools and analytics and all this type of stuff that can already build these campaigns
(17:46) is that are investors even concerned about that as a potential concern for Zeta, or is it entirely different? So Zeta made a choice. Management actually talks about this quite transparently. And the CEO, David Steinberg, actually spoke to me about this personally.
(18:03) Data made a choice years ago to not compete with the walled gardens. The walled gardens or ecosystems kind of define that like Google or Reddit or Meta. These are platforms that kind of control the ability to monetize on their platform or list the ads on their platform.
(18:18) They control that data in that ecosystem, and they control the ability for somebody to come in and activate on it. Instead, what they decided to do was to integrate and partner with them. So Zeta actually has partnerships with Google and with meta to pair.
(18:34) And we'll get to this in a little bit. So yeah, we're jumping the gun in just slightly here. But they get to pair. It is own proprietary data with Meta's proprietary data and the consumer or sorry the customers, the client, the enterprise clients proprietary data. And through that they're able then to triangulate the most likely
(18:53) and the strongest return on ad spend for the client. Yeah. Well, it makes sense because meta doesn't like they don't. Zeta's not their concern. Like they don't want to destroy Zeta. It's not even like a competitor to them. No, they actually bring money to meta.
(19:08) So. Yeah. So it doesn't make sense that they would they just want to make it easy to advertise. So theta does that. That's great. If meta does that themselves that's great. I think that's the way they look at it. They just want people to freely and easily advertise. So that makes total sense okay. So we can continue on I'm going to go.
(19:23) Do you want to go to the next slide then. Yeah. Next slide works. We start getting into more of the proprietary aspect about Zeta here. So this is this is this will be good. And it's very heavy on the front end. Once we get to the back end you know it starts to get a lot more sense. The super graph is the moat beneath the model.
(19:41) There proprietary data cloud continuously resolves who is a consumer is and what they're doing and what they may do next. Yeah, this is what I read about when I did some brief like just looked at the company is basically they have a huge network of customers
(19:56) and they leverage that to be able to like advertise outside of outside of the company's individual knowledge of a of the customer. Is that right? So Zeta is very unique because Zeta has its own proprietary database, specifically 552 million IDs in their own,
(20:14) what they call a super graph or their data cloud. In this proprietary relationship, Zeta is able then to pair their data and their profiles with the enterprise. They're essentially their client, and by doing so, Zeta's
(20:30) then able to identify okay, almost immediately. What does the archetype of this clients customer look like, because they're able then to pair within their database? Somebody from the loyalty membership program of, let's say, American Eagle.
(20:47) And by kind of pooling all these identities together, they're able then to identify common characteristics and or sort of signals that build this sort of archetype of the ideal customer. Then from the remaining IDs or individuals in Zeta's data,
(21:04) Graph Zeta is then able to target and acquire new customers for that client. So in this relationship, and I know that can be a little confusing. And there's going to be some graphics on screen here to help sort of, you know, articulate this sort of technological pairing here.
(21:22) But in this relationship, every single enterprise client that works with Zeta Global never, ever loses the proprietary nature of their own database. They only gain the proprietary nature or knowledge of Zeta's database.
(21:40) But that information can only be shared on Zeta's ecosystem on their platform. So part of Zeta's value proposition. On top of being this unified ecosystem that reduces latency, they also are agnostic activator.
(21:56) So they'll deploy and work with anybody, which incentivizes enterprises to work with them so they can keep their data in one place. But Zeta also brings their own proprietary data that nobody else, no LLM, no other party ever sees this data to the equation.
(22:11) Instead of using third party data assets that anybody could go and subscribe to online, Zeta only has this proprietary data asset that they've been able to produce, which helps enrich targeting. Okay. So that's so basically Zeta is not like it's not like a,
(22:29) you know, Dillard's like hooks up with Zeta and Dillard's has a bunch of like emails and phone numbers of people. Right. Is Dillard's when they agree to hook up with Zeta, are they feeding Zeta their customer information into that data cloud, that zeta
(22:47) that the Zeta uses to benefit other customers of Zeta's. So okay. Yeah, I just want to make this extremely clear, because I know this can be a little complicated if Dillard's or American Eagle were to upload their consumer data and migrate
(23:03) everything to Zeta's Data Cloud, nobody else that is a client of Zeta can see their consumer data or their customer data. Does that make sense? Yeah. So it's proprietary to them. They never share that with anybody. So if we go back to the slideshow really quick.
(23:20) Yeah. Zeta's data graph, like I mentioned, is 552 million people globally and 240 million people in America. That's nearly the entire adult audience, by the way. Every single day. It's their customers data that is in their graph, though.
(23:37) Or is this like proprietary? And if it's proprietary, where are they getting it? That's that'll be on the next slide. Okay. We're getting through. We can actually skip to the next slide. Okay. Let's do it. All right. We're here for we dive into the data rails and how Zeta collects
(23:54) this information from each individual and informs the super graph Zeta. Essentially, out of these 552 million profiles or 240 million Americans specifically, and vast majority of the revenue comes from the US, by the way. So they have this huge ramp into the
(24:12) enterprise world or the sort of this consumer reach world in the rest of the world, whether that be Europe, Canada, etc., etc., etc.. So that's a huge runway in front of them. But circling back to the topic, but the super graph, every single one of these 550, 552
(24:28) million IDs, each one of them are like a little profile within the super graph. Essentially what I wanted to kind of, you know, kind of visualize here as sort of the listener. Imagine every one of those IDs is a Facebook file
(24:43) or an Instagram profile every single day. Zeta refreshes or is updating each one of those profiles of an individual with over 6000 new signals. So those are little bits of information
(24:59) that update that profile with the most recent information. That is relative to marketing that somebody may capitalize on. Where are they sort of like what's what? Where do they get those signals to update? Like where does that come from? Let's go back to the screen okay.
(25:15) So this is sort of capturing what are those data rails. And the data rails essentially a touchpoint a conduit if you will, where Zeta is able to reach in and collect information in a permissive way, which means somebody opted into this,
(25:30) whether it be through cookies, that's just one option for something even more intimate, which is something like signing up for an email and building a profile. One of the most notorious methods or not, maybe notorious isn't the best way to kind of articulated methods.
(25:48) Yeah, one of the most predominant. That's the word I was looking for. More of the most predominant methods Zeta uses to collect data is through something called discuss. Discuss is one of the world's largest open sort of widget commenting platforms.
(26:04) And you could see that on the very bottom right in the middle. And so that's the is that kind of like the free to use like chat commenting system. Correct. Okay. Correct. I've seen you seen that a while ago. Yeah.
(26:19) You make an account once you use an email, you sign in and then you're signed in wherever it's used across any platform for TMZ uses it. Right. And that's just one example. There's other newspapers who use it as well. So essentially this is a permissive relationship.
(26:35) Or Zeta is able to collect your email, hash your email, which again just a fancy way to say, hey, we give it a special ID code to protect your privacy. And then they're able to track certain interactions online across different services or different ecosystems and platforms and update your
(26:53) your profile, essentially your ID in their data graph against that. And that's just one of the mechanisms they use to collect information. So as like discuss own separately. And is that like an expense item. Are they just like licensing the data there? Nope. They acquired discuss back in 2017.
(27:10) Yeah okay. So they actually are like vertically integrating with the rails that capture the actual signals for them. Correct? Correct. And that's just one method. Yeah okay. Are there any other big are there any other big ones that would be recognizable like discuss.
(27:25) Yeah. So one that you probably won't know but you will know the names who use it is called Live Intent. So in 20 I think it was 2024. Zeta acquired live Intent. And Live Intent is essentially one of the largest publishers in the world for newsletters. So companies like Washington Post, New York Times, Uber, Groupon,
(27:44) they all use live intent as this mass publisher so they can send out email notifications to their subscribers for discounts or, you know, your group on codes or your new newsletters, and by proxy, in order to collect information.
(28:00) Because it's a transactional relationship, Zeta gains these hashed IDs for anybody who subscribes to these services, because they're able then to send that email out. And when that person clicks on the email, they interact with it, they go to the site. And Zeta has their own software, little pixel, whether it's depending on
(28:18) if it's an app or whatever it may be on the app, it's a software development kit on the website, it's a pixel. And they're able then to track little bits of information about what that user does on that page, okay. And then feeds the super graph or their data cloud
(28:33) kind of update these profiles about each individual user. So that's the live tag email opens plus click. That's probably the live intent. They're in Live Connect account login and authentication. And that's the publisher websites newsletters 3000 plus publishers.
(28:49) And then you have discuss the auto login comment authentication their widgets and then zinc pixel advertiser site traffic. What does zinc pixel that Zeta's own proprietary pixel. So that's what they started with back in 2017 when they made the shift to be a data
(29:05) first company. They made a couple of acquisitions. Discuss was one boom train was another, and we'll touch on that in a little bit because data, you know, really emphasizes machine learning as a core tenant of their value proposition. And that's kind of how they're able to and to make all these decisions in real time.
(29:21) It's with these own proprietary machine learning models that are not large language models. But what David kind of calls David the CEO. Mid-sized models. Okay. Yeah. Yeah. And so in 2017, it was very pivotal year for Zeta because Zeta
(29:36) did their entire marketing cloud platform to be this unified ecosystem, an ecosystem where you have the data rail on the bottom above that, you have this sort of transactional exchange where the client uploads their data and you pair it to Zeta's data
(29:52) to make a unified ID, and then above that, you're able then to create campaigns and activate upon those campaigns. So Zeta acquired discuss and a company called Boom Train. And one of the founders of Boom Train is now the chief technology officer Chris Malmberg of Zeta Global.
(30:11) So for somebody who built a machine learning company to focus on activation on all these proprietary signals, to better informed decisions, to then become the chief technology officer after an acquisition and still be that chief technology officer nearly ten years later, is quite
(30:29) telling that, you know, he's somebody who's supportive of where the company is going and how they're innovating and how they're progressing as a company now. Yeah, no. That's awesome. I think it's it's cool. They have those their own proprietary rails to gather information. Do you think that they're going to continue to try to acquire new rails
(30:46) and new ways of gathering signals like that? Yeah. So Zeta is known for doing acquisitions like this is something that they kind of take pride in, but they don't take act like they don't take on acquisitions that would, like, revolutionize the company per se.
(31:02) What do I mean by that? I mean, they don't go out and spend $1 billion acquiring a company that would put the Zeta themselves in a financially poor situation, and then also strip back another company to its bare bones and, you know, just gut it out completely.
(31:18) What they like to do is acquire a company that will be a creative from within the first 6 to 12 months. They'll be able to integrate that vertically into their team and then have it become part of that organic structure from here on forward within a 12 month period.
(31:34) Okay, awesome. I'm looking at the next one here. It says there's an identity appearing in Zeta's defining edge. Live signals meet client data. One privacy safe identity exists. So basically you got the signals and then you have them matching that
(31:50) with their customers and how that they basically have to pair. How are these signals useful to what our customers are doing. Is that what's going on here. Yeah. So like back to what I said earlier and I did briefly mention this. And again, this is why it is quite confusing.
(32:05) So if you're not somebody who's spawned a technology, I don't blame you for not being interested in this company. But for me, you know, I've taken a lot of time to understand it. I love technology, so it's something I'm quite passionate about. So essentially what Zeta does here is they take that entire data cloud
(32:20) that can that consumer database, all these customer relationship sort of histories or profiles, whatever may be from let's say American Eagle Zeta has their own database, which we just finished talking about. And in this clean room, they're able then to pair the two together and say,
(32:38) okay, what IDs or what individuals, what emails or people are shared in our database and in American Eagle's database. Through that, Zeta is able to identify in real time without the customer spending a single dollar on marketing.
(32:54) Okay. What are the current customers that we can identify? What is their archetype? So when I mentioned earlier that sort of archetype, the go to profile of that customer for that specific brand or enterprise, this is what happens at this moment.
(33:11) Zeta is able then to identify and match all these different ideas together in order to establish what are the common traits amongst all of their clientele. From that, Zabel Zeta is then able then to target against that archetype to say, hey, we're only going to target individuals
(33:29) with these common signals or these common traits to meet this archetype, and then Zeta is able then to almost guarantee a much better return on investment or return on spend when you then launch your ad campaigns, because they have this deterministic match, you know, for a fact
(33:45) you're deploying and updating the profiles in real time for your current audience. You can guarantee that deterministically because you're able to pair these two databases, but then with a very high degree of confidence, you can activate and try to acquire a brand new audience based on the,
(34:02) you know, that archetype profile of what your current clientele or your current loyalty program may prescribe or capture. They basically they take in all those rails, all that information that's just like broad data that's proprietary to Zeta.
(34:17) So they're not they're not going and sharing that data with other customers per se. But when a customer becomes a client of theirs, they will utilize that data to get a higher ROI for that customer, so long as they're using Zeta.
(34:32) Correct. Okay. That that makes sense. And this is this basically like what most marketing cloud companies like Zeta's competitors are doing, but they have different rails and different matching? Or are they actually doing
(34:48) is a system architected differently for their competitors? So a lot of the architecture of competitors differs fundamentally from what Zeta is created from. What I've been able to identify is it is the only true unified ecosystem, and Zeta is the only one out of all of these competitors
(35:06) who has proprietary data to this extent. Yeah, a lot of the competitors will go out there and Salesforce and Adobe. Artorius for this. And the Trade Desk also is quite notorious for this leaning on third party data signals, meaning, hey, we go to some sort of third party vendor who
(35:23) specialize in data collection, like most literally a company like discuss. Go out there, subscribe to them, lease their data and we get to use it. It's sort of like an LLM using Reddit's data. Well, Salesforce would say, oh, well, we have, you know, how many businesses using us
(35:38) and how many customers going through those businesses, right? Correct. But the relationship. Right. The Salesforce can't just go without permission from their client, like let's just say client a American Eagle,
(35:53) take their data and share it with American Airlines. Yeah. They can't do that without the permission of American Eagle. Right. So not very hard to do that. Sorry. Yeah. They're not going to give permission to do that either. Yes that's my point. Yeah okay.
(36:12) Zeta is 100% agnostic. The best results win. Yep. Okay. So this is to my previous point Zeta. You know this is something fantastic about the company that I really like is an agnostic. Zeta is a durable zeta meaning Zeta
(36:28) wants to integrate with any channel. They potentially can because if they do that, it brings more value to the enterprise, and it actually incentivizes the enterprise to other, you know, migrate to Zeta services and spend more with Zeta or just to stay with Zeta, because that means Zeta staying on that cutting edge.
(36:46) They don't necessarily need to develop their own activation channel. All they need to do is to integrate with them, ensure their gross margin profile differs from some of their competitors because they're agnostic, but as a result, they gain more business over time.
(37:03) And that starts to compound because those customers want to stay with Zeta, because they get to activate across a broad spectrum of channels, and that intelligence are able to create and generate compounds over time, too. So and this is something we'll touch on a little bit, but I kind of want to prime this conversation.
(37:19) The longer a client stays with Zeta, the more proprietary data is generated about that consumer relationship with Zeta's client or the brand. Let's put it that way.
(37:34) The more data that work or not a network, but kind of like a switching cost associated to it. Yes, because that proprietary data stays within the Zeta ecosystem. And the brilliant thing is that Zeta then can utilize their machine learning
(37:50) algorithms to then create a more and more and more targeted result for that enterprise. So then the longer a brand stays with Zeta, the better their outcomes become and better their outcomes to become.
(38:05) The more spend that their customers do with their brand, which then encourages that brand to spend more with Zeta. So it's kind of this flywheel. Yeah. What is their what is their retention rate right now with their current clients. Is it going up or. Yeah. Yeah.
(38:21) So I have it on one of the very last slides. But their net retention rate went from 114 in 20 24 to 2025. It hit 120. If you include the retention rate. With the recent acquisition of Marigold Enterprises, it hits 128.
(38:37) And management's recent commentary said that is actually increasing from there. So what they've seen in some of the top spenders within the company or within their clientele base, they're actually hitting closer to 132. 134. Wow. Okay.
(38:52) The value proposition just gets stronger and stronger for the enterprise with Zeta as the time goes on. Because I mentioned of Zeta's agnostic, right. And it integrates and has this noncompetitive relationship with a meta, for example, what Zeta is able to do.
(39:07) We've already went over how they pair their proprietary data with their clients proprietary data. What if I told you to also goes to meta and says, hey meta, you have a ton of unique profiles in your ecosystem and a lot of proprietary data.
(39:25) What if we pair all three of our databases together in a privacy compliant way where nobody actually discloses, you know, in this sort of gross manner, all of our proprietary data, but we're then able to hyper target to guarantee
(39:41) a much better outcome for our client by activating on meta platforms. That's what Zeta is able to do with clients like OpenAI, with meta, with Google, with Netflix, with Reddit, and the list goes on and on and on. So Zeta is able then to, in these clean rooms in real
(39:58) time, have these paired IDs so that they can almost triangulate who is the best individual to target and when and where to target them. Interesting. So Zeta is you know, they're working with OpenAI.
(40:13) How would how would OpenAI be using that today. So OpenAI so Zeta comes in and it has all these enterprise relationships. And essentially OpenAI is just another channel for these enterprise to act to activate on to either launch Geo,
(40:29) which is essentially brand recognition or launch actual ads within ChatGPT. So for brand wanted to launch an ad we already understand. Like I just mentioned with the meta analogy, Zeta has their own proprietary data, which is paired to that data of the enterprise client American Eagle.
(40:47) But now a third party comes into the picture OpenAI, with their proprietary data set. What's brilliant about OpenAI is that it's not just this brief contextual signal where somebody does a Google search, know it's most likely going to be this conversation, and a very rich conversation
(41:04) about what somebody is interested in or desires or wants, you know? You know what I mean, right? Yeah. It's likely going to be a long conversation in that text. Yeah, exactly. In context, you're able to establish intent. So with greater intent, it provides a lot more signal
(41:22) to somebody like Zeta Global to then capitalize on that. So ChatGPT is fantastic when somebody is logged in. So the vast majority of their profiles are logged in. I think it's around that 70% mark, but there's roughly 30% who are not.
(41:37) So how do they provide some sort of stopgap or to backstop those 30%? Zeta can come in and say, are we can, with a high probability, identify that this individual who's not logged in on ChatGPT is most likely this individual.
(41:55) Let's deploy an ad. So that's how Zeta can help support OpenAI is not only being this, you know, agnostic channel with a ton of enterprises already integrated, but also help with ID resolution when there's a void between ChatGPT having that contextual data
(42:13) through logged in interactions and conversations, but also to help when somebody is not logged in. And it's interesting because more recently at the con, I think it's con my friend. I'm Canadian, but you know, my French isn't great. Yeah,
(42:29) there's a huge marketing festival or conference every single year in France, in Con France cans. I think I'm saying it right, guys, don't ask me, but sorry. Don't ask me, man. I'm trying to just get down my Spanglish here in the U.S..
(42:44) I don't know any French. That being said, so David Steinberg, the CEO, was actually on stage and he talked openly about this and he said, hey, you know, we partner with OpenAI specifically to help them with that problem. It's a big problem. But we also have
(42:59) an entrenched enterprise relationship that just continues to compound. And OpenAI as a very rich signal base, and we have our own proprietary data. So we're able to work together and get the best outcomes. And, you know, for OpenAI, there's an incentive here, because if OpenAI wants to IPO,
(43:15) they're incentivized to have this robust ad revenue. So if they're going to lean on somebody, they're going to lean on somebody with their own proprietary data set, who has this value proposition to return better outcomes to their customer, because that means the customer is more likely to spend more and more money.
(43:31) You kind of want to gravitate towards that technological leader in the industry. And that is at a global interesting. So they're basically just compounding their lead. They have the snowball rolling, the recognition. This is the first time I think we've seen Athena.
(43:47) Yeah. So is the agenda layer. So this is sort of that UI layer that's built on top of the machine learning base that Zeta Global has, and is this really cool tool that reduces the friction,
(44:02) that human level friction for the ecosystem. So David kind of mentions this frequently. If somebody goes and uses Bloomberg Terminal, they likely only use 5% of its capabilities. What if you could develop a little piece of software
(44:18) that is powered by machine learning and sort of activation capabilities, in order to unlock the rest of that platform for the user? The outcome is that you develop this user interface
(44:33) that can act as a flywheel, a flywheel that pushes different use cases or different applications right in front of the user that they're maybe not spending money on now, but could return them a better outcome. And the only reason why they're not spending that money and achieving that better outcome is because they just didn't know it existed.
(44:51) And that's the purpose of Athena. So Athena is essentially this UI layer that allows the enterprise to interact and talked with, and it will auto develop campaigns, give you KPIs, tell you all kinds of different
(45:06) sort of factoids or, you know, data points about your organization, all the data pertaining to your organization and your clientele and then actually activate those campaigns for you. So let's say somebody only used Spotify
(45:23) for whatever reason, to activate and actually reach out to people. But they talked with one day and they said, hey, Tina, what would be the best surface or activation channel for me to use to reach my specific archetype, client or customer?
(45:41) And then the thema comes up to them and gives them three recommendations. Each one of them has a better return on ad spend, and one is Netflix, one is meta, one is Google. Yeah. So what this does is it then unlocks more spending channels for the same client, which then rapidly scales Arpa. A
(46:01) yeah, I see that in the chart. You know, so basically it's trying to be a catalyst to get them to explore other channels and then identify which channels are going to have the highest return, like the lowest hanging fruit, basically. Right. Correct. Correct. And interesting.
(46:16) And a lot of people aren't going to spend the time to do all the discovery and testing and stuff. So this thing will just say, hey, try out these first. They're probably going to do really well. Here's what it's like projecting. Yes, but this is the this is the best point.
(46:31) And I've failed to mention this yet. And I'm kind of kicking myself because it would have been great to mention earlier. Okay. Through Athena and Zeta's machine learning layer, they're able to harness that the predictive ability of their own
(46:46) proprietary data graph and then forward look, what would the outcomes be if you were to activate on this channel? So without the client even spending a dollar, they're able to give you a predictive outlook as to
(47:01) what your return would be if you were to activate on this channel. And that differs from a lot of marketing applications today. Marketing applications today require you to do a B testing spend. They want you to spend first to even see how it works. Right, exactly.
(47:16) So that's a competitive edge for Zeta two, because they build in machine learning as this native core to the application layer and their ecosystem, they're able to leverage the power of that machine learning specifically for each individual client.
(47:32) Interesting. Okay. Yeah, I probably would be more willing to spend on different channels and stuff with advertising if I just had something that told me, here's your likely, you know, there's going to be a return on your ad spend, and if you match it with your lifetime value of a user, I can actually see the math beforehand.
(47:51) But when you just think about, hey, you have to spend $50,000 to test out these three different channels, and hopefully one of them does. Well, that's a that's a little bit different. So yeah, very cool that they have that. I didn't realize that advertising is getting so advanced, but being able
(48:06) to predictably analyze and estimate how much you're going to get in return. Do they have any metrics on how accurate that is? Is it pretty accurate? Yeah. They have like a 92% accuracy rating. At least that's a that's the internal stats that they've given. Yeah that's crazy.
(48:22) Four engines now compound the same data set. So we have yeah the channel flywheel. And then business intelligence owned data rails and Palantir go to market. So Palantir in the mix here I'm blocking that one with Palantir.
(48:37) Go to market there. Yes. So I know exactly what ex post this came from to I kind of chuckle on myself because I talk about the roadmap for the next few years, and I'm pretty sure it extrapolates that message and built this graphic for me.
(48:53) Okay. So if we pull it back up on screen, yeah, just to kind of touch on where Zeta is at. So at its core you have Athena, which is this flywheel, this cross-selling flywheel. That's the purpose of it. That's the intent of Athena. And then you have the proprietary data asset,
(49:09) which is the super graph, which is refreshed daily with over 6000 signals per individual profile. So you have this rich data set, and then you have this flywheel and this, you know, user interface that allows people to interact with data in a much more expedient manner.
(49:25) So you have your own data rails, you have a channel flywheel which is cross-selling. Now you expand into two new categories, one being this new vertical of business intelligence. We could talk about that in a minute. But almost most importantly, you have this Palantir go to market
(49:42) partnership, which is absolutely vital for data moving forward. It's kind of cool because both of these companies share very similar value proposition. Palantir finds itself. And we could go to the next slide here. Palantir finds itself on one side of the enterprise
(49:58) coin, and Zeta finds itself on the other side of the enterprise coin. And we could talk about that here in a second. But yeah, in this slide, marketing essentially was the entry point because it was extremely lucrative. And management openly talks about this. But
(50:14) within that proprietary data touchpoint that data has, they're able to help businesses with more than just marketing. They're able to provide business intelligence. So Zeta can then predictably model based on all the information
(50:29) they have on an individual, where, let's say, Home Depot should open their next five locations across America because they have all that consumer information. They know what the consumer is willing to spend. They know what the key demo is. They have the credit card information. Right. They have these
(50:44) these profiles are updated in real time through that proprietary data. Zeta is able then to help provide other solutions. Yeah. So they're basically they start off with marketing with helping effective advertising. But now they're saying we have so much data that we want to start doing more of,
(51:01) like the consulting side, like we want to go to a businesses and say, based off of this is where you should expand, not this area, because all the data points to this. Like, is that the basic idea? True. And it's a little bit better than a McKinsey, let's say.
(51:16) Okay. Because these companies are making decisions and recommendations based off of historical imperial data. Zeta is seeing data in real time with machine learning layered on top of it. So then they're able to take real time data touchpoints through the data rails,
(51:33) which we've already talked about. And those exact same consumer profiles. Then make predictive models forward looking. No okay, curated to each individual at a very granular lens too. So they have the credit score of every individual
(51:48) in the spending history and spending habits of each individual person. Within that data graph. How big is, in percentage terms, the business intelligence revenue line compared to their just normal business like their marketing spend.
(52:04) So Zeta literally this year launched business intelligence okay so branding that just happened. Management hasn't been able to quantify that yet. They said that's two premature for them to give us numbers. But they said they've been providing these services in the past.
(52:19) They actually haven't been charging customers for it. They're only really formalizing what it is today. Oh interesting. So this is basically just a new growth path. Do you know how much of it is a like how many our investors really buying the company for the business
(52:34) intelligence, or are they doing it mostly just for the marketing? Oh, you're talking about like just retail or Wall Street. Yeah. Like when people look at it is are is the sentiment, are people excited about the business intelligence or is it like just like kind of too nascent and new?
(52:50) Yeah. So the business intelligence equation is really exciting. The investor base. And that's where Palantir kind of comes into this equation. And I bring that up in the next slide, because that's more or less kind of what Palantir focuses on is how do we generate operational intelligence for an enterprise.
(53:07) So on this screen here it says Zeta sees the demand. Palantir sees the enterprise together. They are essentially two sides of the same enterprise coin. Every single enterprise has a demand side, and every single enterprise has a supply side. What do I mean by that?
(53:24) Essentially, the supply side are all those raw data points, all those data nodes of how does the enterprise or how does a company function from day to day? What does X individual do throughout their entire day on their shift in order to contribute to the company?
(53:41) Where are our trucks? Where is our supply chain? Where is this specific shipping or this specific parcel within our ecosystem? All that information is updated into Palantir's ontology through foundry and ingested and then contextualized.
(53:57) That is supply side intelligence. On the flip side of the coin, you have Zeta Global. Zeta global has, as we've already discussed quite early, already has this intrinsic touchpoint with the consumer through their own proprietary dated graph data in real time knows what the consumer is.
(54:15) What do they want? Where do they want it? When do they want it? All of this information pertaining to a specific brand, to a specific enterprise. So Zeta now specializes in the customer facing ecosystem of a business, whereas Palantir focuses on the operational side of a business.
(54:33) They are both one side of the same enterprise coin. What's very unique here, if we flip to the next slide. Was very unique. Here is when Zeta and Palantir come together and unify.
(54:51) You have something very unique, something special that can happen. They both specialize in generation of intelligence for their own particular applications. Palantir more for operational intelligence, Zeta for consumer intelligence specifically for marketing application.
(55:07) But now business intelligence and the business intelligence sort of train of thought is where you kind of see these two overlap a little bit when working together. Zeta and Palantir have the opportunity to inform each other
(55:22) with their own intelligence, to create a more granular and acute level of enterprise intelligence. How does that work? Yeah, so maybe I'll pause there. And if you have a question. Yeah, I do have a couple of questions.
(55:37) So one of them so basically the framing here is like I like the framing a lot by the way, which is you know Palantir's on the supply side and we know them as like the big aggregate ontology company gets all this data, makes all the logistics work better. And obviously that's been wildly successful.
(55:53) They just do it better than anybody else. And then the framing that Zeta is basically like a palantir. But on the consumer demand side, customer signals knowing what your customers want, knowing what direction to move in that way is that is that framing.
(56:09) Is that also held by Palantir's cells? Do they view it that way, or is that just a view? Is that a view that Zeta themselves has of how they're framing it, or is that mostly like a retail view of how we're just doing analysis on it? So Zeta's management's actually come out and set this for beta.
(56:27) This is how that dynamic works, that they're both one side of the enterprise coin. Palantir hasn't actually come out and spoken about this partnership to the same extent. Now part Palantir has come out and announced this partnership with Zeta. They actually flew somebody out for the day of overnight
(56:43) to France to announce his partnership, which was pretty cool. And Zeta and Palantir have done a couple of things more recently at Palantir Sovereign Bootcamp. They've done 1 or 2 of them, but so it's pretty cool. But Palantir actually hasn't come out and said, hey, this is how the partnership is working.
(57:00) Zeta is commented a little bit more on it. They've had a lot of engagement with the investor community on, okay, how does this work? How does it function? Most specifically, I believe there's a technology conference at Chris Greiner, the CFO recently did, and he came out and spoke about this
(57:15) quite openly about how this dynamic functions. Panter focus is so much on supply and logistics and inner workings of the company. Is Palantir a risk of crossing the aisle to the demand side?
(57:30) No, because Palantir doesn't actually own any proprietary data assets. Okay. And from my conversations and I've reached out here to the Palantir community quite a few times, and I've had a couple conversations, because when I look at this, I say to myself, wait, isn't data
(57:46) perhaps this intuitive acquisition target for Palantir? I mean, they're $6 billion company. They have a data asset. Their value proposition is return better outcomes to their customer and guarantee these outcomes, etc., etc., etc. it seems like they have a very similar ethos, but baked into the ethos of Palantir.
(58:03) There's a core tenant and one of their core tenants is that they will never own the proprietary data asset. They never want to do that. Interesting. I don't know now that specifically pertain to the enterprise itself.
(58:18) Zeta doesn't own the enterprise data. That's the unique thing. However, they do harness a proprietary data asset that they own in order to provide more proprietary intelligence to the enterprise. So I don't know if that jives with that ethos Palantir has.
(58:33) Yeah, might be a little bit different. Palantir isn't they're not buying a bunch of supply companies to try to get signals and that type of thing. I don't see them doing that. So when Zeta investors are looking at this in terms of like just a super brief story, is that is a story of the stock,
(58:50) that Zeta is a bit of a palantir for the front end of the company, the demand side, the customer acquisition, the knowing where to expand that type of thing. Whereas Palantir's the palantir of the back end of the company. Is that what investors are conceptually viewing the company as?
(59:08) Yeah, I try to. So obviously retail can be split into multiple different camps. I tried to stay in the lane of data and Palantir share a very similar value proposition, and they achieve very similar outcomes for their customer. Now, Palantir, in my opinion, is a unicorn and Z is not the next Palantir per se.
(59:26) But Zeta has very similar traits. That's how I would describe it. Yeah, well, Palantir, if you didn't get in early, it's up to a hundred price to sales. It's rare for companies to do that. You have to be really excited. I'm not sure how fast Zeta we can look at the revenue growth.
(59:42) It is growing quite fast. The revenue is growing super fast so you could get it's hard for lightning to strike. You know, it's like catching lightning in a bottle. Is Palantir and some companies where the entire community gets behind it. The ethos of the company takes over.
(60:00) All of a sudden you have a company that goes up to 100 times price to sales, right? Or even more where Palantir's today. So I don't think obviously I don't think anybody's going to go in fully expecting that. But it does. The story, I think, is a good story. I think it's a really good story for a stock that we have a proven formula of how this works on the back end.
(60:17) We have a company here that's trying to do a lot of the same things conceptually, but on the front side, it's having a lot of success. There's probably some big differences people would outline. Do you know if, when comparing to Palantir
(60:32) in the amount of revenue that they can grow, charging for their products in logistics and everything they do with all the granular ways that they can dig into the data? Do you know what the total addressable market is of a Palantir compared to what it could be for a Zeta?
(60:47) Like, what is the size of the markets they're going after? So I have a Tam chart, I believe, later on in the slide deck specifically for Zeta. Okay, as for Palantir and their Tam, I do not know what off the top of my head, so I'm not going to pretend to know it, but I will comment on Zeta's
(61:03) Zeta is set to achieve, I believe, over a $2 trillion market. Sorry Tam, not market cap, $2 trillion market cap by the year 2032. Specifically for marketing, not business intelligence. Business intelligence is a whole nother
(61:20) revenue vector that they're able to chase. Now, the Tam for that isn't as large, but it's kind of hard to quantify the Tam for Zeta Global, because right now, all the analytics and these sort of companies who develop any sort of idea of what that Tam could look like and the numbers behind it, they're leaning on this type
(61:38) of legacy business intelligence company that is McKinsey, for example. It is a completely different right, totally different. Zeta is not going in, and I don't think they're going in and just saying, hey, fire all these people and change how your operations work this way. I don't think they're going to.
(61:54) Is it just a totally different business? Yeah, I feel like it's good to have a stock just like a little bit of meta analysis here. I feel like it's good to have a stock when you have a really recognizable, understandable story. And then you've also seen an example of a not the same but similar story
(62:11) that's worked out really well. For example, if you look at Nvidia and how it rocketed up insane demand, a lot of people went to AMD as basically I missed that play. So I'm going to do a catch up play in Achmed.
(62:26) And that worked to a good extent. AMD got leftover demand from Nvidia. Like there's just so much demand. Nvidia couldn't capture all of it. So Achmed got a lot of it, to put it really simply. And the stock went up a lot. So if you look at this where you're like,
(62:42) okay, Palantir is at whatever multiples now, right? Super high multiples, you're probably not getting the best return at this point. Buying into it fresh today, that's not the optimal time to be buying it. If you look at it, our investors, from what you see viewing this is a bit of a I missed Nvidia.
(62:59) I'm going to get AMD like I'm not in any more. Maybe I didn't miss Palantir, but I want to move over to this one because I believe it represents another opportunity, another bite at the apple. So I'm not seeing a lot of investors jump ship on Palantir to join Zeta necessarily in this movement.
(63:18) But I do see a lot of people allocating capital to Zeta and Palantir. Volunteer investors seem to be this very I don't want to say religious, but they're quite they're very loyal. Yeah. Yeah, they're very loyal. They're very loyal. And they go to the church more than more than religious people do, man.
(63:37) They're not there just for Easter, you know, Christmas. They're there all the time. They love Palantir. And it's been a good stock. So it makes sense. Yeah. I feel like it would be a good a great story for the stock. Now, whether that happens to the same extent as Palantir, very unknown.
(63:55) But even if you get and okay, so AMD did not need to go up as much as Nvidia for it to be a great by right. You didn't have to capture Nvidia returns to say this was a huge success. So I feel like there's I don't know.
(64:11) To me it seems very similar. Yeah. Like I said, there's very similar value proposition. And in all honesty, the Zeta story really benefits the Palantir story. And the Palantir story really benefits the Zeta story. Because like I mentioned before,
(64:27) if you're in this permissive environment where a client becomes a client of Zeta Global and a client Palantir, you have a permissive environment. In this permissive environment, that client can agree
(64:42) to have Palantir ontology communicate with Zeta super graph. In that equation, the enterprise is able to produce enterprise intelligence that they can not produce anywhere else. I don't think that level of enterprise intelligence honestly exists
(64:57) because Palantir in itself, creating this ontology for the operational understanding of a business and that contextualization is revolutionary. You bring in and you inform the end with that real time super graph that Zeta has. You're talking about enterprise intelligence that we have not seen before.
(65:14) I think it's honestly this opportunity that Zeta and Palantir could come together and produce something for the enterprise that's hyper focused on guaranteeing outcomes of which legacy SaaS hasn't been able to offer before and revolutionize the entire industry.
(65:32) Yeah, it's just putting Palantir on steroids, adding a huge catalyst to it. So yeah, it makes sense. It's interesting to see these newer companies come in and just overshadow the legacy architecture of all the companies that have been in this so far, and the revenue growth shows,
(65:49) you can argue about a lot of things, how well the product works and whatnot. But when companies are signing up and paying more and more and more for product and they're not held hostage, they can they can sign up for any company. But when you see these revenue lines or companies like Palantir and Zeta, that the proof is there,
(66:05) these companies would not be spending money on their products if they weren't getting an adequate return. So pretty impressive. I'm on this one here. Do you want to stay on this one or you? We can move forward from this. I took some time there just to kind of talk about it a little bit.
(66:20) So again, this is kind of just capturing the core tenets of, you know, the value that Zeta brings to the equation. Something I wanted to capture here though, because now we're getting into some of the KPIs, okay. And we bring up called trim here in a moment too, because I think that'll be very useful for the viewers.
(66:35) But specifically on this note, Zetas given a rough outline of what they could, what they believe over the next 5 to 7 years, what they could become as a company. And this projection only pertains to the marketing side of the business. It doesn't even factor in any of the tailwinds
(66:52) from business intelligence or the Palantir relationship. Okay. This specifically focuses on the incremental increase of wallet share in their marketing business. What is wallet share mean? Essentially, out of all the clients that Zeta has,
(67:09) which is 52% of the fortune 100, which is up, I think about four points to four companies from last year. I may be incorrect about that. I think maybe it's 4 or 5. That being said, they have currently a wallet share
(67:26) of roughly 1.7% of the $110 billion all of those companies spend each year. If Zeta increases that that share price or that share value of roughly somewhere between 0.68 to 0.7,
(67:45) they can incrementally become a larger and larger and larger company just by stealing more of that wallet. Share so or share of the marketing budgets of the companies that they work with. They're only winning 1.7% today of that marketing spend.
(68:02) So basically these companies are signing up their in approval mode. And as they prove it, they'll spend more and more money on Zeta rather than their other their other outlets. Correct. There is actually I believe it's an airline or insurance company.
(68:17) It's one of the two. David talked about these two use cases in the last earnings call. They went from having a $50,000 pilot two years ago, and now they spend over 100 million a year on Zeta's platform. It's crazy. It's absolutely crazy when you have net
(68:34) revenue retention rates, which for people who don't know what that is, basically, if you have a customer paying $10 a month and you just keep them at $10 a month, the most you can hope for is just to get 100% net revenue retention. But if you increase the amount that they're paying every month and they stay with you,
(68:50) your net revenue retention rate goes above 100%. Very good to see when a company can do this and get the same customers to spend more every single year as the product gets proven. But if I'm looking at this company, just the way that I would frame this is if I was making content about it, as I would say, the palantir
(69:07) of the front end or the palantir of the demand side or the next palantir. And that comes with huge caveats, because everybody says the next Amazon and there hasn't been any next. Amazon's really not really. But you know, when you look at Meli, that's pretty close.
(69:25) It's not the same. But when you say, hey, there's this huge retailer growing in South America, but they're different than Palantir's or sorry, they're different than Amazon. They're not just going into retail logistics and AWS and the compute side and Amazon Prime Video.
(69:42) They're going more into the finance fintech side. They're becoming a banking platform. So there's differences. But I think it is accurate to say it's not a stretch to say, hey, this feels like an Amazon. They're following a lot of the same playbooks and principles, but they're doing it in a different context.
(69:58) They're doing it with a different levels of demand, and they've identified things that they can capitalize on instead of capitalizing on compute infrastructure. They can't do that. It's done. Amazon one that Google, one that they're not even going to go into it, but they know that they're underbanked. Everybody's underbanked in South America.
(70:14) So we're going to we're going to make it so that we can catch up and get people banked and have credit cards and have a have infrastructure both in payments and infrastructure and logistics. So those type of stories are really good. I think this one so far feels like a really good story
(70:30) of a company that can evolve to be, like you said, the more demand side of the other flip side of the coin of what Palantir's currently doing. So conceptually, that's a lot easier to understand from my perspective,
(70:45) because when I look at Zeta, I think it's just a run of the mill, like you're first looking at it, you're thinking, oh, this is just some marketing platform, some fancy marketing platform where they slapped in some model, right? They're using some open weight model or something to run the company.
(71:02) But then when you say no, they have all of this proprietary data, they use it in a way where they've made this distinct value proposition. They can offer higher rates of return. Now they're moving into business intelligence, where they're going to start informing customers about the demand side.
(71:17) And it's becoming it's evolving to look more like a fast growing demand side company. That gives it a lot more. I think, of a very interesting, intriguing story to invest in than an AI ad platform. So I see why you don't want to just call this an AI app platform,
(71:36) because I don't I don't think that would get anybody too excited. That's true. That's 100% true. And to my point here, the ad business alone can incrementally increase revenue every single year. Yeah. The tailwind effects of this new line of business and the partnership with Palantir,
(71:54) they can be really dramatic to that top line growth and also incrementally do margin growth as well. Now I do want to mention like that incremental growth to revenue that I captured in that chart. That's without adding any new customers.
(72:09) That's the exact same customers. There was 52% or 54% of the fortune 100. Today. That data has just spending more money on the data platform every single year. So as you can see, the small little number from 1.7% to 2.38 to 3.0
(72:24) 6 to 3.7 4 in 2029, you can see incrementally that increases. That doesn't even account for adding new customers where data just stole their projection of 0.68. How do they arrive on that number? Sorry. Where did they get their projection of the point plus 0.68.
(72:43) annual revenue growth with their customers? So Zeta's management said, we expect to increase incrementally our wallet share by roughly 0.5% per year. I made an assumption here because Zeta is this notorious again, notorious this.
(73:00) There's a weird track record of just continuously beating and raising and beating and raising and beating and raising. Management loves to sandbag, so when every single metric they provide, they tend to sandbag. And they the analysis companies that do numbers they performed are very conservative.
(73:15) Sorry. You can take you can take some companies do that. For example, you can take whatever Netflix says they're going to spend on content in one year, and they usually come in under they almost always come in under. So I look at their content, spend projections as a very high end, which the analysts always look at their free cash
(73:31) flow as that they're going to spend that much. So their free cash flow always comes in higher than analysts projections that just this happens so many years. So eight out of ten years that always you know that's been happening. So it makes sense. You bumped it up. So their own projections were 0.5.
(73:47) That's what they listed. You're saying they usually sandbag. So I'm going to bump that up to 0.68. Yep. Roughly okay. And this model right here captures more or less my current long projection of about five years for what I think Zeta Global could achieve.
(74:04) So to give you an idea of their historic track record in 2025 or sorry for the year 2025, Zeta had a what they had. They had some sort of like long term four year projection of Zeta 1 billion, which was landing in the year 2025.
(74:19) Zeta became a $1 billion a year company a year early in 2024. I project that Zeta is going to beat their current guide for 2028 of being a $2.3 billion company a year early. I believe that they're going to accomplish that in 2027.
(74:34) What is the analyst consensus right now at how big of if they did accomplish that, if they got to the 2.8 that you're projecting in 2027, what would that mean for what investors are currently pricing in right now? It would be I don't think so.
(74:49) Looking at the numbers at the moment, analysts and I made a post about this recently, analysts project that Zeta will do roughly 40% year over year growth this year. Next year they have it dropping to 16% year after that, 14 the year after that, 11%. So analysts right now
(75:04) expect that the business growth is just going to flatline. And they're not actually factoring in any of these tailwinds from business intelligence, the OpenAI channels, the Palantir partnerships, and or the incremental growth of the marketing budget every single year. So right now, rapid deceleration and revenue is being priced
(75:22) in over the next three years. And your thesis on the company is not only is it great for all these reasons, has all these qualitative reasons, but also you just don't think it's going to dislocate as much. You think it will declare it a much slower schedule, right? Yeah. Not only me, but management comes in and says, hey,
(75:38) we will maintain a 20% kegger for the foreseeable future. Yeah, it would be big if that happens. You know, when we look at this and there's there's companies, it's always the case for if you can get anything a little bit above what the analysts estimates are,
(75:54) usually the stock will do so well because investors unless investors are pricing it in and they're smarter than the analysts. But in most cases Wall Street is following right along with the analysts projections. So if they come in way above, they're going to have a really good outcome. The next one here we have 10 billion of marketing inside a 2.46.
(76:12) This is the Tam. So we have the Tam slide here. Yeah. So this is also what Zeta kind of projects what they could become in the next 5 to 7 years. I don't think I've finished that train of thought previously when we were talking about this, but David Steinberg came out
(76:27) and announced that he believes that they will become a company that generates $10 billion annually with a 30% operation operating margin, and that 30% plus free cash flow margin. You know, he said, that's not going to be next year or the year after that.
(76:43) But, you know, in the next 5 to 7 years, this is the type of company they could become. Yeah. That's huge. What are the margins at right now? The margins right now. So they just broke into profitability literally this past quarter okay. So it's just flattish operating margin flattish net margins right now.
(77:01) But they want to get up to 30% operating margins, free cash flow margin 30% plus. Pretty incredible. All of this is a future. Tam. I feel like anything in advertising and if you combine advertising to business intelligence, you obviously have a massive Tam.
(77:17) So I don't think there's any problems when I'm looking at this. I don't I don't see it as a problem, that it's too niche, the Thames too small. I don't think that's an issue with the stock. We have a super skilled customers. Palantir GM scenario.
(77:32) Yes. So in this chart I capture the tailwind effect of that Palantir partnership. And what the potential is are so super skilled customers just to kind of quantify that for the audience. Zeta baskets, these customers as customers actually kind of mean something.
(77:47) They spend more than $1 million a year on marketing on the platform. Now, in some circumstances, it could be a customer spending 100 million. In some circumstances, it could be just at that threshold of a of $1 million. It's just a basket of customers. And, you know, it accounts for roughly 90% of all their revenue.
(78:04) So that being said, Zeta adds roughly 30 new clients a year. In this graphic, I capture what are those tailwind effects of Zeta capturing roughly ten Palantir customers as these cross go to market clients a year.
(78:20) And what sort of tailwinds could be provided to Zeta sort of high tier clientele increasing year after year after year? So that's that projected part off to the right, the purple part of it.
(78:36) Correct. At the moment. Right now, Zeta's management said they have about 20 companies for this year. The analysts first thought that the Palantir deal wasn't going to be a creative until 2027. But Zeta and their team are actually head of schedule.
(78:51) They were fully integrated into Palantir already, and Palantir is already working with them. Walk them into two deals. They got two deals. They signed them. They're two for two now. They have another 18 they're targeting for this year alone. So that gives you sort of some a concept of like, okay, how fast are they moving?
(79:07) What's the attitude of the management teams. How are they working together. They're working well together quite well. They already have. And what is that pipeline. So the pipeline was 20 when they first announced it. They've already secured two of those 20. And I'm estimating okay. Conservatively speaking they add at least ten a year, which I think is also sandbagged.
(79:24) They're probably going to add a lot more than that. If we go to halfway through a full year, full calendar year, and they estimate that they could potentially achieve 20 clients within the first, you know, half a year, I mean, that's quite attractive in my opinion. I think it lines up those estimates.
(79:41) You'd get revisions. Okay. So we have some cash generation, stock based comp versus free cash flow on a trailing 12 month basis. Interesting. So the stock based comp is falling while the free cash flow is expanding,
(79:56) creating those healthy free cash flow margins. Correct? Correct. So Zeta currently has I believe you may be the audience maybe to correct me if I'm wrong, but I believe the 2028 target, which is their latest
(80:11) four year term, that I think that they're going to beat a year early, like I mentioned, as their free cash flow margin set to hit, I think it's 14.5% 30 on track to beat that two years in advance, which is quite remarkable. Interesting.
(80:27) Yeah, it really has tipped down. Did they? What did they do? How did they handle this to make the free cash flow or sorry make the stock based comp go down. Did they just right size their company, do layoffs? What happened. So they stopped issuing stock first of all okay.
(80:42) And secondly, how they structured some of their acquisitions. Has they've changed it quite a bit. They've decided to put up a little bit more cash in their deals rather than stock based compensation. Yeah I see that as well. The share count actually flattened out even with the stock based comp.
(80:57) They're starting to buy back shares and at least eliminating any type of dilution. So it's good they're keeping their share count in order. They're making it so it's actually not spiking up. I think that they've it's interesting.
(81:12) They've been around for a short amount of time. They did that quickly. Like most companies like Palantir, even dilute for a long period of time. They were they seem to get that under control pretty quickly. So it's not it's not some startup, highly dilutive, unprofitable company.
(81:27) They're just getting into profits. Now their free cash flow is good and they're not diluting anymore. Correct. Yeah. And management openly says that hey we heard investors, whether that be investors from the street or retail community.
(81:42) And we understood that stock based compensation was a problem. They were not happy about that. So it's something that we addressed. Okay. Very cool. I like what I see so far with the economics of the business. I think it's in an interesting spot. Super skilled customers versus ARPU.
(82:00) All right. These are these are the ones that are over the 1 million mark. And it says that in the trailing 12 month revenue keeps increasing while average revenue per super Scout customer expands. So these are probably the most important clients. And they continue to spend more and more on the platform.
(82:18) Yes, exactly. Is the this clientele, this base, like I mentioned, attributes to roughly 90% or greater, I think was actually almost 92% in the latest quarter of their entire revenue. So that being said, yeah, they don't have many small
(82:34) mom and pop companies using Zeta. That's not what they're going. Zeta targets the largest enterprises in the world. That's essentially their core clientele, which is something I like a lot because those are that is the most predictable clientele base it is. No, it's for sure is if you look at where to sell a product, the place to sell
(82:51) it is large businesses. That's where you make the money. That's where they're willing to dish out a lot of money for products. What I'm doing selling a consumer product, a software consumer product is not the way to go. Tough competition. People want the lowest priced possible.
(83:07) It's just difficult. So I'm I'm I'm fighting an uphill battle here. Zeta has it's down. They should stick to the customers paying over $1 million. A much better route. Interesting company. Overall though, I really like what I see with it. I like the story of it.
(83:22) It's surprising to me because one of the things that I look at, what I'm hearing about a company like this. I'm naturally going to roll my eyes and look at how much are they actually spending. What are their economics look like? And it looks pretty good. I see these charts and I really like these charts.
(83:39) Now you can say it's unprofitable, but if I remind you of what Uber looked like a couple of years ago. Uber, it tricked us a little bit. It looked economically terrible. But that's because they're doing a land grab. They're going for as much market share.
(83:54) They're throwing everything that they could to expand everywhere in the world. And so you see these companies with these type of trajectories. And obviously I don't think it's going to just go perfectly flat and stop there. They're going to have operating leverage in it. So very cool to see it looks like they're getting to the point where they're
(84:11) pushing for profits while trying to manage growing the business. Do you know how much of a what is what is their big priority right now? Is it to grow at any cost necessary? Or would you say that they're now in a position where they're trying to balance profitability and growth
(84:27) and still post profitable quarters? Yeah, it's definitely the latter. So data is in a position now where they kind of want to lean into Athena and the abilities Athena to cross sell products in different channels to their same clientele, but also coincidentally using the leveraging the Palantir
(84:45) partnership that go to market strategy to bring on new clientele. And then from that scale, the scale, the revenues of the business and the profitability of the business, they kind of went on this conquest for maybe, I want to say, the last 5 or 6 years of acquisitions very similar to Uber.
(85:02) Right? You kind of go back to that analogy. Like you said, if you want to make this comparison to a land grab, right, this territorial land grab, Zeta went out and acquired what it took to grab the data rails and to really own that touchpoint. So since 2017, they started building,
(85:19) you know, every single block required in order for them to get to the strategic position they're in now. Now that they're in the strategic position and their machine learning algorithms are at the point where they're kind of being able to propel the company forward into this predictive modeling ecosystem
(85:36) when pairing with, you know, Palantir ontology, for example, you have this equation that sets them up to rapidly grow, but also keep costs down quite a bit. So to me, do I see Zeta spending a lot of money in the next couple of years?
(85:52) Probably not. They may acquire 1 or 2 companies. They just secured a $1 billion credit facility. They did say that they could either use this for M&A activity, repurchasing shares or consolidating prior debts. It gives them optionality. So I think Zeta is comfortable where they're sitting at the moment.
(86:09) I think they just need to execute. I think this year, moving into next year is just about execution. They've set themselves up now to be a business that throws off a ton of cash flow and can compound year after year. It just comes down to execution. They just need to let the technology speak for itself and keep signing deals.
(86:25) Okay, yeah, I agree with everything you say so far. I want to ask what in terms of we can look at just the quick bear case here. So I have the bear case on Qualtrics. It highlights the bull case in the bear case. We've heard a lot of the bull case. So you've actually gone over a lot of this in super detail, which I appreciate.
(86:43) But it says here and I'll just read some of this. It says skeptic. Skeptical investors argue that the AI narrative is ahead of proof partnership pipeline ZB and OpenAI advertising revenues are not yet material in the forecast. While much of 2026 growth includes the Marigold acquisition,
(87:01) the structural risk, and I think this one's probably the strongest bear case, the structural risk is that Zeta's data advantage becomes less usable or less differentiated if privacy compliance weakens, particularly in acquired data assets. Marigold integration also must deliver expected results.
(87:17) So we have a we have kind of a hodgepodge of bearish things. Bearish points to bring up. I am wondering about one of the concerns I had when listening to this was I feel like when you explain, hey, we're linking all of this data together and we're creating these huge networks
(87:35) and we're we're going to put it from one company to another. Even when you use the word compliant, it's still. And this is one of the problems I ran in with Equifax, even though people are opting in the employment information and sharing their employment history,
(87:51) all of a sudden they'll realize that a company is using this to price their next job and to do this and they go, whoa, I don't like that. What do you feel about not just people. Because people individually don't have too much power to go against a company like Zeta. But what if they get what if it becomes a thing where you have more laws
(88:10) passed, regulatory burdens in terms of compliance and opt in of data, and they make it way more difficult for them to get those touch points. Have you thought about that in the thesis? Yeah. So I have an opinion here
(88:25) say the least. So first of all, I think if those things do happen and privacy regulation, controls and modifications can happen at any point in time, especially public, you know, opinion can change quite quickly. Like look at meta and their situation at the moment. Oh yeah. I think if there is significant privacy laws
(88:40) that are changed or some sort of initiative from the public to overhaul these laws and what is allowed or what may be allowed, it doesn't just affect Zeta in this box, right? This is the likes of Google, meta, Amazon, Netflix, Spotify, the list goes on and on and on and on.
(88:56) Anybody. He utilizes any sort of ID in order to provide some sort of verification of identity, to verify that, yes, this person clicked on an ad, saw an ad, then went to your checkout, bought your product, and were able to verify that
(89:12) anybody who does any sort of sort of technological verification in that sense, in the ad world is going to fall under the scope or the magnifying glass of the regulator. Yeah, it's for sure they're not going to just target Zeta's practices because Zeta is doing something that is legal and every company can do so.
(89:30) It would be a shared bear case throughout the industry, but Zeta could be more, I would say specifically at risk because it would it basically commoditized one of their unique advantages more because let's say that
(89:46) it would be difficult. Basically they did they did something like it's basically if the government did the same thing that Apple did to meta. Remember when Apple is like, no more tracking. We're not going to do any more tracking. I thought that was terrible. I thought that was honestly awful for meta.
(90:03) That's part of the reason that I wasn't bullish on met at the time. The thought of them not letting them track any of their customers or what they're doing anywhere to be able to inform any of their ad spend. This is a whole ad business and they can't track. So I thought meta was in bad shape after that,
(90:18) and immediately their revenue tanked as well. So there was good reason to believe that that had a huge impact on the business. But then Meta's like, we'll use AI to just infer what they're doing next. So companies like meta, I don't know, are they using different techniques? They're not using deterministic things.
(90:34) They're not really tracking users to the end. Is there a risk with companies like meta that can use their own AI models to just infer what a customer likes? Because meta can medic can look at what you're scrolling through and use all these very subtle almost.
(90:51) They use their AI to analyze your little behavior modifications, and then they can just infer what ad to feed you based on that. Is that type of behavior of more of an advanced advertisement driven by AI? Is that is that a risk to Zeta, or do you think it's
(91:10) going to incorporate that and then combine their own unique data? How do you view how do you view that? Zeta's been doing that since 2021. So Zeta has been using machine learning in order to provide a predictive model similar to what I said to you recently, that they've built the product Athena, where they're able then to build a campaign
(91:27) and then provide you with a predictive model of the results of that campaign without even deploying the campaign. Okay, so Zeta has been doing this for quite some time, predictive modeling. So when you're going to try to acquire a customer, which is the most difficult use case out of all three marketing use cases,
(91:43) which once again just quick refresher is retention growth, acquisition. Acquisition is the hardest thing to do. It's acquiring new customers that otherwise haven't seen your product before, because you're essentially using some sort of predictive model
(91:58) to infer what is the result you're going to get based on that specific user's profile and their traits or their signal, whatever may be, in order to then inform the enterprise what the outcome could be. Meta does something very similar,
(92:13) but that's why Zeta and Meta work really well together, because they use both, and they leverage both of their own proprietary data graphs in their databases to help inform the enterprise of the outcome. So I think, yes, in a privacy constrained world,
(92:29) it's very hard for us to map out what that would look like. But if there was something revolutionary that was table that said, hey, you can't use predictive modeling anymore, I think it would affect, you know, much more than just the marketing world. I think it would affect business at large.
(92:44) And if we want to kind of dive into my opinion on this matter, I think this is where the government has to come in. And with a little bit of give and take and understand, okay, what are the implications of a decision? If I come out and I say, hey, I'm going to implement this or regulation or this legislation that says you cannot use
(93:01) predictive modeling or collect data through permissive data policies, right? Like somebody cannot just go sign up. They can do that if they do that. But just theoretically, the government, you're still going to get ads to, they'll just be less targeted.
(93:17) They'll just be not as relevant. So you still get annoyed with ads, but they won't even be effective, which would just make. How much of the US economy is reliant on small businesses being able to advertise? It's a gargantuan part of it. So if they just say, hey, all advertisers have to be far less effective, that's going to have huge implications on the economy.
(93:34) You just want to have businesses selling as much product, which would be a bad thing to do. And there's ways to be concerned about people's privacy and have very sophisticated, intelligent ads. Both of those worlds, I think can fit together.
(93:50) I have another question in terms of the valuation. The stock is up. It's up a hair a little bit this year, up 46%. Sexually doing great. It's doing great this year, up 46%. It's had a good bump in just the past couple of months.
(94:07) We look over the past year I guess it's it's it's doing all right I wouldn't say it's going crazy, but what do you view in terms of the valuation of it? How do you view it right now. Is it your top by the best stock to buy right now or is it more cautious.
(94:22) You know I'm not accumulating right now. Let's put it that way. So when I compared to competition it is definitely cheaper than competition. I think it as a technologically superior product than the competition and is incrementally stealing their own wallet share. Now when more data shares.
(94:39) Is that what you're asking? Yeah. When would you accumulate? If we look at the chart here, it's at what is it at $30, $29. Would you be buying it at 15 if it fell back down there. Yeah. So sub20 is when I was accumulating heavily.
(94:54) The last time I bought shares I think was around that $20 mark when I was buying very heavy. It was between 15 and 1790 ish in that range. So that's when I made a majority of my position. I think my average right now is just hovering above $18 a share.
(95:10) And how much would I have to offer you to buy out your shares? How much would the price have to go up for you to sell this thing? I think perhaps I could sell you my cost basis at maybe $46 a share.
(95:26) Okay, so if it ran up to 46, you'd be going, okay, I have to take some of it off the table after either trim or give up some of it, but you would do so reluctantly because the future would still be right with a company. Yeah, I believe that this company can compound for years and years to come.
(95:42) That's how I feel. It's always the hardest part when a great company that has a great future, the share price gets a little too ahead of itself because it puts you in that that situation where you're going, geez. This is what I'm not supposed to do. I don't want to sell the company that's doing fantastic because the share price is 20% overvalued.
(95:57) And so it puts you in one of those situations where with ASML, I had one of those earlier this year. It went up to like $1,900, $2,000. And I decided I'm going to sell 20% because I see both sides of the coin.
(96:13) I think I smell will do great forever, but it is for sure enthusiastically priced overvalued on the short term for sure. And I wanted to just take a little bit off the table and fund DoorDash and Uber, which aren't as good as Ismael.
(96:28) They're not as good of companies. So there was a tiny bit of degrading portfolio quality. But they are very good companies that also represent tremendously better valuations. At the time that I bought them in terms of their growth and their maturity. And so those are all the tough decisions to make.
(96:45) And with other companies, I've decided I'm just holding it with Costco. I go to Costco and I hate it how busy it is. It's just so it's so busy. Every time I go there I'm thinking, why does anybody shop here? It's too busy. I'll go on like a Tuesday morning or, you know, Tuesday afternoon.
(97:04) It'll be busy. My Costco is busy on Tuesday, midday. They don't. There is no point at which my Costco is not busy. I have to go in the morning before all the other members get in there with the executive membership. Then you'll have like a half hour of where it's not too busy. But that's one that for whatever reason, no matter how high
(97:22) the valuation has gotten, I've just I've just kept this shares in my back pocket because I feel like it's going to be around so long, so long. It's a stock that I really feel like I could pass down to my children when they're adults.
(97:37) You know what, Zeta? You have a good line of sight here. I think it's a great thesis. I don't think there's as much visibility as. I don't think there's 40 years of visibility with Zeta. You know, there's it's like ten years, maybe especially five years or ten years, but I don't think there's 40 years of his ability with it.
(97:53) But I don't think you need that at this stage as well. So yeah, it's overall I really like the story, man. I like the I like the explanation of the unique data set. I like the comparison. I think the best story part of it is the comparison to how Palantir
(98:08) evolved and grew and offered value on all the supply side, and how Zeta is doing that a bit for the demand side. I think it's I think there's some haziness still, even with the explanations of how it works and interacts with other companies. So I'd say
(98:23) after this part that I'll still, I feel like be wanting to research more and go into more is how it interacts with meta, how it interacts with all these different companies. Those relationships are very complex. It just feels, for me at least, it feels difficult to understand. But I think I got the gist of it.
(98:38) I think I got the idea of it and the valuation also for a company growing this quickly, even with acquisitions. So we have some acquisition in this growth. But even with that, a company growing 36%, trading at a 20 6PE ratio,
(98:54) trading at less than five times price to sales, and an actual positive free cash flow, that's really good. This is not a super overvalued company, at least by my estimate. So there is there's not maximum bullishness priced into this thing already.
(99:09) It could go up more. This one could rise up more. Yeah it could really gain a premium. Like imagine if we had this conversation merely three weeks ago. You would have looked at a company with a market cap of roughly, I think $4 billion.
(99:24) I think today it's roughly $6.7 billion. So that entire valuation perspective when you're trying to this conversation three weeks ago screaming by, sorry, what did you say, Joseph? We were trying to have this conversation three weeks ago. We couldn't get word I we had a baby here.
(99:42) Things got busy. It's hard to hard to fit things in. But yeah, I wish we could have because it was at a much cheaper valuation. I like doing these. I like doing these thesis episodes, hopefully when the stock is still like a realistic by.
(99:58) So I'd rather do it if Zeta went up to $50 per share. It's like, sure we can look at it, but what is the point really? Is anybody going to jump in and buy now? And so I think that it's still at a point where I still think it's interesting. Maybe it's not pile in right now
(100:13) and put all your life savings in it, but maybe it's at a point right now where you start taking the research seriously. If you haven't, if you haven't looked at it, you start joining in some of the discussions in the community, and then you buy, you put, you know, you put a little notation or you say, hey, if it drops below $20 per share,
(100:29) that's when I might deploy this money that I have waiting for the next by. So I feel like it's a good situation in that mix right now. That's that. Feel fair? No, I think it's completely fair. And keep in mind so investors out there, please do your research.
(100:44) Complicated company. You can listen to what I said here today. Learn a lot. But then please take that. Go learn more yourself and don't make an investment. If you don't understand fundamentally what the company is doing and what they're trying to solve and how they generate revenue, that's, you know, that's a litmus test for me.
(100:59) If you can't explain to me how a company makes money, there's a problem, right? You probably shouldn't invest in that company. Yeah, it's clear that you've done a ton of research on this. So you're not you're not buying this stock without knowing about it. And I don't think anybody else should really go into stocks like this unless they have done a lot of research and they know how to they know how to
(101:15) portion it correctly in their portfolio to not expose themselves to more risks. And they're willing to handle. I would say, as a last question, what is what is a thing that would derail this for you? Where would you go? Wow, this is not turning out how I liked. I'm going to move on to another opportunity and sell out of this one.
(101:33) So the threat Zeta has is if Zeta like we looked at the numbers and Rhino's data is like the previous quarter, they did 44% year over year growth. Right. And a large portion of the year over year growth comes from Marigold. Now the core business is growing like 29% year over year growth.
(101:50) Still phenomenal. Phenomenal. However, if Athena the cross-selling engine, fails and it doesn't actually push customers into more channels, allowing for the organic growth rate, really to start to churn and start
(102:05) to, you know, compound and compound and compound quarter, over quarter, over quarter. And if Zeta also isn't able to secure a lot more customers from this Palantir relationship, I think there's a problem. I think there's a problem fundamentally. Also, the third one would be if Zeta isn't able
(102:22) to incrementally increase their wallet chair every single year. If we see that Zeta is actually not either achieving their point 5% increase year after year after year, that's a problem. I think bare minimum they have to meet that or exceeded. So they need their they need that acquisition.
(102:38) That was one of the bear cases it highlighted skeptical of the acquisition, the synergy behind it. That's one of them. And if their relationships aren't fruitful being another one and if they're if their wallet share for their companies are already in the door with if that remains flat, that's like a huge red flag.
(102:55) So that means that the companies, they're not convincing companies to spend a meaningful amount more in terms of the wallet share. So interesting, I like those. I like those as things to look at if the thesis isn't working out. Do you have are you willing to share?
(103:11) You don't have to. But about what percentage of your portfolio is this company? So right now I could check. Let me double check. Just so I give you guys the exact number. I believe it's hovering around 40% of my portfolio 40%.
(103:27) So yes, it is a heavy percentage. Yes, 42% of my portfolio is Zeta Global okay. On a cost basis. Is it. It's gone up a little bit. Right. So you put in a little bit less. It's grown a little bit. You might have made it like 30% and it's grown to 40 something like that.
(103:45) Yeah. So I'm up about 50% on my investment. Well cool man you're in it. So a lot of people that say, oh, you know, it's good to have a it's good to have your holding in it and your exposure because obviously you're very bullish on it.
(104:01) This is a bullish thesis. We're not here to we're not here to go through a 20 point bear case on the company. So this isn't even we're not going 1 to 1 even coverage here. But you're very bullish on the company I do the same thing I put the reason I'm bullish on companies. I show how much I've invested in them. And I think it's good.
(104:18) I think it's a really good pitch. I feel like you covered a lot of the company, at least enough that now people can view this and go through and research further with what they what they want. So I appreciate you, man. Coming on. Look at you. It's a great thesis.
(104:33) You've done a great job I appreciate it. Thank you. If I could before we go, I think there's two predictions I want to make for Zeta okay. One being I believe at the end of the year, Zeta will announce a new product.
(104:49) And I've done some investigative research here. This new product is going to be a loss. And I believe this will be a competitive product that Zeta will partner with Palantir in order to compete against the likes of ServiceNow, thus expanding the Tam even further. So I think they'll aim to be more of a holistic
(105:05) enterprise solution offering more of this. What would you call it? A AI control tower alternative, that is services now sort of value proposition in this whole AI era. They have all this data from an enterprise that's integrated in compounding year
(105:21) after year after year. I think Zeta is really trying to attack that and expand their Tam a little bit. Two I think that partnership with Palantir will open up different lanes of business for Zeta Global that they haven't been able to tap before, and also open up different lanes of business.
(105:37) For Palantir specifically, I think given the mandate of Governor Warsh or Chair Warsh, I should say in order to find more data rich signals coming from the private industry, I think
(105:55) Palantir and Zeta are sitting in a very unique position where they could provide a solution to the fed in order to provide the fed with a real time touch point of 240 million Americans, but also provide the contextual understanding that Palantir could through the ontology.
(106:12) So I think in theory, something on the table, if we're looking a year, maybe two years from now, we could see a US government contract for A to global, expanding them into the government lane and outside of the commercial lane, and also opening up a new revenue channel for Palantir. So I think in that sense it could be very beneficial for both parties.
(106:29) And when do you think that these are going to happen? Would be your by the end of this year or just Aquino's I think will be announced by the end of this year, specifically in October. So Zeta does an announcement every single year called or they do. It's sort of like a convention, right?
(106:44) Like Palantir has what is it called? It's slipping tip of mind. Yeah. They all they all have their annual conference call or something like that. Essentially the exact same thing for Zeta, but it's called Zeta Live every single year.
(106:59) And it's like a big conference. They do. And I believe they're going to make a huge announcement. And that announcement will be Athena OS. And the reason why I do not know this, it is now because of retracted job descriptions that specifically are hiring for an individual to work on Athena OS.
(107:15) So again, theorizing here, kind of putting together some different pieces of the puzzle. Don't want to go too far down that rabbit hole if you. But that's a real speculative. You don't know for sure I don't know I don't know. It's speculation but it's informed speculation per se.
(107:31) You know what I mean? I've written about this, so if you guys want to go read it, feel free. But specifically, as for new revenue generation potential, I think a deal with the fed would be quite interesting. Yeah, and very interesting.
(107:46) I like the I like the new products if they're able to get into that as well. If people want to follow you on X what is your X handle? It's wealth. Matica. Well, follow me on x, follow me on Substack.
(108:01) Those are the two main places that I really just provide value and Substack free. So go. If you go finding them, just go on to X and search Zeta and you'll find them. You'll find a wealth. Matica. Well, man, I appreciate you.
(108:17) Thanks for thanks for showing this thesis. I'm going to do more research on this one. Thanks, Joseph. It's been awesome. Yeah.