LiveRamp Holdings, Inc. (RAMP) Earnings Call Transcript & Summary

Welcome, everyone. Thank you so much for being here today. It's great to see so many familiar faces in the audience as well as some new ones. And I also want to thank everybody that's joining us on the webcast today. We realize this is an incredibly busy time of year, earnings are still going on. So we are especially grateful that you're making some time today to learn a little bit more about LiveRamp. We're pretty confident that you'll be -- that you'll find that this is time well spent. We have a lot of new and exciting information to share with you today. First, some housekeeping fan favorite slide. Today, we will be talking about forward-looking statements. These statements have risks and uncertainties, and actual results may differ materially from the statements made today. Please see the Risk Factors section in our SEC filings for additional details. Additionally, we'll also be discussing non-GAAP financial measures. Definitions and reconciliations are available in our Investor Day presentation that's posted to our investor relations website. With that out of the way, here's the agenda for the day. In addition to hearing from our CEO, Scott Howe; and our CFO, Lauren Dillard. You also have an opportunity to hear from one of our leaders in product, Matt Karasick; as well as our Chief Revenue Officer, Vihan Sharma. After the presentations, we'll have plenty of time for your questions. We'll aim to start the Q&A session at approximately 3:00 Pacific. For those of you that are joining on the webcast, you'll be able to ask a question virtually and I'll ask the question on your behalf. And finally, for those of you in the audience, if you're planning to stay for ramp-up, please hold on to the badges that you receive today. That will get you access to the ramp-up conference over the next 2 days. And before I hand it over to Scott, these are the messages, the headlines that we think you should walk away with today. Number one, data collaboration is a significant and scaling market. Two, we are uniquely positioned to win, given our solution and network scale. Three, our revenue growth is durable, levered to the increasing demand for data to support marketing outcomes. And finally, we are driving long-term shareholder value through efficient growth and disciplined capital allocation. We've done our job properly today, these will be the points that stick with you at the end of the day. Now let's get on with the show. I'd like to welcome to the stage our CEO, Scott Howe. Scott?

Thanks, Drew. This is so much fun. Thank you for joining us. And I get the really fun part of today to talk about our strategy. And in particular, share with you what I live every day and 1,000 of my LiveRamp colleagues live every day. Like we believe we are working at such an amazing company at such an amazing time. And the runway in front of us is so long. It's going to be a really fun journey. But let me unpack the spirit of what Drew just did. He said, this what I'm going to tell you. So this is what I'm going to tell you today, this is what you're going to hear from me. We solve at LiveRamp a fundamental problem. And it is a problem that every company on the planet or at least any for-profit company is trying to deal with. And that is all of them, but all of them are trying to generate better performance, better results, better outcomes. And how they measure those outcomes might be a little bit different. A retailer it might be. I want to generate more sales. For a publisher, it might be, I want to generate more yield on my inventory. But everybody, but everybody is trying to generate a better outcome. And the way to do it, the way to do it is to collect data along the way. Because if you collect the data, it tells you whether you're succeeding or failing. It allows you to course correct. It allows you to optimize and it allows you to achieve a better outcome. So it's what every company is trying to do, but it is so hard. And with your permission for a few minutes today, I'll take you on a journey of what a typical company, a typical client that we work with, how they see the world. And I'll show you why what they're doing is so complex, unless they start to work with LiveRamp because we're the easy button for collecting, ingesting, collaborating, using, activating all of that data such that you can unlock better performance. And that's why like all of us at LiveRamp like are so passionate about what we do because we feel like that's almost a holy mission. Like we help everyone. And it's so much fun. You're going to go -- many of you will go to ramp up over the next couple of days. And you'll walk around and you'll say, "Oh, there's Google. There's Meta, there's trade desk. There's Samsung, there's P&G." and without exception, you can look at those companies and say, those aren't our competitors -- those are our partners. Those are the companies that we're helping achieve a better outcome. So almost everybody in the world is a potential client for us. And so we feel like we're so uniquely positioned for ongoing market leadership because we do this thing that's naturally scalable for anyone and everyone in the industry. And so that builds strong moats around what we're doing. And then as -- you'll hear a lot from Lauren, we have this natural network effect in our business where the more clients we bring on and the more they collaborate with one another, the stickier the whole thing gets because we met ourselves into the very fabric of what they're trying to do, and they can't possibly ever leave us. And then with that, there's a strong natural network effect from an economics perspective, our upfront fixed costs are high. And those of you who have been with us for a few years know that because you can remember when we were so unprofitable. But every incremental dollar falls disproportionately to the bottom line. And so as we scale, not only does revenue grow for a decade of nice double-digit top line growth, we think, but it falls disproportionately to the bottom line. So really need opportunity, and I'm glad that you're spending a little bit of time with us today to learn more about why we're so excited about it. All right. Let's go on a journey of what a typical company sees. And I'm going to give you this example of a global electronics manufacturer. I could have chosen a gazillion clients. Why I chose this? I don't know. But it's pretty representative of what our clients are thinking about. And I saw Alec over there earlier. So Alec, I'm cold calling you. You just put up your hand. Let's pretend Alex as a Silicon Valley technical savvy person needs to buy a new phone. I'm the CMO of this company, I want to sell him a new phone. And I know that the more I know about Alec, the more I know about like, oh, let's say, up here in the upper left corner, did Alec go visit YouTube and explore new phones. did he go to the New York Times and read an article about the best new cell phones. If I knew that, I'm the CMO of this company, of course, that would help me deliver the right information next time I saw Alec, such that I can get them to buy a phone, the right model more efficiently, right? And that's what I'm trying to do. Now I have the natural advantage. If I'm the CMO of this company. There's a whole bunch of stuff I might already know about Alec. And metaphorically represent -- it's represented in the inside of this television screen, right? So I might know in the bottom right there that Alec visited my website to learn more about some of the other products I sell. And if I knew he was already a customer of other products that I sell, well, that's a really good thing to know because he probably likes my company, he might be more inclined to buy a cell phone from me. So every company is dealing with this, but this incredible array of information, some of which I know internally because Alec's gone to my website, the vast majority of which live outside that television screen because it lives in other touch points. It's information collected at my channel partners like Best Buy, where Alec might have bought consumer electronics in the past. It's information that might sit at Google or YouTube where Alec has actually gone and done some research for the product. I kind of bring all that together. And oh, by the way, if this looks like a complicated chart, which it kind of is, right, this is nothing compared to what the typical CMO is actually dealing with because they're not dealing with a dozen different touch points, they're dealing with hundreds of different touch points, far more than I could ever fit on this screen. And all the way, like if I'm the CMO of this company, I'm not just worried about Alec. I'm worried about everybody else in this room. And what is it that I can sell you what might you be interested in besides cell phones. Maybe it's a smart television to hang on your wall. Maybe it's a washer dryer. And so I'm trying to manage all of that complex information, knowing that if I can get my hands on the right data, I can do it a lot more effectively, right? It is overwhelming complexity and it's even more overwhelming when you stop and pause and consider that a company like this isn't just a marketer, but they're also a publisher because they're selling advertising and they're smart televisions. They have so much going on. They're interacting with so many companies in the world, all of whom they need to partner and share data with. It is overwhelmingly complex. It's ridiculous. And so it gets even more ridiculous because if you think of the challenge that I have to sell Alec a phone, it's not just bringing together all these things, it's the proverbial, the very fundamental nature of advertising. The thing that we've all learned, like the goal of advertising is to deliver the right message to the right person at the right moment in time to get them to convert. But it gets so confusing because if you just look at the left here, of course, if I knew that Alec had gone to, say, YouTube to do some research, I would probably treat him differently, if I saw him on the New York Times because every ad impression, I've seen every interaction I've had with the brand influences the next interaction that I'm going to have. And so it's not just individual data collaboration and interpretation at each point, it's thinking about it holistically because on any given day as consumers, Alec has probably seen if he's like a typical consumer, 3,000 advertisements already today for a whole bunch of different products across a whole bunch of different media. And so it's trying to navigate that complexity to figure out what is the right recipe that me as a CMO, can deliver to him to get him to buy my phone. And if you think that's complex, let me make it even more complex because it's not just about pushing out the right messages to the places that Alec frequents. It's about learning what happens after he's seeing those messages. And so I want to know, did Alex go to Best Buy, then he buys some of my other products there. Did he go to Home Depot and buy a smart washer because if I knew that about him, I would actually have something really valuable, maybe a bundled offer, maybe something different to offer them to get him to buy my phone even more rapidly. And so this concept of bringing information back, which is measurement from all these different partners. That's another challenge that any CMO on the planet, any business on the planet has to be thinking about. And so if I haven't made it quite complicated enough yet, let me add 3 more things into the mix. Privacy. Consumers are really smart now about how their data is used. They recognize their data has value. We all signed up for loyalty programs that we care about. We sign up for the content that we care about. We give permissions appropriately and that privacy, those permissions need to be respected at every step along the way. And so it's really hard for a company like this to go and navigate thousands of different partnerships and ensure that privacy is maintained at every single one. It's even harder for them to start to collaborate with all these different partners and maintain security because do you really think Home Depot and this big global electronics retailer want to mingle their data together. No. they want to ensure that whatever data they contribute is there for a moment in time in tightly controlled conditions, probably encrypted maybe not even moved. And that's a hard technical challenge. And oh, by the way, the last challenge, the challenge we're just starting to crack. And the one that should get you most excited is that if I were a developer and I were looking at the picture I'm talking about, I wouldn't see it through a marketing lens. I'd look at all those dots and say, oh my gosh, this is the most interesting programming challenge ever because each one of those dots is a signal. And if I collected all those signals, and I ran an optimization program across them. I could crack the code. And then instead of doing marketing, I would be doing outcomes. And so when people talk about AI and the lure of AI, who gets really excited? Well, it's people who own businesses because they've been doing stuff inefficiently in a hard way for dozens of years as long as businesses existed, and now they see this optimization problem that can be unlocked with AI. And oh, by the way, AI, what's interesting about it? Well, we all use that. And to date, like it's built on all the public information in the world. But the best information isn't the public information for businesses, it's their proprietary data. right? And so what better way to unlock it, then if you had somehow could plug into the network to access that data with permissions the right security. So that is the overwhelming problem. And that's why you you're a business owner, you look at this and you say, that's hard. Well, it has been hard unless -- well, it has been hard, but let me make it even harder because I shared with you one example, but in fact, we're talking about planets. And we're -- what we should be talking about solar systems because there is a QSR universe, there is a health and beauty of universe. There is a travel universe, and every company is dealing with a different set of partners. And oh, by the way, when you start to overlap all these different solar systems you realize we're not talking about a solar system anymore, we're talking about a universe of businesses, and they are all connected. They are all connected. There is some degree of overlap between all of them. And so what have I described here, I have described a natural network business, where everyone is best served by aligning with a neutral party who can unlock value for everyone. And that is what LiveRamp is. So what we've done over the last decade, so it's crazy. Some of you in the room have asked me before, they're saying, Oh, I didn't realize you guys were a CDP. And we're not a CDP. I met with a big company the other day. And they said, Oh, yes, we know you. You were like the best DSP, like we're not a DSP. We're not any of these things that are so commonly referred to, and I think that's why we're confusing to investors because we're unique. We're the thing that ties them all together. We sit in the middle of the universe, and we're the wiring and the cabling that connects all these different companies. And that's why I can go to next door to ramp up tomorrow and every company is my partner. I can help them all by connecting them to one another. All right. So how do we do that? Well, we work with everyone. And I replaced the universe in with like just practically, let me talk about who we work with. So let's start with brands. 400 plus of the world's largest companies. I'll show you the logo slide here in a second. But that includes 20 of the 25 largest U.S. advertisers. We work with every major agency a tick down there. On the publisher side, we represent 91% of time spent online, right? And it's not just one lane of the -- one swim lane of the pool, it's everything. So it's open web. It's the walled gardens, it's Meta, it's Google, it's Bing, it's Microsoft, it's Yahoo!, It's Amazon, it's all of the major CTV providers. And it's a whole host of new companies that we're now securing -- that you haven't even heard of companies. Well, maybe you have her to some of them like we'll talk about perplexity tomorrow. And what you can do with perplexity to embed your data into their very different kind of search decision engine AI-driven. It's consumers, 4 trillion consumer records processed every month that puts us on par with a company called Google, over 1 billion permissioned consumers through our network that we are helping match to one another. A massive amount of data, but not data that we own. Let me be really clear about that. We are not a data broker. We are a match maker. So oftentimes, our clients are doing really interesting stuff with their first-party data and the natural next thing they want to do is append other data elements to it from partners. And so we provide that. We're a matchmaking service. We introduce them. They use our connectivity to collaborate around data. And then virtually every ad tech partners, some of whom -- people often say, oh, that's a competitor. No, we work virtually everybody in the system and whatever they do, we just make it better through the connectivity to everyone else in the ecosystem. So really fun place to be because everyone is our friend or so it feels like most of the time. 400 brand subscription customers and again, huge network benefit because it's a little bit like having a cell phone, right? Why do you want a cell phone? It's because if you use it, I can reach every single other person on the planet who has a cell phone or who has a phone number. Well, likewise, why would someone work with us at LiveRamp, it's because they see this network benefit. I was at Best Buy a couple of weeks ago, and we were talking about our relationship and they said, hey, everyone we talk to, publishers, ad tech companies, they all keep saying they work with LiveRamp. And so for us, the choice is obvious because we know these are the folks we want to partner with and they all use you, so we ought to use you as well. Great network economics that I talked about earlier, and Lauren is going to talk about a little bit more later. And the structural advantage, having a scale position. It's a deep moat because scale breeds more scale. If you have everybody signed up and the next person who wants to extract value once to develop value from collaboration, it's obvious where they should plug into. All right. When our clients work with us, 80% of them are using a subscription. It is largely historically SaaS-based, 80% of it is subscription model. And there are 4 components to the subscriptions that our clients purchase from us. And going from left to right. The first, Matt's going to talk about this in more detail, is identity. So if I have a large data set that I have permission for, and I want to match that with someone else's data set that they have permission for, I need a matching key. I need some way of taking 2 tables of data and saying, "Oh, here's the overlap between them." well, that overlap that Rosetta Stone, we call that identity. And it's foundational if you want to do something with your data. Second piece is this data marketplace. So I start with my own data, but I want to enhance it with other second or third-party data, and we allow that. The third piece is what we'll call addressability. And that is the concept of if I'm doing something with my data, I have developed an insight, well, it's kind of a useless insight in a can, if I can't activate it at the moments that matter. So when I was trying to message Alec earlier, I want to identify him when he goes and downloads an article in the New York Times about cell phones, because I want to influence the content of what he sees. I want to deliver the right ad to him at that moment of time. And so that addressability is the activations we have with all of the different touch points in the world, it's Meta, it's Google, it's Pintrest, it's Reddit. Virtually every publisher you could name, they are our partner in this addressability network. And then a lot of companies that you wouldn't think of as publishers. And so a lot of them are commerce media networks. Companies that are big retailers who have historically worked with merchants and now they're figuring out how do they merchandise their store, how do they merchandise their website given these signals. And then finally, the last piece is this data clean room. So bringing information back in an anonymous encrypted privacy-compliant way between multiple partners that requires a clean room such that disparate partners can share information and know it's done securely. Now the other thing I'll point out on this slide is you noticed that I have a multiplication between each of these swim lines. And that's how our clients think about and that's how I think about it as well. And the reason I put that is because this is a multiplicative function. And in math, what that means is like to deliver the full value, you need to have all 4 pieces. If you have an empty fit in any one of those, you can't deliver the entirety, the benefit of the network. And where this becomes really relevant is this next slide here. I'll call this the swim lane slide because so often people say, hey, who are your competitors? And I often say, hey, we don't really face a whole lot of competitors. We have a whole lot of companies that do a small slice of what we do. And so you can see here the rows are areas of potential overlap. So ad agencies or data clouds or ad tech platforms like DSPs. And you can see that each of them does some portion of what we do. But when someone says, "Hey, I'm using Trade Desk" I can go in and say, "Well, great, I can help you use Trade Desk better. And importantly, I can also help you advertise on all of the other DSPs, DV360 and Amazon and Yahoo!, and Microsoft's Xandr, DSP. Likewise, on an ad agency, they might have some element of the identity, but they don't have all the connectivity and the clean rooms and the measurement that makes us so successful. The market here is really big, and I think it's getting bigger. And so we estimate it as a $13 billion TAM. We've used some external consultants to help validate that number as well. and we've split it into the 4 underlying pieces of the market. And I often times say, is it a big number or it's a small number. This is a big number. So we would over a couple of degrees off. It's still going to be a big number. More importantly, we think it's a growing number, and I'll unpack that here in a second. Part of the reason it's growing is because we feel fortunate to be aligned against all of the existential market trends. There are winds at our back right now. And so for instance, let's take CTV, high growth, 13% compound annual growth. We work with all the major CTV providers, and they're just getting started as linear television tips to CTV. And I talked about this earlier, like hey, for years in linear television, everybody has used the same kind of way to buy where you use like panel data. Well, you can continue to do that or you can just go directly to Netflix who knows exactly what we've all viewed has our demographic information has our permissions and so -- and can build predictive models out of it. And that is so much more robust. It will unlock so much better performance than anything we've done historically. And the same is true in commerce media companies like Albertsons or Target or Walmart. We work with most of the major commerce media networks. They're replacing their merchant partner programs, ones where they talk about the partnerships or funding the Sunday circulars and the couponing with instead, they're bringing it all digitally. So you can go into the app and have your shopping list, they can suggest things for you. And they'll even increasingly use that to merchandise the stores based on what their consumers want. And so what we're doing has applicability in such a greater surface area in the future than it has in past and nowhere as much as AI, which I talked about earlier, unless you have relationships with commerce, media, CTV and social, you can't possibly be fueling the AI models of the future, the ones that are going to unlock transformative performance for our clients. Speaking of our clients, we got a lot of them. I talked about 400-plus really big clients, 125 of them spend more than $1 million a year. They tend to grow faster than smaller the clients because they're more sophisticated and they're unlocking greater value. Now we see an opportunity over time that what the big clients are going to do, someday all clients are going to do. And we just have to make it easier for smaller advertisers, smaller businesses through more self-serve functionality and just make things easier and more intuitive, that will also unlock future growth. I'll give you an example of how clients use us. And this is a good one because it's not kind of one-to-one. It's, I think, representative of the future where companies are going to collaborate one to many. And in this case, we had Mondelez, who's a major packaged goods advertisers. Mondelez is the owner of brands such as Triscuit, which was trying to advertise in this case. We had Albertsons. Now actually, Mondelez wants to sell more crackers. They have an ad budget, but they don't necessarily know who buys more crackers who knows who buys more crackers, Albertsons because they have a huge loyalty program, they get scanner data that says who bought those crackers. And they also have a retail media network where they can advertise directly to a lot of those shoppers. But it's still limited to a small slice of the population. And so what does Albertsons not have? Well, they don't have the kind of global reach that a company like Pinterest has because they see everybody, but Pinterest doesn't know who's going to buy crackers and they don't know whether they actually bought crackers. So everybody in this partnership brought something the other 2 didn't have. And by collaborating their efforts through a clean room and sharing data, it was a pretty transformational case study. In this case, Mondelez achieved 16% lift in sales and a 19% lift in new buyers to the franchise. And when we're talking about double-digit lift in either of those things for a company that's been in existence that have packaged goods sales, that is a huge deal to that kind of company. All right. You see I'm passionate about stuff, right? And I'm in the home stretch here, I'll end with a few slides, like I talked about the complexity earlier. I talked about, hey, super complicated, imperfect marketing, measurements hard, privacy and security and optimization. Well, all of the problems that I started out with, like we saw a big checkmark next to, through our scale, through the integrations that we have because we have the foundational data that's going to enable future AI innovation. That's why we're all so excited here. because we feel like we're at this moment in time where really interesting things are happening. I'll go back to this eye chart again. And Alec, thank you for shopping for a phone with me earlier. But I want to make a couple of other points that I didn't make on this slide earlier. Number one, like there are a lot of data points on here, but there are a lot more that aren't on here. And that represents a massive TAM expansion lives, a massive opportunity because things like your e-mail, the text you get, that's not linked in here yet, but that is a surface area that we are pursuing. We will sign up those partners because by having those data points, it makes everything else more valued. Likewise, if you think about this picture, I mean, this is representative of any company that has fragmented data that is owned by other partners outside of their walls. Well, that's true of every marketing, every marketer's challenge, but it's also true of most other industries. So risk and fraud, same thing, health care, the average consumer has their health records with 100 different partners. And so the ability to bring those things together in a permission, secure compliant way that will help people live longer lives. And so while our killer app to date has been in marketing, we think there's application here to expand to other verticals. And then finally, I'll double down on what I said earlier, a developer wouldn't look at this and describe it at all like what I described it, they would say that's a beautiful picture. That is signal. I can harness that signal, and I can optimize it. And so they see the answer to a whole bunch of AI optimization opportunities. And so when we go back to the TAM, hey, $13 billion, we're a fraction of that today. But as we expand into MarTech and consumer experience, we think there's a bigger opportunity. Our TAM naturally grows bigger. And new verticals, health care, fraud and risk, public, that can get us bigger in AI. How big can that be? Well, we don't even know. But we know we're right in the center of some of those really interesting conversations right now. And so that's why this conference is so much fun. It's because all of the companies that I talked about on those slides, they're here. they're collaborating. And the 1 common denominator across them all is us because we sit at the center. Two last slides. One is no great company is great without having amazing people. Amazing companies are always built around the amazing people. And LiveRamp is no exception. I really like our management team here. And what I will tell you is we have probably a little bit different philosophy than a lot of companies. We look for people who have a decade of industry experience. They tend to be in the positions of leadership in our company. But they're earlier in their careers and they're hungry. I mean they want to succeed so desperately. They are so intellectually curious. They are so excited to go to ramp up and have a gazillion conversations about what is possible. And all of us a lot coming to work every day because we feel like we're changing the world. I think we are changing the world. So I leave you with this. It's where we started. Every company, every company on the planet is looking for better results. And it's really hard. But if they work with LiveRamp, we can solve that problem for them. And as we solve that problem, we think we're a really interesting stock to have on your radar screen because we think we solve that problem in a way that has really compelling network economics. A nice path for a decade plus of sustained growth, but also every quarter, every year, never may be completely linear, but always in the right march, the right direction of just increasing margins because of our fall-through. So again, thank you for joining us today. I love the fact that you're investing the time to learn about us. And with that, I will turn it over to Matt, who's going to take you through our products.

All right. Thank you, Scott. Scott said, I'm Matt. I'm one of our product leaders here. I came over with the Habu acquisition, where I was the Chief Product Officer over there. And so key takeaways I want to walk you through are by delivering the largest collaboration network and the use cases that it can power, our platform is purpose-built to solve those challenges for marketers and deliver those better results. We have earned the right to this network by several different key differentiators that are extremely difficult to replicate without a huge amount of investment and innovation. And the last bit is we're not done. We will never be done. We will always continue to improve and build out this platform to deliver better and better results for our marketer customers. And so Scott walked you through why this is complicated. As the resident product guy in the room, I get to get a little geekier about what makes all of it so hard. And so first, you look in this top left, the high-tech switcher. We all started with the high-tech switcher. Well, how did this company even know that this was a high-tech switcher, right? This individual likely showed up as 6, 7 different individuals within just their own 4 walls. And maybe it never showed up in their own 4 walls and maybe that was data that they needed to go and get from a partner who knew more about a purchase from a partner retailer who saw the kinds of technical equipment beyond just the screen that they were going to buy. And so first, they just needed more data and to make better sense of that data. That was really, really hard. Next, if you look across this and the one Scott ended on had even more logos here, you're talking about 6, 7, 10, 15, 20 different companies. That means just by definition that if you look across all of this, this is data that's coming across all different clouds from all different schemas, all different platforms out there. And that's hard. If you're a marketer, you wouldn't even know how to go about trying to figure out what to do about that. And then the last piece is, let's say you can solve all of that. Well, now you need to help companies connect and be willing to be able to collaborate in this way, which means all 15, 20 of those companies need an easy way to set and enforce the rules about how their data can be used, right? No longer will the company say, "All right, write me 1,000 page contract about all the things you will and won't do about with this data." you're talking about some of the largest companies in the world where this data cannot leave their ecosystem whatsoever without absolute technical guarantees that only what they are comfortable with can occur and nothing else. And lastly, if you solve all of those problems, you now have access to huge amounts of data to take that data and turn that into the analytics, the insights and the outcomes, then require building out and orchestrating all of those workflows. And so that is a huge amount of work that the entire ecosystem would have to solve for to try and deliver these better results that can happen. And that's what LiveRamp can do with our collaboration network and the platform that powers it. And so Scott talked about what each of these components do, how identity gives -- allows a company to rationalize and have a single view of their customer, how they can enjoy that with each and every one of their partners view of that customer, how they can add more data to that view to have even more signals there. How they can then use that to then go reach their target audience to meet their objectives and how they can use clean rooms to then measure that. What you need to understand is that when we go and pitch a marketer, let's stick with this consumer electronics brand. And let's say they want to use commerce media as a way to infuse and deliver results. Well, that is the use case. How do I tap into commerce data use that data to segment, build audiences, reach my audience and then measure what happen, meaning they're buying the entire use case from us 1 at a time, not each of these pieces. And that's why if you look down at the revenue distribution, you see that it's fairly even across because nearly all of our customers are using the entire platform, not each individual of these components that make up for it. And so I talked about the moats that help us get here and the competitive advantage, right? The first is, again, identity. We have the largest graph out there, making it so that we can have -- help customers have them clearest view across all of the data and marry that view to the entire ecosystem. That's hugely important and a requirement. I can talk that prior to the acquisition, Habu was a clean room company here, and we had this slide that talked about all of the challenges that we solve for. And the thing that we never would like to say out loud is there was a huge box missing from it, which is if you're really just only matching your identifier exactly as it's typed into your database, to your partner's identifier exactly how it's typed into your database, it's not going to work very well. It's going to miss a huge amount of fidelity of information. Adding this piece to the overall staff was a requirement, let alone an important differentiator piece. Interoperability. If you were trying to collaborate with the entire ecosystem, this is a multi-cloud world, a multi-platform world. If 2, 3, 10, 15 companies want to say, hey, let's power this retail media use case together, an invalid question for one of them to say to the others is, okay, which cloud should we use? Almost every enterprise is multicloud even on their own, let alone the fact that if you took across the entire ecosystem, it is fragmented and the cloud wars are not going to be fought and won and done on this hill. Data governance, again, you are talking about some of the largest companies on the planet, the largest walled gardens social platform, CTV providers, retailers where their data is their gold. And if they cannot honor their commitment to their consumers and be able to answer the questions that their privacy, legal and governance departments are going to say, are required in order for them to collaborate this way. If we do not deliver the provable technical guarantees that ensure that everything is going to go the way, well, it needs to. This doesn't work. There is no network. There's gaping holes in the network, which doesn't solve the overall problem. And finally, because we've solved these problems, it has earned us the right to the most important differentiator, which is the network out itself. We get to a walk into a marketer and say all of the places you are buying, all of the places you might want to buy, all of the places you want to plan and look for opportunities? The answer is yes. They are in our network, and you will be able to collaborate with them and get those insights and outcomes that you are looking for. And so we talked about how this network, coupled with the platform delivers the ultimate end results for these marketers. So marketers, this is not a new concept. The cycle has never changed. If you're a marketer, you plan, who am I going to reach, where am I going to reach them, you segment and start to then figure out who that message is going to be. You then go and reach them where they are, you then optimize those things as they are running and then you measure to find out what's going on. What we have long known what the industry has long known for decades now, is when you use data to infuse intelligence in each of these steps, you achieve better results period. And so let's just take a practical example of how all this works. Hill's Pet, right, one of the largest pet food companies on the planet. First thing they're going to do is they're going to say, hey, I have some data about a portion of my very loyal specific customers who may be using our mobile app to learn and track their pet's health. But that's a small sliver of their entire addressable market. So then they're going to then say, all right, in addition to that data, I need to add more data to this picture to fill in the holes of the data I have as well as get a whole new picture, so I can scale and grow beyond what I already know in my walls. Now I need to rationalize. That's why I have a consistent view of those customers, and I need to connect it to the ecosystem in a way that makes it joinable because my view of these customers may be different than the social platform or the CTV provider or how someone logs into this mobile app. Once I do that, and I'm able to then segment this data. Now it's time for me to go and address these consumers in all different types of services and experience to create that awareness and consideration and intent. And then you're going to push that out. And then you need an ability to then have every single one of those touch points be measurable, so you can understand, did it work, did it not? What do I do from here? And again, all of this is happening. And if you're the marketer, you don't want to hear about all these squiggly lines and clouds and data formats and identifiers. That is too complicated. What you need is the -- to be able to just ask a natural language question, give an answer, know that it's optimized and get your results. And so if we look at each of these individual pieces and what we've been investing in and doing. Identity is foundational to the entire piece of it. In order to be a customer-centric organization, you need to have a 360 view of who that customer is, right? It's a simple fact. And in order to do this is incredibly difficult. You were talking -- I mean these are real people, the same as all of us using different devices, different log-ins from home, sometimes doing things not logged in. And it is only because of LiveRamp's graph, which is the largest graph in the world, coupled with the way we infuse it into this ecosystem and into the network that allows this to work and have this consistent view. Where, again, if I was still sitting here on the Habu side and I just had a blank page for this piece, what I know is that just being able to match what direct strings of whatever I had whichever e-mail address I was using to log in. I'd be at less than 50% of the scale and accuracy that I would need to be here. Access. You -- it will be very, very hard for you to go and talk to any marketer who says, I have all the data I need I know all about every consumer and every project I want to reach. I know everything I need to know about them. I know everything about where I've reached them, what to do and how to do it. And so what you need to do is add -- fill in the holes of the data that you have and then get more data so that you can scale and grow beyond where you already are, right? I am not aware of any company that is not looking to grow which means you can't just sit still with what you have right now. And so that's why our marketplace is an important part of this entire equation with hundreds of valuable data providers that our marketers are able to use to scale and grow their entire efforts. Connectivity. This is another moat again where -- this is a network of integrations across so many different types of services, so many different channels across hundreds of different endpoints and destinations where consumers interact, where this is key to taking action on all of those insights and actually reaching those consumers to create those outcomes to create that awareness, consideration, intent and ultimately conversions. And this also is extremely difficult, right? When you start to look at what the ecosystem, we've seen so many different eye charts in the marketing and advertising industry. over the course of years. I remember the first scape that was put out there, I talked to the guy who created it. It was meant to be a joke, like look how complicated this is, turns out the industry said finally, you've rationalized it for me. But the answer is the amount of investment and innovation, you need to do this to have these connections where you can reach consumers anywhere and everywhere. At petabyte scale is not just an upfront investment, the type of an investment that is ongoing and something that we are in such a leadership position on today. And then insights, and this was an important key component to closing the loop of this entire collaboration network, is, again, if you're trying to go to that marketer and say, I can deliver you better results. I can't mean in tiny pockets and in tiny silos. You need to represent 100% of that marketers plan and addressable market. And that's where we've been able to build this collaboration network by having the largest, most sensitive companies in the world, be able to get comfortable connecting their data, not having to move and centralize it, being able to set their individual rules and have full control over who, how and where their data can be used all in a way that it can plug into templated, well-understood use cases that the market needs and addresses without removing the ability to customize and configure and iterate and innovate from those initial use cases. And so you may have seen that we have announced cross media intelligence today. This is an example of one of the most challenging use cases the marketing and advertising industry has had and how do we not just, I as a marketer measure within each 1 channel, measure and optimize within each channel, but look across my channels. and get a sense of global reach, the duplicated reach, global frequency, true attribution across my entire plan. That is an incredibly challenging problem to solve. And what we realized is the answer to solving it is data collaboration, is allowing everyone to plug in their data, set their own rules and then be able to provide this answer in a way where if the business user doesn't have to understand all of those complexities and can just come in and say, is my measurement ready, please help me get it. Now solving this problem, we've had the blessing and the curse of having to take on something that most technologies don't do which is a technologies either have to choose, am I going after builder persona? Am I building my product for developers and data scientists and data engineers or am I more of a SaaS platform that gives it to business users. And we've had, again, the honor and privilege of having to solve both. Because if you want to get these large nodes, these large companies, the social platforms, the CTP platforms who have road maps of their own to build on top of our collaboration network. You need to not put a ceiling over their head and what they can innovate and iterate and how they can build things. But -- and so all of that flexibility allows us to scale to any type of use case, but you can't have all of that flexibility and complexity limit the ability for a business user to be able to have the easy button to be able to say, this is the exact use case I want. We know what those use cases are. Here is an easy button where all I can do is just come in, ask a natural language question, define my outcomes either with data or words and have the system just go do it and figure it out. And so our platform has to meet both of these personas because in a lot of ways, it is a 2-sided network. And will never be done. We will always have to continue to modernize our platform, right? If you look 5 years ago, most of the industry would be sending each other data, centralizing it in one place, with the birth of the adoption of the cloud. Now you need to let everyone be able to just connect and bring their data from their cloud as is. The amount of data that we're talking about continues to go up and to the right. being able to do everything that we do at petabyte scale incredibly fast, incredibly reliably, again, is a moat where we have made a huge amount of investment and will never be done investing here. Improving match rates more signals, more devices, more surfaces meant that huge lead that we have with our identity graph, we're never done. We'll always continue to enhance it and be able to rationalize and connect all of this data together. And then a modern user experience, right? If you look at the way things were done in point and click with 80 clicks before, as you just saw in that quick demo of how cross-media intelligence delivers make it easier for users to get access to those insights and outcomes that they want. And AI is a huge opportunity and it's really in 2 buckets here. One is powered by AI. Again, you just saw that quick little bit in the demo where AI just makes our products better, easier, faster to use. And then on the other side is we actually enable empower AI right? With AI, Scott mentioned this, right? It's great to go scan the public Internet and have conversations with it. But in the world of business, you need data that is private not just your own data, but your partner's data provided they give the permission to use it in this way. So being able to take all of the data that you can access through the LiveRamp ecosystem, and be able to use that to build and train your own AI to create pipelines for AI agents is the next frontier for how people are going to use the data from the collaboration network. So as we think about what it is that we're doing across each of these components, Live/Identity, that is always going to be us trying to make sure that we can bring identity to where the data is and be able to have that identity get resolved and connected directly from customers' clouds while always increasing accuracy and scale. Live/Access again, I just mentioned it, the -- being able to take more and more data, not just for sort of marketing and optimization in those types of use cases, but now also to power new AI and agenetic experiences as well as having people who may not have been able to sell our lessons data in the traditional way, but with the benefit of a clean room and with rules now are able to become a data provider or a data seller. Live/Connectivity. How do we -- how do you go and reach these consumers in all the new types of formats and use cases. So Scott mentioned it earlier, you're right, not just seeing regular ads, but also maybe in marketing use cases such as e-mail or SMS, as well as a new type of agentic shopping experiences. And then Live/Insights powered by clean rooms, right? You saw us talk through that cross media intelligence to answer even more questions. And that's what it is, is continuing to add more and more of these very, very valuable use cases on top of the collaboration network to make it easy for folks to tackle these new use cases faster and more effectively. As far as where all this goes from here, what we're focused on today in marketing and advertising, the answer is more data and better outcomes, right? A whole lot of data that we're talking about here is data such as audience data, transaction data, impression data and it often helps to power optimization within each channel and help you measure across -- both within and across the channels now after our announcement today. Where this goes is how do you optimize omnichannel with even more data in 1 system versus still having to optimize within each of these channels, all powered by AI, being able to just do more with more data. And then as far as where this goes from there along the x-axis, it's the use cases. The technology we have built is unbelievably flexible and horizontally applicable. The ability to craft a new use case to create a new value is absolutely part of the technology platform. This would be us, again, starting to then use that flexible technology to build in those use cases that are relevant to more and more industries and financial services, in health care, as Scott talked about. And so key takeaways, this collaboration network and the platform that powers it is purpose-built to solve marketers' largest use cases and ultimately deliver better outcomes and better results. We have this network, and we have this platform only because of the advantages we have in identity, in addressability, in connectivity and in insights with clean rooms. And we have a road map that will continue to innovate and iterate here and keep our leadership position. Thank you. I'll hand it over to Vihan.

Good afternoon, everybody. My name is Vihan Sharma, and I'm the Chief Revenue Officer of LiveRamp. In my section, what I will cover is 3 things. One, we are going to look at why LiveRamp is a critical platform for our customers. We are going to look at how our network is creating net new levers of growth for our company. And finally, why we are just getting started. Scott in his section actually talked about the different customers we serve. But let me take a moment and help you explain how these customers leverage LiveRamp today. On one side, you have the marketers and their agencies who leverage LiveRamp really for audience targeting and measurement use cases. On the other side, you have companies who are leveraging our platform to build net new capabilities on top of us to unlock growth and capture net new revenue streams. These are traditionally the top 500 ad tech platforms. These are top 250 data providers, and they are the ComScore 250 publishers. Interestingly, over the last couple of years, marketers with huge volumes of first-party data are now leveraging LiveRamp to unlock net new revenue streams for themselves. A great example here would be retail media. Most retailers leverage LiveRamp today to capture this retail media growth that you are seeing in the market. Scott talked about how we are at the center of massive industry transformation. For our customers, we are helping them navigate these key trends that we are seeing in the market. I just talked about retail media. But over the last, let's say, 12 months, you have seen financial services creating their new media engagements. This is the case for JPMC and PayPal. You have other mobility companies like Uber and others who are also launching their advertising businesses. And our experience in retail media is going to help us unlock the growth we are about to see in Commerce Media. Secondly, we talked a lot about CTV and social. Finally, these companies are becoming more open to allowing their data to be available via clean rooms. This is a huge opportunity for our advertisers and their agencies to finally understand how their media is performing across platforms, across publishers. And finally, AI. We fundamentally believe this is going to be a highly fragmented market where you will have multiple models, you will have multiple applications for advertisers to use. And LiveRamp's role really will be to securely connect the first-party data our advertisers have to this highly fragmented ecosystem. This is the reason why having a network is so important. And we fundamentally believe that our network is going to be a core differentiator for our company going forward. which will unlock net new land and expand capabilities, which will unlock net new partners who will drive incremental demand. And finally, it will allow LiveRamp, our partners and our customers to continuously innovate on top of net new signals we are seeing across the network. So I mentioned that our network is really going to be our core commercial differentiator. Let me actually help you explain how we built this network over the last 10 years or so. We started this journey really very much by helping advertisers take their first-party data and push this out to the destinations which mattered. This was for advertising or targeting use cases. Over a period of time, over a period of time, we have not only helped our customers push data, but also to access net new signals to ensure that they are able to measure the effectiveness of their campaigns going forward. So our network today looks like the circle you see in the middle or the [ petri ] dish you see in the middle. However, when you ask our customers what they want, they actually want to have access to more signals so that they can map the full customer journey, right? They want to have access to transactional signals coming from retailers. They want to have an understanding of mobility signals, which they can get today from the different apps which are available. And so we believe that our network strength is going to continue to grow as we add these net new capabilities for our customers. How are we going to take the network strength and drive net new revenue? This is the question I think everybody here is looking for. And so to answer this, I'm going to focus really very much on our brand customers who are the largest customer cohort we have available here at LiveRamp. If you look at the work we have done, we have very successfully penetrated the Fortune 100 companies or Fortune 100 advertisers. However, when you expand this view and look at the Fortune 1000 companies, we really have a long runway for customer growth, and it unlocks really a huge potential for acquiring net new customers going forward. Our network actually enables multiple land motions, which will allow us to accelerate net new logo growth going forward. On the left-hand side, what you see here is our traditional land motion where our customers leverage LiveRamp for targeting use cases. These are traditionally companies who have access to first-party data. On the right-hand -- or my right so your left, what you're seeing is a net new land, which is available to us because of the acquisition of Habu that we did last year. Today, companies, let's say, like CPG brands who don't really have a lot of first-party data, if they want to understand what is happening across the media channels they are using, they can come to us, and we are actually able to help them get new access, which unlocks net new use cases for our customers. And so this new land motion allows us to go after net new customers who we were not able to go after before. Finally, once we land these customers, we have multiple paths with which we can grow them. I think Scott talked about perplexity. We have a significant number of destinations, which can be used for a customer to push their data. The more destinations they use, the more data they are generating, the more data that needs to be measured. This allows us to go and upsell live identity and live connectivity solutions that we have. However, as our customers are maturing, they want to have access to net new signals. For example, if you are looking for -- if you're a CPG brand and you're looking for a transactional signal, you will need to have access to retail data. the retailers no longer are going to take their data and just make it available to you. They have never done this, so they're not going to start today. However, with clean room access, you have the opportunity to complete this journey and better understand how your marketing has been doing. So this allows us to unlock a net new cross-sell motion, which is via clean rooms, which we believe is a vast opportunity for our company. On this slide, you can see only 25% of our customers actually have a clean room today. Each of them represents about 4x the annual recurring revenue versus a customer who doesn't have a clean room. The white space available to our commercial team here to go and continuously upsell and cross-sell or continuously cross-sell live insights and clean room solutions is huge, and we look forward to continuing accelerating adoption of our clean room solutions. This all sounds theoretical. So let me take you through a land-and-expand journey for a leading global electronics manufacturer that Scott was talking about earlier. This company started very simply with onboarding their first-party data. They took their data and they wanted us to distribute it to a couple of destinations to see whether their first-party data delivers better outcomes. Very quickly, they started generating a lot more data from -- or a lot more signals from a measurement perspective. and they wanted to continue understanding how their different partners are delivering results for them. And this allowed us to actually sell or sell identity and upsell different connectivity solutions at LiveRamp. However, this global electronics manufacturer doesn't really have access to all the transactions. Not all consumers purchase their products directly in their -- on their website or in their stores. So they needed to partner with a retailer, in this case, a Best Buy or a Home Depot. And that allowed us the opportunity to cross-sell clean room solutions. And you can see the value of the contract significantly increased. Today, this company not only uses LiveRamp for their own first-party marketing outcomes. They are using the clean room solutions for their own advertising business. They want their advertisers to access their impression data so that they can continue differentiating their own products and drive more adoption for their own marketing -- own advertising business. And this is just one example. If you look at this next one, you have a leading global automaker who spends a lot of money in advertising. They also just started with a connectivity land, which is onboarding. Very quickly, all the advertising they were doing actually results in foot traffic at the dealerships. And they had no way of connecting this data. So what do they do? They leverage clean room connections, allowing us to upsell again clean rooms, which help them connect transaction data and foot traffic data to whatever advertising they're doing. And they started a very small, maybe a couple of areas where the dealers were okay to connect their data. And very quickly, the results of this solution led them to grow across a wide -- a large number of dealers. Similarly, this is a different example from a grocery chain, where the grocery chain leveraged LiveRamp to launch their retail media business. When they started their media business, they were really very interested in their owned and operated properties. Very quickly, they wanted to go beyond. So they needed to have additional channels where they can advertise and start generating more revenue. Very quickly, they wanted to go and showcase and bring transparency and allowed us to continue upselling and cross-selling identity and clean rooms. And today, we believe that this company not only has about 600 clients, but they have access to about 5,000 different suppliers. In the future, they are not just going to focus on their top advertisers. They're going to focus across the 5,000 companies they work with. And therefore, each and every one of them, they will use clean rooms as a way to share insights, which can be activated across the ecosystem. And finally, this is a leading global pharmaceutical company, which, again, from a privacy perspective, as Scott mentioned, as Matt mentioned, a lot harder to navigate. But because of the partnerships we have, because of the network we have, we started very small onboarding, but very quickly, we're able to go into a clean room solution because we could bring TV data, our TV viewership data, along with transactional data, which is scripts in this case, to unlock much better understanding of how their advertising is performing. As you can see, this is just 4 examples, but this is a trend you can see across our entire customer base, where everybody starts very small, either through an onboarding solution or through a simple measurement package. But very quickly, as they mature, they are able to unlock net new opportunities for themselves and for -- and allow LiveRamp to upsell and cross-sell the multiple products we have in our suite. The channel partners, I think we have talked several times with you where we talk about clouds and the global system integrators. We have had about 2 or 3 years of history working with these platforms and partners, and they are very important because when you're a CTO or you're a CMO, you are using one of these partners to either their technology or their services to help you navigate the challenging ecosystem you have in front of us. However, we believe in the future, the channel will actually be coming from our network. So retailers, I talked about a retail customer who has 5,000 suppliers. They want the 5,000 suppliers to leverage LiveRamp's clean room solution so that they can unlock revenue for themselves. This gives both of us a joint opportunity to go after this market. Similarly, the publishers and media partners, they are using clean rooms today to differentiate their own advertising value proposition in the market, and they jointly want to bring LiveRamp and their own sales team to their customers so that they can increase the volume of media being purchased through their platforms. And we absolutely believe our network strength unlocks net new incremental demand we have not seen across our ecosystem before, and we are very excited about going and capturing this value for our partners and for our clients. I talked about how innovation -- how our network strategy and differentiation is leading to greater innovation. As we create more and more demand, we believe we need to have the right scale. I think Matt just helped you understand how we are investing in ensuring that we have a scalable platform. But what I'm really excited about is this whole notion of how our product is making it easy for everybody in the network to actually discover new partners, understand the value that each partner brings to the table so that we can accelerate collaboration across the network we have. Similarly, I talked about the Commerce Media capabilities or Commerce Media partners and publishers and media partners. They need support, and they are willing to invest in joint go-to-market capabilities with LiveRamp to unlock net new demand for themselves and for us. This is a huge opportunity, and we are really investing in dedicated teams who are going to go after this opportunity. And finally, as we grow our network, we have net new signals coming into the platform, which we are trying to innovate on. We have a specific team who is focused on using our current technology and the data signals which are available to work on market expansion. And today, we are focused on 3 key outcomes. Number one, it's about landing. So prescreened acquisition as a service. It's about fraud. So bring your own data science capabilities to the data which is available to you in our network. And finally, government, which, again, has a lot of data but doesn't necessarily collaborate across themselves very easily. You saw all the opportunity that is in front of us. And so in order to capture this opportunity, you need to have a right kind of go-to-market setup with the right teams in place. What we have done is organize our business in 2 specific teams: one, which services the CMO or the marketing office and the other, which is very much focused around platforms, data providers and publishers because they are different expertise and they use LiveRamp in a different way. However, we want to be strategic partners to our customers as they navigate the challenges I talked about earlier. And so each of these teams are verticalized and have vertical expertise, which allows us to differentiate ourselves when we are in front of our customers. Similarly, on the post-sale side, if you are talking about significant investments as some of our customers are making, you need to have the industry expertise in order to be good partners. So we are investing in that. And we are unlocking specialized support for key outcomes like measurement services, like analytical services, which will allow us to serve our customers better. So this is how we are going to use our network. But as I said earlier, we are not really done yet. We have a pricing motion today, which is very focused around adoption. So we go and we try to set up pricing in a way that is easy for our customers to consume every time we launch a new product, we have to go through this whole process. However, we know that the current state of our pricing is complex. There are too many KPIs or too many levers of pricing, and it is creating a fractured customer experience in some cases. When customers are using multiple capabilities at LiveRamp, they have to navigate the challenges we have created with pricing. So this is something we are going to work on to simplify to ensure that we are -- there is less friction in our commercial motion. It is going to be easier for our customers to understand. And they are going to be able to leverage multiple products with a single pricing unit. which is going to unlock huge opportunities for our commercial team, but also for our customers to use LiveRamp in a much better way. Again, this is not something which we are going to be doing tomorrow. This is something we are thinking about in terms of accelerating and supercharging our commercial motion, but this is something which is going to take time. And as we start this journey, we will keep you updated. So finally, what you heard me today -- heard -- what you heard from me is we are a critical platform for our customers. We really do have multiple growth levers driven by our network strength. And finally, we are just getting started to deliver incremental value to our customers and to our investors. Thank you very much. [Break]

All right, everyone. Hi. Welcome back from the break. My name is Lauren Dillard. I'm LiveRamp's CFO. And throughout the course of this afternoon, you've heard a lot about our vision and strategy, our product and product differentiation and our go-to-market. So now I want to round out the afternoon by talking about how all of that translates back to our financial model and outlook. And if you take nothing away from the next 20 to 30 minutes, I hope you walk away with the following 3 points. First, over the past decade, LiveRamp has built a solid foundation for durable growth at scale. Second, that foundation is supported by a large and growing addressable market with multiple growth levers. And finally, we have an advantaged financial model. We're a SaaS business with great revenue predictability and a proven land-and-expand motion. We have a strong track record of balancing top line growth with steady margin expansion. We're debt-free with growing cash flow, and we have consistently and meaningfully returned capital to our shareholders through our share repurchase program. So with that, let's dive in and we'll begin with the foundation we've built. Over the past decade plus since LiveRamp was founded and certainly over the past 6 or so years since it's been a public company, we've built a solid foundation for future growth. We're a SaaS model with more than 75% of our revenue coming from subscription contracts. We have an ARR base of nearly $500 million. As Vihan mentioned, we're winning with the largest companies and expanding our relationship with them over time as evidenced by our strong and improving net retention rates. We're profitable, we're cash generative, and we have a very healthy balance sheet, which over the years has afforded us the ability to make smart, strategic acquisitions while at the same time, returning excess cash to our shareholders. I'll double-click into a few of these areas. While we would be the first to acknowledge growth is never perfectly linear, we have a strong track record of consistently growing our top line by double digits. Our revenue is also well diversified across industries and customers. No single customer today makes up more than 10% of our revenue. And even within our largest industry segment, IT, multiple subindustries are represented, including digital publishers, media buying platforms, consumer software and so on. As Vihan mentioned, we serve multiple customer types. And while brand marketers make up our largest customer group and the group that we believe will drive our growth over the long term, because we're neutral, because we're interoperable, because we occupy the center of that network diagram you've seen now a few times today, we have the opportunity to not only work with brands, but also the partners and builders that those brands use to support their advertising efforts. Another point I'd make on this slide, and Vihan made it, but it's worth reiterating, we're thriving with the largest brands. And a growing portion of our revenue is coming from these sticky customers. I want to spend a minute or 2 on this. While we're certainly proud of all 865 direct customers we work with, this is a cohort we're especially proud of. And over the past 5 years, growth in this cohort as well as across all companies who spend more than $500,000 with us annually has outpaced the growth in total customer count. This reflects a very deliberate and recent shift in sales focus to focus on the largest, highest LTV customers, those customers with the maturity and sophistication to attach multiple LiveRamp products over time. Now as Vihan also discussed, most of these companies didn't start as million customers of LiveRamp. Once upon a time, they paid us $100,000, $200,000. And we've been able to land at that level and expand and grow our relationship with them over time. And so we think this chart is just a really nice representation of the land-and-expand motion Vihan took you through. A final point I'll make here is we compare very favorably to peers on this metric. So in preparing this slide, we wanted to see how we stacked up. We went out, we looked at a universe of over 50 peer software companies with a similar revenue base to LiveRamp. And among the companies who report $1 million-plus customers, those customers on average represented roughly 5% of those peers' customer base. Whereas for LiveRamp, that number is nearly 3x higher at 14%, which speaks volumes to the criticality of what we do. I want to spend a minute or 2 taking you through our revenue model. As I mentioned earlier, about 75% of our revenue comes from subscription contracts. And while most are annual, a growing portion are now multiyear, which is a reflection of the strategic partnership we strive to build with our customers. Of our subscription revenue, 85% of it is fixed and tiered based on the total data volume supported by our platform. The other, call it, 10% to 15% is more usage or usage-based or variable and is generated when customers and particularly our media platform customers and data provider customers exceed their monthly allotment of data volume. Because of the nature of our model, our revenue is very durable and predictable. And in fact, we go into most years with the majority of our revenue outlook for the following year committed and under contract. This is in part why in the 26 quarters LiveRamp has been a stand-alone public company, we have never once missed our quarterly revenue guidance. Trying to advance one slide, please. Okay. Great. Unlike other software companies, we also have a second revenue segment, marketplace and other. And for our business, this includes primarily our data marketplace as well as a small but important and growing services business. I want to spend a couple of minutes here because we believe our data marketplace differentiates our model and is a real competitive advantage for LiveRamp. Over the course of LiveRamp's history, as we've scaled our network and expanded our technology, we've identified new and different ways to leverage our platform to both deliver greater value to our customers as well as to unlock new revenue streams for LiveRamp. And our data marketplace is an awesome example of this. Data marketplace leverages our core platform infrastructure, our identity capabilities and the network that you've heard about today. And it brings together more than 200 providers of high-quality third-party data with over 300 active buyers of that data. And those buyers span our different customer types. We have over 500,000 segments of consented differentiated data available through our marketplace that our brand marketer customers leverage to enhance their own first-party data and targeting efforts and our media platform customers leverage to build custom audiences that they then sell to their advertisers. We take a percentage of each transaction. So this is a take rate model. That percentage varies depending on the data type and the channel it's being used in, but on average, is right around 20%, 25%. As we've discussed in the past, this is also the component of our business that most closely correlates to the health of the overall advertising market. And you can see here, we've overlaid our data marketplace growth with that of the U.S. digital advertising market as measured by the Standard Media index. And you can see the correlation, but hopefully, you're also picking up that on average, the growth of our data marketplace has outpaced market growth by roughly 10 points each quarter. We believe this is a reflection of a few things. First, as I mentioned, the high-quality nature of the data available through our marketplace and the fact that, that data is tied to LiveRamp identity, which makes it joinable or connectable across our ecosystem of platforms and partners. Network scale and ubiquity is another key factor here. We've effectively created an easy button that allows for multiple data sets to be aggregated, joined and integrated. And then finally, our performance reflects the exposure we have to some of the fastest-growing segments of the digital ad market, namely CTV and commerce media. As an example, in each of the past 2 years, data purchased off of our marketplace to fuel CTV ad buying has grown by more than 50%. And today, CTV represents roughly 20% of our marketplace revenue. A final point I'll make on marketplace because it leverages the same infrastructure and capabilities as our subscription business and because we book the revenue on a net basis, this is a very high-margin revenue stream for us. So to summarize my first key point, over the past decade, we've built a solid foundation for growth. The majority of our revenue is under subscription contract and predictable. Our revenue is well diversified. And importantly, we're winning with the largest customers. We believe this foundation is a competitive advantage and supports the investments we're making in pursuing the large data collaboration opportunity. which perhaps is a great segue to my second key point. We have a large addressable market and multiple growth levers. As Scott shared, we're sizing our current addressable market at roughly $13 billion and growing to north of $30 billion when you point our technology and you point our network and new use cases and verticals. Underneath that, I'll make a couple more comments. We're assuming spend in this market grows by roughly 15% and for the majority of that growth to come from connectivity and clean room. We believe the clean room market is very nascent today, but on its own will represent a multibillion-dollar market and opportunity for LiveRamp. Vihan talked you through the multiple levers we're pulling for growth. I'm not going to go through each of these in detail, but rather just rehit the highlights. We have a network-driven growth strategy that accelerates our land and expand motion. We expect channel partnerships, both in the traditional sense and those unique to LiveRamp as Vihan was discussing, to represent a bigger component of our growth equation over time. And we will continue to innovate to deliver greater value to customers and unlock new use cases and verticals that should support our growth over the long term. We often talk about our aspiration to be a Rule of 40 company. And within that framework, grow our top line 10% to 15%. Again, acknowledging growth will never be perfectly linear, we believe we have the knobs and dials to sustain double-digit growth over the medium to long term. With respect to subscription growth, we're targeting a growth mix of roughly 30-70 between new logo and expansion, including an expansion, both upsell and cross-sell. And this mix is roughly consistent with what we've seen over recent years. In the near term, we expect our customer count to stabilize and to grow modestly in each of the next several years. And again, we would expect our $1 million customer cohort to continue to outpace the growth of our total base. When you look at our customer cohorts by age, you can see we have a long track record of successfully upselling and cross-selling our brand customers. This is a slightly different way to represent the bubble charts that Vihan shared. I think the point here remains the same, which is we have a strong land and expand motion. In addition, within this framework, we're targeting a 105% to 110% net retention rate over the medium term and 110% plus over the long term. And we're really proud of the trend here. The recent improvement has been driven by a couple of factors that I'll talk about. First, as Matt mentioned, there's been a lot of investment in recent quarters and years to modernize our platform and upgrade our back-end platform and product infrastructure. This has not only improved processing speeds for our customers and delivered greater reliability and stability, but it's also driven our gross retention rates higher. Over the long term, we're targeting 90% plus gross retention rate. We believe this is best-in-class. We have made tremendous progress in the last 18 to 24 months toward that goal, and we still have a little bit more work to do. Second, from an expansion perspective, the cross-sell or attach of our clean room and then the subsequent upsell as companies move from one-to-one collaboration to one to many has been the biggest driver here. And as we look ahead, we would expect more of the same with clean room cross-sell and upsell being the biggest driver of this metric over the medium term. Of course, we have a final revenue driver in Marketplace and Other. And here, we expect our data marketplace to grow mid-teens over the medium term, supported by the secular tailwinds that you've heard all of us discuss today as well as the new opportunities that are unlocked by expanding our clean room solution. So to summarize my second key point, a big market, a lot of ways to attack it and for these reasons, a lot of confidence in our ability to grow double digits over the medium to long term. Which brings me to my final key message, we have an attractive long-term model. We've talked a lot today about the top of our income statement or the things we're doing to support and sustain our revenue growth. And so I want to spend a few minutes now talking about profitability and how we intend to use our balance sheet and growing cash flow to continue to deliver value to our shareholders. And in preparing for today, we actually went back to a really similar presentation we gave in 2018 on the heels of the divestiture of Acxiom. And it was actually -- it was a really fun exercise because it so clearly highlighted the elements of our story that have been consistent over that period. And I'm looking at folks like Tim who've been following us that long. But if you think about it, like strategy, vision, product differentiators have all remained pretty consistent. But if you look at our financial profile, it looks pretty different than it did in 2018. So in 2018, as we were standing up LiveRamp as a public company, our gross margins were in the 60%. We were operating at a negative 20% plus operating margin and burning through more than $40 million in cash annually. It's hard to believe that was only 6 years ago, but it was. Because of the nature of our cost base with a high portion of our costs being fixed, as revenue has scaled, we've generated very high levels of fall-through, and Scott alluded to this earlier. In fact, over the period I mentioned, our incremental margin was roughly 40%, while we continue to invest in the business. This year, we're guiding to high teens operating margin, which again really shows you how far we've come. But importantly, we aren't finished, and we're going to continue to improve profitability. We've long stated that we target a 75% plus gross margin. And you can see here, we've been more or less in that range over the past several years while also standing up a professional services business. Services was super important to our retention and expansion strategy runs us is 2, 3 points dilutive to overall gross margin. So not included here, but worth highlighting is that product gross margins have consistently trended in the high 70s. 78%, 79% in recent years, which for a company of our size and scale is quite respectable. Looking ahead, we think we have 2 key levers to pull to continue to improve gross margin over the medium term. The first is the platform modernization effort that Matt and I discussed. We believe this will not only allow us to better serve our customers, but to serve them more efficiently over time as well. And then second, as we scale and mature our services and international businesses, that should unlock another couple of points of margin expansion. So targeting 75% plus with an underscore under the plus. Beneath gross margin, we've also made steady improvement driving leverage across our entire OpEx base, and we would expect this to continue. We're going to continue to smartly manage costs and outside of our revenue-generating areas, continue to moderate our hiring. The other big near-term cost lever we have is offshoring. And if you've listened to any of our recent earnings calls, you've probably heard me talk about it. This is a journey that started about 18 months or so ago. Today, we have over 270 team members in Hyderabad, India, and we expect that number to be north of 300 exiting this fiscal year. It's an effort that I've been personally involved with and a really fun one. We remain so impressed with the quality and the talent that we're able to recruit in Hyderabad and the culture we're beginning to build in the office there. So we look forward to continuing to scale our presence in Hyderabad over time. And as we do so, pull this lever. This is a view of our long-term model. We continue to target 25% to 30% operating margin. You can see here, we are not that far off. We've committed to 20% to 25% operating margins in FY '26. And sitting here today, feel like we have the levers in place to kind of put us in the middle of that range without assuming a huge inflection in growth next year. Of course, we'll have more to share in May when we provide formal guidance for FY '26 and have a better read on our top line. All right. So where does that all leave us from a Rule of 40 standpoint? We'll cross the 30% threshold in FY '25, which is an accomplishment we're all very proud of. However, we've set our sights higher, and we are committing to being a Rule of 40 company by FY '28 or sooner. While ambitious, we think this is entirely achievable and supported by both the top and bottom line levers we've discussed today. All right. A few more topics to round this out. Free cash flow. This is another chart we're particularly proud of. As revenue has scaled, so has cash flow. And looking ahead, we're targeting a roughly 75% EBITDA to free cash flow conversion ratio. Over the past couple of years, there's been a lot of good work to rightsize our real estate footprint. And as a result, our CapEx needs have been very modest. We would expect that to continue into the foreseeable future. In terms of capital allocation, if you've been following the story for a while, there's not too much new to report here. Our approach to capital allocation has been remarkably consistent over the past decade plus. We're going to continue to maintain a strong balance sheet and preserve financial flexibility. We're going to continue to invest in the business to expand our network and differentiate our product. And we're going to continue to return excess cash to our shareholders through our share repurchase program. Over the past decade, we've returned well north of $1 billion through our buyback program and more recently, over the past 4 years, have leveraged it to offset the majority of dilution coming from stock-based comp. Going forward, we would expect to continue to allocate a substantial portion, north of 50% of annual free cash flow to buybacks. And of course, we will continue to do this in a targeted and opportunistic way as we always have. One final topic before wrapping up, stock-based compensation. This is a topic we've discussed with several of you over the past few quarters and years. As a reminder, equity is a very important component of our overall compensation philosophy and practice. We believe it helps us attract, retain, motivate top talent and in particular, top technical talent. And we also believe it helps to align our team members' interest with that of all of yours. Over the years, we've also used it strategically in structuring acquisitions to ensure the talent that was so core to our acquisition thesis can be retained and grown at LiveRamp. All that said, we also know it comes at a cost and at an expense to our shareholders. And that is why we have and remain committed to reducing our burn over time. Over the past couple of years, we've been more disciplined, targeted and performance-based with respect to equity, and we would expect that to continue. Over the medium term, we are targeting to be at or below the median of our peer group from a burn rate perspective. And as a percentage of revenue are targeting stock-based comp to be sub-10%. We would expect next year to show progress towards each of these targets. Before wrapping up, I wanted to take the opportunity to reiterate our guidance from a few weeks ago. You'll note here that the Q4 guide does assume a slight moderation in growth, which as we discussed on our last call, could extend into the early part of next year due to the sales pressures we felt in the earlier part of this fiscal year. That said, if you were also on the call, you heard about what an outstanding quarter we had in Q3, one of the best in LiveRamp's history. And sitting here today on the eve of ramp-up, we feel really good about our ability to deliver on Q4. And so if we can string a couple more quarters together that look like Q3, we'll be in a position to accelerate growth as we move through next year. So with that, let me summarize the key points for my presentation. First, over the past decade, we've built a solid foundation for durable growth at scale. We're pursuing a big opportunity with lots of ways to attack it, and we have a very attractive long-term financial profile. And of course, these are all in service of continuing to deliver long-term value to all of you, our shareholders. Before turning to Q&A, I'm just going to end where Drew began. We hope you walk away from this afternoon with the following 4 messages. First, data collaboration is a significant and scaling market. Second, we are uniquely positioned to win given our solution set and network scale. Third, our revenue growth is durable and leverage the increasing demand for data to deliver, measure and optimize marketing. And finally, we're driving long-term shareholder value through efficient growth and disciplined capital allocation. With that, I'm going to turn it over to Drew, who's going to set us up for Q&A.

Excellent. So at this time, we're going to do our Q&A session. I'm going to invite our executives up to stage. We got the 4 speakers that you all know. In addition to that, we're also going to welcome to the stage our Chief Strategy Officer, Dave Eisenberg; and Travis Klinger, who's our Chief Ecosystem and Connectivity Officer. Travis manages all of our sort of publisher relationships and is incredibly knowledgeable about the ad tech space, generally speaking. [Operator Instructions] Why don't we start with some questions in the audience, if we may, and we'll start with Mark, the Benchmark.

Vihan, very interested in the discussion about pricing redesign. I think there's -- could be a lot of opportunity here to sort of move the ARPU up going forward. So maybe if you could just talk about sort of how that was contemplated, sort of what went into your thinking there? I assume it's post Taboo, perhaps started to move a bit. And then maybe talk about what you expect the net impact may be to ARPU or however you'd like to -- or average revenue per subscriber, however you define that?

Okay. Let me start by setting up the scene and then maybe I'll ask Lauren to help here on understanding the total impact. It's really started happening about a couple of years ago when our marketers and advertisers who have first-party data, they started building out net new capabilities on top of our platform. And so they were kind of growing and they didn't want to have a hefty fee, which marketers were okay to pay before, right? So you have 2 different kind of motions. One, which is for kind of the CMO office who would love to have a kind of flat rate versus the same advertisers in a different department wanting to scale and wanting LiveRamp's pricing to scale with them. And that kind of complexity is difficult to manage for us today under the current pricing model. So we -- it's not just me, but Lauren, Matt and everybody in product actually thought about, hey, if you look across the different product suites we are selling today, is it complex? Is it -- can we actually simplify the pricing structure so that our clients can easily go through the upsell, cross-sell motion. And so that was really the reason behind this focus. But I'll let Lauren take us through what kind of impacts we expect in terms of any revenue or profitability.

Yes. And just a few other points I might add on. I mean we, to date, have priced for adoption. And in doing so, I think, have made concessions or just pricing decisions that have been right for our customers but are not scalable over the long term. So as a result, today, our pricing is fairly complex. It's complicated for our sellers. It's complicated for our customers. I'll kind of look -- it's really complicated for our teams as I think about the team we have that manages the billing of our contracts. And so we're in the early stages of this shift, but we think it's going to drastically simplify our pricing and provide more flexibility to companies who, as Vihan mentioned, prefer to work with us on more of a usage basis. It's going to have fewer metrics. and it can be applied to all customer types, which is one of the challenges we ran into before. As Vihan mentioned, we think it's going to unlock the following 4 benefits: First, help simplify our sales cycle; second, provide a more accessible entry price, especially for mid-market brands. Third, encourage the frictionless usage of all products. So instead of having to contract on a product-by-product basis, customers will contract once and have the ability to use any product across our portfolio. And then finally, as I alluded to, we think it's going to unlock a lot of internal efficiency. In terms of sizing the impact, it's hard to do today. We're kind of in the early stages of this journey. We're planning to pilot at the end of next -- middle end of next fiscal year with a full rollout in FY '27. We don't expect a huge change to our revenue day 1. Again, this is something that's going to bleed in over multiple quarters as we renew deals. But we think over time, it can be a growth catalyst and also from just a revenue composition standpoint, could shift a small percentage of fixed subscription revenue into usage. But again, we'll have more to share as we work through this over the next year.

Thanks so much for the question. Alec Brondolo from Wells Fargo. I think at multiple times during the presentation, the opportunity in the middle market was alluded to. I think you had the slide that showed like 45% of the Fortune 100 uses the product and then some amount less of the Fortune 1000. And Scott, at the beginning of the presentation, you kind of indicated you go into customer meetings and they say, well, LiveRamp is a DSP, LiveRamp is a CDP and you say, like, no, we're not any of those things. And I think my question is, is that a problem? And does the technology need to be packaged in a way that's more familiar to buyers in order to penetrate the middle market? Like should LiveRamp maybe have a CDP? That's the question.

Yes. I'll start. And Matt, I think you'll have something to add on here. To me, it's more than just product or go-to-market. It's actually our pricing model. It comes back to that. Why have we not been successful with those SMBs? It's in large part because our cost to serve is through the roof. And we have built a category. We're educating the market on what to do. And for sophisticated marketers, they get it right away. And in fact, in many cases, you look at a P&G, you look at a Walmart, sometimes they're a step or 2 in front of us, asking us hard questions, leading us towards what they'd like to see happen next. For those SMBs, we got to go in and do a lot of education. around how they should use the product. And so over time, Vihan did this analysis of our ideal customer profile, and we just realized we have to go after the bigger sophisticated clients given our current pricing model. Now over time, as we -- as several things happen. One, everyone gets more sophisticated and starts to realize what data collaboration can do. And so there's less kind of upfront and holding that needs to take place, less services that needs to be directed at those SMBs, that helps a lot. Two, having a pricing model where you can price for adoption with those folks and have a small activation fee and then kind of scaled usage. But then the third piece is you can only do that if you have a product that is intuitive and scalable. This is where I see AI helping so much. And we're probably still a year away from seeing the full benefit of that. But so much of our product usage has been by sophisticated data analysts writing SQL queries. Well, anybody who's ever written a SQL query knows, like it is horrific. It takes a couple of hours. It's like rewriting the same line of code over and over again. And it is just pure draft work. So you hire this team of data scientists to do it or in AI, you can just say, "Hey, who are my highest performing segments? And the query is done automatically and all that work is automated, and it's almost like magic. So I think that is the real gating factor to what opens it up for the SMBs. And then the final thing I'll say is I think the network will drive adoption. And so if I look at like at Albertsons, Walmart, at Target, their retail media networks, well, they've really focused to date on their top partners. You could talk to Albertsons, like who do they really want to work with? They want to work with P&G and Unilever. And there's like a dozen others that it's the 80-20 rule. Over time, I don't know how many products are in the SKUs are in the typical grocery shelf. But it's got to be hundreds, if not thousands. It's not dozens. And so I think over time, we're going to start to get the walk-ins, the introductions to the smaller and smaller merchants that we're not working with today. And they'll walk us into those. As we standardize the product, it's going to be a lot easier to stand those up. I don't know, Matt, what did I miss?

Yes. I think the -- I agree with all that. I also agree with the sentiment of the question, if you go down to the mid-marketer, things need to be easy and familiar. And that is where we're going and frankly, what's led to the success and the traction of the clean room today, which is the clean room is a how, right? You go to even the most sophisticated marketers and say, I have a clean room for you. That isn't value. What we have done and we'll continue to need to do is package up the use cases that those enable. And so when you speak to those use cases and offer that as the end-to-end solution because, listen, I've been in the industry for almost 25 years now. If everyone just tries to position it as the right sort of product category in 3-letter acronym, when CDPs came around, there were 80 companies saying, I'm a CVP, I'm a CVP. That doesn't necessarily simplify the life for a mid-marketer. What you need to do is offer that mid-marketer the actual use case that they're trying to do and have that be packaged. And that's been our strategy, and we'll continue to do so.

Why don't we take a question from the online audience. This might be best for, I guess, maybe Travis. Can you explain why a brand would use LiveRamp versus going to Trade Desk directly, especially with UID and OpenPath?

Yes, absolutely. So I think when you look at our brands, on average, they use many destinations, right? So when we -- and this will be in Scott's keynote tomorrow, when we talk to our brands, we talk about reaching consumer experiences. Consumer experiences are way beyond programmatic. So they exist on CTV. They are on Netflix, on Disney+, on Paramount, on Peacock. Most of that inventory isn't bought programmatically. It's bought direct. They exist in AI, like our new integration with Perplexity, where you can use AI-powered search. They exist in search. That's going to be Google, Yahoo!, Microsoft, all not available programmatically. So all of these experiences are ways for our brands to connect with their consumers. And programmatic is one way, but it's not the only way. So I think when you look at our profile, our ICP, our customers aren't looking for just one DSP. They say, I'm going to work with 1 or 2 DSPs, and then they're going to work with a couple of dozen other destinations. And that's the differentiation we drive of our network. If a brand just wanted to work with a single DSP, then yes, it might make sense to go directly into the Trade Desk, but that's not the profile of our brands.

We'll do another one from the online audience. Maybe this is kind of for Dave and maybe a little bit of Lauren. Looking at the TAM slide and the TAM expansion as you kind of move from the base $13 billion and up to the maybe $35 billion over time, can you help us think about sort of the time line and potential investment and sort of the gating factors to kind of expand into the larger TAM opportunity.

Sure. You want to kick it off and yes, you can add on.

Yes.

So first of all, I mean, we -- as you'd expect, we invested a significant amount of time and resources in sizing the TAM. We also did work ourselves, but engaged a boutique consulting firm LEK to help us do a bunch of kind of primary research to size the opportunity. And so when we got -- we did -- we approached it kind of a few ways. We approached the advertising market, which we serve today, doing both a top-down and a bottoms-up approach kind of looking at the total number of entities that could buy our products in our core market and some theoretical maximum spend that they could spend. And then as we look beyond that, we looked at the same sort of core capabilities, call it, kind of middleware for or enabling kind of layer with the 4 capabilities that we offer today, but for Martech or consumer experience, the markets that brands like Adobe or Braze vs Klaviyo kind of serve. And then we -- but we did a top-down kind of view of our capabilities in that applied to that marketplace for channels like SMS and e-mail and others. In reality, what we do today can actually be extended to those marketplaces. It requires us to kind of go out and build business development relationships and integrations with all those platforms beyond what we do today with them, which is largely in their advertising-related applications, but to a whole new set of kind of ecosystem partners. And that should be fairly -- while work and it will take time to kind of reach the scale, the capabilities we have today can serve that market. And we already began this year to kind of build out those capabilities in the Martech and CX space. As we think about stepping even beyond that to new markets, applying our technology to create connectivity around fragmented markets of like public sector or health or beyond, that's -- the technology is there. What we do is directly applicable to those markets. It will take us time to kind of go learn the use cases to kind of build the relationships kind of to bring on the talent that knows the market incredibly well. So I would say that we have so much opportunity today with what we're doing in advertising and our next step in CX and MarTech, that will be a step beyond that, where we'll plant seeds in the next -- this year and next, but it probably won't be for a few years until we go really attack those markets. Do you want to add anything?

No, I thought that was great. I mean, as Matt mentioned in his presentation, our platform and technology is horizontally applicable. And so when we talk about entering a new market, we're not talking about a huge amount of R&D spend. This is really more of a BD effort for us. So it obviously comes down to prioritization. As Dave mentioned, we think there's a big opportunity within advertising that we're attacking right now, and we'll continue to kind of plant seeds in some of these other areas in order to support future growth.

Great. Tim?

It's Tim Nollen. Could you please express a bit about the competitive environment, maybe going back to the split of Acxiom compared with now? Because at that time, identity resolution and the identity graph was pretty revolutionary concept at least it seems to me. And now maybe the technology has evolved and yet so have you spread into some of the other areas. So just how would you characterize your competitive environment then versus now?

Yes, Tim, I think about the pre-Axiom divestiture, boy, when I first joined Acxiom, we were named Agency of the Year. And I was like, what, we're an agency, and I was kind of stunned. But at the time, because we had such a big professional services business, and we're also a data broker, we divested both of those pieces. We really had a broad swath of competition. We found ourselves on the data broker side, for instance, competing with Epsilon and Experian. And on the professional services side, we competed with every major agency. And so we were constantly going in and having to prove ourselves. Now I look at Epsilon, Experian, every single major agency, there are partners on that screen. And so I can go in and sell alongside of them, and you're going to see them at ramp-up over the next couple of days. I will say where we have always faced competition and where it's a nuisance, I was joking about this with President of one of the largest CDPs. They have a CDP and Creative Cloud business. So we were chatting the other day and he said, "Well, we don't compete. And I said, no. But sometimes our sales reps, and we're guilty of it, they're guilty of it, they go in and the client says, well, why do I need anyone other than LiveRamp and our sales reps will go, "Oh, you don't need anybody or their sales reps, well, you don't need LiveRamp, you just got us." And it's like, no, come on. And across the ecosystem and whether it's Trade Desk, whether it's Adobe, whether it's Salesforce, whether it's Hublicissist, we got to do a better job of evangelizing with the front lines why we catalyze one another. senior folks at all those organizations get it. But boy, in the trench warfare where a sales rep is trying to close the deal now, the go-to is always you don't need anyone but me. And so we got to fight that a little bit more.

Okay. We'll take another question from online audience. We have a question about clean room competition from cloud hyperscalers. And the question is, could you elaborate a little bit on sort of the competitive advantages that LiveRamp's Cleanroom has against some of these larger hyperscalers who may be heavily discounting clean room capabilities as part of a broader bundled offering?

Yes, happy to take this. As each and every one of the clouds released their clean room pattern, their clean room primitives, quite honestly, that was hugely confirming that we were on the right track and that this was a path forward for how so much of the industry is going to use data collaboration. The -- going with a cloud set of primitives is a little bit like a DIY, right? That's sort of the competition of how to think about it. the drawbacks and the reason why we don't really run into DIY as a practical alternative here is a few fold. First of all, if you take any one customer and you just ask them to sort of write down who are the 10, 20, 30 companies they're going to need to collaborate with, they're going to find out that, that one cloud that they're on represents a drastic subset. So are they going to DIY it across every single cloud because they want to be everywhere. Second of all is the use cases themselves. Again, to the earlier question, a clean room is a how and DIY has a lot of work. And in this case, it's not just work for you, it's also work that your partner then has to do. So for each individual query, each individual analytic, you and your partner would then need to go and build those primitives in order to be able to make all of that work. And that's just friction and often prohibitive to get started. And then the third is all of the other components that you need to connect to this. When you're in a clean room, you need that data to be joinable. Again, you're going to need an identity solution in there to make that joinable. What are you going to do once you get all these insights from the clean room? What you want to do is be able to then take action and power that into experiences and couple that with our connectivity and our activation network. What are you going to do when you need additional data to add value to that? So ultimately, the cloud is adding in those types of capabilities and primitives is very good for sort of awareness in the market and confirmation that this is a safe, viable path ahead. But in terms of building a collaboration network that powers these end-to-end outcomes, DIY was something we saw a whole lot more earlier. It's a good place to sort of do testing. But when you're trying to do this at scale, often our customers see sort of the entire network and then the use cases that are packaged in with it is a viable path ahead.

Okay. I think this question is for you, Lauren. A little more financially oriented. During your presentation, you mentioned the possibility of hitting the Rule of 40 by FY '28 target sooner. Could you maybe discuss the gating factors or what would cause you to maybe achieve that earlier?

Yes, I'm certainly happy to. And I think in short, revenue growth is going to dictate how quickly we get to 40%. As discussed today, we have several margin or cost levers in place over the medium term to show really nice margin expansion. And because of the nature of our model, which we also discussed today, as revenue expands, so does margin. And so really, our ability to get to 40% sooner kind of hinges on our ability to accelerate revenue growth. We talked a little bit about the fact that over the near term, we expect a slight deceleration in growth based on some of the selling challenges we faced in the early part of this year. But we also talked about the great third quarter we had, one of the best in LiveRamp's history. and how good we feel kind of in the middle of Q4. We believe in the opportunity. We believe in the customer set and product set to take advantage of that opportunity. And now it's really on us to execute. And if we can execute like we did in Q3 consistently, we will be in a position to show improving revenue growth over time, which hopefully gets us to that Rule of 40 milestone a little bit sooner.

Lauren, maybe just digging into the cost side of that equation a bit more. If you think about, obviously, your outsourcing opportunity, you also have AI leverage as well on the cost side. What does that, I guess, translate to in terms of headcount growth over the next to 3 years. And so that's cost. And then as it relates to AI specific to improving just overall yield of your solutions, perhaps helping the revenue side of the equation. A broader question there, but. ...

Yes. So in terms of headcount growth, we would expect pretty moderate growth over the next couple of years with the majority of the growth happening offshore and Hyderabad. The areas of our business where we're going to continue to add headcount are going to be engineering and then revenue-generating roles. And on the engineering side, we think we can get a lot of leverage out of our investment in Hyderabad and offshoring. And then do you mind repeating the second part of your question?

Yes. I guess on the AI side of both. So yield improvements on the revenue side that are on the come, I guess, but also on the cost side. I guess more specifically is if you think about headcount growth within the company, excluding outsourcing, is it -- can we see a down type trend line over the next 3 to 5 years? Or how would you...

Yes, potentially a down trend line over the next 3 to 5. I mean, I don't want to call that today because we're hoping over the next 3 to 5 years, our business is growing 10% to 15%, and we're going to need to invest to support that, but can we invest more efficiently, leveraging AI and offshoring, like, sure. I mean, we absolutely can. In terms of kind of the top line AI opportunity, I don't know if Travis or Matt want to share a little bit more about that.

I can start by sharing what we're doing with the ecosystem and then Matt can talk about the product. So I think we see a whole slew of opportunities in AI in the ecosystem. I'd break down kind of 4 near-term opportunities we're focusing into AI search. So we think the search industry is pretty ripe for disruption. So if you think about today, you've got Google, Yahoo and Microsoft. Now you have perplexity coming in there. You have other things like Cloud that are coming in there. These guys are going to redefine search. You, of course, have Gemini and Copilot from Google and Microsoft coming out. So we think this is good. If you think about LiveRamp, we thrive in a complex ecosystem. The more consumer experiences there are for brands to connect to, the more reason to work with LiveRamp. The other 3 areas we see in AI is AI chat. So this is going to be things like ChatGPT. This is actually -- we used to put this research. It's kind of bifurcated out now and it's a different experience. OpenAI announced in December that they're going to be using advertising there. We're looking forward to helping power that. Then you've got kind of 2 things on the infrastructure side, dynamic creative. So more and more now platforms investing into how to make real-time creative, make it cheaper for brands and then use data to make it more effective and then also do better A/B testing. We're going to be helping powering that. And then finally, AI optimization. So we think just as in 2016, 2017, you saw a whole new wave of DSPs emerge. We think there's a whole new paradigm shift in how media is bought over the coming years, and we're going to see this with existing players and then new players where you have the opportunity to use AI to much more efficiently manage the performance and where we send signals in. And so our conversion API program and our onboarding business are well set up to support that. I'll let Matt speak to what we're doing on the product side.

Yes. There's 2 buckets of revenue upside that AI brings. First is -- and certainly in line with sort of the pricing dynamic and the pricing changes that we're looking to make. It drives utilization. It drives value, right? Take an example of a marketer looking to create segments and get access to more and better data to create those segments. Today, that requires a human to be thoughtful about which segments would work, how would that work? In a world of AI going and discovering and searching through far more data, creating far more performant and more and better segments that drives utilization. That utilization will lead to revenue, right, as it creates more value. The other side is AI as a new use case product category itself, right? As enterprises are investing in their own AI for their own purposes, building their own internal and optimization systems, they will need data using the LiveRamp collaborative network to build data pipelines to build and train new AI to power pipelines for agentic experiences, that's a net new use case and more utilization of the same data that's in the system today.

Thanks. One more, yes. Could you address the topic of measurement. You had announcement this morning. You mentioned measurement a couple of times in the presentations today. Is LiveRamp becoming a measurement provider in this new massively open different digital ad environment?

I'm happy to take it. Short answer is no. What needed to get solved here is a new way of getting measurement done, being able to access all of the data you need to in a privacy and governance safe way. And what we realized is that data collaboration using clean rooms was the way to make that work. And so we are super excited as a fast follow to our announcement to invite our measurement partners that we've collaborated with for years to come and build on top of the be able to access that data and have their brands be able to bring them in, bring their measurement methodology. What we are releasing is a set of standard templates that the industry wants. We are not out there trying to say, we have a better measurement methodology, a better measurement mousetrap. What we are doing is providing a pathway where all of the data that needs to get put together in order to deliver accurate and understandable measurement can happen in a privacy and governance safe way, interoperable across clouds, and that's what we focused on.

We're just about out of time, but we'll take one last question virtually. I think, Lauren, this is probably for you. It sounds like the clean room and data collaboration market is really inflecting here. There's a lot of headroom towards the $13 billion TAM. You guys showed a tremendous amount of momentum last quarter. So the question is, can you help us think about that opportunity next to the long-term guidance of 10% to 15% revenue growth, which is consistent with the trend. How do you think about the upside opportunity of maybe outperforming?

Yes. It's a great question, and we would agree. We think we have a significant runway for growth in front of us as this market scales. As I mentioned earlier, we believe we have the network, the client roster and the solution set to really take advantage of this growing market. That said, it's still early. When we talk about the clean room opportunity, we're talking about a very, very nascent market today. And so as we typically do, we would rather err on the side of caution of conservatism and then over deliver. And if we can consistently execute, as I mentioned earlier, then we should be in a position to outperform our expectation.

Well, great. Once again, I want to thank everybody for joining us. For those of you in the room, we're going to have a reception. We hope you can stick around for beverage and meet some of our management team and our leaders. But with that, that's it. the LiveRamp 2025 Investor Day. Thank you again.