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

Good morning, and thank you, everybody, for joining us at the TMT Conference. My name is Lucas Cerisola. I'm on the U.S. software equity research team here at Morgan Stanley. I'm here on behalf of Elizabeth Porter. Today, I have the pleasure of hosting LiveRamp's CEO, Scott Howe; and CFO, Lauren Dillard, Welcome, guys. And before we get started, just a few disclosures for more disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. And if you have any questions, please direct them to your nearest Morgan Stanley sales representative. And with that, thanks for joining today.

Thank you for having us.

And a shout out to Elizabeth if she listens to these, we miss her.

We miss Her as well. So for those who aren't as familiar with the LiveRamp story, give us a quick overview of your business and your customer base.

Yes. We were about $800 million in revenue. We work with a huge slice of clients, most of whom you've heard of, and they use us for data collaboration, i.e., how to take data that they or others have and use it at the moments that matter. And so that includes big destinations, the likes of which you're all familiar with, Meta, Google, Trade Desk. Increasingly, I think what's interesting is it includes a rapidly growing array of AI applications. And so what's interesting is we're one of the rare software plays. It seems like the market is hammering all of software, but we love AI because we catalyze it and we should benefit. It should be a real tailwind for us.

Awesome. That's super helpful. And this week, you're hosting your user conference RampUp. What are customers most focused on right now? And how are you positioning LiveRamp to better align with those trends?

Yes. So RampUp is like 5 blocks away from here in San Francisco. And if any investors want to swing by, you're more than welcome. We'll have a few thousand clients and partners there. And my guess is a lot of overlap between other companies in which you may be interested. So it tends to be a couple of great days of content. We're rarely on stage. We put our clients and partners on stage because after all, what we do is run a data collaboration platform. And so it's a chance for them to share their insights and observations with one another. As probably comes as no surprise, Lucas, the big theme this year, it's AI, AI, AI because all of our clients and partners are impacted by it. And if I think about the market overall, we really see the impact of AI happening in 2 important areas. One is AI is shifting consumer behavior. And so the way that each of us as consumers interact with content is changing. Many of you, like me, probably don't go to search as your [ NAV page ] anymore. Maybe you're using ChatGPT or Anthropic, something like that and using that to find information. And so consumer behavior is shifting. At the same time, marketing and company behavior is shifting. The way work gets done is transforming from very automated, unchanging to increasingly agentic, the concept of agents automation. Now regardless of whether we're talking about door 1 or door 2, there is a tremendous benefit for LiveRamp there because all of that AI activity to perform well, it needs data. And anybody can harness the data available in publicly available models, but then your performance is no better than anybody else. It starts to flatline after a while. What differentiates all of these models is inserting private data into them, first-party and second-party data and enter LiveRamp because that's been our business for the last 15 years. We're -- we have kind of 4 things that we do, Lucas, as you know. And number one is we help companies figure out identity that makes disparate data sets usable and it allows you to co-mingle data. That concept of identity, hey, Drew, becomes more important in the world going forward because in AI. I mean, who wants a nameless, faceless algorithm making decisions about them, you need to give consent. You need to have deterministic identity that plays to our strength. The second big piece is interoperability. We work with virtually every destination, every major marketer. Our share of wallet is still far from what we'd like it to be, but that interoperability allows data to be utilized at any AI application at any destination or you can ingest data from one of your partners as well. The third piece is data governance and it is so important in AI. I read a stat recently that 50% of traffic on major websites is coming -- 50% plus is coming from nonhumans. And publishers are concerned that some of those AI agents are scraping their sites, stealing their content and not giving them any compensation for it. Well, if that's the case, it stands to reason that everybody in the industry is equally paranoid about is their data being safeguarded. And so to use AI appropriately, you need to have a clean room, you need to have the right data security in place, and that's what we offer. And we do it all at scale. And that's the fourth thing and probably the most important thing, we are the scale leader. We work with everyone in the industry, and that's super defensible because whether you're a buyer or a seller of data, whatever you're trying to do, you want to work with us because we have the most partners that you can do it effectively with. And those partners, as I mentioned earlier, increasingly include a bunch of AI applications.

Very interesting. And in the landscape, AI agents have become very popular. And you recently launched an Agentic orchestration platform that positions LiveRamp to enable AI agents to access identity, segmentation and measurement in a governed way. What are you seeing in early use cases? And how differentiated is this versus what clean room competitors are building right now?

Yes. For us, it's another step in a journey that's been underway for well over a decade because if you have data, having the data itself isn't useful. It's actually being able to activate it in the places that matter. And so as these AI applications take flight regardless of who's creating them, they're hungry for data. And so if our most popular applications historically have been Google and Meta, probably in a decade, it's going to be more of the AI applications. And we're already starting to see that. We have over 20 AI partners active. About 1/3 of those partners are what I would call greenfield AI. They range from companies you've heard of to some of you haven't. There's a company called Chalice that does AI data segmentation on top of our stack. Everything is through APIs. There's a company called Scout that does a Agentic AI buying, Scope 3, Agentic AI buying. And then 2/3 of them are companies that you've heard of. So as an example, Google has AI shopping. And if you want to go to Google Shopping and turn on AI, you get a better result. And we have a dozen clients that are kind of the alpha or beta partners in that effort already. And I expect our entire client portfolio will turn that on over time. Likewise, a company like the Trade Desk, so much more of their decisioning in terms of who gets ads will be done by AI in the future as opposed to legacy algorithms. They're a major partner of ours. And so we'll consider that an AI activation. And so that's really starting to take off. And again, I think it gives us a tailwind. We've never been a seat-based model. And increasingly, the way we charge clients is usage-based. So the trends that are rattling the markets are kind of crazy. Lauren and I look at what's going on in the broader software market, and we scratch our heads because as people take the time to understand LiveRamp, they will see that we should be a major beneficiary of these megatrends.

Completely agree. And you've also expanded the data marketplace to include AI models and training data sets. How do you envision that evolving? And could this become a more meaningful revenue stream? Or is it more of a complementary capability?

Yes. And I think to Scott's point on kind of orchestration being just a natural evolution. I mean we also see this as a natural next step for our data marketplace. For those of you who are less familiar with LiveRamp, in addition to our core platform or Subscription offering, we also operate a data marketplace. So think about this as a virtual marketplace and Apple App store for data, if you will, that enables our marketing customers to buy data from third-party data sellers to basically enhance or refine their ad targeting efforts. The model here is we take a transaction fee or rev share on each transaction paid to us by the seller. And for reference, our data marketplace has been growing high teens over the past several years and now represents about 20% of total revenue. So from that position of strength, we're excited to expand our marketplace to include data licensing for AI model building and training as well as the licensing of AI models and agents themselves. We think this transforms our data marketplace into a centralized hub for AI-enabled marketing and importantly, it should allow customers to really easily and seamlessly kind of bring AI capabilities to bear. On our most recent earnings call, we gave a couple of examples of this. So we have a gaming customer who is licensing data off our marketplace right now to build a model to predict in-game behavior, which is allowing them to better tailor and personalize the in-game experience for their users. Scott mentioned Chalice, they will be on stage with us at RampUp. They're also licensing data off our marketplace to kind of build and provide models that allow for better audience segmentation, specifically for customer acquisition. So we've seen some kind of real-world examples that give us a lot of optimism for kind of where this could go.

Great. And shifting gears to product depth. You have clean rooms, CTV and Cross-Media Intelligence across your product portfolio. And touching on the clean room specifically, they sit increasingly at the center of the privacy safe collaboration. How is clean room adoption changing the way customers expand across the broader LiveRamp platform?

Yes. If I think back a couple of years, clean rooms really enabled data collaboration in the retail and commerce media space because if you're a packaged goods provider and you want to collaborate with, say, a Walmart or an Amazon, you want to have a clean room such that your data is protected. Likewise, if you're a retailer, you want to have a clean room, if you're at Albertsons or a [ Roundel, ] you want to have a clean room. So your audience is protected from your merchant partners. And so clean rooms really started to become a purchase essential at that point. Serendipitously, what we realized through the explosive growth of AI is our presence in clean rooms was equally essential there because no one but no one trust one another. Everybody thinks that their data is a unique asset, and they're right. And so they want to protect it from being misappropriated by whether it's a technology partner or an AI technology, clean rooms keep it safe. And our advantages that I talked about earlier, whether it be identity or interoperability or scale are super important in clean rooms because by virtue of spinning up a clean room, you're basically telling the world, hey, you want to work with different partners. And so by virtue of working with the scale leader, it's much easier to make that clean room work with the destinations and the partners that matter.

Got it. And then CTV, this now represents a more meaningful portion of marketplace revenue. And as CTV buying becomes more automated and identity-driven, how important is that channel in reinforcing LiveRamp's role in AI-enabled workflows?

Yes, it's a great question. And we see CTV driving our business in the near term in a couple of areas. So first is, as you mentioned, Lucas, our data marketplace, where TV data sales today represent 20% to 25% of the data volume through the marketplace and are outpacing the growth of overall data marketplace revenue. And then the second, and Scott's touched on this a little bit today, but CTV is driving an increased need for brand customers to collaborate with publishers, whether it be for the basic use case of ad targeting, but also increasingly measurement and specifically cross-channel measurement. So today, roughly 70% of our largest integrations or destinations as measured by data volume are either pure-play CTV providers or media platforms where CTV can be purchased. A year ago, that number was closer to 50%. So I think that really does speak to the growing importance of CTV nodes in our network.

Definitely. And on Cross-Media Intelligence, you launched this offering about a year ago with a few marquee customers. What does adoption look like today? And when customers layer this capability on top of connectivity and clean rooms, what kind of uplift or stickiness do you see?

Yes. To me, AI is about a battle for signals that the smartest companies are going to emerge as the winners. And so too often when you think about signals, people are thinking about their own CRM first-party data. But the other side to that is whenever you're activating data at a destination, you want to get back some measurement capability, the measurement signal back to describe did it work or not. And if you're like most advertisers or marketers and you might be activating across dozens, if not hundreds, of different destinations, having an apples-to-apples comparison of which techniques are working and which are not, so you can optimize to the things that are working most effectively or start to think about what is the recipe of different techniques that's going to work most effectively. Well, that becomes really important. And so our Cross-Media Intelligence capability, we've gone out and not only do we activate at all these different destinations, but we also bring signal back. And so that's a capability that more and more of our marketers are starting to embrace. And we think in an AI world, in a CTV world, it's going to be more important still. That said, we're in the early stages. It's crazy to me. There's been so much over rotation to AI that last year's flavor, CTV seems to be forgotten. Well, if you want to do audience targeting on Netflix, you got to work with us because we provide the technology underneath Netflix, great company. And yet we still don't have all of our advertisers activated on even Netflix and bringing back measurement signals. So we look at that as a real area of continued growth for us and one in which, quite frankly, the CTV providers, like I don't see a world where that's replaced by an agentic agent. If you want to watch content, you're going to Netflix, you're probably not doing it through your agent. So I really like that as a vector for growth for us as well.

Great. Let's talk about pricing. You're shifting from fixed subscriptions towards a more usage-based model tied to data activation and platform consumption. What customer behaviors are you trying to drive with this change? And as we lean further into usage-based pricing, how should we think about net retention evolving?

Yes. I'm happy to take that. And Scott mentioned it earlier, but I think it bears repeating. We've never been a [ seat-based ] based model. Our pricing has always been tied to data volume across our network. And with this new pricing model, we're trying to drive a couple of different behaviors depending on customer type. So with existing customers ultimately, we're trying to reduce the upsell and cross-sell friction. So the new model introduces the concept of a usage token that can be used fungibly across our product suite. And hopefully, that enables customers to more easily explore, identify and attach new product capabilities or use cases available through our platform. The tokens are also valid across an entire 12-month contract period as well, which is a change from our previous model. We've had a couple of really nice customer wins with existing customers, transitioning them to this new model. We've talked about American Airlines and McDonald's on recent earnings calls. And both of them were really drawn to the flexibility that the new model enables for them. And then with new customers, we're introducing a lower entry price point and the option or flexibility for a less fixed, more usage-based structure should they choose to opt into that. We're finding that, that is particularly appealing with midsized brands as well as media platforms and data providers whose models are just inherently more variable. We've been in a pilot for this new model for the past 6 months. We've seen a lot of traction, specifically with new logo -- a new logo acquisition. And in fact, in our most recent quarter, we reported a nice tick up, and you noted this in net new customers, in part driven by this new model. So early signs are really encouraging.

That's really exciting. And then zooming out a bit, you've outlined the $13 billion TAM today in data collaboration for advertising with a path towards a $35 billion TAM as you move into new channels and verticals. If AI increases how frequently and deeply data gets used, does that accelerate TAM expansion beyond these original assumptions?

Yes, we think so. We're not unique in -- I would say we saw AI coming. But even a year ago, when we talked about those TAM numbers, that didn't really factor in AI. And we think AI is just going to fuel more and more data usage. And to the extent that we have usage-based pricing, that's really good for us. And so I think it accelerates our path to growth. We've been following kind of a Rule of 40 mantra. We talk about it all the time. We pay our people that way. And part of that Rule of 40, the most important part is double-digit top line growth. Last quarter, we're at 9%. I think this gives us greater conviction that we will get back to strong double digits.

Got it. And that's a great segue to my next question around the Rule of 40 goal that you have in mind. Last year, you delivered a Rule of 31 and have outlined this ambition to get back to Rule of 40. What are the key pillars that get you back to 40?

Yes. And starting with the top line, and I think we see a path to getting Subscription revenue growth back, as Scott mentioned, to kind of high single, low double digits in the near term. And that's going to be driven by a lot of the things we've talked about today, whether it be the cross-sale of our clean room or AI catalyzing greater data volume across our network. And then from a new logo acquisition standpoint, we just mentioned kind of the new pricing model. We think that in combination with some very targeted changes we've implemented over the past year, 18 months with our sales force should continue to allow us to grow our customer base. And then data marketplace. So as we mentioned, that business has been growing double digits over the past several years. We expect that to continue over the next couple of years and hopefully over the long term as well. We think we've got a large runway here, especially as we expand the use cases for our data marketplace to include AI as we just discussed. And then on the margin front, I think we've got a really strong track record of driving margin. So in this year alone, we'll deliver 4 points of margin expansion. We see clear line of sight to a similar level of performance in fiscal '27. We've got an offshoring initiative underway that's unlocking a lot of cost efficiency. And then the continued use of AI in our business to unlock productivity and especially in engineering should give us dollars that we can either drop to the bottom line or reinvest for future growth. So I guess a long way of saying we're on track to be at Rule of 40 by fiscal '28, which is the commitment we've outlined.

Yes. And the one thing I'd add is one of the greatest margin improvements comes from our ability to increase our top line because as a network-type business, our marginal profitability is off the charts. Our fall-through rates are extremely high. So as we do better on revenue growth, we do disproportionately better on margin expansion.

Got it. And then one of the things that Morgan Stanley is most excited about for LiveRamp is embedding more AI directly in the platform to then expand customers to more nontechnical users, and that should expand the customer base. So where do you believe LiveRamp needs to invest further to facilitate that expansion?

Yes, I think you just hit it, which is to be a network business, you got to serve the entire network. And historically, we've been really good with a very sophisticated both clients and partners, companies that you've heard of. But some of the growth going forward has to come from smaller advertisers. An example, we work with Uber up the road. Well, for Uber to really take advantage of its data and its customer relationships, it's got to extend their insight to its universe of Uber Eats partners. Well, for every McDonald's or Burger King that is a major Uber Eats partner, there are a number of smaller restaurants that are regional or even local. And so to the extent that we can make LiveRamp intuitive and simple, easy to use, we're going to attract a legion of new partners. And even if those aren't major revenue drivers, and they will contribute to revenue, they contribute disproportionately to the density of our network. By bringing on those SMBs, we make the experience better for Uber. We make the experience better for our payments partners, whoever works with SMBs or companies that aren't in our network today will benefit from our ability to build more network density over time.

Great. And then speaking about partnerships, you announced the extended partnership with Publicis beyond simple connectivity to encompass your broader platform. What friction points still exist today? And what has to improve to unlock further partner-driven growth?

Yes, Lucas, it's interesting. If I think back over the evolution of LiveRamp's history, for a long time, since we invented a category of data collaboration, we had to spend a lot of time educating, evangelizing, teaching our clients and partners how to use our products. Increasingly, what we're realizing is there's also an opportunity as they've gotten smarter to work with other intermediaries major tech platforms and allow them to build using our composable pieces of our platform. And so Publicis is a great example of that because the technology that they're building to automate the way an agency works actually is fueled by our platform. And so look at this as the first of potentially many, we hope, many of these kinds of partnerships because what we did with Publicis, we can do with every other agency. We can do with every major DSP. We can do with every AI tech platform. And so I talk sometimes internally and people kind of glaze over when I talk about Intel Inside. Those of us of a certain age remember that Intel prided itself on being a key component of a whole bunch of partner technology. Well, that's who we are. We don't necessarily need to be out in front of the stage. We can be behind the curtain and our partners will grow for us. And Publicis, again, coming back to that, great example. The ability for them to bring us clients, we get paid through usage-based pricing. In this instance, when they use us, someone has to have LiveRamp paper, but I don't envision that always being a requirement. That will make it easier for us to attract new clients because our partners will be doing the selling for us.

Let's go back to the broader opportunity set as you expand beyond your core retail strength into verticals like health care and the public sector, what are the biggest execution challenges that you face?

Oh gosh, it's a timely question because later this afternoon, I'm going to a health care conference here in the city by one of our partners, Schafer, who invests only in AI and health care. Typically the convergence of the 2. And as you can imagine, as we expand into some of these new verticals, there's an opportunity for us to parlay our strength, which is -- one of which is data governance, another of which is scale. So to the extent that the things that other regulated industries need is to consume massive amounts of data, make sense of it and activate it in the places that matter in some of those industries, the whole concept of data governance is even more important because it's sensitive data. And so in health care, it's HIPAA, I mean, there are also things in financial services. They play to our strengths. And we're already finding even in generic categories like retail, increasingly the business that we serve started out being retail advertising, but increasingly, it's operations. People are using our data collaboration platform to think about how do they merchandise their physical in-store inventory more effectively. So we didn't call ourselves an advertising collaboration platform. We're a data collaboration platform. And we think that there are opportunities well outside of our traditional advertising and marketing strengths.

Great. And one last one to wrap us up. We've seen a lot of disruption over the past decade. What has made LiveRamp resilient over that time frame?

Oh, I think more than anything, it's our ability to transform complexity into simplicity. And if I think about Lucas, you at Elizabeth's time here, people were scared about this cookie deprecation, is that going to change the industry? COVID, is that going to change the industry. CTV or the invention of new IDs, is that going to fundamentally change the industry. And the one thing that is always the case is that despite all this disruption, data matters that smart companies make better decisions than those that don't have the data. And if you want to do something with the data in a complicated world, LiveRamp is your best partner to do that because we have such experience with all the destinations that matter, with all the data sources that matter and with all of the regulatory complexity that increasingly matters. All this to say, we think everybody in the world should be using LiveRamp.

Great. Thank you for the insights.

Thank you, Lucas.

Thank you. Thanks for having us.

[ Thanks everyone. ]

Thank you.

Thank you.