DoubleVerify Holdings, Inc. (DV) Earnings Call Transcript & Summary

Good afternoon, everyone. It is so great to see you all here, and welcome to those who have joined us online. My name is Tejal Engman, and I'm the Senior Vice President of Investor Relations at DoubleVerify. So you will be pleased to know that we are making quite a few forward-looking statements today. So please make sure you read the disclaimer at the front of our presentation. And we have an action-packed agenda for you. So we're going to kick off the afternoon with our CEO, Mark Zagorski. We'll then hear from one of our advertiser clients, Kenvue and their real-world approach to simplifying very fragmented media. We look into how DV's strategic vision plays out in practice with TikTok, Meta, Away Travel and Rockerbox, all joining us on stage. There will be a product deep dive with our CTO and CPO showing how DV's data and AI become real-time intelligence. And all of this will be followed by a clear view of our revenue model, our growth drivers and an updated financial outlook from our CFO, Nicola Allais. So 2 important notes. We will take a 15-minute break at roughly 2:20. And we'll host a Q&A session, taking questions from folks online at roughly 3:30-ish or 3:35. So let's get going. [Presentation]

Please welcome DoubleVerify's Chief Executive Officer, Mark Zagorski.

All right. I was going to do my Steve Ballmer dance here to get everyone fired up but no one needs to see that again. Thank you all for joining us. As Tejal noted, we understand that you all are super busy, but this is a really exciting opportunity for us today to share with you not only where we have been and where we are today, but where we are going in the future. Now, I was told you usually start these things with a joke. So at first, I said let's look at one of the AI tools, ChatGPT and see if you could find a joke that's relevant for the room. But I said, why am I doing that when I have my IR lead who's one of the funniest people we know, and the analysts in this room know that. When you think of Tejal, you think of funny. So she gave me a digital ad joke. She said, "All right. Digital ad walks into a bar, bartenders says, "What brought you here?" Digital ad says, "I don't know, attribution is a mess." Come on. That's the -- you have to be digital ad people to get that joke. All right, that's why she's great at IR, okay? But anyways, in all seriousness, we know that some of the things that are going on in digital ad world today are to the extent of not humorous, but ridiculous. There's a lack of clarity and an increased complexity, which is incredibly challenging. And what DV is here to do is help not only lead the way for advertisers, but give them the tools, the transparency and build trust so they can navigate an increasingly complex universe. Today, we're going to talk about all of those things. And it's not just a discussion of where we are and where we're heading, but it's a pivotal point for us as a company and the investments that we're making to leverage our core to build something totally new. This isn't just Innovation Day. It's a turning point for us. Protection and performance have never been more important. And today is our chance to share that with you. So before we talk about the past or we talk about the future, let's talk a little bit about where we've been since our last Investor Day. So we met in August of 2023. And since then, we've delivered some great financial and operational results. On the finance side, last year, we grew 15%. Q1, we grew at 17%. And we announced today that, that increasing momentum in our business allowed us to give guidance at a midpoint of 17% for the Q2 of this year, giving us a 17% growth rate for the first half of this year. Since we've IPO-ed, we've maintained a rule of 40 plus, which means we've driven profitable growth over the last 4 years. And on the operations side, our achievements have been just as exceptional. Most recently, we launched suitability measurement and activation on Meta and TikTok, opening up an entire new world of activation business for us on the largest platforms in the world. We've doubled down on CTV since the last time we've met. In 2023, CTV measurement was about 5% of our business. Today, that's 11% of our business, and it continues to grow. On Scibids, which was a new acquisition last time we met, we mentioned that we would target $100 million of revenue in 5 years. We're well on our way there with over 200 customers -- DV customers have been upsold to that and many in our top 10 and top 100. We've doubled our AI-powered implementations, accelerating some projects and some dev cycles by up to 28x. And then most recently, we acquired Rockerbox, which has given us unique closed-loop data that allows us to power that last leg of the stool on verify, optimize, improve. It's been a lot of work done, we've done in the last few years, and there's much more to come. If we walk away with one thing from this room today, that's 4 things we'd love for you all to remember. First off, and as represented in our numbers that we released -- prereleased this morning, DV's core is incredibly strong and growing. Core verification is an essential part of the digital ad ecosystem and we've got great momentum. We're adding customers, we're adding scale and we're adding markets. That business continues to grow at double-digit rates and is incredibly valuable part of our overall platform. It's the fuel that's driving our performance evolution. As we've moved into media optimization and now into media attribution, those independent insights are driven by a core verification data that's unmatched on the planet. Those things drive new products, which also grow our TAM. And AI, we can't come to one of these conferences and you can't do anything in this space without talking about AI. We're going to spend some time today showing why we have an advantage. Everyone has access to AI. But what's different about DV is the data we train it on, the experience that we have and the customers that we can implement it through. Those are not easily duplicated. And then finally, bringing all these things together and our unique capabilities and scale, ubiquity, customer engagements and technology gives us the reason to be the winners in this space. It's an exciting time for us in the company, and we're excited to share that with you. So what are we kicking off here today? Well, in a few short minutes, we'll launch to you the press, the announcement of our Media AdVantage Platform. The Media AdVantage Platform is bringing together all of the assets that we are -- that we've acquired and built over the last several quarters. Advertisers, as we showed in that video, which, by the way, as you know, none of you have to worry about me leaving for a career in Hollywood. It's very clear from that intro video, that advertisers need clarity. They need transparency and direction. And that's where DV's MAP, the Media AdVantage Platform, gives them direction. At its core is our verification solution to ensure quality and trust. Next, there's optimization to make sure that we're finding those impressions at the most efficient rate possible. And finally, outcomes. We bring it all together. Think of it as a compass. A compass that guides every marketing decision across all of those essential points. And no other company can do this. The combined verification, optimization and outcomes, transparently and independent of and complement to media buying. So why now? If our core is great, why move into this new space? Well, we think there's an unbelievable opportunity for unlocking the value of that core data through our relationships, our independence -- and independence. The Media AdVantage Platform helps us answer 3 key questions that advertisers have: Was the ad in the right context and was it seen? Was there quality? Did I deliver that ad efficiently and did it drive a result? No other independent company can do this across multiple different types of media than DV. So it's the right company, right time, the right tools to address the challenges that are affecting marketers the most. These challenges that marketers have, have never been greater. And we alluded to them in the video, but the reality of it is digital advertising continues to get lost in complexity. There's more channels and more platforms than ever before. Teams are wrangling 230% more data since 2020, driven by the explosion of channels like CTV, retail media and game ads. It's never been this fragmented. There's lots less clarity. There's disconnected tools. Over 60% of advertising teams now use 6 to 15 tools to manage campaigns and more than half of agency folks rely on 8 or more tools every day, and the systems rarely talk to each other. And the pressure has never been greater. We have some marketers in the room today, and they'll be the first ones to say, they love their CFOs, but their CFOs are breathing down their necks. Every day, it's about how can we efficiently deliver more sales for less? How can we ensure that our ads are working? 61% of CMOs say they're under more pressure than ever to prove ROI, yet fewer than half actually trust the measurement tools they're using. More channels, more clarity, higher stakes, and this is all in an environment where complexity has created less trust. In the IAB, the Internet Advertising Bureau did a 2024 survey of advertising decision-makers, and they found that 59% say, trust in social media is falling. And it's not very different on the OpenWeb where they had found 57% say the same about programmatic media. They're challenged with determining what the results are of their spend there, they're challenged with determining -- figuring out what works, what doesn't work and are they getting a fair shake. Complexity is rising, confidence is falling. And with that complexity, they have to manage it, they're making more compromises. Driving performance has been handing the keys over, in many cases to black boxes in which there's no idea what's happening or what's coming out on the other side. Simplifying operations by targeting lower overhead has made opaque procedures outsourced to entities that are sometimes conflicted. And the complexity is driven to move fast and make decisions quickly has meant that there's lack of consistency, platforms that don't talk to each other and solutions that don't scale from one to the next. We believe that DV's Media AdVantage Platform, driven by the core of DV data and the momentum that we've had allows advertisers to manage this complexity independently without compromise. As I mentioned, DV's independent trusted transparent guide brings together these 3 solutions. And today, we're going to talk about how they're actually being enacted in the marketplace today. This is not just a slide where these are real tools that advertisers are using today. The first of which, which we're launching today officially is DV Authentic AdVantage. We're incredibly excited about this product. This is the combination of core verification with optimization through Scibids. You'll hear today how DV Authentic AdVantage is driving results on social video platforms. We're driving efficient delivery of spend with high-quality impressions with lower overhead. It's the dream come true for advertisers, give me high-quality social video, give it to me in an effective price, but don't force me to use multiple tools. Steve Mougis will come out here and share what this is, how we're doing this with customers today and the amazing results we've been aimed to deliver. That's live today. Coming soon is Performance AdVantage. This is the integration of our optimization Scibids technology with our Rockerbox attribution technology. It brings together the idea of where we can take proof points of performance and drive them into the optimization tool -- into the optimization wheel to drive better results. And finally, bringing it all together will be in an Agentic AI -- Agentic UI that allows advertisers to simply put in their requests into our system and drive results from verification to optimization to proof. Jack Smith, our Chief Innovation Officer, is going to talk a little bit later today about how that comes to life. And by the end of the year, how the first iterations of this product will come together. Providing the opportunity to lead our customers through complexity is not just valuable for them, it's valuable for all of our stakeholders. When we think about the addressable market that we're now moving into with the new solutions, it's grown considerably. From our core verification TAM of $21 billion to our optimization opportunities where you look at $110 million of programmatic spend, and the opportunity for Scibids on just programmatic of being $1 billion. We're also starting to move Scibids into social optimization. And by the end of the year, we'll be launching social optimization at scale. And finally, the outcomes business, which is represented by Rockerbox. There's a $5 billion TAM in outcomes and outcomes solutions measurement. Altogether, this has provided a TAM of $27 billion plus for DV to go after, and we're just starting. There's lots of opportunity here. And Nicola later will tell you, will talk about the size and scale that we have in each of these opportunities and where we're attached and where we're not, and the opportunity for growth there. The Media AdVantage Platform strategy is already working. Although we're launching it in earnest today, we've already been in market with these integrated solutions and a broader platform. First way is we're offering bundled solutions that pay for themselves. Many of you know that since we bought Scibids, we've been presenting Scibids and DV's core verification as a single solution set to many of our customers. The amount of money that advertisers can save via Scibids, on average, is $4 for every $1 spent. We have won RFPs based on the fact that we will save an advertiser enough money and optimization that it pays for our verification services. No other company and verification can make that guarantee, and that's helping us win big deals, which brings us to the second point. We're winning large RFPs based on this platform strategy. Kenvue is going to join us today to talk a little bit about our relationship. But folks like Microsoft and other major brands are joining with DV because they love the vision of a larger platform that not only gives them insight into verification, but allows them to find impressions cheaper and determine whether they worked. And we've seen in the marketplace that solutions that haven't innovated and haven't integrated and haven't adapted are failing. They're losing market share or even going out of business. We are doing the stuff that's driving not only growth but longevity and stickiness with our customers. And that's the final point here. We're creating higher-value, stickier relationships with all of our customers based on a platform, which not only makes them feel like they have to work with us to protect their spend, but they want to work with us to ensure that they're driving the best results possible. That MAP strategy underscores our greater aspect of our business is why we win. And why we win and why we'll have the longevity in this space that we do and have over the last 17 years is based on the fact that we're embedded. I would challenge anyone who could find another company that's integrated with more platforms in more markets across more different types of media than DV. Starting with the OpenWeb, Where the business emerged evolving into social on platforms like TikTok and Meta, to CTV with Netflix and Hulu and others. DV is everywhere where advertisers want to spend and the places where they'll spend next like ad-supported AI, which is the next up for DV. We're ubiquitous, from Sydney to London, from Singapore to San Francisco. DV people and solutions are in the biggest markets with the biggest ad spend in every language that matters to advertisers. And we're trusted. The 2 largest CPG companies in the world, the 2 largest software companies in the world, the 2 largest pharma companies in the world, the 2 largest digital ad companies in the world, all trust their ad spend to DoubleVerify. What this means is not only do they trust us, but they've spent time building data with us. When you have scale, ubiquity and trust, you acquire data, and that data makes us smarter, building a flywheel, which is unmatched in the industry. We built independent, differentiated, scaled proprietary data sets that we can then leverage AI across. It's a different story than anyone else in the space. That will fuel our growth. Our growth for the future is about expansion always in all ways. It starts with channel expansion. As I mentioned, it's not just about OpenWeb and social. It's about what comes next, whether it's retail media where we're driving great growth or ad-supported AI. Channel expansion allows us to drive more revenue from more customers. Then there's full funnel expansion. Many advertisers feel that we're just a top of the funnel company. You work with big brands to protect their brand. But it's clear now through optimization, we're a performance marketer company. And it's even clear that with companies like Rockerbox, we're a company that focuses on driving results and doing so independent of the media spend wherever it may be. That gives us even more customers, which is the middle part. Our expansion across new channels and new solutions allows us to not only grow with customers, but grow by adding customers. This is great for our future and gives us longevity and stability and growth for the long term. In closing, DV was built to verify media. Today, we're optimizing it and in the future, we'll be proving its impact. We are becoming a mission-critical partner, bringing clarity, confidence and control to every marketing dollar. We've built great partnerships with great companies. And now I would like to talk about and with one of those great companies. Kenvue was a recent win for DV that kicked off in January of this year. You may know Kenvue by its previous name of Johnson & Johnson, which we work with their consumer brands, brands like Band-Aid, brands like Benadryl, brands like Neutrogena and Aveeno are now trusting their digital brand environment or digital brand spend to DV. So without further ado, thank you for your time, and I'd like to invite Adam Benaroya from Kenvue to join me on the stage and talk about the future of digital advertising.

All right. So not too much pressure because you've got one of the most important and powerful brand suites in the world. But you also have pressure to deliver on those brand messages, right? When you look at the global portfolio that you handle, how have things changed for you with the complexity and change the way your team acts, how they execute and plan? I mean it's got to be tough for someone in your seat.

Yes. And thank you for having me here today. Yes. I appreciate all the discussion on complexity because that's what, from a media standpoint, we're living in every single day. It's a combination of how we're managing the external complexity. You spoke about the fragmentation. Our brands are needing to invest more in new channels like CTV, influencer, retail media. As a CPG company, it's a huge focus for ours as well. So the fragmentation is there, the complexity that the industry brings to us is there. And of course, we're managing the internal complexity. We're a new organization, spun out of Johnson & Johnson, and we're continuing to develop ourselves and what our organization looks like. So how do we manage all of that complexity is similar to your data. We're trying to find how we can bring some simplification to our approach, whether that's simplification in what we invest in from a capability standpoint, how we manage consistency across our brands. But it's a challenge. And there truly is a limit to us on how we have to accept some of that fragmentation to deliver what our brands need to deliver in market.

Yes. It's got to be hard, too, because with that comes compromises, right? Some lack of transparency, lack of control. How do you deal with that?

There's truly choice points of how we either need to go fast or where we need to push for transparency. Yes, it's not something that we can just accept as an either/or. Of course, we have to work with the walled gardens, that's a large part of our investment and operate within the confines of what transparency they offer to us. But is that what we want to see in the long term? No, I think we need to get to a point where we are able to have a view on our total investment, the quality of our investment, how we optimize across that investment, the outcomes delivered by that investment and speak it in a language that qualifies our total investment in media. But there's no single answer of what the industry has been able to offer us.

There's one coming out. Well, let's talk a little bit about performance. I mentioned in my intro, no one is immune from the pressures of driving results, right? That's the world we live in. But, and this is interesting because I was just out in New Jersey a couple of weeks ago, and this actually even came up in our discussions, which is balancing short-term kind of ROI results with long-term brand building. What's your perspective on that? And what presses you one way or the other?

Yes. So our legacy was certainly on the performance side, even as a large brand, marketing organization, there was that pressure on what is the ROI against our investment. And that's the backbone that we're building on. Now but -- so what we spoke about a couple of weeks ago, it's about how do we balance that short-term nature of what the business needs to deliver, how do we validate that our working media investment is delivering, what it needs to deliver in your sales, But we need to build our brands, and we're trying to instill that balance of a performance culture that is our history as J&J with what those -- how do we allow ourselves that ability to build long-term brand equity, which we're not going to -- how do we balance those 2 together. I think there's fundamentals of our buying if we invest in quality media, we can -- we believe we can deliver both. And of course, different parts of the channel mix might impact different outcomes, but instilling a culture of how do we validate our quality, how do we optimize against our quality because that's something that can deliver short-term performance can deliver long-term brand equity as well.

Yes. I think it's really interesting. I heard this term the other day, brandformance, right, which is there is no one who is just a brand company or just a performance company anymore, right? You have to manage both. And I think it is part of the driver behind DV's continued evolution, which is brand is important and brand lasts, but performance matters as well. So the core of brand is quality media. The core of performance used to be cheap media, right? If you can bring the 2 together, you really do deliver on the promise of this brandformance idea, which is efficiently delivering high-quality media that aligns with who you are as a company.

Yes, exactly. And I think we don't treat those investments differently because we do believe fundamentally that we can -- the messaging should change. The brands used to evolve for what we're trying to deliver, whether they're trying to announce a new product or deliver evergreen sales. But we agree with that statement that we should be delivering both and that our investment as a whole should be delivering both.

I think that's interesting, too, because it does show that the need for agility and flexibility, not just from your strategy perspective, but from your partner's perspective. Well, it needs to be there. There are times when you're going to amp up performance because it's coming to a certain part of the season or end of the quarter. There's times where you really want to focus on building brand and finding quality media. I think agility matters. It's hard to be agile, though in -- with kind of a lot of tools, right? Every marketer talks about, hey, wanting fewer tools and more clarity. What do you think it takes for the industry to kind of get there? And what does progress look like from where you're sitting?

I think progress is a partner that is able to have data across enough of our media mix to whether that's from a planning standpoint or an optimization standpoint or how we're validating outcomes that solution that DoubleVerify is speaking about is trying to give marketers visibility to a broader set of our mix, whether that's the walled gardens or programmatic, CTV, retail media, those are all sizable percents of our media investment. And until there's a partner and until there are tools that can give us that visibility to enough of the media mix, then we're working in a reality where we have to deal with fragmented solutions because those parts of our mix, we have to include as part of our mix, that is a nonnegotiable. So breaking through the walled gardens and giving marketers visibility to the walled gardens and of course, the newer platforms and retail media networks that are still a little bit in their infancy is the critical point because then the use cases can be built on top of that. If the data access is there, if the visibility is there, whether it's optimization use cases or measurement use cases, that gives us a new ability that we haven't had to date.

Yes. No. I mean, look, I think you sit in a seat which many marketers do, which is spend is everywhere and moves very fluidly from platform to platform, and having consistent metrics and platforms to be able to understand what's happening between all of them and have comparable statistics is key, right? Because you can -- as we all know, and every platform will say they're the best and every platform will say, they're driving the needle the most and they're pushing things the highest level possible. But from your perspective, you need to be able to compare one versus the other.

Yes, which is kind of going back, ROI is our baseline because that's something that we do have the ability to measure across the majority of our media mix. But it's ROI still very much a lagging indicator for us. We get a couple of quarters after a performance in market. So the ability to connect those lagging outcomes with media quality, what is the relationship between quality and the return on our investment. That's something that we haven't gotten to yet. And that's something that, yes, would give us the ability to optimize a larger part of our media mix on a more frequent basis and not be as reliant on the lagging indicators, which, of course, are the basis of how we're going to validate the investment with our finance teams.

Yes. I mean ROI has become such a, I don't want to say a controversial term, but one in which so much of everything is based on, right? What is return on investment? And everyone has their own definition of it. But I think the challenge for you is to determine not only your own definition of ROI, but to have independent data and clarity on being able to do that. And I think, look, selfishly, we believe DV is helping provide that data to companies like Kenvue. To close off, I'd love to just -- I mentioned that we just launched our relationship with Kenvue earlier this year. You've been working with other companies in the space and in a self-serving way, we'd love to hear and share with -- have you share with the audience of your move to DV and what drove that shift in your experience of it.

Yes. I mean certainly, there are a lot of factors. I'll focus on two. We were excited about the road map. And truly, it's where you're seeing the industry converge on verification, quality optimization outcomes. We're looking for partners that can help us expand from a capability standpoint, not just kind of the current use cases that maybe we are working off of very much focused on the core verification business. So we see the opportunity to integrate more of these methodologies and tools together. And a partner that was investing in that product through acquisition and not just external partnerships was important because we want to have fewer partners, strategic partners that can help us deliver on more use cases for our investment. And the second key point was around the partnership model. We are a large global team. We have many markets that we're supporting. We have agency teams in many markets. So it's a complex internal plus agency ecosystem. And we need a partner who is truly set up to deliver that global model for us, especially as now, Kenvue, we're becoming a more centralized organization as well, trying to build tighter controls across what all of our markets are doing. And that's tough unless we have a partner like DoubleVerify that truly can help us build that global centralized model with the representation in markets as well.

Awesome. Well, Adam took a moment to give us some kudos. And I have to give Adam and the Kenvue team some kudos. We have never worked with a partner that's moved faster in a more cohesive way and more coordinated across more markets and more products than Kenvue. So if your boss is listening right now, Adam, you and your team did an amazing job. We launched literally a global implementation of DV in a matter of weeks, where in many cases, it can take months and months and months. So great job there. So again, I want to thank you for your partnership, I think, and your insights. And we're truly excited about where we are with Kenvue today and where we can take it in the future. So thanks very much. Appreciate it.

Thank you. Thanks.

Welcome to the stage, Jack Smith, Chief Innovation Officer at DoubleVerify.

Thank you. So Mark talked about DV Media AdVantage Platform or MAP and how it brings unity to a highly fragmented media landscape. So we're verifying media quality, optimizing performance and improving outcomes, all in one connected system. But let's shift from strategy at a high level to go deeper into execution, and that's what we're going to do over the next few sessions. So MAP is designed to work everywhere, whether it's the OpenWeb or CTV or social. But we're starting in one of the most dynamic and complex environments in media today, and that's social. And the reason we started there is because we've heard from customers that social presents a lot of unique challenges. It's a combination of high scale, high stakes and limited transparency, and all those things are linked very tightly together. And it's where opportunity and complexity collide. And it means that it's a huge amount of opportunity for us as we solve this problem. So that's why we're starting there, putting MAP to work live and in market now. Now if you look at the North, you see verification, and we offer prebid suitability controls in TikTok, in Meta and YouTube, which makes campaigns there more transparent and more scalable. Optimization is in the East, and that's where Scibids AI being embedded directly into social buying flows, enables real-time optimization and you're able to do that at the impression level. And in the south are outcomes, and I'm really excited to have Rockerbox as a part of the company because we're now able to link all of this together in a through line that starts with exposure and goes to business outcomes. And that's cross-platform and in real time, and as always, with all of our DV products independently, no other company can do that. And it's the same MAP, verify, optimize, prove, and that operates at the speed of social, social has -- it's very fast moving and high volume. And also, it's complex. So we're trying to simplify that complexity for clients. Now if you think about the complexity and you're an advertiser, it's pretty fragmented. So historically, you've had to cobble all this stuff together. So verification came from one vendor. Analytics came from another vendor, attribution came from somewhere else. And all these tools were disjointed. You had inconsistent signals, endless numbers of spreadsheets, and I've worked with some of the largest advertisers in the world, and I've seen firsthand how this breaks workflow and makes it impossible to have a consistent view across everything from quality to outcomes, it's almost like each team has to have their own roll of duct tape to kind of cobble this together. And what DV is delivering is different. It's a connected platform that replaces trade-offs with trust. So pre-bid controls that protect your brand equity, in-flight optimization that optimizes performance and this closed-loop measurement proves value and clients can do that on their own. There's one set of signals, one system and full control. So let's take a closer look at how DV is helping marketers apply these real-time standards at scale in a way that actually works performance. And we're going to speak with TikTok. And we'll start with the end game, which is customer success, and we'll work backwards from there. So I'll just bring up our next guest quickly. He leads Global Measurement at one of the most influential media platforms in the world. That's TikTok. So please welcome Jorge Ruiz, Global Head of Marketing Science at TikTok. Come on up, Jorge.

I feel that I didn't ask you for your own walk-up song.

I got the best intro possible.

It's good to see you again.

Hello there.

Okay. So we've known each other for, what, 15 plus years now?

15 years. Feels just like yesterday.

Yes. It feels like only yesterday. That's what I say to my wife too. She doesn't say that back. But what's interesting is that this kind of cobble together nature of how measurement happens was around 15 years ago, and it even precedes that time period. So there's -- does this -- I'll just start with the most basic question, does this integrated approach makes sense to you?

It does. I think in the history of building measurement and insights, I think as you grow your business and as you grow each of your brands, you will have many more things that you need to have, analytical rigor and capability. And so having the capability and the ability to actually have integrated options, it's always great for brands.

Now for your platform, that's kind of a high-level statement, right? So connecting it all together makes it easier for brands. And what does that mean to you as Head of Measurement or Global Head of Measurement for a platform when you think about how -- what it unlocks for advertisers?

So let me wind the clock back about 5.5 years. So when I started building the practice, I've had the great privilege, I'm fortunate to kind of had to build marketing science from the ground up at TikTok globally. And so the first thing you have to do is you have to follow and understand what clients are looking for and meet marketers where they are. And a lot of the areas is where clients are smart, and they already have a lot of measurement frameworks and tools and expectations. So you have to build very quickly, have a lot of very robust third-party partnerships and making sure while doing that, you have to deliver for the present but investing for the future. And that's why for TikTok and the work that my team is doing, it's very important to have a very strong third-party strategy because we want to advance, not just the things like we have across the world, across smaller businesses, but for larger midsized clients. They already have measurement frameworks. They already have expectations. So it's this balance of build for the present and invest with the future.

Got it. And there's this balance that's like multiple different kinds of ways that advertisers, marketers balance requirements and trade-offs. So they're always under pressure to balance performance or ROI in brand protection. So how do you see quality signals fitting into that framework and specifically, around performance for TikTok?

I think it's -- part of it is -- you could argue it's a measurement journey. You have to start where you're looking at using measurement -- many different measurement systems to solve very specific set of questions. But ultimately, as you're building marketing effectiveness, you know there's going to be some constant things that tend to be best practices in driving towards that. And then some clients will be starting from a brand safety perspective, some will be from viability, some will be from outcomes. They're going to start at different places. But as they grow their campaigns, as they start growing and building their effectiveness, the way you answer million-dollar questions is very different from answering $10 million questions. And that's -- we're going to have a lot of measurement complexity because the questions you're asking are very different, and how you have to evolve your measurement systems and how then you get them to be able to have that level of synergy on how they're speaking.

Yes. And just to build on that a little bit deep, are there specific best practices that kind of span that? Because you're dealing with a lot of very, very large advertisers all the way down to folks that are much smaller, who are selling through different kinds of products and commerce on your platform. Are there consistent best practices across there? Or are you trying to group those into one area or another?

There is one, which is having amazing creative.

Yes, yes, yes.

But that's the magic and science of advertising. I mean the reality is, yes, there are some media best practices, having things like effective frequency, efficient reach, having like, yes, having great creative because of things. But increasingly, if you look at it from the areas of TikTok as a business we've been putting together and from the vantage point that I have is there are multiple entry places where an advertiser could start building on TikTok. You could have awareness, you could have consideration, you could have outcomes or you can even have commerce. But what happens is as clients grow, grow, grow, the questions I'll get is, how can I be more effective? And then the question -- the answers I have is, well, use all of TikTok effectively, not just one component of it, but at the heart of it is the quality of that creative.

Yes. Yes. No, that's very true. The creative and the offer still really matter in advertising. You just want to be able to measure and prove that those are working.

That's right.

Yes. Now you've also got an interesting -- like thinking about it from the consumer side, it's kind of this multimodal consumer experience where you've got images, videos, text, all these things combined together. And when you look for partners who are providing quality signals about all of that to help maintain trust, what are you looking for there when you're working with third parties?

I think there's 2 components. One is obviously, high-quality service. That's critical for this kind of advertisers that are seeking the solutions. But more importantly, the thing that I really think matters a lot is having partners that are really hands-on operators because increasingly, in this world, you do need to be fluent in understanding how the technical implementations work because clients are not -- clients get it. It's not like 15 years ago where a lot of clients were doing their digital transformation. They won't understand that. Today, most clients will understand things like incrementality. It's a question of how do you do it, how do you navigate those trade-offs? How do you have the right signal strategy? How do you have the right implementation in all these different solutions? How do you adhere to the right trade-offs because you have some things that may conflict with different departments inside the client organization? So having -- understanding clients, not just from a relationship perspective, but also like the people in the front lines having technical acumen is so much more important today.

Yes. And I think it's important for us, too, to have high-quality product engineering teams on the partner side because we're trying to mask a lot of the complexity of that -- of the technical implementation because it's the Internet. It is challenging to do that. Now there's another kind of balance that -- and this goes back to the earliest days of marketing where you're trying to -- you used to have to balance top of funnel and bottom of funnel and how spend went into each one. But now you're looking at more of a full funnel flow and the ability to track all the way through is important. So can you break down what that looks like from top to bottom and the linkage of that inside TikTok when you're working with advertisers?

So it could be top to bottom, it could be bottoms up. Again, it's like -- the world is a big place, and clients have many different places where they can start their journey. What ends up happening is -- and this is one of the things that -- it's actually marketers that have led on looking at outcomes, looking at attribution, they've kind of found this kind of recipes. They may not call it the same, but like I mentioned before, when you are solving million dollar problems, there's a very discrete campaign objectives kind of solutions. When you're solving 5 million, 10 million larger kind of problems, you quickly realize that you have to do more with your campaign. And you may realize that I need more than just performance budgets. I need more than just this line item. I need to figure out how to bring in the synergy or the halo effects of brand or consideration. So what happens is, looking at outcomes in my mind is one of the -- like analytically, it's one of the great equivalizers. It's a great way for you to bring and oversee all of your media investment in different ways. But it's a planning practice of what are you going to do with this thing. So for some clients, they need to be very outcome-first, very efficient CPA-driven first. But then they'll realize, okay, I need to figure out how to bring in more inventory balance by effective CPMs. I need to bring in more quality creative that I need to have like an always-on strategy. So there, for every marketer, it will be somewhat different, but the reality is that as your budget increases, you have to make those broader full funnel kind of trade-offs. And yes, if you have to be doing things on TikTok and you have a TikTok shop and you're doing brand campaigns or you're doing things across other media channels, it's just having that flexibility and that nuance that says, okay, part of the journey has been on making sure I have brand safety. I have verification. But when it comes to what you do with outcomes, there's a big shift when you're doing pure measurement for like a small-sized campaign. So when you have really large campaigns, you're running a measurement strategy.

Yes, absolutely. And I know you worked with Rockerbox before. We acquired them. And the ability for an advertiser to look at all those different things you described, whether it's commerce or reach strategy or whatever it might be is important to proving the value of your platform. So now that we've linked it together, what additional does that give you as a media platform that helps you service clients better or maybe take some of workload off you or...

Well, first, it means we're going to keep running, very busy.

Yes. Yes.

So I'm very happy with the partnership with you and the team and especially the crew over at Rockerbox. We were one of the early partners. We made sure that when we heard a lot of demand, a lot of requests for attribution in the early days, which is -- we looked very quickly. And a lot of the questions around full funnel, we were getting them 2, 3, 4 different years ago. Clients that run attribution may not always call it that from the beginning, but it is a very common thing that says, as I grow my business and I'm looking for attribution, I need to understand incrementality, now I need to understand comparability. And then how do I grow? These kind of questions of full funnel continue to be growing in importance. And it is important to be able to have those kind of capabilities in place.

Right, right. And we launched pre-bid suitability controls at TikTok. How important is that to you? And how do you think about how that fits in because we started with kind of the end part, which is Rockerbox improving value. Now we're going back up to sort of planning or a little after planning. What does that mean to you? And how does that help in focusing in on the value that you're bringing to clients?

I think another vector to look at this within the way you're describing it is that it's not just about a funnel. I think one of the things that often happens is a lot of the measurement questions become very -- like it has to be like this or like this. But the reality is that, like I said, it's very different for different clients, and it's especially true for different verticals. So you may have the SMB, the small business path and you may have a large client path. But when it comes to -- for pre-bid, there's verticals like packaged goods and retail. There's many different clients to where having that capability and also for having proactive planning, it's very crucial. These are clients and these are verticals where they already have an established process for what they expect on safety and verification, and they're having the capability to be more proactive because at the heart of it, what all of us in this industry are trying to do is we have, in many ways, a lot of answers on the methods. We can always improve them. But what all of us are really trying to improve is on the quality of the operations to bring in more automation and speed. And that's part of the thing that's driving a lot of the innovation today. So being able to actually have proactive planning on prebidding, it's a very reasonable thing that clients that are active on this are looking for.

Yes. And especially if you look at like if it's an auto client, their funnel, I went to a client's office, an auto client's office, and their funnel took up an entire wall. And really, to focus in on the planning piece helps manage that because there are many different customer entry points that are happening on your platform in addition to other places, too. And if you're a CMO who's managing that and you're going to spend your next dollar, what new measurement or performance signals would you kind of focus someone in on. And I know you're going to say it depends on where you want to start and what kind of campaign it is. But let's just start with pure performance, like what are some of the newer signals that you're seeing have meaning in your platform environment?

I think it...

And you can't say creative either because we know that, that's important.

In the world of measurement, incrementality is something that you want to have a point of view on. Within your organization, the way you define incrementality may have different ways, but you should have a clear understanding of what incrementality means to your organization. I also think that in terms of like a new thing -- I mean, yes, we're going to -- we talked about the credit piece. But I do think that one of the things that often is getting missed is this notion of having the right practice on building full funnel measurement strategies because I do see a lot of opportunity in terms of like typically, you're going to have clients or you're going to have situations where there's a very clear brand campaign and a very clear performance campaign. But what does a consideration campaign look like? And that's often the case where clients need to get that support to translate because those are 2 different worlds.

Yes, very much so. Yes.

And it can be a very tricky thing to say, just do everything because organizations will be like, well, I'm going to do that.

Yes. Yes.

So you need to have a very disciplined way of helping to migrate clients with accountable checkpoints that says this strategy is actually heading in the right direction. It's meeting your goals. There will be some things that expand the way that you're looking at it. But having that discipline to understand the progression across the funnel becomes even more important than it is before.

Yes. And if you're starting -- if you're thinking about a campaign that sits in the middle of this, let's just call it a very simplistic funnel, a 4-stage funnel, just to simplify it, something gets starts in the middle, you do need to connect to the other pieces. Now you have the ability to do that in 15 years ago, even 5 or 6 years ago, it wasn't even possible to do that.

That's right.

Yes. Okay. So final question. For us being independent is important. And you have, obviously, your own -- targeting your own measurement on platform but still work with us as an independent company. Why is that important?

It's reasonable. Clients are asking for it. And it's also important to have -- for me, I want to be able to have a very strong third-party ecosystem because client questions will grow in sophistication, and there will be things where we're going to need a lot of help. And clients are going to be able to say, look, I need to be able to go deeper. And the bigger the client or the bigger the campaign, it is important to have something to where -- I've always told my team measurement is a team sport. And it is something.

Yes, it is.

It is truer than ever and there are a lot of complexities in how the measurement world works today, but the reality is that clients are going to be asking the same set of questions in a more advanced different ways and having strong partners that really kind of bring in that kind of penetration to the industry just makes sense.

Absolutely. So thank you very much for showing up and speaking with us in the crowd here and online. We appreciate it.

Thank you, my friends. Thank you.

Bye, bye. Thank you.

Please welcome DoubleVerify's Chief Growth Officer, Steve Mougis; and Dor Levy, Senior Vice President of Product Management.

Good afternoon, everyone. Let's talk about walled gardens. Okay. First of all, there's a lot to love. They're fast growing. They have high engagement. But of course, they can also be incredibly complex. They're walled gardens. So by definition, there's going to be some element of limited control for our advertisers. There's also complex targeting, which makes it hard to drive a return on investment and you need to protect your brand. And with YouTube, it's an incredibly dynamic environment with 500 hours of content being uploaded every single minute. So it's not just complexity. It can also feel like complexity, chaos. As Mark said earlier, it can also feel like chaos. Okay. So this is what we call the marketers dilemma, right? It is the trade-offs that our advertisers need to make on a daily basis. Do they push for performance or protection? Do they try and get scale or drive suitability? Or do they optimize to cost or control? Dor and I have been working with advertisers for years, and they're always managing these trade-offs. No matter what optimization they're trying to make, it seems like they pull one lever and it compromises another. It could be painful watching clients juggle these priorities, and the trade-offs are more pronounced in walled gardens. So let's talk about why. So on the OpenWeb, filters drive better return on investment because they reduce waste, But on social platforms, filtering can restrict scale and increase cost. And the stakes are incredibly high. So it's a really tough trade-off to make because 87% of consumers expect brands to monitor unsafe content and 2/3 say they'll walk away if they don't. So it's not just an efficiency challenge, it's also a trust and brand equity issue. So that's why we built Authentic AdVantage. It combines DV's media quality engine with Scibid's optimization into a singular platform. And for the first time, advertisers can dynamically optimize towards safety and cost at the same time. Authentic AdVantage connects 3 core capabilities: measurement, pre-bid and Scibids in an integrated solution that automatically delivers transparency, protection and performance with no trade-offs. Today, we're live with YouTube, but we're working towards expanding across all of the platforms. So let's take a quick look at how it works.

All right. Thank you, Steve. So I'm going to show you today how it works and then we'll go into a case study. So the scale of social is massive and so are the trade-off, right? For example, how do you scale efficiently without losing control of your brand or your margins? Imagine yourself as the marketing team. You're juggling 3 different priorities, right? You have audience reach, you have media cost and you have brand risk. You can really do all of those things on hundreds of campaigns, that takes hours and hours and hours, and it doesn't always work. And so what we do, we give us control, right? We let the social platform do it for you. You trust their algorithm, but you give up control. By doing this, you lose transparency. Where did my campaign actually run, I lose precision, which sensitive placement can actually exclude, can I exclude them all. I lose on performance insights because it's a black box, right? I put my money in. I don't know how the outcomes came in. And then last but not least is brand alignment. Brand alignment, a lot of times, we see the damage after it's done, but we can prevent it with those tools. These tools are actually optimized for platform objectives, but not necessarily the advertiser is one. In contrast, DV is a neutral third party and is not part of the media buying process. Instead, we empower marketer with visibility and control. We use DV's Scibids AI to steer campaigns to a better outcome. And so enter Authentic AdVantage. As Steve said before, this is why we built this system, a real-time command center that enhances your campaign performance without being part of the media transaction. So let's see how it works. Here on the top, you can see your ad campaigns live on the social media ad platform and implementation is extremely simple. With one connection, Authentic AdVantage pulls all of your campaign data across your platform and give you a single unified view. Then what it does, it optimizes, you need to optimize this for cost, for performance and for brand alignment, using AI to guide bidding logic inside the platform. Authentic AdVantage gives you a lot of suitability controls, very granular ones, right? Those need to reflect your brand standard. From answerable content categories to creator types, all deployed in a second. What used to take advertiser hours, now it takes seconds. And that's critical in an environment like social, everything moves and shifts every moment. The advertiser team stays in the driver's seat, but now they can drive much faster with more confidence without compromising their standards. So now you've got everything in one single dashboard, cost and delivery data, brand suitability metrics, contextual and quality insights. You can see what works, what doesn't work and where to go next, all in one place. But there's one question that every marketer asks. What can I do right now to improve performance and reduce risk? At the same time, right, as Steve said, no trade-offs. So this is the power of Authentic AdVantage. It makes it easy for advertisers to optimize for outcomes, control for content quality and maximize ROI with 0 effort. So looking at the overview page, you can clearly see your campaign spend, you can see key performance trends and media quality metrics. As you can see, as we scroll down, you basically see our AI recommendation. Our AI estimates that we can save a lot of money. So we can cut CPM by 32%. We can boost scale by 46%. And we can improve suitability by 6%. But we need to accept a lot of those recommendations. So we apply them. We accept each one. And we are seeing as Authentic AdVantage basically updates the profile real time each and every single one. And then you save and you publish and you see the magic happens. So Authentic AdVantage now doesn't -- it's not a post bid sort of contained analysis. It actually goes into the platform and pulls the data and cost data. Delivery data, et cetera, and it makes real-time adjustment to your campaigns within the platform, many times per day before it's too late. It is a set it and forget it, but even better. The more the system learns, the better it operates for you. And the results, if we're going to show them, there we go, are clear. Cost efficiency is up, brand suitability is up and scale is up, all with dramatically less manual effort. That's Authentic AdVantage in action. And I'm excited. I hope you all are very excited. The reason I'm excited is we think that this will actually solve a real client challenge. Client on the other -- on the one side will get their value, right? They get better outcome with less effort unlocking operational leverage for them. And then on the DV side, we get a lot of product adoption, both on measurement and activation and optimization leading to growth for our business. So this is really the system that ends a lot of the trade-offs. It provides outcomes and operational leverage. And unlike companies that blend media buying with optimization, DV maintains an independent position. We don't buy the media. Instead, we optimize how your bid -- how do you bid within the platform based on your goals and your constraint. So DV Authentic AdVantage, performance and protection automated. So let's take a look how it looks in the real world. Yes. We're going to go through 2 case studies. So in the first one, a global CPG client of ours came to us with a familiar challenge. They needed to lower costs without sacrificing safety or quality. But they had some challenges standing in the way. There were too many unsuitable placements, CPMs were rising and campaign execution was manual, reactive and not scalable. They challenged us to deliver an automated optimization for them. Exactly. So what did we do? We activated Authentic AdVantage. What it does, it combines pre-bid automation with AI optimization, and the results are pretty clear. 60% increase in impression volume, 35% decrease in media CPM and 10% increase in CPM. Everything is great, right? And you can only achieve this when you put everything together from the start. Steve, do you want to take us through another one?

Yes. So another example. We had a global footwear company that came to us with the same challenge, right? They had rising CPMs and inconsistent media quality. So we activated Authentic AdVantage, and again, the results were incredible. In this client, we saw a 200% increase in volume, right? So for the same budget, they were able to increase their volume by 200%. Their media costs in CPM decreased by 70% and their brand suitability increased by 30%. So for us, we know how incredible these results are because, again, for years, we've been working with our clients trying to manually create them. But in the case of Authentic AdVantage, we're able to do all with AI automatically. So for the client at the end of the day, they got these results, but they were also able to reach more consumers. Their campaign had a better return on investments and their consumer engagements were better and stronger. So it was a win-win for them. So let's talk a little bit about why clients are excited. They're excited because Authentic AdVantage ends the trade-offs. They no longer have to choose between return on investment and brand safety. For the first time, they can get all of it. So for us, why are we excited? Well, for one, we're excited because our clients are excited. Hakuhodo, which is one of our agency partners...

Yes, keep going.

Yes. Sorry about that, which is one of our agency partners built an entire advertising platform on top of Authentic AdVantage. So they built an Advertising as a Service platform, and DV is powering it with Authentic AdVantage. It's not only delivering great outcomes for them, but it's also delivering great outcomes for their clients. We're also excited because we have great momentum. We're already live across 50 campaigns. And as you could see from the case studies, the results are exceptionally strong. And lastly and maybe most importantly, right, we're really excited about the growth opportunity here. Authentic AdVantage is an $82 million to $105 million opportunity of incremental revenue for us. So we're very excited about that. And Nicola is going to walk through that in his section in a few minutes. So to sum it up, Authentic AdVantage drives real protection, real performance with no trade-offs. And like always, it's independent, trusted and transparent. So thank you, everyone. Appreciate it.

Up next, DoubleVerify's Senior Director of Product Management, Amanda Carlton; and Rockerbox Founder, Ron Jacobson.

Everyone, good afternoon. Now we just heard about Authentic AdVantage. And before we dig into Rockerbox, I'm going to give you an update on Scibids' progress since DV's acquisition in 2023. In the past 2 years, the solutions have gone through significant growth while delivering strong performance gains towards our client KPIs. 200 DV clients have scaled and been upsold on Scibids. The average improvement of ROI across clients is 4x, meaning they would have to dedicate 4x more budget on their campaigns without Scibids to get the same performance outcomes. And we've also seen a 67% increase in campaigns optimized, meaning we're now creating more models across more platforms for more clients. Now let me give you an example of how we improved ROI for Icelandair. Icelandair wanted to increase the number of flight bookings at the lowest possible cost. Additionally, they wanted to scale across 11 different markets. This would have been a heavy workload on their agency team. In order to test the efficacy of Scibids, Icelandair ran A/B test across their markets, comparing Scibids to a control group running without Scibids. Scibids analyze millions of advertising context, supplying the ones that maximize the probability of conversion. Through regular updated custom bidding models, the AI maintained a high frequency of optimization, constantly adapting its models to new market signals. And the results were outstanding. Scibid's AI drove a 70% reduction in cost per booking, which resulted in a 10x ROI for Icelandair, and we enabled the client to achieve more bookings for cheaper at scale across 11 markets. So now I'm going to pass it over to Ron to talk about Rockerbox.

Thanks. So I want to talk about one of the most important yet most broken pieces of modern marketing: attribution. Let's start with the simple truth. Every platform claims to be the hero of the same conversion. Snap says as they drove it, Google wants to take credit or DSP says it's theirs, but none of them actually see the full customer journey. And that's why we built Rockerbox to deliver one source of truth across every channel from search to TCV to programmatic, Rockerbox can see across it all. By adding Rockerbox attribution, DV is finally able to deliver a full closed-loop system for media effectiveness. DV verifies media quality, DV's Scibids optimizes media performance and Rockerbox proves the outcome. This system doesn't just tell you what happened, but it drives what happens next. Now let's step back and paint a picture of where this is headed. Today, most attribution models treat every impression the same. But we know that's not the case. Not all impressions are equal. Some were never seen. Others were ignored. With DV's attention and viewability data, we're able to finally change that. Viewability shows whether an ad had a chance to be seen, while attention tells us if someone actually interacted or noticed the ad. When we feed those signals into Rockerbox, we stop rewarding impressions that had no impact and start recognizing the ones that actually move the needle. And that's how we build smarter models that reflect real ROI. That's what makes Rockerbox such a critical part of DV's Media AdVantage Platform. Verification gives marketers trust. Optimization makes every dollar go further, and Rockerbox is how we prove it all worked. So before we dive in, I want to take a moment to step back and talk about some of the foundational principles of Rockerbox and why these matter to both marketers and investors. First, there's cross-channel measurement. This is one consistent view across search, social, retail media, programmatic, all of your channels. It's normalized, it's deduplicated and it's privacy safe. Second, we have first-party outcome data. We measure what actually happened, not what platforms claim happened. Our models are powered by verified business outcomes, not inflated clicks or last such bias. Last is independence. We don't own media. We don't care where the media is served. All we care about is that the media helps businesses grow profitably. These 3 foundational principles is what makes Rockerbox so effective. For Rockerbox, that means independent answers, better decisions and fewer black boxes. And for DV, this is the gateway to a much larger opportunity, performance, planning and outcome intelligence at scale. Now let's dive into how Rockerbox actually goes about measuring outcomes. To start, Rockerbox is plugged into everything. We have hundreds of integrations across a really wide range of channels, from linear TV to social, to e-mail to SMS, to direct mail, even e-mail and search. We bring it all together in a very easy way, going across both digital and offline channels. Next, we centralize 100% of ad spend. Being able to have one location where every single dollar is centralized is super critical if we're going to then measure whether those dollars actually drove business outcomes. Next, we focus on the key business KPIs that marketers are really looking after, things like revenue, conversions, even being able to cut the analysis by whether or not the media is driving new versus repeat customers. Really interesting dimensions on their business that Rockerbox is able to bring to the table. All this comes together to enable measurement that now goes from quality to outcomes. And it starts to manifest with different KPIs, things like cost per acquisition, CPA, return on ad spend, even cost per site visits. These are all lower funnel KPIs that Rockerbox is able to bring to the table as we're helping to measure outcomes. With Rockerbox, we help marketers plan. We have media mix modeling functionality that enables marketers to understand what would the impact be of adjusting budget, what would be the anticipated impact on things like revenue and conversions. And even more, we help to verify that those decisions were correct through a really extensive incrementality testing that runs test and control scenarios to verify the outcomes of those media changes. But truth without action is wasted. And that's really where the power happens when we can flow Rockerbox' outcomes into optimization. And to show you how that works. I'm going to pass it back to Amanda to dive a little bit deeper.

Thanks, Ron. Let's talk about a consumer subscription brand that tested with Scibids. Like many brands, they were managing high spend across DSPs and social media platforms. But they had 2 persistent challenges. First, they wanted to reduce the cost per acquisition, the cost of a customer signing up for a membership. Second, they were spending too much time mainly activating and analyzing their conversion data in a way that would genuinely improve performance. This is where DV, Rockerbox and Scibids come together. Rockerbox begins sending verified CPA data to Scibids at the impression level using custom text. This enabled real performance-based optimization within the DSP, eliminating the need for a data science team to manually clean and analyze the data. So we ran the test. DV optimized live campaigns using Rockerbox CPA data, benchmarked against a control group running without Scibids. The results were clear and compelling. The brand saw immediate performance gains, which were a 39% reduction in cost per acquisition over an 8-week period. Based on these results, they scale from a test campaign to 3 campaigns during their busiest season. No more A/B test, just full adoption. And perhaps most importantly, they didn't need to spend up a data science team to do it. Scibids turned Rockerbox data into turnkey activation, saving time, lowering CPA and reducing internal operational lift. This is a great example of how DV, Rockerbox and Scibids make sophisticated performance marketing available to any brand, not just those with deep engineering and analytics resources. And this approach is now unlocking broader opportunities. Scibids can activate on social media platforms and DSPs using the same independent outcome signal. Soon, Scibids will do cross-platform optimization using Rockerbox multitouch data. And in the future, we can align around ROAS, customer lifetime value and other business-centric goals, which we're already exploring with our clients. So what do we actually deliver here? Better outcomes informed by verified performance signals activated through AI at scale without the operational drag. That's not just optimization, that's marketing intelligence made actionable. And now with Rockerbox and Scibids, DV closes the loop with DV Performance AdVantage, from measurement to optimization to proven business outcomes, one platform, one trusted signal, one system designed to drive smarter marketing and stronger margins for our clients and for DV. This is exactly what advertisers have been asking for, and we're in a uniquely strong position to deliver. With the data, the infrastructure and the independence to make it work at scale. Thank you all for your time today. [Presentation]

We will now take a short 15-minute break. We'll see you back at 2:30. [Break]

Please welcome DoubleVerify's Executive Vice President, Global Chief Commercial Officer; Julie Eddleman.

All right. All right. All right. This girl's on fire. DV is on fire. We're super excited that you're all here today. We know that you are very, very busy. You could be anywhere, and you're here, hearing about some really exciting innovation that DoubleVerify already has in market and is going to bring to market over the coming months. Again, my name is Julie Eddleman. I'm the Global Chief Commercial Officer. And we are going to have an amazing panel hearing about how we're bringing everything to life with our clients and our partners that you've already heard about today. As a former CMO of North America for Procter & Gamble, I used to have a $3 billion budget, a $3 billion budget. I was responsible for making sure that, that was maximized and optimized across all of the brands. If I would have had what you all have and are hearing about today 10 years ago, my job would have been a lot easier. So we've heard a lot today about unification, how DV will integrate verification, optimization and outcomes into a single intelligent system. But unification doesn't happen in a vacuum. It happens through partnership. If there's no partnership, it does not happen. You need to be aligned on a shared source of truth that drives faster and smarter decisions. During this next conversation, we will look at how that's happening in real time. This example brings together one of the most powerful performance platforms in the world in Meta, a brand that is redefining modern marketing in a way, and a measurement partner that sits at the middle of all of it in Rockerbox. Please welcome to the stage, Diana Lucas from Meta; Valeria Dediu from Away; and Ron Jacobson from Rockerbox.

Ron loved this choice of music. I've got 2 amazing, powerful women and an incredible ally in Ron. So thank you all for being here. Diana, we're going to start with you. Again, thank you very much for being here and both of you for being such incredible partners and clients. So Meta has consistently, over the years, delivered on performance, but marketers today are really under more pressure than ever to validate those results with independent data. From your perspective, why is Meta starting to embrace third-party measurement and how have partnerships with Rockerbox and DV reinforced Meta's value proposition in this truly accountable driven landscape?

Well, first, thank you for having us here today. It's great to be here with some clients and with some esteemed partners. So at Meta, we've been evolving. We used to anchor ourselves on sort of what I would describe as like a single publisher focus on attribution. So focusing on clicks and non-clicks data and focusing on a view of our channel sort of in isolation of itself, but we recognize that advertisers and brands have to look across their marketing mix and understand the different components of that marketing and how that -- and understand the value that it contributes compared to other platforms. And so as part of that, we see that there is an opportunity. And we've partnered with Rockerbox to ensure that we're feeding in for our clients who are using their services, their data into our platform so that we can better inform our optimization models and be delivering value against those metrics and those measurements that they've decided as their source of truth. And so that has been the source of like our partnership.

Awesome. And Val, can you just tell us first a little bit about Away. We were doing a little bit of a focus group in the green room before we are prepping so I learned even more. But if you can just make sure the audience understands what you do as a company and the products that you have.

Of course. At Away Travel, we've been focusing on making travel seamless with our products since 2016. So we're coming up on our 10th year anniversary. We've disrupted the hardside luggage market with our carry-ons. And we proved that you don't have to sacrifice style and function when choosing suitcase. We have millions of fans out there and hopefully many in this room today, and we've since expanded beyond luggage into bags and accessories and into more points of sale. So all products are available not only on our D2C site or branded stores, but also in wholesale partners like Amazon or Nordstrom.

Awesome. I travel a ton. And it is going to be the first thing I do after this session is buy some luggage on Away. So Away has really, really been a standout example of a brand that prioritizes data-driven decisions. What drove you to implement Rockerbox as your management platform? And how has it changed the way that your team evaluates your marketing performance and even more specifically as it relates to Meta?

Julie, to your early example, my team might not have a $3 billion budget, but it's still massive in terms of the paid media investments that we manage. And our goal is to drive profitable growth. It's also to support strategic imperatives like driving new customer acquisition, brand awareness or establishing new product categories. So for us to do that, we truly need to understand what is the relative efficiency of each channel to make continuous data-driven optimizations both across channels to optimize the media mix as well as within each channel to really focus on the strategies that work. Otherwise, the risk would be for us to continue spending on ideas that seem good, but are actually not incremental or even inefficient. So being able to manage that like-for-like channel performance is absolutely critical. And equally important, it's important to understand the overlap between channels or what is the role of each channel across the path to conversion, especially for a product like ours where the consideration cycle is longer. Gone are the days when you have a linear marketing funnel, you see a TV ad, then you see a Meta ad and then you convert on a paid search ad, right? The customer experience, especially when you're researching a $300 luggage, is very fluid. So for me to empower my team with that understanding and with data is absolutely important, especially for a channel like Meta, where we know that so much of the value that Meta drives is not last click. So we were excited to partner with Rockerbox due to the capabilities in their MTA, Multi-Touch Attribution platform, the ease of implementation as well as a very strong endorsement that we got from some brands and marketers that I deeply respect. I wouldn't be overselling it by saying that it's been a game changer, and it's a key pillar in our measurement toolbox. It's truly allowing my team to operate at the level that I've envisioned since I joined almost 3 years ago and make continuous data optimizations. For Meta, an example specifically, I was just checking the numbers, for Q1, to give you a sense of how much triangulation you have to -- the team needs to work with. The Rockerbox MTA attributed revenue from Meta is 6x -- more than 6x higher than last click revenue, but it's a fraction of what Meta is reporting in terms of that revenue. So having that independent unified source of measurement is extremely critical.

Great. And we'll talk a little bit later with Nicola well about how this really builds upon each other and how important it is for the clients that we already have at DoubleVerify to be exposed to Rockerbox. So Ron, as the connective tissue between platforms like Meta and brands like Away, Rockerbox plays a central role in transforming disparate data into actionable insights. What do you see as the key ingredients that make shared measurement framework truly work? And what's the unique value that DoubleVerify brings to that equation?

Yes. So to really be that kind of connective tissue requires trust. I think that, that's kind of, first and foremost, the most important thing. If our clients don't trust our data, they're not going to use it to guide their business, like Val was just discussing. And that trust is something we take really seriously. And it's very hard to develop and very easy to lose. So I think trust is the most critical thing. A way that you engender trust is with transparency. And that's something we think about all the time at Rockerbox is how can we be as transparent as possible with our measurement, how we got to the results that we got to, so our customers don't think they're looking into a black box, but can kind of understand how the sausage was made. Even that point about varying CPAs between Last Touch versus Rockerbox versus Meta, that's a very common story. And that could also be really disconcerting for brands because it's really difficult to make decisions when you see such a wide variance in performance. And that's where Rockerbox as an independent third-party platform, who that doesn't care where dollars are being spent, just wanted to be spent in channels that will grow their business is really well positioned to be able to be that neutral arbiter, transparent and kind of opening the box on how we got to the conclusion that we got to. So I'd say trust and transparency. The other parts are probably around speed. Businesses move really, really fast. Every single day when a marketer logs into or just on their desk, opens up all the platforms, they have to make a lot of decisions that happens in the morning and the afternoon, at night as well. So just being able to keep up with that pace is really difficult, especially as there are a wide range of measure methodologies, some of that are faster and slower than others. So helping to bridge the gap between something like multitouch, which is available daily versus media mix modeling, which is historical in nature, even incrementality tests can take 4, 6, 8 weeks to run. How do we kind of bridge across all of them so that we can provide results exactly when our brands see them. Lastly on how DV fits in? I mean, I think DV helps to bring an entirely different dimension to Rockerbox's measurement. I talked about this before about being able to look not just as all -- at all impressions as equal, but were they seeing how much attention was paid to them, I think that's going to be a super powerful lever for Rockerbox in the future.

Great. Diana, with so many tools and measurement options available, how do advertisers ensure that they effectively measure campaigns across the full customer journey and not just the last click?

Yes. So I think everyone knows, and I heard Mark's opening earlier, like the consumer journey has become even more complex, right? The way that people are consuming media and the way that consumers are shopping is changing. So people are generally using multiple media sources at the same time. And we're seeing a shift towards more engaging formats of media like video. So as an example, on Meta, about 60% of time spent is on video. And then when you look at shopping behaviors, you also see that's changing. So for example, we did some research and looked at the Gen Z-ers, and they are twice as likely to make a purchase without ever having clicked on an ad. So that means if you're focusing just on those clicks, you're missing a lot of that value. And so you really have to be focusing on in your measurement, understanding what's driving the most incremental, right, conversions to your business and to be optimizing against that in order to be increasing the business value that you're getting out of it. And so from a Meta perspective, we believe incrementality should be -- it's like the North Star or the gold standard of causality, but we also realize like that advertisers need to use multiple sources of truth. We talk about a suite of truth now, not just a source of truth, right? And so when we have looked at comparing -- when we've looked at performance like what you capture in -- across multiple studies, we did an analysis to understand like how pervasive is this problem where you're misattributing value. And we found that of -- and across the selection of campaigns, I think it was about 31% of conversions were misattributed from Meta to clicky based failure that we weren't getting the credit for in the systems. And so again, this focus is for us on incrementality as being the gold standard that you should be using. But we also recognize it can be hard to totally focus on incrementality as your only source of truth. And so we recommend to advertisers to think about how are you -- within the source of truth that you're using, how are you optimizing ensuring the best recognition of the value that's resulting in those conversions. So for example, if you're relying on click-based attribution, considering incorporating view data within your attribution model or if you're using multi-touch attribution or MMM, considering running incrementality tests, whether that's Meta's conversion lift or whether that is GeoLift and calibrating your models.

Great. Val, let's talk about application. Can you give an example of where insights from Rockerbox specifically have helped your team really make smarter and faster decisions across channels?

Of course, the example I picked is also one that literally had the highest stakes for us last year, and it's during Black Friday, Cyber Monday. I think everyone here probably wouldn't be surprised to know that for us, Black Friday, Cyber Monday is our Super Bowl. I think it's generally for retail, it's not different for our brands. You have a disproportionate amount of revenue orders, new customers and media investment that goes behind delivering on that. So at that point, we have been live with Rockerbox for almost 2 quarters. So I think the team had a good handle. As context, we have been pulling back in our Meta's investment based on relative efficiency leading up to Black Friday, Cyber Monday, but our MMM from last year was actually showing that Meta was outperforming both itself and some of the -- most of the digital channels during that big holiday, even when the costs are some of the highest in the year. So I would say, it was a big risk that we wouldn't have taken without the confidence that we had from Rockerbox is another data point in the triangulation. And during those 5 days between Thanksgiving and Cyber Monday, my team is hands on the board. I mean, of course, AI is doing a lot of the work, but we truly make optimization every single day and multiple times within each day. So it was an amazing partnership between my team, Meta and Rockerbox where Meta was providing us some headroom analysis, telling us how much more room do we have to spend, what is the competitive dynamic. Rockerbox was, to Ron's point earlier, providing almost real-time data at a very granular level. So the team was able to reallocate and rebalance -- reallocate dollars to the campaigns that we're performing the best and ramp investment or rebalance the portfolio live. So in the end, we ended up actually doubling our investment in Meta compared to the prior year while maintaining efficiency and driving almost equal revenue impact, which was massive. And again, honestly, for me, beyond the results, I think it was just a very -- it was a highlight for me personally to see my team operate at a whole different level, leaning into those insights and enable a different kind of collaboration, both with the media partners directly, but also just -- this is how I aspire my team to deliver on, and they delivered when it mattered the most.

Awesome. It is so cool to be able to see, being able to make those decisions in real time based on real data, so very cool. Diana, as marketers adopt more sophisticated measurement strategies and lean on a broader set of tools like Rockerbox to understand campaign performance, how is Meta evolving to support integration into these external platforms? And what does this shift mean for advertisers who are increasingly looking for that flexibility?

Yes. So while at Meta, I think a lot of marketers do lean into the fact that we have a wide range of capabilities, ranging from ad creative development to targeting to measurement. A lot of advertisers are looking to not just understand their Meta channel investments, but understand this in context of other channels. And as the investment decisions they're making have become more and more complex, right, they're leaning into different types of third parties like Rockerbox. And so it's really important from our perspective to ensure that we are understanding what our advertisers are focusing on, right, and how they're measuring their investments. And so we see that as an opportunity to work with parties like Rockerbox, and ingest their signal in secure ways and be able to use that to input into our optimizations and let AI ensure that it's delivering against the outcomes that matter the most to our advertisers.

And Val, as a marketer, again, going back to P&G, I would have loved to have been able to compare like you can compare channels using Rockerbox. So how important is it to be able to have those KPIs comparable across platforms?

It's a very leading question. But my joke is always saying, if I added up all the revenue as reported by the different platforms, we would be $1 billion company in terms of revenue, and we're not there just yet, right? So it's extremely important for us again to be able to have that independent consolidated consistent view of like-for-like channel efficiency. Ron, you alluded to some of the differences in attribution logic that different platforms have. Even if -- so you can compare a CPO or a customer in customer acquisition from Meta with Google directly, that would just lead to bad decisions, or we also -- it's just very complex or almost impossible to verify the numbers that we have from -- get from our partners across different metrics. So it would be virtually impossible for us to deliver the results at the scale and complexity of what we're trying to do without having -- not having that independent unified source of truth. And to your point earlier, Julie, it is a triangulation where we've expanded since to add in the MMM within Rockerbox as well as their testing support. So it is truly a triangulation to get closer to the truth.

Cool. All right. We're going to wrap up. Kind of a lightning round, one line to close out from each of you. What is your call to action for marketers trying to simplify the complexity and improve performance? Ron, I'm going to start with you.

Have a good measurement in place. I'll stick with that. No, but really, have a good measurement in place. There's no perfection in measurement. I think about it as like minimizing imperfection is really what the name of the game is, and you just need a lot of signal for that. So go out there and find that signal.

Val?

You truly can't succeed long term without deeply understanding and operationalizing attribution and incrementality. So you have to invest in the data, the tools, the partnerships to get you there.

Diana?

Unlocking performance by closing the loop between optimization and measurement.

Awesome. So hopefully, you can see by the, really, the stories that we were able to tell today why we are so excited about Rockerbox and the value that we think that it's going to bring the already existing clients that Rockerbox has and the hundreds of clients that we have at DoubleVerify that we think are going to be really excited about the capability. So thank you again for your time.

Up next, DoubleVerify's Chief Technology Officer, Nisim Tal; and Chief Product Officer, Alex Valle.

Awesome. All right. Good afternoon. I asked our AI model to help me write this talk, but it politely refused. It claimed like emotional burden, optimizing all of our clients' campaigns and dealing with all of our data. So here we are with our own words. So I'm Nisim Tal, I'm Chief Technology Officer at DoubleVerify. I've been with the company for 15 years now. And prior to that, I built AI and classification system for Nielsen. And today, I'm going to walk you through the intelligence engine powering DV's platform and why that gives us a commercial and competitive advantage leveraging AI to its full extent in a way that's hard to replicate. All right. So Mark presented the vision for DV Media AdVantage, the unified platform that controls verification, that powers optimization and that proves outcomes. And this unified platform brings together data and technologies in a way that perfectly aligns with how our customers think about ad campaign ROI. So DoubleVerify is positioned to best succeed in executing the vision, thanks to 3 elements that yield unparalleled innovation and success deploying AI. So let's take a look. So one is scale. The scale of the combined solution and particularly the coverage of the ecosystem that we have and the robust integration, that's a major asset that we have. Second is the richness, the breadth and the depth of our data. And third is the unique expertise in media analysis and in the advertising space. All of those 3 combined drive our innovation and turn the unified vision into a reality building smart AI solutions. All right. So let's break it down, and let's start with scale. Throughout partnerships with over 2,000 advertisers and direct integrations across the media landscape, for Meta to DV 360, we tag impressions in real time in over 100 countries and 40-plus languages, covering 98% of all ad-supported web content. And through this integrated technology, we built a unique platform that spans the entire media ecosystem, DSPs, SSPs, CTV, retail media, publishers, social media and whatnot, all of which can be accessed and controlled with DV's unified tools. For example, once a client builds an ABS profile, the ABS profile is immediately available across DSPs, across geographies. They are making change to the profile. It's constantly updated for their entire scale. Another example is a client can see their social data and their web data and analyze them with the same tools through the same UI. Now building AI solution without those integration is like cars in a place with no roads. The integrations actually connect our solution to the real world where our customers are operating and they bring them to life. But the value that scale and coverage and integration gives us goes beyond the value to our customers. It also creates a flywheel effect and a moat, a moat that expands every day with every impression we measure. More integrations leads to more signals. More signals leads to better models. Better models will lead to stronger outcomes. Stronger outcomes immediately lead to more client wins. And this, in return, gives us the power to have additional and more integrations in the ecosystem. And then the flywheel continues. So we talked about scale. Let's switch now to data. Not only are we integrated into an entire ecosystem where we exchange data at scale, meaning the breadth of our data is huge, but also each of these integrations are very deep and rich. So the depth of the data and signal we receive is unparalleled in our space. But remember that more data isn't the answer, quality and clarity are. And everybody knows in AI, garbage in means garbage out. And that's why we put a lot of focus on quality data. We don't just access data, and we just don't just collect data. We also generate some of it, we clean it, we normalize it and we add a meaning. So let's take a look at some of the data points we are collecting. We are starting with data that we are collecting directly from our customers, from the brands. So first-party data can be campaign goals, settings, budgets, plans. We continue with our delivery data. So campaign delivery data can be what time the impression ran on what device, all kinds of [ fraud ] signals, operating system and so on and so forth. And then we layer on data about the content next to which they had appeared. So it can be sentiment analysis, contextual analysis, all kinds of brand suitability segments and other signals that gives us more color about the content itself. But it doesn't stop there. With the Rockerbox acquisition, now we are capturing more of their user journey. So if before Rockerbox, we were primarily doing viewability engagement, now it goes also to actions, to clicks, to purchases, to conversions. And then on top of that, we run attribution models. We add our own data and we collect the business results. We collect sales data, we collect other types of outcomes and we layer it on. And all of these data types together, which again, that's very, very unique to DoubleVerify. There is no other company in our space that have such a robust data. All of this, this is what leads to achieving the clients' KPIs, helping them meet their goals and drive more value to our customers. All right. And you know what, like another nice thing is that the consistent metric. I'll go back to that. The consistent metrics here are being carried throughout the entire campaign life cycle, whether we are talking about activation solutions, measurement solutions, optimization solutions, attribution, the same data sets are being transferred throughout this life cycle. All right. So I said that we have a lot of breadth of data and depth of data and that's a lot of data we process. Let me quantify it for you just a little bit. So you can see here how much data was used to train the, I would say, some of the biggest foundational large language models out there. So you can see that GPT 4 was trained on 26 terabytes of data. Meta's Llama 4 was trained on about 160 terabytes of data. And in comparison, DoubleVerify processes every day more than 160 terabytes of verified signal data. Again, every single day. That's almost 2x the volume, the training volume of the largest foundational models. On top of that, our data is uniquely structured and is purposed for high-frequency advertising decisions. So it is marketing specific data, which is even more powerful than generic LLM data when you come to solve our problem. And that's why we take a smarter path, where we leverage LLMs, and we leverage them a lot. But we feed them on top of the strong infrastructure that we already have. While we are on data, let's talk a little bit about data for content classification. We have many types of data, but data for content classification. If we take a naive approach to classify content, okay, with LLMs, some companies are claiming to do this, okay? So we will need to analyze all of this content, right? And we are talking about a lot of content. And then each content needs to be evaluated against all of the profiles of the customers against each profile, right? So if you take all of the size of the content and the size of the profile and you multiply them together, you get a huge number. And let me remind you, like for LLMs, you are paying by the token. What does it mean? It means that is payment per usage. It's similar to minutes in a payphone or similar to kilowatt per hour in electricity. So more tokens means more money. And the token is roughly equivalent, I would say, to one word in a text. So if we are classifying all of the content and all of the texts, and we want to count how many tokens are there. I don't know if somebody wants to guess, but we are talking about 56 quadrillion tokens, and that's something that is unrealistic to do. That's a naive approach that whoever is claiming that they are doing this, I don't buy it, 56 quadrillion tokens is around $15 billion a day. And I'm sure that none of the investor or analysts here will want us to spend $15 billion a day on classifying texts. And that's why we chose a smarter approach. And even if LLM costs continue to drop, tenfold annually, which they may, this approach won't be economically viable for years if ever at production scale unless you do it in the smart way that we are going to present to you. So operating at our scale with our granularity and richness and with the unique profiles and needs of our customers requires a smarter path built on years of expertise where we leverage LLMs, but we feed them on top of a strong infrastructure that we already have and the strong infrastructure is the rule-based systems. And this is the neural networks and traditional AI models that we built. So again, I want to repeat it. So our smarter path is to leverage the use of scale of data and technology and layer LLMs where they make sense, okay? And let me give you some example where LLMs makes sense. For example, we are using LLMs to train lightweight, high-precision expert models or agents that can run efficiently at scale. We are using them to classify highly nuanced content types, such as copyright infringement and compliance aspect and custom categories. We use them to generate labeled data sets faster in collaboration with human review, and we are using them to distill brand policies into efficiently enforceable rules. And this hybrid architecture combines the adaptive capabilities of LLMs with a deterministic approach and the reliability of symbolic and rule-based system tailored for brand policy enforcement. All right. So with that, yes. So with that, the result will be like scalable automation with human aligned precision, and it is built for enterprise-grade execution. And you know what, now it's patent pending. We filed a patent on this. This is a testament to the uniqueness and the sensibility of our design. For summary, from signal to insight to action, our platform doesn't just evaluate media. It drives measurable outcomes marketers can trust. And critically, this hybrid architecture isn't just a technical differentiator. It's what makes the unified platform Mark described become feasible. It connects DV's quality verification to optimization and to outcomes in a way that is scalable, auditable, cost efficient, which is exactly what advertisers are demanding. So with that, let me pass it over to Alex Valle, our Chief Product Officer, to share some of the exciting things we are building.

Thank you, Nisim. It's an honor to be here with all of you. I've been building AI products for about 25 years now. It's been a while. It's been ways, and it's just really exciting to see what AI has done to transform this entire industry and continues doing so. As Nisim mentioned, the foundation for AI, as you all know, is this rich structured data set that we've been working on for many, many, many years, right, and we've built on top of that. So let me walk you through a bit today, the products we already have. the value they're already bringing to our customers. And then paint a bit of a picture of where we're going next with all this data and the agents that Nisim mentioned. So we have 300 billion signals daily, as he mentioned. Again, structured, rich, contextual, et cetera. We've taken all that, and we've now infused it into our customer life cycle, right? And you're familiar with some of these products already, right? When we activate, obviously, we have our authentic brand suitability product that we've had for years. We have our pre-bid products on social that have launched more recently, and we continue to have a rich set of activation products. We also have, of course, been known for measurement for a really long time, but we continue to enhance that with stronger authentic attention measurement, outcome measurement. And so our measurement suite continues to get stronger. And again, built on that same foundation of rich quality data and signal that Nisim was talking about. We now optimize with it. We optimize across full funnel. You heard here on the panel, folks talking about full funnel. Well, we do that already today, right? We already can optimize towards upper funnel goals like viewable CPM. We can optimize towards lower-funnel goals like CPC or ROAS, for example. And all those are live customers are using that with us today, and it's a very powerful solution that optimizes and drives outcomes for customers. Well, with the acquisition of Rockerbox, which is super exciting for us, we can go even further in this customer life cycle, right? So now we can actually prove with multi-touch attribution, as you heard earlier from Ron and Rockerbox, and they can help us plan, right, help us with media mix modeling, for example, on the planning stage. And really the platform that we're talking about, this Media AdVantage Platform that we're talking about brings it all together in a seamless intelligence layer, right? It is a consistent data set that connects across all of these parts of the life cycle. And we're using that to build really revolutionary new products, right? You heard earlier today about Authentic AdVantage, right? Authentic AdVantage, in fact, connects the first 3 parts that we talked about, right, activation, measurement, optimize. And that's a product that now, as Dor and Steve mentioned earlier today, allows a customer to balance suitability, performance and cost in a way that has been extremely difficult to do until now, and we are offering it in an integrated solution. And similarly, we have ideas for how we're going to build integrated solutions across other parts of the platform. So that's a bit about how we're building products today. We do this as well across the entire ecosystem, right? We do it across open web, social, CTV, retail media. And why is this unique, right? It's because of years and years of strategic partnerships that we've struck with the entire ecosystem, right, not only these channels, but buy side and sell side. And with these rich relationships, with these strategic partnerships, again, it allows us to fuel that foundation of data that Nisim was talking about. And we continue to offer products that can meet customer needs across their entire media life cycle and across all these channels that they operate in today. So what? That's a nice picture, right? What does that mean? What does that do for your business? Let me share some numbers of what this is already doing to transform our business today. And then let me talk about where we're going next in our future, right? So already today for our customers, our thousands of customers that we have today using our products benefit from a lot of what Nisim and I have been talking about today, right? Campaign creation has already fit twice as fast as it used to be. So we're able to take a new customer through the entire journey and onboard them much, much faster. Our model is this optimization layer that I talked about. We're able to train hundreds of thousands of models and constantly tune them, add features, make them stronger, make them perform better on behalf of very specific customer needs, and we're able to do this at scale, like I say, hundreds of thousands of models already today. Nisim mentioned content labeling, right, auto labeling. We're now able to improve how much scale we can -- how much content we can label by 2,000 times. And if you think of, for example, social, how much content comes out regularly and how that's constantly changing, the speed at which we can label content becomes incredibly important for us to be able to continue scaling and supporting these different formats. And again, all this is built on this foundation of several things that we've built over the years, right? One is the scale of data; two is the strategic integrations that I mentioned across the entire ecosystem; three is consistent metrics across all these parts of the life cycle, right? Nisim talked about structuring the data and this takes time and its effort to do the science behind that to connect at all. And then finally, these proprietary AI models that again, we use to leverage the best models already in the market, and we continue to tune the way that we build our models. In fact, we've been early adopters of Gemini, Copilot, Cursor. So a lot of the software development that we do is quite modern by using these latest AI tools. The product team, of course, uses like Lovable for prototyping. We use Glean, et cetera, for information. So there's a lot of tools that we're already using within our workforce. Again, it's just part of our DNA. I think we've done that for years. And it leads to more efficient internal operations. So internally, right, in addition to the benefit that our customers are already getting, because we've adopted and had this discipline of AI for years now, again, a lot of benefits internally, right? We can define policies twice as fast as we used to. We're able to triage all of our IT tickets now automatically through an AI tool. And we're able to develop code 40% faster. So all this is already bearing fruit. Let me talk a bit more about where we're going next, right? So we talked about the models that Nisim mentioned and an approach where we have hybrid AI and leverage language models and rule-based and neural networks in a very unique way. Well, what comes next, of course, is language models have their limits, right? They're singular, and with agents, you can actually carry state and take action more importantly, right? And so I'm sure you're all pretty familiar with agents. Agents bring a new set of capabilities to us that not only can we now take the models we've already built, but enhance them with autonomous context to where intelligent effective agents, think of like advanced employees, that can actually take a decision and run with it and make decisions in between or come back with information and have the right constraints and the right context awareness. That's something that we're doing right now. What are we doing it on? We're trying to apply it to different parts of basically a customer's life cycle, right? So in terms of implementing brand guidelines, we have tailored agents that are purpose-built for this media workforce, for that part of the workflow. We are enforcing brand preferences as well. You can imagine an agent that does that specifically. You can imagine an agent that optimizes campaigns and really learn specifically how to work with that data and work on that use case and so on and so forth, right? Reallocating budgets and ultimately, driving the performance. These are different stages today and language models reach a constraint if you don't apply agents along with them again, so that they can carry state and they can take actions with it. So let's take a look at what this looks like in practice. Let me show you a video of kind of where we want to go next to give you a picture of what this looks like. [Presentation]

Pretty cool, right? Hopefully, that gives you a sense of how we're going to connect everything we've talked about today, right? This is a small glimpse into our agentic AI future. And if you imagine the product that we talked about earlier, Authentic AdVantage, already able to optimize several parts, again, suitability, performance, et cetera, you can imagine now agents being trained on specific aspects of this and being able to do much more powerful things in the next version of that and the next version of our platform as we tie all of those together. So in summary, what we've shared today reflects not just what DV does today, but why we feel confident that we can continue winning in the market, right? We offer a unified platform that's built on real verified signals, rich signals that Nisim mentioned that we've been building and tuning for years. We offer the intelligence that is embedded across the full media life cycle. And so we are uniquely positioned, I would say, to be able to deliver value at each part of the life cycle and tie it all together and be able to not only stitch this data together, but now take actions and build agents on top of that. We have infrastructure that operates at a speed, scale and precision that I think is unparalleled. And finally, we're building, I think, really cool products on top of that, right? We're building products that take the complexity that our customers today struggle with and able to take actions for them, able to bring clarity into the world of advertising for them, and basically enrich a lot of the value that we can add for them. So look, with the things that we're launching here, we think this drives margin expansion. This drives the ability for us to operate more efficiently, internally as you saw from some of the stats. And it offers the innovation for the market that we think our customers are thirsty for, right? We think we can add a lot more value to our customers with this integrated and differentiated platform. Thank you so much for your time. Thank you.

Welcome to the stage, DoubleVerify's Chief Financial Officer, Nicola Allais.

Hey, everyone. Good to see everybody in person. I'm standing between you and Q&A. So I will be sharp and direct, and hopefully, we'll get to that as well. So I love Innovation Day because you guys get to see product innovation, thought leadership, some real developments and you get to see the DV team with their depth, expertise and product stories. I'm going to try today to put it together and connect this vision to the value opportunity for DV. I'm going to do 3 things. I'm going to talk about the business model. I'm going to talk about actual revenue opportunities that are available today based on the products that you heard about today. And then I'll give some commentary around guidance that we announced this morning. So the way this works for us is you heard about verify, optimize, proof, and I'm going to talk about how this creates greater customer value and compounding growth. The model that you heard today is attach, stack and scale. Attach is where we start. It's where we start generally with just open web measurement. And this is where we increase the share of digital media that we can verify. Stack is where we create deeper monetization. This is where we turn simple measurement into a multiproduct platform relationship. And finally, scale is where we increase operational efficiency as the business continue to expand, utilizing tools that you heard about today to reduce the cost and complexity as we service larger functions. So how does attach, stack and scale works? What it matters is how these 3 levers compound. Attach expands what DV measures. And in a moment, I will go through slides, I will show you how verification is very far from being done. Stack increases how DV monetizes each impression with all the product suites that you're familiar with. What's unique about what you heard today is the optimization and outcomes. There are very few companies in the market that are able to provide both the optimization and outcomes on top of the verification. And scale is how DV monetizes its impression at an even more efficient way, and that creates a compounding revenue growth. Now let's start with attach. As I just said, verification measurement isn't done growing. Their major swath of digital ad spend that remain unmeasured. This is data, actual data for the U.S. market only, so not even of our entire market. And as you can see, we're attached to about 20% of open web impressions, a little less for CTV at 19%, and only 6% for social. Social is 2x the number of impressions compared to the open web, and that 6% is still very low. You heard today about new products that are going to allow us to go deeper into the social channel, this is where there's a large opportunity. That mismatch of high volume and low attach is the key to unlocking revenue growth. Now just 1% point more of attach generates a lot of revenue. It's $4 million more revenue on the open web, it's $1 million more for CTV, and it's $5 million more for social. And the key here is the catalyst, like what needs to happen for this to unlock. And as you heard today, the activation solutions are live on Meta, YouTube and TikTok. And we know that activation drives measurement. We have the experience of the open web to know that on social, now that these solutions are available, the attach rate will continue to grow. This is where attach begins to compound. Now on Stack, this is the second lever of the growth model, and how DV can monetize more value for every impression that we measure. Stack starts at the base, early Stack, 1 to 2 products, we have a 2-year tenure. Those clients are generating about $100,000 of revenue to DV. As the client moves up the Stack to the mid Stack with 3 to 4 products used, the tenure grows to about 3 years and that generates about $0.5 million of revenue for DV on an annual basis. And these are real numbers based on our top 700 measurement advertisers as of last year. Now the deep Stack with 5 to 7 products use is when our clients are using our most sophisticated products and create a much stickier relationship with us. Those clients have a tenure of 5-plus years and generate over $1.8 million of revenue to DV. Now the real key to unlock is part of the Stack is that the majority of our clients are still early in their Stack journey. 27% of our clients are in the early Stack, which is the onetime baseline revenue, 50% are a mid-Stack, which will be 9x more revenue than the baseline revenue, and only 23% of our clients are using 5 to 7 products, which would generate 35x the baseline revenue. That's the power Stack. It's a platform adoption that compounds over time, creates a more durable and more embedded relationship with our clients. Attach, Stack. Now let's talk about scale and let's talk about the catalyst that you heard about today. Today, we talked about social activation, Authentic AdVantage, optimization and outcomes. Optimization is DV's Scibids, which we presented to you 2 years ago at Innovation. We had, at that point, committed to $100 million by 2028, and we're well on our way to making that number. So let's talk about the 3 other opportunities. And it's -- I will give you some concrete numbers around how we're thinking about the opportunity. And let's start with social activation. Social activation is the opportunity to stack social, basically on top of measurement. If you know that, as we've said in the past, we have about $100 million of social measurement revenue in 2024, and about $40 million of that is Meta measurement. Now if you assume that 50% of those campaigns will adopt activation, at an average price of 2x to 3x the measurement pricing, the activation revenue potential on Meta alone is 40 to 60. Now we've used 50% attached. That's a fairly conservative one. If you know that ABS is on 100% of our top 100 clients. So we've been conservative on the 50% assumption, and we've used a 2x to 3x measurement -- times the measurement pricing on the activation side. That's one opportunity that's available today for us to go after. The second one I want to talk about is the Authentic AdVantage. This is the YouTube opportunity. So again, starting with the $100 million of social measurement in 2024, and YouTube measurement being about $44 million of that. If we assume that 70% of those campaigns are a good fit for Scibids, and that's a $5 billion media spend, 70% of that. If we assume 33% of them buy the Scibids product, which is -- could be a pretty low number at a bundled price between 7% to 9% of media, that gives you an $82 million to $105 million number. That's the number that Steve mentioned earlier today. We are achieving about 8% right now of media spend on the Scibids product. So we're trying to be a little conservative on that number as well. And the 33% sell-through could also be a much higher number once the clients understand the value of Scibids. As you saw in the video, $1 of Scibids can save you up to $4 of media spend. So the product is extremely powerful. So that's another opportunity that's available to us today to go after. And then the final one that I want to talk about is Rockerbox. So this is beyond brand. This is us going after performance marketing dollars, well beyond just brand advertising dollars. Rockerbox is targeting about 700 advertisers and the combined media spend of those advertisers is $23 billion. 200 of those advertisers actually already have a relationship with DV. So there's already a crossover there between the advertisers that Rockerbox was going after and the one that are already a DV advertiser. That's $8 billion of spend. If you assume that we have a cross-sell opportunity of about 0.4 to 0.6 take rate on that spend, and Rockerbox achieved 0.5 last year on its own data, that's a $31 million to $47 million opportunity. The purpose of these 3 slides is to show you that we have opportunities to go after today with products that we have integrated into our own product suite. And this is an opportunity for us to not only attach, stack, but really scale on the opportunities that are available to us today. Now scale is one of the factors that allow us to diversify our revenue. One of the questions that we always get is how you're diversifying your base, how you're diversifying your revenue streams. What I spoke to right now was really new product introduction and upsell, which diversifies the revenue streams. We talked about channel expansions where we're not just open web, but we're able to achieved quite a bit on social as well. Client growth and scale is important to us. The top 100 customers are spending $4.2 million, but we're able to add more and more enterprise clients and the ones that we acquired last year, in particular, are scaling quite well, and I will speak to it when we get to the updated guidance. International expansion is important to us, so we're not just tied to one geography. And you've seen the benefit of strategic M&A for us, especially around product extension and product adjacency. So this is the scale -- we work in an environment that we're obviously not immune to macro trends. We're not immune to changes in media spend, but we are working on what we control, which is diversify across more than one product, more than one channel and continuing to increase the client's base, especially on the top of the funnel. And I don't know why it's a SCH listed in [indiscernible]. Scale is not just on the revenue side. We have scale on the expense side. So you heard about Alex from Alex and his team. This is a business that's inherently very profitable. The tools that you heard about today are going to allow us to innovate at a clip that's going to be even more efficient than we've been able to -- to date. This is not a 1-quarter opportunity. This is not a 1-year opportunity. We are investing in all the product opportunities that you heard about today. But there are inherently efficiencies that are underway to continue to deliver long-term operating leverage. Now turning to updated guidance. We issued the updated guidance today. We are raising the Q2 guidance to 17%, 30% margin and the full year to 13% and 32%. The 17% growth in Q2 matches the Q1 growth that we've achieved. And all the drivers that we discussed for the Q1 performance have continued into Q2, namely, we're able to continue to upsell to the large enterprise clients that we gained last year. The base is performing well, and we're having strong initial reaction to social activation, optimization and now Rockerbox. The exact same drivers that helped us achieve 17% in Q1 is continuing to Q2. The 13% revenue growth for the year is up from 10% and that assumes a prudent view on the macro. We have not seen any signs as we're guiding to 17% in Q2, but we're going to remain prudent for the second half of the year. All the reasons why we are continuing to perform well have continued from January and now into May and to the beginning of June. So to conclude, we have a proven history of growth and profitability. We are just focused on what we can control, which is more products, deeper relationships with our clients, providing a product offering that's very different and very unique in the market. You've seen the opportunities on a dollar basis for what is out there now that we've been able to launch authentic Advantage, now that we have Scibids, and now that we have Rockerbox and our commitment is to continue to grow top line and high margins for '25 and beyond. And with that, I will call the DV leadership team on stage for Q&A.

Awesome. Well, thank you all for hanging in there [indiscernible] beautiful day and we are excited. To do Q&A now. So we have some rolling mics roaming in the room. So if you raise your hand.

So just wanted to drill down first on pricing strategy. I think I just heard -- it's Laura Martin from Needham. I think I just heard Nicolas say you're achieving an 8% average on Scibids product. And the best thing about that product is you get paid as a percent ad spending. My question is you've introduced a bunch of new products today that bundle in Scibids, Rockerbox, have we kept that business model or have we gone back to the original DoubleVerify model, which is a fixed fee?

Yes. So Authentic Advantage, which is the first product that we announced today and that we've launched that bundles in Scibids is a percentage of media products. So we'll extend the capabilities of us taking advantage of higher CPMs and pushing through a percentage of media opportunity. What we've wanted to show with all of our solutions is pricing flexibility for clients to buy our solutions the way that they are most comfortable with and then drive the highest ROI. Authentic Advantage is a percentage of media bundled solution that brings together an opportunity for us to take advantage of those higher CPMs.

Okay. And then my second and last question is on international. Lots of silence about international today until that last slide. My question is with the Digital Markets Act, where they're not allowing European data to come back. Does that make international tougher for you to grow some of these new products outside the U.S.

It's a great question. I mean considering that a lot of the data that evolves around that act has to do with personalized data, PII data, and we don't trade or our products are not built on that type of data. For the most part, we're not subject to a lot of those provisions. Otherwise, we do have data centers around the world. We adhere to whatever rules and regs that need to be adhered to. And our customers regional, we're looking for to generate results for them on a regional basis, keeping that data where it needs to be, if they're global, in most cases, we're not -- the data that we're pulling together has not been tied to those issues. So if you want to talk a little bit about data, we can.

Yes. Yes. So I will add a little bit. So you mentioned that we have data centers all around the world, and we are also working cloud. So we have cloud instances. So if any jurisdiction require data locality, so we can definitely and we are doing so. Another thing to remember is the wide range of compliance framework that we are currently part of. So we are SOC 2, ISO 27001 and lots more. So specifically, ISO 27001 is the bleeding standard in Europe. So we are making sure that as we adopt compliance framework that we are that you are adopting international compliance. Also from a technology perspective, there is no barrier to operate in globally.

This is Matt Condon from Citizens. Today, it was really about more tightly integrating the products together and driving attach. Can you just talk about just how does that change your go-to-market strategy here as you think about going forward?

Yes. So if we didn't hammer enough this idea of attach, stack and scale, don't put those letters together, any tighter acronym. That is a strategy that has really started emerging over the last 18 to 24 months as we've started building new integrated solutions and adding on functions outside of core verification. There is the ability, as Nicola noted, to create more value with individual customer engagements by doing so, right? And I think that's changed the conversations we have with our current customers. But to be very direct, it's differentiated ourselves wholly from our competitive set. It's a different discussion now. We're not talking about just verifying impressions. But as I noted earlier, we're talking about driving quality impressions more efficiently and showing how they solved the problem or drove a result. That is a different discussion. In many cases, it's a different group of folks that we have the discussion with at the marketer, but it also sets us apart. It means we're not competing one-to-one on features and functions. We're competing on a concept. And as you saw, we had folks up here like Kenvue, who that vision and the drive behind a complete platform is how we won that business. So our go-to-market has evolved pretty rapidly over the last 12 to 18 months from one where we're just talking about bringing solutions together to one where we're bundling them together from a pricing perspective to 1 in which we're actually selling an entire platform vision, which means that we can help advertisers solve the problems they have with one solution independent of the media platform they're on. It's changed go-to-market altogether. It's changed how we compete, and it's resulting in big wins like Kenvue Microsoft and others over the last 12 months, who have bought into that vision and we're no longer scrapping it out on features and functions

Youssef Squali at Truist. Maybe just a follow-up to that question about selling a unified platform. Does that also mean that your pricing strategy is changing in any way? Does that mean that you may be getting more aggressive in trying to price the platform as opposed to the product that you haven't necessarily talked about? And Nicola. Hello, On the guidance. So it's nice to see that you guys are not seeing any weakness implied guidance for the second half, when I look at the full year guidance is a major deceleration from like 17% in first half to like 10% in the second half, maybe explain kind of what's going on there, especially on the back of expectations for an easy Q4 comp from last year.

Yes. I'll start with the first one. So as I mentioned off Laura's question, I think now with a suite of solutions or a platform solutions, it gives us a ton of flexibility when it comes to pricing. We can look at percentage of media when we're now bringing in things like optimization. It allows us to be more holistic, but also more aggressive in how we attack customers because of the fact that we're selling them a broader bundle of goods that gives us an opportunity to take more budget, but each individual product in itself can be more competitively priced. So I think it gives us an advantage in go-to-market. It gives us an advantage of the conversations that we have because we're touching lots of different budgets now within the advertiser themselves. And we don't need to take the highest rate for each one of those budgets. We can take a smaller rate and deliver a much bigger relationship with that customer over time. I'll ask Steve just to comment quickly. Steve's, our Chief Revenue Officer, and he's out there in the market in selling these solutions and selling the platform and what's your take then?

So the main thing is that the solution actually works together. All of the little bits actually feed off of each other, and that was really important to us. The second thing is we wanted to make sure that we were offering a pricing structure that made sense for our clients, an easy way for them to predict their costs when they're buying the services and easy for us to prove a return on investment whenever they use it. So frankly, it was a lot of just looking at the things that we've done in the past with ABS and the way that we've sort of bundled solutions, like ABS is a bundle and collection of services that are in one segment. And the idea for authentic advantage is really the same thing. It's taking a bunch of services and solutions that kind of work together and then charging one way that a client actually wants and being able to prove a return on investment for what they pay for it. So -- it was just a connected way of doing it, and it's working so far. So it's a good strategy for us.

Yes. And on the guidance question, Youssef, we started the year saying this will be a transition year at a 10% growth rate. We now have 1 quarter at 17%, and we're guiding to a second quarter at 17% and the drivers of the over performance are, I think, the important drivers here, which are the base is performing well. We're able to upsell the large enterprise clients that we won last year, especially around the clients that came from Moat to our premium priced product at a clip that's faster than we had planned for, which is good news. And then the early indication for social activation, optimization, the bundle and for the proven outcomes of Rockabox are also very positive. So you can look at that and say, okay, the momentum from the first quarter into the second quarter is very strong and it's been consistent month-on-month. For the second half of the year, frankly, we're just -- we have not seen anything from the macro, but we're remaining a little prudent.

Brian Pitz from BMO Capital Markets. When I listen to what you guys are saying all day today, it sounds like the core is really some of the newer acquisitions, Rockerbox and Scibids driving the value. If you kind of look across the platform, what's missing? Are there other pieces on our friend, Doug Campbell, looks at a lot of opportunities out there. But are there other verticals that you would add to the platform here? And then maybe just a follow-on on Scibids I think I heard the stat, $1 spend drives $4 in savings for advertisers, a very impressive metric. What's the bigger adoption sooner with stats like that, what's really kind of the slowdown in that adoption?

Yes. So I mean, I think you called it out, which is 1 of the things we wanted to stress here is our core is strong. Our core, as Nicola noted, is what's driving the overperformance in the first half of the year, and that's a great thing for us because that core is what's fueling the next set of solutions. And when we look at kind of what's next for us, we obviously covered a lot of ground here, right? We're talking about optimization, we're talking about performance. I think those 3 tent poles are things that we're going to stick with is our value prop. Because they're outside of the media transaction. There are things that advertisers are looking for, and they want an independent partner to provide them transparent data around. The things that we would look to maybe build those out will be any enhancements. If we can get more data, more granular insights in specific verticals to help drive verification optimization and performance. I think probably the largest one for us is CTV. We talked about growing from 5% of our impressions to 11% in just 2 years. I think it could be bigger than that. I think the take can be bigger than that. And to get there may be other assets that we want to look at or partner with or drive relationships with to get more out of that. But right now, I think we've got a great basket, a great bundle of goods that we're continuing to grow. And optimization, as you noted, the last part of your question, it is a catalyst not only for new products like authentic Advantage, which enhances our verification solution, right? Verification can be challenging. Verification can drive the cost of impressions up because you're taking higher quality media. Optimization now lowers the cost of quality, and that's a huge thing. So when we see drivers of that 1 to 4 ratio, it's -- when we talk about Scibids today, it's really been about optimizing against other KPIs. Now optimizing against our own quality data is a driver of growth outside of the normal driver. Scibids grew at over 40% in Q1 without the benefit of authentic advantage as being now a driver as well. So I think that business has catalyst behind it. You're right, it is -- if you're saving that much money, it seems like a no-brainer. For most of the customers, it is. We've upsold a significant number of them. it does take a little bit more of a sophisticated customer to start putting their KPIs into the system, so that does slow adoption. But the beauty of authentic advantage is we're taking that out of their hands and by that meaning not out of their hands, but we're taking the overhead and the work to say, what should I be optimizing against. And we're going to optimize against something that's really straightforward, which is quality. And I think that will help accelerate the growth of that optimization layer, but do so in a way that benefits both our verification and optimization business.

Robert Sanderson from Loop Capital. A couple of quick ones. Just thinking about go-to-market on an authentic advantage, you're obviously selling a bundled solution -- does that elongate the sales cycle do you think? Do you -- is it more of an upsell product to existing customers in its early innings? Do you continue to focus on point solutions for new customers, sort of how do we think about any changes to go to market?

I mean you want to talk about this, Steve, yes.

Yes, sure. So it actually does the opposite, right? When we think about engaging with the customer and working with them in the social platforms, there's a lot of things that they used to be able to do with us. They would have measurement they would have prebid and then we would be doing Scibids. The bundle actually consolidates the sales cycle into one actual motion. So it's actually streamlining the process and making it easier for customers and for us to actually engage them on the topics because they could only get so many things done on their side. So the consolidation is actually speeding things up for us quite a bit.

And then maybe a follow-up, if I could. So we heard Ron talk a bunch about just enthusiasm for combining DV data into Rockerbox, and it sounds like it's a pretty powerful combination. What are we expecting in terms of timing? And do we think this is potentially a material catalyst for sort of kickstarting your efforts and performance.

So on a -- we've already started on a beta base is combining optimization and Rockerbox and through the Solution Performance Advantage, which we'll launch later this year. We are already starting to provision certain data sets into Rockerbox, and we'll continue to do that over time. I think to have the 2 data sets fully engaged, we're probably looking at 12 to 18 months realistically. But I think out of that, what we're trying to show is that -- these are not just integrations for integration's sake. These are integrations that drive value, but also have products behind them, right, and products that we can sell. And I think that's what our thesis has been between all of our acquisitions, which is we're not building a bundle of goods that are separate that we go out and try to sell. We're building an integrated solution that each piece where 1 plus 1 will equal 3 when we talk to a customer, and that's authentic advantage, that will be performance advantage when we launch it. And when we start provisioning data back and forth, all of this will have greater value than the individual pieces.

Arjun Bhatia from William Blair. Maybe if I can follow up on some of the bundled solutions. I think it makes a lot of sense how it makes the conversation easy with new customers who are evaluating you versus a competitor. What does it do for existing customers? And is there going to be some sort of migration maybe to bundled solution from customers that are using the traditional products? And then I have a quick follow-up after that.

I'll take it from the top view, but again, Steve's in the trenches, I mean, I think it's important to think about how the cola frame this attach stack and scale. So current customers are in that attach bucket, right? They're there. We've always tried to stack solutions on top by selling another one and selling another one. This becomes much easier to do because it's an integrated solution based on something they're already doing. And going back to Laura's question is, if we can do that with a pricing model that makes it pretty seamless and ensure that there's ROI behind it. It's made the go-to-market quite easy as opposed to saying. Hey, I want to sell you something else that's going to cost more money. We're going to sell them something that's going to save them money and drive an outcome and do it on a pricing model or a bundled pricing model that makes sense for them. It's actually lowered, I believe, the barriers to upselling. And Steve, you're in the market, so maybe you can talk about some of the recent discussions you've had.

It's -- It's very similar to the way that we always approached ABS. And one of the greatest advantages that we had with ABS is that we had the measurement data and we understood the challenges that the clients had. And so we could sell a solution directly into the data that we had. On authentic advantage, it's actually the same exact thing, right? We've got a huge installed base of clients that are already measuring on YouTube and all the other social platforms, but specifically on YouTube. And we can see what their quality metrics look like today. So we already have a really good handle on which clients we can add value to out the gate. And so all of those conversations are in motion. So I think having the baseline measurement is a huge advantage, and then we can obviously predict how much value we can add through the solutions that we're going to add to the bundle.

Okay. Perfect. And then maybe a little bit more forward-looking. We obviously kind of hear a lot of what's happening with search and search volumes maybe moving to AI chatbots. How do you think about that opportunity? Is that -- are those conversations you're having with the AI search companies? And if there is more -- if there are more ad-supported models that get rolled out. How quickly might you be able to integrate and monetize them?

Yes. So I mentioned kind of in the ubiquity discussion at the beginning that wherever advertisers are spending and wherever ad dollars are going, we will be there. We started Open Web. We have evolved to social, then CTV, retail media an ad-supported AI is next, and we're in conversations with those companies today because the one thing that hasn't changed from media to media is advertisers' demand for independent insight and independent verification. That's the role we played. If you remember when Netflix who was never going to sell ads, just like every AI agent platform says. We are never going to sell ads, right? Many of them have come out and said that's the stupidest thing I've ever heard of. I was on a stage 7 or 8 years ago in a video conference and they're like, "I've made the calculation. I said, Netflix will sell ads in the next 5 years, right? And it was like you're crazy. They've already said, their CEOs never said what do they do sell ads. But the reason I bring it up, the first thing they did before they sold ads as they engaged us, another company on verification and Nielsen because they knew it to go out and actually sell ads, they needed to have third-party verification and third-party insights. We believe that will be the same pattern that we see with AI platforms that are going to be ad supported. And I think it's an exciting opportunity for us as well. It will change the dynamics of the open web. But Again, we see spend moving to social. We see spend on the Open Web continuing to be robust. But as AI takes over, it's an opportunity for us that we're going to be leaning into pretty hard.

We have time for one last question.

Thank you very much for the presentation today, very helpful and I appreciate the chance to ask a question. I had 2. One is kind of a clarification for Nicola. Just -- when you outlined the incremental opportunities in your presentation, does that take into account the new or modified bundling and pricing strategy? Or is that on discrete products? I guess that's the first one. And then the second one is just on CTV. I guess I would love to ask everyone up on stage. When do you think CTV will be more of a performance channel relative to what it is today?

Yes, I'll start on the first question. The reason you saw ranges on the opportunities because we're still figuring out the pricing. So we're moving into a situation where we're able to provide different pricing depending on how the client wants to go after it, right, especially if it's an existing client already spending on an MTF basis and they want to bundle it. So the reason there are ranges because we haven't yet set the exact pricing, and we are moving towards being able to provide more than one option depending on where the client is coming from.

Yes, we're already -- I mean, if you watch CTV, profile of advertisers that you're seeing on their direct-to-consumer advertisers are spending a lot of money there. So that's already starting to happen and we expect that to continue. One thing that will -- I think will accelerate it as you've probably seen some of the new AI video generation tools. So it's going to make it easier for many more advertisers to access that. And the pricing dynamics are also changing as well as more inventory comes online. So all those things in convergence will make it attractive to performance advertisers and then our ability to measure in those environments, which increases all the time. Means that we will not only be able to protect it, but also show that full funnel through proof.

I think the [gauntlet] was dropped when Amazon decided to get prime members and of them into advertising, the supply-demand imbalance totally shifted and the largest retailer online retailer on the planet who can close the loop and show the performance of CTV ads basically said, this is now a performance media. And everyone since then is now playing catch up. The great place where we sit is we now have performance date metrics through Rockerbox that can show that. We get verification metrics through our relationships directly plugged into the CTV platforms. And I think it's inevitable that advertisers, as Jack noted, as they get tools, we'll be moving down funnel. CPMs will start to compress but be driven much more based on what they deliver as opposed who they delivered.

Yes. The -- just one thing I'll add to that is that within Rockerbox story, we can measure linear TV and CTV. So we can actually track that migration and what it means from a performance standpoint.

All right. Well, thank you all. Appreciate your questions. Team will leave and we'll leave -- we have 3 minutes left to do a close before they turn the lights out on the big stock exchange. Thank you very much. Okay. It's been a long day. It's been a long day, we've gone through a lot. And the first thing I would like to do is thank the great crew here from DV who have set this up and worked tirelessly led by our Champion IR lead Tejal, who you all know, so thank you. In a sea of boring blue suits standing up here with receding hairlines. She stands alone in her wonderful red jacket and amazing insights. So I want to thank her. Look, we said we wanted to walk away from today with some key takeaways for all of you, as all who are stakeholders as partners, stakeholders as investors and stakeholders and people who cover us. If we haven't beat this drum enough, we have got a strong core. As Nicola noted, the raising guidance, the optimism through the end of the year has been solid, and we see great momentum behind that. The second is that great core is helping to fuel a performance evolution into new solutions that as we also showed, will have incremental revenue value, but also help drive our core wins. We are winning business for verification based on the fact we had a broader platform that is also increasing our TAM and the opportunities that lie ahead. As our CTO and CPO and Chief Innovation Officer mentioned, AI is not a competitor. AI is a tool for us to use to make our business stronger. That tool learns and breathes off of great data, no one has greater and broader data to train from in a smart way of doing it than DV. And finally, those things put together create a formula for future success, from analyzing every piece of media that goes across social or the open web or mobile, which is our goal, to going into emerging platforms like ad-supported AI. Our capabilities and our customer engagements gives us the power and interest and focus to do so. We think we're well positioned to win, and we're glad that you're all here as part of that story. Our story is not over. Our story is just beginning, much more attach, much more growth, attach, stack and scale is the future of DV, and we're excited to have you all there as part of it. So thanks very much for joining us today. We have demo stations to show things to you all later. You can do that while you're drinking a cocktail, which is amazing because the demos come off so much better after a bourbon. And again, thank you all for joining us. Appreciate it.