All right. Good afternoon, everybody. Thanks for joining us this afternoon for Clarivate presentation from Clarivate. My name is Andrew Nicholas, and I'm the research analyst covering the information services, consulting, and HR technology sectors here at William Blair. Before getting started, I'm required to inform you that for a complete list of research disclosures or potential conflicts of interest, please visit our website at williamblair.com. With that out of the way, I'm very pleased to welcome Clarivate CEO, Jonathan Gear, to the 44th Annual Growth Stock Conference. Thanks, Jonathan, for being here, and I'll turn it over to Jonathan to give a brief overview before we do some Q&A. Okay, great, Andrew. Thanks so much. Thanks for having me back again this year. It's great to be in front of so many of you again. I'm gonna kick this off and just walk through a few slides for those of you who don't know our story or don't know where we are in the process, just to kind of set the stage for what I hope should be a good set of discussions this morning. So we look at Clarivate. You know the story about Clarivate. We're a highly recurring revenue business operating in three different segments with significant moats around those segments. That being said, each one of the segments is we're focused on addressing one or two key pieces of the business to turn around this business and get to the point of driving consistent, very solid organic growth figures. On the next few slides, I'm just gonna walk through each of those segments just to remind you of where we are in that journey. I'm gonna start with our Academia and Government segment. We call it Academia and Government. It's primarily academia, where we're serving the world's largest research-intensive universities. We also serve the government side around public libraries and the like like that. And we have three really core pieces of our portfolio. Our research portion, we're helping to support academic integrity, which is core and critical around driving tenure decisions, funding decisions, promotion decisions. It's actually, it's the piece I get the most inbounds every day from professors around the world, asking me to double-check their research, as we help the industry understand what's happening on academic integrity. Our content business, where we aggregate and pull together content that is consumed by libraries globally. This is both physical books as well as primarily e-books, e-journals, and e-content. And finally, our workflow solutions. This is our SaaS-based solutions that both academic libraries and public libraries operate their entire systems on. These are the core systems that drive and enable libraries to happen. As you look at through the journey of this turnaround, we got started with a decent place where our content and workflow solutions were driving fairly close to market, workflow a little bit under, but we had one issue we needed to address to get to the market growth rates, which in this segment should be around 3%-4%, and that was in our research sub-segment, primarily the Web of Science solution, which many of you will know that name. And this solution certainly had underlying incredibly strong content. It's viewed as the gold standard content underneath this solution that helped drive research integrity. However, the actual application on top of it, the solution on top of it, had become very dated and underused. We began heavily investing in this solution really in late 2022, late 2022, and this is something I stood in front of you, Andrew, last year and talked about this being our first of our key examples of being able to turn around and accelerate innovation. Where in Q1 of last year, we talked about this business turning around, this business which had been in a flat, slight decline mode for a few years. The lift we're seeing in usage rates, our ability to capture more market share, and that is well on the path we see to getting to the right market potential. A few more executions to come to this year, a little more next year. With that and the tailwinds we have there on research, with the execution we already have in content and workflow solutions, we feel very good that our Academia and Government segment, which represents about half of our business, is well on the path to getting to its market potential, again, 3%-4% organic growth rate. If I move now on to our second segment of Intellectual Property, this is where we support our customers, both our IP attorney customers directly, IP firms, as well as corporate IP attorneys, as they protect their core clients, either the corporate clients or the individual clients' core assets around patents and trademarks. It is a tremendous industry that is, again, very recurring in nature. We're a market leader in this space. This is a segment which, for us, the market, going through some slight ups and downs, but the market should be growing consistently around the mid-single-digit organic growth rate. This is one for us, which certainly has underperformed us in two areas the last few years. Three different types of solutions that we provide here. First is around Patent Intelligence. These are workflow analytical tools that are used in the, again, in the patent attorney space that help them understand when they're doing a patent search, if they have a client to understand, "Can I patent this idea? Where is there space? Where do I perhaps have to license other patents to create a product?" Fundamental tool there. It's also used when you have existing patents or trademarks to watch and monitor what is happening around the world. So you can see if someone's made an application in a patent trade office that you believe will infringe your core asset, we're providing the intelligence to alert them to that so they can form appropriate defense around it. So this is an analytical tool set. We have the best underlying patent database in the world, the Derwent Patent Intelligence database. It's a phenomenal database, proprietary, that's been developed over decades. That's the good news. We also have some tools on top of it, Derwent itself, the workflow tool, Innography, and IncoPat, which is our China-specific solution, that had become tired and stale. And as a result, in our customers, we had been losing share. We'd been losing share for a number of years as customers, as we were losing wallet share within customers, as some of these, as some of our smaller competitors were rising up without the benefit of our proprietary database, but much easier to use tools. So just as within Academia and Government, we focus heavily on Web of Science and improving it. In IP, our key innovation space has been around these collection of tools in patent intelligence. We had several key milestones in Q2 as we launched four key elements of the refresh. We had a new beta around the Derwent solution, a new search tool going out to all of our Derwent customers globally. So a significant refresh of that platform. We're seeing an improvement and a lift in our renewal rates as a result. We shared in the Q1 call, 200 basis points improvement from our own internal plans of what we expected to see on the renewal rate. So great progress taking place there. We also launched 3 new alpha products. These are specific new products, also in the patent intelligence space, which will be new revenue opportunities. Each of those has gone off to about 20 or 40 alpha testers. We're getting their feedback right now, hope to convert that into market launch sometime in the second half. So we feel very good about this key element of revenue for us. It has to be... That is critical for us in improving, improving this portfolio. Look forward, Andrew, to sharing with you and the street more to come in our Q2 call and Q3 call as these key metrics, as we see this business turn around. The other two elements I'll just mention briefly is management. So here we provide workflow solutions that help people, again, corporations or attorneys, literally manage their assets. So think about these as the core workflow tools that they look at their total portfolio of trademarks, of patents, understand what's up for renewal, help them make decisions on what to renew or not. We're a market leader for this. You'll see green here. This has been across all of our Clarivate, our top-performing product line for a number of years, and we continue to expect to see that continue to perform well. Finally, in maintenance, this is our annuities line. You'll see it as we've reported. This is the solution set where we renew patents and trademarks on behalf of the clients globally, across every country in the world. This has been a, I would say, if we put a slight green, slight underperformer versus market, part of it was a slight slowing market the last couple of years. We are seeing that begin to stabilize and do expect a recovery here, second half of the year and beyond. So again, as we look forward to 2026, the key product piece we need to fix, heavily focus on, is that set of solutions around Patent Intelligence. And again, this most recent, or the quarter we're in right now, Q2, has been a critical quarter as we have launched those new solutions. Finally, moving on to Life Sciences and Healthcare. This represents around 20% of our total revenue. And in Life Science and Healthcare, we are supporting the drug development process, both with pharmaceutical solutions as well with MedTech devices. That process includes all the way from the very beginning, what type of pharmaceutical or therapeutic solutions do I, as a biotech or pharma, want to invest in? What's happening with my competition? Where is there space for me to land a solution? Once that decision has been made, we support, through information and analytics, the content required to actually create that therapeutic solution. We take that through to support clinical trials, through to market access, which is getting governments around the world, insurance companies, to agree to cover these solutions. And finally, once that's done, through market launch and help with the commercialization. Underlying that are the core databases and content and solutions we have created over years to help support these processes. We look in terms of three buckets here. First is around corporate. This is around the strategic biz dev decision. Do I want to enter this market with this type of drug? We're gonna leverage the Clarivate Cortellis competitive information database, as an example, to see where I have space around that. Research and development is around, "Okay, I've made a decision to enter this space. How can I get it done? How can I get it done?" They're, again, leveraging our content around this. And finally, is commercialization. Now, what you'll see in terms of our progress right now is in both corporate and research and development, largely including what's under our Cortellis portfolio suite of products, is fantastic gold standard content, where the interfaces have become sleepy and frankly, a little underinvested in over the years. We've been investing in this. We made a great small but very mighty acquisition called MotionHall in Q1, bringing on board with us some great AI capabilities, some great product leadership. We're injecting that into the portfolio right now, and we believe with some modest effort there, we'll be able to lift those two pieces. You'll see on the very bottom, commercial. This is predominantly around our real-world data solutions. Real-world data, there are really two ways you can see the efficacy of a drug. One is in the lab, so that's clinical information. The second is what happens when it actually comes to all of us, comes out into what's called the real world. We're one of the leading providers of that real-world information, where we collect information from medical records, hospital records, insurance records, various pieces, bring it together in an anonymized basis, and are able to track the actual efficiency and effectiveness of drugs through the process post-launch. It is a very, very important tool, critical right now to the pharmaceutical and drug industry. This is a tool set where we've had this great underlying data... as I've shared with many of you before, we had a flawed commercial strategy a few years ago coming into this of collecting data, doing modest enhancements, and then selling it onto other data aggregators who were then building the enhancements, building the workflow tools about it, building the analytics, and selling that enhanced solution on to the ultimate user, which is pharma, either pharma or biotech. We made a pivot about a year ago to move away from that, to invest ourselves in that product, make sure we were creating that platform, and we have two very important milestones we've announced so far in Q2 of 2022, two very important, top 20 pharma wins in this space, where we've sold this solution to, to top 20 pharma companies. Very important validation to us that we've got this solution right. Look forward to rolling that out to the rest of the industry. When we roll this together, this is our, our solution, which today is about, again, about 20% of our revenues. Of the three segments, this has the most, the highest ceiling, if you will, of growth potential. We get these. When we get these pieces right, we expect this to be growing high single digits. You bring those pieces together, that would bring us together the total Clarivate story of getting 2026 to kind of that 4%-6% range that we have indicated. So, Andrew, with that, maybe we'll go to Q&A, where you can kind of unpack any questions you may have. Sure. Maybe just to start to wrap up some of those slides. So you have, you know, 4% type market growth in A&G, 5% or mid-single digits in IP, and then high single digits in LSNH. I mean, what does that ultimately get you to in the aggregate? And maybe you could talk about kind of the financial attributes of the business, because this is, you know, a business with a ton of data assets. These are higher incremental margin businesses when they're growing the right way. So just to kinda wrap up the- Sure ... the financial profile a bit. Sure, will do. So if we add together those pieces, and you, you have those components exactly right. A&G should be bobbing between 3% and 4%. Very predictable business, and the, the least, least amount of work to do of the three segments. IP should be around mid-single digit, and as I mentioned, life science should be high single digits. You get that together, you get to a growth rate blended of 4%-6% in any, any given year. Now, the attributes of this business is the, the space we're in, and you know it so well, Andrew, we've worked together for years. In information analytics, it is a remarkable financial model. When we're growing at mid-single digit organically, the beautiful things about this model is it requires relatively low CapEx, very high EBITDA margins, very high incremental growth on EBITDA margins, very incremental growth rate on top of around 3%-4%, and then very high free cash flow conversion. So the model, when I pull this thing together, is when we're performing in the mid-single digit on the organic growth rate, that's gonna kick out an EBITDA growth rate of high single digits. That'll kick out an EPS growth of low teens. And it's this remarkably predictive model once we get to these three green buttons on every slide. Absolutely. And maybe I'll ask you to switch back to some of these as we go through them. But just to stay on that topic, so you have, you know, a really attractive financial model. You have, you know, a clear set of goals and a path to get each one of these segments to a certain level of growth. I mean, can you talk about capital allocation as you're embarking on that set of paths or goals? Because I know there is some leverage on the business. You have some investment needs, organic, particularly around gen AI, which we can talk about. Ultimately, would you expect kind of the capital allocation to veer more towards M&A over time? Or if you could just kinda talk about the path over the next couple of years there. Yes. Sure, Andrew. And I've been at the company, running the company for almost two years. And when I came to this company two years ago, we had a challenging capital structure. Our leverage was quite high, kind of over 4.5 times on a net basis. We had a convertible pref. There was a lot of kind of noise in our system. Our, the pref, mandatory convert, converted this past Monday, so we have that noise now kind of off our balance sheet. And my message coming on here on day one, when I joined, is we had to focus on getting our debt down. We have zero issues servicing our debt. Zero. We generate, with the pref now gone, the pref dividend gone, close to $500 million of cash flow a year. So there's no real issue from an economic basis. However, it's a screening issue, and I think it puts questions in the mind of several investors. So the first commitment was to get under 4x. We exited Q4 last year with our net debt under 4x. I do think the arrival point for this company in a steady state is right under 3x, a good place for it to kinda hang out, 2.9, something like that. Now, given where we are right now and given our first commitment being done, and myself and the board, we're looking at all uses of cash at this point, including buybacks, including continue to M&A tuck-ins, like the two we did in Q1. And so we'll continue to kind of focus on those areas. Perfect. Maybe one more big picture one, actually, maybe a couple before we get into- Sure ... your underlying segments. You know, you mentioned, I think it's, it'll be a two-year anniversary in a couple months, if I'm not mistaken. Can you talk about a little bit more about maybe what you're, you're happy with in terms of the accomplishments to this point, and what you're most focused on from an execution standpoint? And I know we can talk about the individual segments, but maybe at a corporate level, from, like, a sales force growth or refinement or go-to-market strategy, things of that sort, that you're most happy with executing on over the past 12, 24 months. Sure, will do. So I mean, it's a very different business from the one I joined 24 months ago, or 23 months ago. Very, very different. I think the key thing is when I came in here, I mean, I'll say, what are some good things that have happened? A collection of very good assets had come together, and, you know the story. For those of you who don't know the story, Clarivate was a former Thomson Scientific business... roughly $1 billion in revenue. This business tripled in size from 2019 to 2022, all in the middle of COVID. And so it had work to do. It had work to do on the integration. I joined believing that focus was gonna be on product innovation. It ended up being that, plus some work being done on how we served our customers and how we're structured. So I feel very good about the steps taken to restructure the company. I restructured the company around these three core segments that we just went through here. Huge benefit in terms of customer touch point. I think we were, quite frankly, damaging some customer relationships with the approach we were taking before, where we lost the alignment internally between sales, product management, operations, customer care, around these three segments. We rebuilt those, brought in some great leadership, both leaders of each one of those divisions. They themselves have brought in the teams underneath them. We've elevated the entire bench across the company. Feel very, very good about that. Feel great about the clear progress we made in Web of Science, the fact we had the line of sight on that one. Feel very good about. Again, Q2 was critical for us. Critical for us in terms of product launch, Patent intelligence with those four products I talked about. Critical for us in getting the affirmation on the work being done in real-world data. It's great to see the affirmation from the market that we're making progress there. So I'm feeling good about the progress. I mean, there's some things I wish we could, of course, be moving faster, any CEO believes we wish we could be moving faster around those pieces. But we're in a much more stable place and much more positioned to provide those steady, predictable returns than when I joined two years ago. Great. So maybe that's a good segue into Academia and Government, which, to this point, and really, as long as I've been covering it, has been among the more stable pieces of the business. Yeah ... you have up there research, content, workflow. I think it's well understood, and we've written some research on kind of the market share that Web of Science has as a kind of linchpin within the industry. So I want to spend a little bit more time on the workflow tools and what you got from ProQuest, both specifically from, like, a software perspective. Can you talk about maybe some of the solutions that you have, what you sell into that market and your right to win amongst kind of library information systems and the like? Okay, great. So, yeah, so with our work— And that's, you'll see it under workflow, that last line there. These are our solutions that literally, if you walk into a library, an academic library or a public library, they should all be running on our solutions. Not all are, but, but, certainly they should all be running our solutions. These are absolutely workflow-driven, SaaS-based solutions, industry-leading. And, it was interesting, three weeks ago, I was in Minneapolis. We had our user group from this group of products, and I think it's very indicative of the type of impact we have. It's a user group. It's run by our users. It is literally their conference. We had 820 librarians or universities represented from North America. We have a separate one for Europe and Asia. Just the passion and sense of ownership they have over these solutions kind of is indicative to me of how impactful they are, how sticky they are, and just, just how critical they are to the core, core workflow. Again, we're the market leader in this space. We continue to see opportunities both to convert legacy platforms we have onto Alma, our leading SaaS-based solution. We see opportunities to continue to roll out additional kind of adjacent workflow tools around, for instance, lending collections, having solutions so that libraries around the world, they, they can't own everything. And so they enter an agreement saying: "Okay, we'll own this. You down the road, you own that. We'll kind of cross, cross-leverage tools." It, it requires a technical solution to do that effectively. Solutions to be able to acquire content. We continue to have ways to chip away at expanding what the universe looks like within our existing customers. We continue to capture new customers. We talked end of last year about some key new wins we have with some high-profile universities as we roll this out. It is a tremendous solution, and I invite all of you to drop into your local library and take a look at it. Great. I think this is one area where there's a lot of investor interest in terms of the Gen AI opportunity. So maybe that's a good segue to talk about generative AI, specifically here, but also across the firm, because we only have a handful of minutes left. Can you talk about kind of the investment that you've made to this point, and maybe even bigger picture, how you think about its impact on data businesses more holistically? Sure. Because you've even before leading this company, you've been running them for quite a bit longer than that. Well, I mean, I mean, if you harken back to a year from today at this very conference- Yeah ... the world went crazy. It's when ChatGPT was kind of unleashed, and there was panic about what it meant for companies like us. And I think at the time, Andrew, I talked about a framework, how I think about how Gen AI tools can be helpful or hurtful. And it comes down to this: if you own the content, if it is your proprietary content, which it is in our three segments, this is a tremendously helpful and important tool for us. We've been using AI tools forever. We could not exist without machine learning, without similar AI tools along the spectrum. We couldn't, and nor could other information businesses like us. So this wasn't exactly new to us. Now, what Gen AI allows us to do for our clients, there really I see two huge benefits. First, there's a big benefit. It allows our clients, as we embed these tools into our solutions and into our processes, and in fact, some of the new solutions I mentioned in alpha release in Patent Intelligence are specifically driven by these new Gen AI tools. It allows our clients—it allows us to provide a way for our clients to unlock value at even a greater way, to be able to find the content they're looking for in a much more faster, much more efficient way. It lifts the value of our content... and I think you'll see that at us, you'll see that at all the peer groups, Andrew, that you cover, is a tremendously additive tool for us. And so we're pursuing it. So like in the world of Academia and Government, and I'll speak for this, I have two sons at university right now, one great way AI can be used is a way to become a virtual specific tutor. And we bought a great company, which has Alethea, which has this ability to create very specific tools to help a professor work with their students, their 20 students, test them. But as the students ask the question, the next set of questions morph and change based upon the first answer. And really a way for both professors and students to test their knowledge or their underlying capabilities and understanding, leveraging our workflow tools and leveraging our underlying content. To me, it's, it's just going to be a great, great way to make our solutions and tools all that more valuable. The other piece of Ledger, which we don't talk a lot about, but it does drive efficiency, is a way for us to continue to maintain, unlock value, to be able to invest back into our products, and we're seeing significant opportunities to leverage that also across our organization. I'm going to ask a Gen AI question a little bit different way, 'cause you alluded to kind of the hype that we saw 12... You know, a year ago, a year, a little bit over a year ago. I'm curious, like, from a technology perspective, from a cost perspective, from a kind of monetization perspective, how does your perspective on all those three kind of levers compare today to what you thought then? Yeah. Is it, is it more expensive? Is it less expensive? Is it more impactful? You know, just kind of curious how your perception of the technology's capability and potential impact has evolved. The world has learned a lot on the commercial application of some of these models. We use the term Gen AI. There are slices of what that means underneath it. First, in terms of just AI tools that we're using, those can be developed by us internally. We can partner with other people if we want to on a buy or build, develop, and then we own those, we roll those out. Those are like any type of SaaS solution. Very scalable, very, very cost efficient, very easy way to unlock value in a very margin-accretive way to us. I think the other bucket that gets a lot of noise in the world is around LLMs, and those I would put in a different bucket. And even within that, you have to carve between LLMs that we may use internally, public LLMs, and then client LLMs. So a lot of our content that we do today, I'll take pharma, for example. Pharma as an industry is one of our biggest end industries, and we're able to take our content, feed it into their own private LLMs, that they're co-mingling with content they have, and elevate the decision they're making around what drugs to bring to market, their pace of change and innovation, all the pieces we can help with, sitting within their own private LLM. So it's a way to enhance investments they're making around those LLMs. When I look to a public LLM, it's a different story, and I think that's where content providers like us have to be enormously careful and prudent on, on how we, how we do it. Because if we're using our content to train the models, there is a risk of leakage of our own proprietary content. And in all three of our segments, you'll see the foundation of what we have are these great gold standard proprietary content. So we protect those. We protect those. We're very cautious around those. The third element is on using LLMs themselves, and they are crazy expensive. Crazy expensive. So one, one of the output releases we're doing right now does leverage a third-party LLM that we're using. One of the reasons we're laying it out very carefully is just to understand the usage and the cost that's going to come back around this and get that balance right of how much of the cost the clients are willing to absorb for the benefit that they're seeing. You see three elements. There's private LLMs, there's making sure companies like ours are protecting their core assets and training LLMs, and then there's the cost of using LLMs yourselves in these products. They are today quite, quite pricey. I do fully expect, like anything, they're going to come down in price. They're going to come down. Do you feel like you guys are kind of leading the way on the AI and Gen AI front relative to your peers? Or how would you put our stack, kind of your peers, without having to name names- Yeah ... their investment and kind of aggression as it relates to leveraging those tools within the products they have? It's a tricky question 'cause we've. The truth is we've all been using AI for a very long time in many, many facets. So it just... Again, a year ago, it went crazy, but there's a lot of hype. We're all using them. We're being very careful of not getting out with hype over substance in terms of our tools. I think our clients appreciate that. We're all investing in these tools in our normal CapEx spend. So I don't see and it, yeah, it's going to be very hard for any one person to get a competitive advantage based on the tools themselves. The competitive advantage is in the content. That is where the moat is. That is where the moat is. And so if you go through the companies you cover, that are pure companies, either in adjacent or just similar business model spaces, I always tell investors, "Look at the proprietariness of the data. That tells you the moat that, that we have and our competitors have. Great. I'm not sure. Do we … I thought we were at 2:30 P.M., but do we have more time? It is 2:30 P.M. Okay, maybe one more to wrap up- Okay, great ... just because I do think it's a good segue to wrap up. So you talk about the proprietary nature of your data. The real-world data sales is a huge initiative, a huge part of the growth- Yes ...acceleration story. Can you talk about how that data is different relative to your peers, or what makes it unique, and what makes it, in your opinion, you know, a high demand product when it's all said and done? Sure, sure will do, and I'll comment a couple of things on this. First, on the data, second on where we are in that transformation from a financial lens. So we, we're one of, I don't know, three or four large providers of real-world data in the industry. There are others as well, but I would say three or four large ones, and we've each kind of captured a certain space. Because the underlying data we have, if you Venn diagrammed our data, core data versus others, they're not gonna be on top of it. There's gonna be some overlap, some differential, and that differential matters on where someone has chosen to focus. Some have chosen to focus, for instance, on the clinical trials process. If you're a CRO, and you have CRO capabilities, and you've built out an RWD capability, that's gonna kind of be your sweet spot. We have focused primarily on the commercial end of the datasets we have collected. We have leadership there, and we believe we also have abilities to focus on a few very specific therapeutic areas. No one's gonna have all therapeutic areas. You kind of tend to focus on those pieces. So, each of us has carved out kind of our particular niche in the market. Again, the two wins we have, which were head-to-head against those competitors, we feel great about as the first of that size of scale that we've had. I'll comment quickly on the model. As you think about this model, historically the model that I inherited, that we've changed, was a model where we took this data, did a little bit of enhancement, and then we sold it to others who did the value add. Financially, it looked great in quarter, because we would get a one-time drop of seven-figure revenue coming in quarter. From a long-term point of view, it was terrible. We were enabling competition. We were moving away from our core promise to shareholders, which is predictable, recurring subscription model. So the new model we're focusing on and building is indeed a recurring subscription model piece, which, as we build it, will be much more predictable than what we've had in the past. All right, great. We'll wrap it up there. Thank you for joining us, Jonathan, and for those interested, we're heading to Gen AI for the breakout. Thanks again.