Good afternoon, everyone. Thanks for coming to our conference and this fireside chat. My name is Kelsey Zhu. I'm the research analyst covering the financial information services space here at Bernstein Autonomous. With me on stage, we have the Founder, CEO, and Chairman of ZoomInfo, Henry Schuck. Welcome, Henry. Thank you so much for joining us today. Yeah, absolutely. Thank you for having me. Before we kickstart the session, I'd like to just remind all the investors that there is a Pigeonhole link for you to submit and vote for questions, and I'll try to incorporate them in our session today. Henry, I know you know, started the company back in 2007. Can you just kind of walk us through the ZoomInfo story, where you started 16 years ago, where you are now, and where you envision the company will be in the next 10 years? Yeah, sure. When we started the business in 2007, the idea was, the market opportunity we saw, was that sales and marketing professionals actually didn't have the data on their prospects or customers that they needed to grow sales, to find new customers, to prospect into new industries. You would kind of imagine that large companies, sort of somewhere organically, have a database or data asset that has high-quality information on their existing customers, then high-quality information on prospects that they should be selling to. It turns out that that didn't exist. It didn't exist in 2007. Today, none of that happens organically either. Originally, what we said was, "Hey, let's provide sellers and marketers originally at technology companies, with information on their total addressable market, what companies they could be selling to, then information on the decision makers at those companies, and contact information that would allow them to get ahold of those folks. Then let's keep it really up to date. Let's constantly cleanse it. Let's make sure that we're tracking when people move companies, when people change emails, when companies change." That was the foundational element of the software that we were building and the data that we were collecting. Over time, what happened was customers said, "We want sort of a broader set of companies. We want a broader set of individuals at those companies that we should be selling to. We want data that tells us not only that these are the companies and the people that we should be selling to, but when is the right time for us to be selling to them?" Over the years, we added a number of additional signal data points that help a company know when one of the companies in that total addressable market should be interested in their products or services. They said, "Okay, now we know all the companies, we know the people who should be buying our products and services at those companies. We know when the companies are in market for our products and services. We know when they're researching our competitors. We know when they're researching our products and solutions on the web. We know when they visited our own website. Now, we wanna automate the motion from that signal to some action. That action could be anything from sending an email directly to the buying committee, notifying the sales rep that this activity is happening, doing a display ad, or launching a display ad to that company, doing marketing automation or social media advertising to that company. Over those years, we went from, you know, strictly this data asset on company and contact information, and we built in signal information, we built in automation on top of that, and then we continued to add different channels that our customers can execute and activate communication with their customers from. The next decade? The next decade. The next decade is really taking all of... You know, well, first let's talk about kinda, like, the next few years. The next few years are really about being able to take those signals and making it really easy for customers to be able to see that. Today, a sales rep, you know, may have to go into the platform, set up their territory, look for companies that are spiking on what we call intent in their territory, look for companies that came to your website. You know, I've already built betas internally, leveraging generative AI, that can go and triangulate all of that information, and then build you a list of your next best customers. You can automate actually, or the next 10 best customers, take these types of actions against them, do the display ads, send the emails, make the phone calls, line up the phone calls to be made, and then learn from that. As you're sending those emails, as you're doing those display ads, get smarter about what type of messaging works with which customer. You know, does this messaging work best with VPs of IT at pharmaceutical companies with over 100 employees, while this messaging works through. The campaigns and the outreach get smarter and better, and you're getting more engagement from your customers. Got it. That sounds super interesting. The current macro conditions, I think, present a lot of challenges. You know, we've seen firms pulling back on hiring activities. We're seeing a lot of financial services firms pulling back on marketing activities as well. I was wondering if you can tell us a little bit more about how ZoomInfo can achieve growth in this macro backdrop. Yeah, I mean, I think the macro backdrop is kind of a tale of three tapes. On one side, you have our technology customers, and today at ZoomInfo, our lineage, when we started the company, was focused on providing this solution to software and technology companies. Today, about 40% of our customer base are software businesses. Those businesses, when you look across our customer base, they're the most affected customers. While in 2018, 2019, 2020, 2021, those were our fastest-growing segment of customers, today they are sort of our slowest growing segment of customers. There's the new business side and then the customer base side. New business for us has stayed relatively stable, and so we're still generating a meaningful amount of demand at the top of the funnel, and we're converting that at historic level, at the same historic levels as we always have, and the same efficiency levels. We feel really good about what's happening on the new business side. Then the customer base side, we're challenged by those software companies that, in most cases, are just, they're not upselling with us, which they've done historically, and in many cases are downselling their contracts with us. While gross retention has stayed the same and the customers are staying with us, in some places, they're actually contracting their contracts with us, particularly in software. You see that happening because a lot of these customers sort of came to us. You know, I have one customer in mind. Two years ago, they're a $100,000 customer. They grow to a $200,000 customer. They stay flat. Today, they went from having 200 sales reps to 20 sales reps. Because ZoomInfo is a seat-based model, that customer naturally contracts from 200 customers to 20 customers. While we're seeing that happening, we're also seeing a tremendous amount of success in non-technology industries. What we're doing today is making sure on the new business side and in the customer base, that we're focused on those industries where we're seeing good growth potential, and making sure that our account management and our account executive teams are focused on growing those customers. We talked about at the end of the quarter, our million-dollar cohort, customers that spend over $1 million a year with us. What you saw in that cohort was, if you went back a year or two years, you saw the companies coming into that cohort being software, software, software. Today, if you look at that cohort and the growth that we saw quarter-over-quarter, what you saw was a textile rental company, a financial services business, a pest control company, an insurance business, you know, another financial services business, and a couple of fintech companies. The makeup of growth looks different. Look, we're in the very, very, very early innings of companies actually modernizing the way they go to market. You know, I mentioned the textile rental services company. This is a company with thousands of sellers, where their fundamental go-to-market motion was, "Here's your territory. It's this section of Las Vegas. Go drive around that area, find the businesses, walk in, try to find the office manager or the CEO, and try to get them to buy products and services from us." That worked. It's also the least efficient way to acquire your next customer. All of these companies, and, you know, 60% of our business is in non-tech, which means, like, today, around $700 million of our revenue comes from the non-tech industry. Those companies are coming to a point where they're saying, "Oh, look, you know, it doesn't make sense that we're not using digital technologies to go to market, that we're not using data to go to market, that we bought CRM, but we never fully deployed it, because the data inside of the CRM doesn't just magically keep itself up to date." You're seeing those companies come to us and say, "We want to modernize the way that we acquire new customers and grow our existing customers." There's this tremendous amount of continued demand that we see. I think right now there's so much uncertainty about the macro, that that's kind of the worst emotion to have in a sales process, is that you just don't know what's gonna happen. As that fades away and there's more normalcy, we think we're gonna be in a really good place to re-accelerate growth. Just to follow up on that, in all the non-tech industries that you're seeing pick up in demand, which ones are you seeing the fastest growth? Over the long term, are you aiming for, you know, software eventually is going to be like, whatever, pick a number, 10%, 20% of your business, and you want to have a broader customer profile? Yeah. How are you thinking about that? Yeah, I think, you know, over the last few days, I met with numerous of our financial services customers. You know, what we hear so often with our financial services customers is, they did a major multi-hundred million dollar deployment of CRM, and then as they did it, they realized that they had no company and professional reference data plugged into the CRM. It's really interesting because it's not just non-tech, it's not just financial services, but financial services lately has been really heavy on deployments of CRM, so you hear this the most there. CRM, over the last 20 years, has just kind of been an open door for data to go in. Sales reps can put in data, marketing can do a webinar, buy a list, go to an event, put data into the CRM. There, CRM doesn't have built-in control to make sure all of that data is complete, to make sure that as people move jobs, as companies grow or do M&A, that all of that information gets reflected inside of the CRM. Data goes in, and then it immediately starts decaying. There's never been an infrastructural investment, which is necessary in between the database and the applications you build on top of the database. There hasn't been an infrastructural investment in a mechanism to keep that information cleansed, a mechanism to keep that information updated, a mechanism to help you keep track of your customers and where they're going, 'cause when you put them in in 2015, they're very likely not at the same companies and not in the same roles as when you put them in. There hasn't been a standardized play to say: We're gonna plug ZoomInfo in as an infrastructural element of our CRM deployment to make sure all of that information stays cleansed and up to date, so we actually understand about our customers. Lots of talk about, we wanna know our customers, we want a 360-degree view, customer 360, we want... No investment in actually, like, understanding even what company your customers are at, whether they're still in the same role or what the companies they work at look like. There's not a single company that looks the same on December 31st as they did on January 1st. How is that being reflected in the CRM system you're using to make all of your go-to-market decisions? Today, it's not. It's all static information that continues to decay. That's a major opportunity for us. When we're talking to our large financial services customers, insurance customers, large banking customers, that is what they're telling us, is that their commercial banking reps, their investment banking reps, their producers are telling them, "I'm not leveraging the CRM because the data in the CRM is not accurate. I know that the people that I need to talk to are not the ones that are being reflected in the CRM. That big $100 million investment that you made in CRM, it's gonna land flat 'cause I'm not gonna leverage it because the data and the insights are not accurate or current." That is gonna be a continued tailwind for us into the future, I think like, financial services, that's a major growth opportunity for us. I might steal a future question of yours. When we think about generative AI as in the marketplace, especially around go-to-market, motions, one of the things that's really obvious is today, if you go talk to any C-level executive, and you ask them, like, "What are the top five use cases for generative AI in your business?" Almost always in the top five, you're gonna find prospecting and customer outreach in those in that top five generative AI use case. In fact, you can go out, and there's, like, lots of videos about how you might leverage generative AI against your CRM to identify your next best customer, identify the buyer at that company to engage with, and do all of that, leveraging the data inside of your CRM. I've been doing this for 20 years. There's not a single company I've met that would trust generative AI against all of the data in their CRM system. There's not a single customer out there who looks at their CRM system and says, "Yep, all of this data is up to date and accurate, and we should be leveraging it even in automated ways where we can't see what's happening. We should just leverage it. We trust it. It's really insightful. It's really accurate." Nobody feels that way about the data inside of their CRM system. What we really believe is gonna happen is, as chief revenue officers, as COOs, as CFOs, go to try to implement generative AI in their go-to-market motions, they're gonna run right into a wall that says: Hey, we actually don't have the data for you to leverage to run a generative AI motion. That finds your next customer using your CRM data? Not possible. That finds your next buyer inside of your CRM to engage with? Not possible. We think we're positioned really well because we have this incredibly proprietary data asset that we can leverage to help customers actually get value out of any generative AI go-to-market motion. Got it. That's super interesting. Let's just stay on generative AI for a second. How does, you know, the evolving AI landscape change how you think about your competitive landscape? I was wondering, you know, you mentioned a few interesting tidbits about your data collection process. I know you have a very unique methodology in terms of how you collect and ensure all the data are accurate. Can you tell us- Yep. a little bit more about that process? Yeah, sure. I'll take the end of the generative AI question. Internally, you know, first, we think about the data asset as very unique in a universe where proprietary data becomes incredibly valuable. Second, just internally, there's a big opportunity to leverage generative AI to increase adoption, increase usage, and just make the platform more simple to use. If I'm a sales rep, I don't really wanna go triangulate a bunch of data points to figure out who my next best customer is. I want the system to just tell me and then narrate the reasons why. We think there are big opportunities to simplify the platform using generative AI. On the data collection side, you know, we collect data in a number of different ways. The two key ways are through contributory networks. The first contributory network is a freemium model, where people get limited free access to ZoomInfo in exchange for the contacts in their email system. The other is a contributory network, where our SMB and middle market customers share data from their CRM, share data from their marketing automation systems that we cleanse, validate, and send back to them. In that process, we gather exhaust data, we gather signal data, and we gather contact data that may not be in our platform, that we can then leverage and publish at the right time, and then feed a published version of that record back to our clients as well. You have these two really big contributory networks that are driving data collection at scale. We see over 100 million contact points every day across those networks that we're leveraging to cleanse the database to make sure it's incredibly accurate. You know, our customers demand two big things, the Holy Grail of our type of solution, which is really high accuracy and really broad coverage. I want every company in the world, and I want really high accuracy on everything you give me about those companies. The only way to really do that is to have a really robust contributory network, and we have two really robust contributory networks that are feeding our ability to be both accurate and broad. So what percent, of your data asset is truly proprietary and really hard to build? Yeah. That, so that asset, which drives our two contributory networks, all of that is proprietary and, you know, basically impossible to build unless you have that same type of contributory network. You'd have to go out and, you know, build a contributory network of customers and build a big freemium network. In order to build either of those, you have to start with the data asset, so you can't, like, work the other way. You have to have a really great data asset that attracts a community, a community of people who wanna contribute to get access to that data asset. You have to have a really big data asset to get your customers to contribute data that you cleanse and validate for them. There's a chicken or the egg issue there. That information is highly proprietary. There are... We have something called an IP to company graph. That IP to company graph, basically takes an IP address and can tell you that that IP address is, belongs to, Bernstein or Atlassian or Datadog or whatever the company is. We have, you know, this very robust database of IP addresses that we can route back to a company. That's really valuable for two big reasons. One, we have a visitor identification product, so when 98% of the visitors to any B2B website are anonymous. You don't know who they are. And 2% of them actually fill out a form, and then you know who they are. The other 98, they're just ghosts on your website, and you might have spent a lot of money driving a lot of traffic to your website, and you actually don't know who 90% of that traffic is. Because of this IP to company graph, we can take that IP address and then tell you, "That's actually this company, that's actually that company," and you can start informing your sales and marketing motion based on that traffic that you're driving to your website. That IP to company graph is also an instrumental part of the way that we provide intent data to our customers. intent data is basically our ability to tell you what companies are researching on the web. We can tell you, "Hey, in your total addressable market, these 50 companies have significantly increased their research on your competitors. They've significantly increased their research on key products and solutions that are related to what you sell." You can start ranking and thinking about who you prioritize, marketing and sales efforts with based on that information. All of that gets driven by this IP to company graph, which is built against the community network that provides a significant amount of that data, and our contributory network, that provides a meaningful amount of that data as well. Those are really proprietary data assets. Now, there are a number of data assets that we collect that are very hard to build, and require real expertise, and familiarity with B2B companies and businesses. That's like. You can think of that as our, hierarchy data. Today we cover 100 million companies and 260 million business professionals at those companies. In between the, in between those two contributory networks is an evidence-based machine learning engine that is making sense of all the data that comes in. For example, Cameron Hyzer is our CFO. He used to work at a company called Eze. Maybe tomorrow, somebody's CRM data says that Cameron Hyzer is the CFO of Eze. When that comes in, we're not just gonna go, "Oh, well, we saw it, so let's publish it." There's a machine learning engine that's in there that's saying: "You know what? We've seen this, we saw it before, but it was a long time ago, and we have all this other evidence that says Cameron Hyzer is the CFO of ZoomInfo, so that's not gonna tip the scale. We unpublish Cameron as the CFO of ZoomInfo and republish him as the CFO of Eze, of Eze. " All of that data that comes in is being managed by this machine learning engine that's helping us make the right decisions about what to publish and what not to publish in the database. But the company information, that hierarchy information across those 100 million companies. That information you can piece together, right? You can go get every Secretary of State tax filing across the United States. You can bring that all together, you can start putting together what are the subsidiaries of Coca-Cola, and where are they all based, and what employees are connected to those subsidiaries. You can do that across 100 million companies. It takes a lot of work, a lot of sort of like industry know-how to do, but it is doable without the contributory network. There are elements of the platform that are really hard to build, take expertise and understanding that are not driven by the contributory network. I would say that is easily the small minority of the data asset that we have. Got it. That's super helpful. Thank you. You know, when I think about data, immediately, I'm thinking about, you know, data privacy regulations and any changes there. You know, in the U.K., they have GDPR. In the U.S., we don't really have a regulation that's, you know, protecting consumer in the same way. How do you think about the risk there? You know, in your view, what's sort of like the biggest regulatory? Yeah, yeah. I think a couple things. One, like before the GDPR came out, I think in 2017, 2018, we decided that we read the regulations. We had a sense for what the regulations were gonna mean. We decided that if you were a European resident, that we were gonna go out and proactively give you notice that we had collected your information, what types of information on you we've collected, and then give you the opportunity to opt out of our database. We've been doing it. We started doing that just in Europe. We decided shortly thereafter that it made sense to just do that across the world. It didn't matter if you were in a GDPR jurisdiction or you were in California or the United States or Alabama, where there's no data privacy law. We were just gonna give you notice that we had collected your information, and we were gonna give you the ability to opt out of collection of that information. We do that now globally. We've done it now for multiple years. We're actually on our second pass of giving notice, not because... First of all, that notice is not required in the vast majority of jurisdictions, and certainly a second pass of it is not required either. We feel like that should be an industry best practice, and we should lead the way on that. We've always led the way on being a data privacy-first company. From a regulatory perspective, what you actually see happening, today in the United States, there are now, like, close to a dozen different data privacy laws. Every single data privacy law in the United States includes an opt-out for data in the commercial context or business contact information. We've seen enough of a trajectory there that every subsequent law is now copying the laws that are already on the books, oftentimes with the same grammatical and spelling errors as the laws in the other states. That's created a model for the future. In Canada, you see something called the PIPEDA, which says that the business contact information used for business purposes is non-sensitive data, and it doesn't apply to the PIPEDA. It's the only type of information that they've carved out as non-sensitive data. In the U.K., there was recently a decision by an appellate tribunal, where Experian was the appellant, where they actually spelled out that the process that we've been running in Europe to stay validated with the GDPR is exactly the process that they laid out for Experian to run. So we feel really good about the decisions we've made from a data privacy perspective. Last year, we hired Simon McDougall, who was the GDPR enforcement officer in the United Kingdom and Ireland, he's our Chief Compliance Officer in the U.S. This creates a major moat for us, especially in the enterprise. Because when we go into the enterprise from a privacy perspective, there is nobody around us who can, who's made the investments in privacy, who's understand the regulatory environments across the world and can help our customers comply with them. When you think about our customer base, Google, Oracle, SAP, those are customers who are really compliance and privacy first, and they've run us through all of their privacy motions, and continue to be really large customers of ours. We're really proud of the work that we've done there. Got it. Super interesting. Let's switch gears to talk about the competitive landscape a little bit. My understanding is D&B is also in this space. LinkedIn is also in this space. There may be a whole slew of smaller private companies where in the current environment, I don't know, you know, if their funding sources are still secure. Just how do you compare your data accuracy, data coverage, pricing, and services for ZoomInfo with D&B and LinkedIn? Sure. If we talk about LinkedIn, you know, LinkedIn has this incredible data asset as well, but it's all trapped on LinkedIn. I've never met a company that you go, "Hey, what's your go-to-market motion?" And they say, "It's just LinkedIn. We're just sending InMail and just managing our customers on LinkedIn." It's an important channel, a channel that we use, a channel that all of our customers use, but it is one channel to communicate and engage with your customers. Every other channel is unlocked with ZoomInfo data. You know, you wanna connect with people on display ads outside of LinkedIn, we can help you unlock that and build audiences around it. You want to email your clients, you wanna have accurate data on your customers inside of your CRM, we unlock that. You want a sales motion that calls your clients, we unlock that. All of the other channels that you wanna go to market against, we can help you unlock that, and then LinkedIn can help you unlock it on LinkedIn. D&B, we've historically not really competed with D&B. Where we cross paths is on company data, company hierarchy data, or company-specific data, where LinkedIn has long had like a credit and risk data asset that they've turned into a go-to-market data asset. We don't see them, you know, very often, and so where we're competing is primarily on company reference data with LinkedIn. I'm sorry, with D&B. Downstream, there have always been kind of a number of one-off solutions, where we might compete with one company on technographic data or data that we collect on what technologies a company uses. We might compete with another company on intent data, this sort of like, what are companies researching on the web? We might compete with D&B or others on company data. We might compete with others on contact data. The real power of our platform is our ability to bring all of this together in one place and make it really easy for our customers to have access to really a mosaic of that data in one platform. To try to get that same thing, you'd be plugging in 10 vendors from different places, trying to merge all that data together, match it together with company data. It would be really difficult to do, but that's kind of like what the landscape is. It's point solutions, it's. Mm. It's really point solutions, company data, and then LinkedIn's not particularly competitive. We don't really find customers who come to us and say, "Hey, I'm gonna buy this or that." They're usually supplementing what they're doing on LinkedIn with ZoomInfo. Gotcha. When you go out and pitch customers, you know, you tell them that, "You know, we'll help you increase your pipeline. We'll help you increase your marketing ROI." What's sort of like the common pushback from customers, especially in this environment, and how would you describe your go-to-market strategy? Yeah, it's interesting. You know, when you put ZoomInfo in front of a chief revenue officer or a VP of sales or head of marketing, the ability to understand the value that unlocks is incredibly quick. Like, Think about this textile rental company. You're going to market literally by driving around a neighborhood and walking into businesses. What if, instead of doing that, your rep at a computer can see all the companies researching textile rental, uniform rental, whatever else it is? We can tell you who the owner of that company is, who the CEO is, who the CFO is. We can give you their email and contact information. We can set up an automated email sequence and call sequence, so you can drive efficiency of your sales force, leveraging digital technologies, or you can drive around the neighborhood and try to walk into these offices. The light bulb goes off pretty quickly when we're talking to customers. Our go-to-market strategy has always been we have really quick sales cycles, sub 30 days, oftentimes much faster than that. Our strategy has been to land customers and then expand them as we show value, really expand them. If we use, like, you know, IBM as an example, we might sell to IBM in their storage division, and it might be just a $100,000 agreement. We sell that agreement, we start showing value in that storage division, and then we go over to the security division, and we say, like, "Look at what's happening over there in storage. Look at the value that we're driving for your storage counterparts. Let's add user seats and licenses for ZoomInfo over into the security department." We go from security to Watson and Watson to whatever else across IBM, until we get to a point where we kinda have $10, $100,000 agreements. We have a $1 million customer, and then we take that and go up and try to get an ELA at the corporate level and really expand it from there. It's been this land and expand strategy. The textile rentals business started off as a $50,000 customer in their first year in one division, in one geography, grew to a $200,000 customer the next year, and now today is a multimillion-dollar customer. That's why we really believe that enterprise opportunity is our biggest opportunity at ZoomInfo, because you see these large enterprise customers where we might be like, penetrated in one small division, but have this really big opportunity to continue to grow in them. Getting a precise, repeatable, predictable motion in the enterprise to drive that is really what we continue to be focused on. Let's dive into the enterprise customers for a little bit. Can you tell us a little bit more about how different the sales processes would be between enterprise customers and sort of SMB mid-market? Yeah. I know you've invested a lot in that segment as well. Could you talk us through kind of your expansion plan? Yep. I think, you can think about SMB and like the lower half of mid-market, they act largely the same. They're quick sales cycles. There's one decision maker. That decision maker can make the decision for the whole company. You're kind of like a inside of a 30-day sales cycle for those companies. When you get into the upper end of the mid-market and the enterprise, and you're trying to do something that's larger, it actually takes a different type of sales motion. A sales motion that requires sort of getting collaboration across multiple business units, bringing people together, plugging into an initiative often. Often, what we're finding more and more at our large enterprise clients, is there's some sort of modernization initiative happening at these companies. Plugging into that modernization initiative to come in and say, "Hey, if you're modernizing the way you go to market, the first thing you need is accurate information on your customers and your prospects, and let's get plugged in there." Finding a global initiative to plug into is very important, and then coordinating across the enterprise is incredibly important next. The seller is different. The type of seller that really succeeds in the enterprise motion is someone who can drive collaboration and coordination and bringing people together. The type of seller who's really successful in SMB and mid-market is someone who can show quick value to a customer, who understands the customer well enough that in, you know, in a 30-minute demo, can show them how they're gonna get immediate value out of buying the solution. In the enterprise, it's much different than that. It's coordination, it's collaboration. They still need to know how to show value to the customer, but they need to actually be able to coordinate a much larger group of stakeholders to get a big multi-million dollar deal done, multi-million dollar, multi-year deal done. Those deals are obviously much larger, where our typical sales cycle is sub 30 days, an enterprise sales cycle is really more like a 90-day sales cycle. It's a pretty intense 90-day sales cycle. You're doing a lot, You know, you're getting that buy-in, then you're getting through privacy, and you're getting through security, and you're getting through compliance, and you're getting through procurement, and then you're going to another round of sign-offs. Over the last two and a half years, we've invested in leadership who run enterprise sales at large software companies. We've invested in individual contributors who have run enterprise sales at large software companies. We recently hired Dave Justice, who's our new chief revenue officer, who ran enterprise sales at Cisco and at Salesforce and at PagerDuty. We feel really good about continuing to invest behind that opportunity. Typically, when you pitch to customers, you're not replacing an existing solution, it's net new dollars. Yeah. Right? We're creating the demand for the product. There's almost never a displacement that's happening. Gotcha. Which I think goes to the maturity of the market. There's not a lot of everything really is a lot of white space in this market. We don't need to, and we almost never displace somebody in order to get the dollars to invest in ZoomInfo. You know, talking about the potential of this market and how much large white space there is, in your most recent Investor Day, you've outlined a $100 billion TAM for ZoomInfo. I was wondering if you can just kinda walk us through, you know, that $100 billion, how does that break down between your different products, different regions, and different customer segments? Sure. We kinda think about that as, you know, north of 750,000 companies that sell to other companies that can be our clients. We've broken out our-- While we haven't verticalized our platforms, we have broken them out by persona. We have our SalesOS platform, our MarketingOS platform, which sells to marketers. Our TalentOS platform that sells to talent acquisition, executives. Our OperationsOS platform that sells to sales ops, marketing ops, data ops inside of a company, a BI unit, potentially in a company. We kinda think about the universe of the TAM across those four platforms, and then we've built sort of like product within those platforms. If you think of Sales OS was the first, platform that we launched, it's kinda $25 billion of that total addressable market. It's focused on driving productivity of salespeople by leveraging that underlying data. Our Marketing OS platform is pointed at marketing professionals and gives them the ability to build a B2B audience, and then a specific persona-based audience, and then target them on the display ad networks, on social media, through marketing automation. Our Talent OS platform is focused on talent acquisition and recruitment professionals. You can think of it as like a sourcing database married with engagement technology. While I might use LinkedIn to send emails or connect with potential recruits, I'm gonna also use ZoomInfo to send them an email, to send them a text, to engage with them on the other channels, to increase the likelihood of them responding and being a candidate in the pipeline. Then our OperationsOS platform is our ability to take our data, all of our data asset, and then plug it into your CRM, plug it into Snowflake or Google BigQuery or Amazon Redshift or Databricks, and allow you to take advantage of our data anywhere where you're manipulating or making go-to-market or business decisions. That's sort of that platform, then our total addressable market kinda fits into those four buckets. Got it. Super helpful. I want to talk about international markets for a second. I understand international right now is probably 13% of your revenues. You know, going forward for the next 5 years, which markets are you most excited about, and what's your strategy to expand there? Today, 13% of our revenue international is focused primarily on English-speaking Europe and English-speaking rest of the world. You can kind of think of it as Australia, New Zealand, and then English-speaking Europe. We think there continues to be a big growth opportunity, although, you know, today, the international markets have been more affected than the U.S. markets as it relates to the economy. We think that's a big growth opportunity. If I'm kind of think of international in a five-year horizon, I would imagine it going from kind of the 13% that it is today to 20% of our overall revenue. I think that means that we execute really well in those English-speaking countries. We find opportunities selectively to go into non-English speaking Europe and non-English speaking rest of the world. We think that continues to represent a meaningful opportunity for us. I think what's interesting in those internationally is customers want two things. One, they want to be able to sell to companies in their, in their countries. So if I'm a UK-based company, I want to sell to other UK-based companies, but even more so than that, they want access to the US market. While we've built up this really robust international data asset, our US data asset ends up being just as valuable to those companies because they want to be able to sell their software, their products, their consulting to US-based companies as well. Investment to grow internationally from a data perspective, requires investment both in international data and continued investment in the U.S. data, because both of them serve as unlocks for those international companies. Got it. The biggest challenges to expand in those international markets that you just mentioned, is it more so just it takes time to build that network effect? Is it's harder to collect data, harder to ensure data accuracy, or how are you thinking about the main challenges? Yeah, the systems will maintain data accuracy across regions. I think today, you know, we haven't invested in non-English-speaking countries and translation layers in those countries. Like, for example, Asia Pac, I think represents a pretty meaningful opportunity for us, but we haven't collected data in Asia Pac. We don't have in-region employees. I think it just takes investment in the region to build out the data asset, and then once you have that data asset, it's. The go-to-market piece of it is the easier piece. You know, we work with close to half of the Fortune 1000, and so being able to go to them, all of whom have some sort of Asia Pac selling and marketing motion, being able to go to them and say, "Hey, you're already a customer of ZoomInfo's for U.S. data and European data, why don't we add in this Asia Pac module?" That's a selling motion we're really comfortable doing, and one I'm confident we'd be really successful with. You have to have a really good data asset first. Got it. I know even in this recessionary environment, you guys are still investing in your R&D, you're still investing in sales. I guess my question is, when we think about the next three to five years, what potential new products or new use cases or new features of this platform are you most excited about? Look, I think we have a team today who's building generative AI into the existing platform. I think over the next, you know, the next three, next five, next ten years, that's gonna be a meaningful part of how we deliver product to our customers. I think every company out there, especially in the enterprise, is driving some sort of modernization effort inside of their companies and inside of their go-to-market motions. I think we're just really, really well positioned to help them drive that modernization effort. You know, when you walk into a pest control company and they're a 6, 7-figure customer of ours, you know, you're pretty excited about the potential of all the other markets because you're not really thinking that the pest control company is gonna modernize the way that they go to market. When you see that happening, and you look at everything in between pest control and really sophisticated technology, so, you know, software go-to-market, you really appreciate the magnitude of the opportunity. I think like, as we're thinking about the product we're gonna build and the market opportunity in front of us, we get a lot of energy by seeing sort of the broad breadth of companies that are really investing behind modernizing the way that they acquire and grow their customers. We'll just continue to stay focused on that user base. Got it. I think we're at the top of the 50-minute session. Maybe just one last question from me. Capital allocation, how would you rank all the organic, inorganic investments? I know you've recently announced a $100 million share buyback program, dividends. Look, I think we're a really interesting company because we generate a lot of free cash flow, and that's pretty rare for software businesses. We recently did a share buyback, and where we announced a $100 million share buyback. We think that that was sort of our first foray into being able to do that. It was an opportunity to see how we would execute against that share buyback. We think that there are meaningful opportunities to continue to return capital to shareholders. We're pretty focused on free cash flow per share as a metric inside of the company. It's how a number of our executives hit their bonus numbers and their bonus targets, and so that will continue to be an area of a metric that we operate against. We're playing in this, in this really dynamic environment where historically we've used M&A to transform the business, and we've been really successful at that. We'll continue to be opportunistic about M&A. You know, we're more focused on bringing together all of the assets that we've acquired and building a really seamless end-to-end platform for our customers. Got it. Super helpful. Thank you so much, Henry, for sharing with us, and thanks everyone for coming to the fireside chat. Thank you, Kelsey.