Episode 53: Jon Bischke - Fixing Stale Recruiting Data

Thanks for trenching in. You're listening to TA in the Trenches, the podcast for HR and TA leaders making workforce decisions with real financial and business consequences. I'm Matt Reimer, co founder and COO at Rogue Hire. Rogue Hire runs the largest healthcare TA benchmark program in the land, where we know one thing acutely, that top performers don't have more applicants. They waste fewer of the ones they have. Each episode I sit down with CHROs, VPs of TA and recruitment operation leaders living at the front of that problem. Operator to operator, less opinion, more evidence. Let's get into it. Talent Acquisition in the Trenches is brought to you by Rogue Hire, the team behind the 17 year Healthcare TA benchmark program and the makers of medics. Your ATS tells you what happened. Medics tells you what to do next. A patient monitor for every open rec, continuous vitals, early warnings, one clear signal on track, watch or critical. Your team works from the same truth. Your dollars go only where hiring risk actually exists. Learn more@roguehire.com Foreign. So thanks everyone for trenching in live. I am your host, Matt Reimer, co founder and COO at a company called Rogue Hire. And so today we're going to be continuing our series of live events with my buddy Ryan here and soon to be friend John, tackling some of the bigger challenges in our field. And so today we're going to be squarely hunkered on and really, I think from my perspective, excited to be able to engage John a bit deeper here. He's a seasoned tech executive, a serial entrepreneur, and so we just had talked a little bit about that with him currently serving as a VP and general manager at Zoom Info Talent Solutions. And so they in the end are one of the largest and most formative data solutions in the world. And so he's got a deep expertise, not only data, but also SAS and AI with his companies that he's founded, co founded and has been a part of. And so super excited to be able to dig into a conversation here with John. And so we also have Ryan Affilter and so he's a good buddy of mine here at Rogue Hire. He's going to be co hosting us today. And so Ryan maybe talk a little bit about what we hope to get educated on here over the next half hour, 45 minutes or so. All right, thanks Matt. For today's talk, I think we're diving right into the critical exploring the necessity of a solid, fully optimized data foundation. Specifically the reality that nearly a third of your healthcare candidate data becomes obsolete every 12 months. So this idea of data decay creates a massive speed to contact deficit where recruiting teams waste hours chasing those disconnected phone numbers and outdated emails. So ultimately forcing your organizations into expensive agency spend because you can't reach the talent you already own in your database. So excited to jump right in, John. Hey, welcome to the show. I guess let's just lean into what we were talking about before we jumped on here and this idea of data foundation, and I think Ryan had mentioned it in his opening remarks, but would love to maybe educate those listening on exactly what that is. I mean, I think that we know the old analogy garbage in, garbage out, but how does that apply to specifically what we do in talent acquisition day in and day out? Yeah. Well, first of all, Matt Ryan, thanks for having me today. Excited to spend some time with this group. So I have this interesting perch that I sit in running the talent business for ZoomInfo because ZoomInfo sits on, if not the largest, certainly one of a very small number of extremely large data sets to be able to help companies out in terms of hitting their goals. Company started 18 years ago, really focusing on sales, prospecting and helping people that were selling into companies to be able to find the right people to buy. And at some point along the way, they figured out, ooh, a lot of people who are using our product are actually recruiters that are trying to use the Zoom Info product to find people to hire. And so then ultimately launched Talent Solutions, which I run today. The thing that's really interesting right now is that there's so much focus in the world broadly and definitely in ta on what's happening with AI and increasingly agentic technology. And so as I walk the world and we talk to customers, one of the things that's very top of mind for them is if they're going to build out true AI, true agentic technologies inside of their companies, they need to build it on top of a strong data foundation. Because going back to what you said, garbage in, garbage out, if the data is really shaky, the agent's going to be spitting out really not great things for your company. Right. And so ultimately I'm seeing, we're seeing more investment, more interest in investment on the data side than we've seen really ever. And ZoomInfo currently works with a very large number of customers to improve the data in their sales and marketing and rev ops departments. So think of like a sales CRM that might be rapidly decaying. Zooming for has been in that business of improving that data for years. So when I got to the company a year ago, pretty obvious leap to say, hey, we should be, we should be doing this in talent as well. And I knew that it was possible because for nine years I ran a business called Intello. And one of the things that we did was we sell data to TA teams to be able to help them improve their capabilities in the market. So I think this is only going to intensify. I think the talk of about data right now is real. It's happening in so many companies and as we think about what the next two, three, four, five years are going to bring, I think it's only going to increase in terms of the importance. So let's riff on maybe an example here for a little bit. And so when we're talking about data foundation and data integrity, and I think Ryan had mentioned it, in a certain amount of months, data becomes stale. It's not accurate. We've got in our client base a lot of the harder to fill roles in healthcare that we work with our clients day in and day out, ensuring that their processes are completely optimized, that they're not wasting funnel end. But ultimately we get to the point where in certain job families we've got to go hunt, we got to go find more talent. And so oftentimes a lot of the talent that organizations are looking for actually hanging out in their database and so they've applied previously and just not doing a great job managing it. But maybe talk to us a little bit about, you know, let's use the physical therapy as an example. I think that's when we were talking about before we got on here, a large client of ours, you know, fox rehab, 20 different markets in the US they do in home PT, which is even more challenging breed of PT. And we're working with them, you know, they're just constantly asking not only us, but all of their partners, hey, am I actually engaging and do I have the right tools to ensure my CEO and my COO that we're engaging 100% of the market. Right. That we're actually touching all of the pts that are available to us. And so that's a question that we're constantly trying to answer. And so maybe using that example, talk to us about data integrity and how we might look at a challenge like that, you know, in our ecosystem for sure, yeah. It's an example that hits near and dear to me because my mom was a PT for many years and so definitely have a bit of an understanding of what that life is like. I think it's a great example because if you think about the two biggest players in the recruiting space at the top of the funnel, it's LinkedIn and indeed. Right. You know, we very often refer to this as a bit of a duopoly. And if you think about, I don't know what the exact number, percentage of pts are that's on LinkedIn, but it's sure as heck it is not 100%. Right. Most of the studies that we've seen, JAMA and others have published data on this, suggest that the number of healthcare professionals on LinkedIn is probably in the ballpark of like 40%, 45%. Which means if you're using LinkedIn to source for healthcare, you're going to find some people for sure, but you're going to miss more than half the market. And I think that's very interesting to people. And I would imagine PTS, that number might even be lower than 40, 45%. I know my mom does not have LinkedIn account as an example, so I think that that's part of it, right, is you got to, you have to make sure that you have the most complete data set. What's good about so many people in health care is that that data, at least some data exists about them somewhere in the world. You know, a licensure, a certification, a degree. Like there's different databases or different places where you can get that information. And so I always really start from this. When you're thinking about sourcing candidates, are you sourcing them from call it a First Party Network? LinkedIn would be a great example. Or are you sourcing them from call it a third party aggregator, which would be more zoom info. People don't come to zoom info and create profiles and connect with their friends and post videos. That's not who we are. We're getting that data through a variety of methodologies. Happy to speak to that later, but because we're pulling this data from a bunch of different places, we're not subject to. Did this individual come to our platform and create a profile which LinkedIn is subject to? Indeed is subject to. And that's part of the challenge that those businesses have. So I think that the third party aggregation has really become very prevalent. When I started Intello in 2011, it didn't exist. It wasn't even an industry. There were resume databases, you know, things like that existed, but there wasn't, there weren't 30 party aggregators. Now you have a whole bunch of companies out there that do that. And so I think almost every company right now that's thinking about recruiting aggressively has access to one or more services that do 30 party data aggregation to pull that data in. And then I think the question becomes, what's the quality of the data? How deep can you go? What's the coverage? What's the accuracy of the data? And happy to speak to that, but we've had a lot of really good luck with companies in part because we have such good firmographic data. So things like SIC codes, right? Somebody comes in and they want to pick a specific title, but they want to know that that person's worked in a specialty hospital or a home health care service or at a nursing care facility. Well, that all ties back to an SIC code. If got all that data mapped now, you can get a bit more granular and you can figure out not only do I have the right type of person, but they've worked in the right type of setting. This is exactly the type of person that I want to reach out to. So that would be an example that zoom info, an area where zoom info might have an advantage because we have all this company data, over 18 years of being in business that might not always be prevalent in some of these databases. So, like the reverse of that. And then, Ryan, I'm going to kick it over to you for maybe a question here next. It's like, all right, you know, I've got, got the, let's go back to the Fox. And I see, I got Kara on the line here who's one of the, the tips of the spear at Fox, trying to, to kind of figure this out. So let's say, hey, yeah, all right, I feel good about that. I got my arms around the data. And, and we would love to learn more, certainly, you know, kind of how you look at quality of data. And, and so like we, we obviously know that it's quality when we hit an email or we make a phone call or we do a text, whether we get a response. But maybe there's a different way that we should be thinking about that or looking about that. But we often get asked, you know, all right, so you have all this data and so then I'm the CEO or the CEO at Fox. Well, you know, tell me more about what's going on in this market. And you know, you know, is there more in this market? Or how do I know that the market that I'm in is the right market maybe for us to be doing business in, for example. And so we're starting to get more and more of these types of, of conversations, you know, so just kind of curious how you look at it from like, you know, a trend perspective. Like is a market growing or is it decreasing? And how you actually put your thumbs on that for your clients. Yeah, yeah, there's pieces of that that we play in and there's areas we don't too. And one of the things that I'm always game to do with any customer or prospect is to say, hey, this is an area where we're not an expert. Right. There's somebody else you should go talk to because I think that's, that's the right thing to do to service a customer. We do have a lot of data on individuals. We have 500 million plus people in our database. And so just simply by running searches on our platform, you can get a pretty good sense of, you know, for this job title and this geography, this many people come up and that data is pretty comprehensive. But there are other companies out there that do specialize in more of the, call it like the talent mapping use case. You know, talent neuron jumps to mind. Light cash jumps to mind. Companies that a lot of other customers will utilize when they're looking. Not so much to get the actual individual person's profile, but maybe more broad kind of market trends. And we're very happy to send business their way and they send business our way as well. I do think one of the things that is increasingly interesting, and we talk a little bit about this in some other areas outside of healthcare that we work in, is kind of this concept of proof of work. Right. So the challenge that I think folks have with databases like LinkedIn is there's really no verification on LinkedIn when you create a profile that you are the person you say you are. Obviously Most people on LinkedIn are actual people and they're who they say they are. There's social mechanisms that we're able to figure that out. But increasingly, when you're looking at somebody and you want to put somebody to work inside your company, there's going to be a set of criteria that you need to be able to figure out about that person. If you're looking at someone's profile online, is that something that they made up and maybe they have that credential or maybe they don't? Or are you looking at something that has been authenticated, has been validated. So as an example of that, here at Zoom Info, we have a 400 person research team. And one of the things that they're doing is they're constantly validating this information. And I think it's to a higher degree of accuracy, not perfect like no database out there, be accurate 100% of the time. But if you never do any manual verification, how are you going to catch situations where that data is inaccurate? So I do think that there's an advantage that we have, and I think that when companies go out to the market, that's the question that has to be asked. Whether it's us or Lightcast or Talent Neuron or LinkedIn, it's what do you do to validate the data that underlies what you're selling? And if there is method to do that, I mean, I'd be skeptical. Doesn't mean that you won't find value, but I would certainly start with a position of skepticism. Yeah, Ryan, what's, what are you thinking here? Any, any, any questions coming to mind on this thread? I know I've got a couple of places I would love to go here next, but what. Where are you at here? Yeah, a question for, for John. And you know, maybe this is, this is relevant to ZoomInfo or not, but we talked about speed to contact as a competitive advantage and then kind of leaned into the quality factor and sounds like you have a pretty massive team manually checking. Do you have partnerships or integrations or relationships with some of the specific organizations within healthcare? I'm thinking like the state licensure and certification boards. Do you have any ways to kind of integrate and pull in and validate data from that side? Yeah, well, one of the things that we're very good at is crawling public data. Right. Where public data exists. I'll use an example outside of healthcare, but it'll kind of prove the point. We had a customer come to us at one point this year and asked about data on roofing contractors. Right. They want to sell into roofing contractors. And, you know, all of this data is out there in government databases and can be accessed, but for any one company to go and try to pull all that information is very challenging. And so we did that here at Zoom Info, and we were able to pull that information together in a way that was super helpful and super powerful. And the same activities are things that we're doing right now in the healthcare space. So we're looking to pull information. And recently did that that actually in the space around therapy. So speech therapists, OTs, PTs, pulling that data in. And what generally is a win for customers is in theory, anybody could go get this data, right? It's sitting in a government database somewhere. But very few companies have the economies of scale that we do. So if you were going to take a team and go after it, sure, you could do it, but it probably is going to be way more cost effective, way more efficient to work with a company like ours to be able to pull that data together, because we potentially can sell that data set to multiple companies versus a company doing it just for their own benefit. So we do get pulled into a lot of those conversations. When it comes to partnerships, we do have a number of different data vendors that we work with. And then the other thing that we have is something called the ZoomInfo contributory network, which is basically people who access our network in kind of a give to get fashion. So they get data out of the system, but then they also provide data back into the system. And by doing that, that give to get, we end up getting a lot of data that just doesn't really live out in the public domain. It's data that people have access to, but it's not necessarily something you just go to the Internet, look up. And so by combining these methods together, you get the most powerful data set. So I always tell people, when you're looking at data, you should evaluate multiple vendors. You should never just go with one. There's obviously a lot of different people that have strengths and weaknesses, but when it comes down to this cost versus quality analysis, there's a really big cost to your company if you've got substandard data. And I think increasingly companies are aware of that. They're looking to kind of more as a flight to quality to find who are the best data vendors out there that I could be working with. So you had said when we started into this, that in your travels on this earth that you're finding that this data foundation conversation is, is kind of where you're starting. So that, right. You're able to enable maybe some of the advanced technologies that are in flight coming our way. Who knows how that evolves. And so, you know, obviously being in healthcare here, which is where we're primarily hunkered down right now, you know, when we're out speaking to, you know, CEOs and CHROs and name a C, right. It's, you know, find me a way to automate something, help me reduce some waste, help me increase some productivity. Productivity or help me reduce some cost. Right? And so it's basically that conversation. And how does, right, in the end, you know, what you offer, do one of those four things and then prove it type of thing. And so I guess, like when, when you think about, you know, the challenge that we have in, you know, this top of the funnel, right, which Is is not necessarily gone away and there's lots of opportunity there. When you think about what is going to be built on that strong foundation that does one of those four things, you know, is there anything that comes to mind, you know, for you, you know, as it relates to how things are going to change in the future, as it relates to sourcing and data? Yeah, I think there's a couple things that jump to mind. Number one is I do think increasingly a lot of top of funnel work is going to be handled agentically. And that doesn't mean that recruiters are going away, that doesn't mean sources are going away. I think the nature of the job will change because some of this work you will be able to handle via technology. And so if you think about it, today, most communication and recruiting is happening human to human, right? It's a person who's going into a database. It could be ours, it could be LinkedIn, it could be another database. They're finding people, they're sending those people messages and then humans are responding on the other side to it. And I think what you're going to see is you're going to see two shifts. One that's going to come first and one that's going to come second. The first one's going to be increasingly agents doing outreach. And you do see signs of this with some of the products now that are coming to market right now. If I'm reaching out to somebody to be the head of my hospital system, I'm probably not using an agent to do that because that's really a valuable role that I'm going to put a person on and they're going to be sending very bespoke messaging. And that's probably going to happen for a long time. If I have to staff up 300 travel nurses, I don't think I'm going to be putting a person on doing all the individual recruiting for all those individuals. But an agent acting on behalf of a company could do a very effective job with that. So I think the first wave of this is going to be agentic tech. Coming in the second wave we won't get to because it's more sci fi, but it's going to be, you're going to have agents representing the job seekers too, and that's going to be fun. So it's going to be agent stocking agents. But for now, you know, I think the main shift that'll happen in the next probably two to three years is a lot of agentic work top of funnel. And then what Happens is people in talent acquisition, you start to shift your behavior so that rather than your day being, you know, I bang on a database, I send out messages to hundreds or thousands of candidates. I don't have the agent doing most of that work for me. I get to really focus in on the higher value roles, the hardest to fill roles. And then I'm going to with very bespoke perspective on those because I have the time to do that in a way that most people in TA right now feel overwhelmed. They don't feel like they've got the time to really do the work that they should because there's just too much going on, too much on their plate. I think the agents take care of 80, 90, 95% of the too much on their plate, allowing them to really focus on the critical roles for their company, the ones that are hardest to fill and highest impact for the company's success. Yeah, I've seen some glimmers of this as it relates to agents doing even for volume roles as you know, and we're not talking about sourcing, we're just talking about inbound, right. And so hey, you know, I get the poster job, I get 50 applicants. And so really the idea of, of the agents doing the outreach, doing the screening and then getting a first layer of, you know, scoring before a recruiter even sees it, you know, before they even touch it, you know. And so I definitely see it from a high volume perspective. It'll be interesting to see how it evolves on the outbound reach and so how it doesn't become, you know, you know, whatever, I guess spammy like the back in the day, you know, the voice machines that then turned into, you know, having to have an answering machine and everybody's home, you know. And so it's like how does it not become telemarketing at scale I think will be super interesting how that. But you can see it coming like. And so I guess tying all this together though, what you're saying is, is like, hey listen, and if you don't have this data foundation of quality data, you can't actually move the agents to do their work on your behalf because basically they're pinging bad data, right? Yep, yep, that's right. And it's both inaccurate data, it's also incomplete data. And I think this is one of the biggest challenges we see in a lot of companies is they may have information about an individual, but it's, it's pretty shallow information. And so when you think about how these large language models work, right, The Precursor to agents. They thrive on huge amounts of information and it's why they're able to produce. The magic that they produce is they're canvassing the entire Internet and all of this post training information that they have. And if you compare that to the typical ATS or the typical hiring CRM inside of a company, I mean it's just, it's so thin in comparison. So, so what you want to be able to do is first of all obviously refresh your data. There's a lot of people that have information about candidates on file, but it's old information. They submitted a resume five years ago, so and so had this job tell at this company what they know is they know the five year old version of that individual. That person's done some new things in the last five years. Maybe they've moved, maybe they've gotten a new job, maybe they've picked up new skills. They might have a new phone number, they might have a new email address. Right. So you got to refresh your information. But I'd also argue that you ideally should deepen that information as well. So if you know a little bit of information about a person, it's going to move you to have a lot of information about that person as long as that data is accurate. And then the agents, the AI that kind of sinks their teeth into this data set is going to have more to work with. Because going back to what you said earlier, which I think is a really important point, how do we prevent this from becoming spammy? Typically, spam is a data problem, right? That when you get a, you know, someone offers you an internship at some company, I past that part of my career, somebody had incorrect data to begin with. So if you get better data, you get more accurate data. In theory, the agents, the AI gets very smart and they're not sending spam because spam doesn't do anybody any good. It doesn't do you any good as a hiring entity. It doesn't do the job seeker any good either. And so I think there's an opportunity here to be able to say we can do better for both the companies that are seeking employees and also for the job seekers by having better information that then the AI, the automation agents can work top of. I'm curious on this is very like maybe, maybe too far in the weeds. I'm just kind of curious in your point of view on this because this is a, this is a challenge that we just, we see everywhere we go and you know, we run in healthcare and I want to talk also Here before we get off about healthcare and other industries and maybe similarities and differences, but you know, in a lot of our shops, right, and this is a byproduct of the applicant tracking system, basically, right. Being designed to fill a wreck and not to engage a human. We see a lot of waste down in these databases, right? You see all these potential leads that have different levels of age on them. Six months, 18 months, three years, five years, so on and so forth. And so if I Socratically kind of walk down the line of kind of what you're talking about is that that database is useful to a certain extent and it's also a compliance based database. It's like, hey, I'm trying to keep you in compliance. And so tactically speaking, I was like, all right, these are people, these are connections. At one point in time they were interested in my company. At one point in time they showed they expressed interest. Is the best way to refresh that set of data it in my own database or am I doing that in another piece of technology or another. Another area? Like, like just talk to me a little bit about that strategy because I think sometimes we struggle with it. And then you see, just to finish this thread, you see technologies showing up. That's basically suggesting to you that they can rediscover talent out of your database. And that's super exciting. That's a super exciting idea. But if we go back to where we started our conversation, potentially we're rediscovering talent that we can't actually action on. So just kind of curious on how you view that. Yeah, yeah. I think that this has evolved a lot in recent years. When I started Intello in 2011, it was really just the ATS at that time. And then maybe you call it five, six, seven years ago, there's been more of the rise of the hiring CRM that might sit alongside the ATS. And so whether you're talking about phenotype people gem or 8 volt and there's a lot of players out there be marine in that space. What I think is interesting and good about that is they are designed more to do that can engagement piece versus the ATS really is that compliant based thing that is all about filling a wreck and then once the rec's done, we kind of throw it away and we start over again. So I think the movement towards CRM is a very healthy move in general for the industry. So I like that. I think that feeds into one of the things you talked about, which is this concept of rediscovery. And so when I think about a job that gets opened, you know, you really have multiple buckets that you could potentially fill that job from. You obviously have the current applicants. That's where most people try to fill from. You have everybody just applied for any job ever inside your company? Right. So there's somebody who applied for a different job three years ago, another really good fit for that job you just opened, but you don't know about them because they didn't apply to that job, they applied to a different job. You have your current employee base, right. So you have internal mobility and how much do you know about them and can you match them back to a job? And so, so what I think the true systems of the future will do is they will not really care at the end of the day. They'll look for the best people. Now, there's some propensity signals here. If I just applied to a job today, I'm interested, I'm active, I've got that intent. I applied to different job four years ago, I might be a better fit than that person who applied yesterday, but maybe my intent's lower. So then the algorithm has to kind of balance out that piece of it as well. But I think you start to move away from silicon silos is my fundamental premise here. And so where that data lives, maybe it's the ats, maybe it's a CRM, maybe it's your snowflake instance, maybe it's your databricks instance. Like, I don't, I think the where the data lives thing becomes a little bit less important and it's more about, hey, can we access all of the data? And then do we have an intelligence layer on top of all of the data that helps to sort this out? And I'd say very few companies are there today, although I think almost every large company we talk about, talk to is talking about go going in that direction. Yeah, I mean, it's the age old challenge. And Ryan, I'll kick it over to you here for a question. But it's the age old challenge, right? Which is like, hey, I'm trying to back again, I'm trying to automate stuff, I'm trying to reduce waste, I'm trying to increase productivity of my team. And I'm putting them in two systems, I'm putting them over here in the ats, then I'm asking to do some work over in the CRM. And the data is not always sinking and lined up. And so the point of contact when I'm at the manager, I don't have some of the intelligence that I need out of the CRM to actually do my job or solve that problem. And so it's just I like though where you landed, right, which is this idea of an intelligence layer that's sitting over that and it may not in the end matter where it lives. It just matters what that intelligence layer is doing or not doing for you to make it efficient. And so it's an interesting idea. Yeah. Ryan, what are you thinking about how much time we have? We have 15 minutes left. 45. Is that what we're doing today? About 15. Rolling to 45. Yeah. So John, just to kind of further add an intelligence layer that sits on time. So we're imagining like a system that is continually looking at your data and offering automated kind of of enrichment of data as it learns kind of real time if phone numbers change, if emails change. And with that, is it looking at just kind of the public scrapable databases and things? Are you thinking about accessing more of the consumer based data sets out there? Think about candidate behavior outside of searching for a job. Right. They might have a dedicated email address or their school university email address for the job search, but that might not be their primary or might be outdated. What about their day to day activities of you know, shopping, purchasing, credit, type of behavior? How is that taken into consideration? Yeah, it's interesting and you know, one of the opportunities but also challenges we have at some info is, you know, we're an international company, right. We have of customers in all sorts of different parts of the world. And so part of the challenge in our industry increasingly is less around technology, more around legal and regulatory frameworks. And so what we could do in the US versus what we might be able to do in Europe, for example, given gdpr, could be different things. I think there's a lot of interesting data out there that's currently not being utilized in recruiting and I think some of that will change over time. And when we got into the business with INTELLO Back in 2011, so almost 15 years ago, one of the reasons we got into the business was because we were seeing all this information in these different niche social networks that was not being leveraged by recruiters. An example going a little outside of healthcare right now was GitHub. Back in the day, a lot of engineers were going to posting information on GitHub. If you were a really smart recruiter, technical recruiter in 2011 and you knew how to navigate GitHub, the world was your oyster. Because here are all these people talking about what they do and there's their email address and I can reach out to them and, and I can reference the project on GitHub and boom, like I'm going to win. Right? Well, most recruiters were not that sophisticated and the original premise for the Intello product was build a user friendly search engine on top of all that information. Right. And so I think that increasingly people are pulling this data in because they do see it's useful. But I also think we have to be very careful about impinging on, on privacy. Right. And doing things that potentially turn off the job seeker of, oh, you looked at my shopping history. Something I don't, that's not, doesn't feel relevant to whether or not I'd be a good fit inside your company. We don't do any of that today. No intention to do any of that. I don't think there are companies out there that are really doing that today. But I think there's other signals out there. One of the areas that we have spent a lot of time on is what signals might show a propensity to leave a job to be able to find a new opportunity. And I think that's really important because a lot of times we talk about the who, but we don't talk about the winner. And you can reach out to people until you're blue in the face, but if they're not looking for something new, if they're super happy where they are probably isn't necessarily going to be very productive. So one of the things as an advantage, for example, that we have at Zoom info is in 2022 we bought a company called Comparably. And Comparably is similar in many ways to Glassdoor but with some key differences. We have some information from the Comparably data set about how happy people are. Are they happy with their benefits, are they happy at their employer, what's morale like, things like that. You can again envision similar data at a place like Glassdoor that potentially can get encapsulated into an offering to try to help show, hey, this person's at a company that Morrell looks like it's on the way down. Maybe it's a good time to be able to reach out to that individual about a job. And there's other signals out there as well. So I think that will be another area of, of a lot of focus in coming years is what are the data points that are out there that might indicate that somebody is more likely to leave their current position and how do you incorporate that into your search process as well? So like back to the, maybe back to the pt, you know, analogy. And I guess does Zoom see any behavior shifts especially? And so like, you know, this just is. It's always interesting because I've got a 17 year old in my house, soon to be 18 year old, right? And so, so just seeing. And then we have job search processes happening with her and filling out an application or whatever is in her world, challenging. So that's something she doesn't want to do. Right. She's not used to that from how she experiences this digital world and how she engages in the digital world that she grew up in. So I guess as that cohort, the TikTok cohort, and that cohort begins to, to further penetrate into the workforce. Like, what is ZoomInfo preparing for as it relates to how they exchange information and how that, that group, that cohort records data? Like, is it similar to the GitHub example, which is like too soon to tell, but somebody's going to figure it out type of thing and there's opportunity there or how does that evolve? Yeah. Well, I'll break this one again into really the two camps of challenge, right? Which is technical challenge and then, and then legal and regulatory challenge. The technical challenge is interesting because there's been this push pull with the ETS over the years where all of a sudden it's too easy to apply to jobs. And then the ATS just gets deliberately obfuscated. So it's really hard so that you can't apply to everything because the ATS makes it hard. And then AI comes along, it's like, oh, well, now AI can apply like a thousand jobs in one shot, right? And that is the problem right now, where we sit today in 2026, that a lot of companies are finding we've opened up recs here where we'll get a thousand in an hour. And we're like, this is crazy, right? And it's not a thousand people that are applying clearly, it's bots that are going, scanning, see, zoom info, just post a new job, boom, it's auto applies, right? And it knows how to sequence through all the steps and get through all the hurdles. You give it and you know, that's just where we are with AI. So then the question is, what? How do you stop that? And of course, AI could be really good on the other side of it. All this stuff comes in and you run AI across. You're like, I have a thousand people, 10 of them are real. And here's your real applicants. We discard the other 990. But here's the challenge. If you look at AI legislation right now, places like New York, California, it prohibits you from using AI to make an appointment decision. So if I reject an applicant who comes in because AI says, hey, this person is not the right person, it's actually against the law, as I understand. I'm not a lawyer, not legal advice, but that's a problem. Right. So we're going to evolve on these two tracks of technical challenge and legal and regulatory challenge for a while. I think technical is going to be a far easier battle. Right. When you look at what Chat, GPT and Gemini and Anthropic can do nowadays, I mean, it's amazing compared to a few years ago. The legal and regulatory framework has to catch up and we have to figure out a way that this becomes more win win, because otherwise we're going to get into this challenge where technically we can do it, but legally this might not work. And we don't. We operate Zoom Info Talent Solutions. We operate very top of funnel. So when people are reaching out to people at a job, that's not employment decision, it's a marketing decision decision. So we kind of move away to the side a little bit from this issue. But there are applicant tracking system companies right now. They're trying to solve candidate fraud and how do you sell candidate. How do you solve candidate fraud without making an automated employment decision on somebody? I don't know the answer to that question and I leave it to other people to figure that one out because I think it's going to be a tough one, frankly. Yeah, yeah, that's great answer. All right, Ryan, maybe fire one last question away here. I'll close this out and we'll, we'll probably land right on time here. All right, let's, let's see if I can find a good, good question for you. So sorry, I had something pulled up here and I'm missing it. Can you come back to me in just one second? This is the greatest question ever, Ryan. My question is this really in the end. And so like I'm always interested and so obviously we're focused right now on the healthcare group. It's kind of where, you know, this company has evolved from. It's where we've been focused. Not to say that we'll always be there, but that's where we're focused right now. And I think sometimes healthcare gets a little bit of a bad rap as being behind the curve or, you know, there's other, you know, and I don't know. Right. But there's other more advanced practices or things happening in other industries that are way ahead of where healthcare, you know, talent sits. And I sometimes don't agree with that premise, frankly. And I think sometimes, and you know, this is all about who you're around and who you're with. Sometimes the healthcare organizations that we're with have some of the most sophisticated, you know, sourcing functions and abilities because the challenge is so stiff on those hardest to fill roles. And so I guess just from your perspective, where do you see and where does maybe Zoom see the healthcare challenge? Knowing that, you know, we've got, you know, pinched margins and you know, we're in a, we're in a, we're in a business that is evolving pretty dramatically both politically and other. Right. And so do you see innovation in healthcare? Are you bullish about it or do you see other industries that are really maybe taking some of the things that we've talked about and running up the hill quicker than healthcare? And if so, that's great. What can we learn from them being in a healthcare domain? Yeah, I mean it's interesting. I spent my four years between Intello and my relative info was in health tech adults, a couple health tech companies during that time. And so I got to think a lot about innovation broadly in healthcare. And I do think there's some huge challenges with innovation in healthcare, especially with implementing AI in healthcare because of CPT codes. A lot of stuff we won't get into today. However, I actually think in the recruiting space that it's a pretty good spot to be. And I say that for a couple reasons. I mean, number one, I think healthcare is something like 20% of our GDP right now. So just from an industry perspective, big getting bigger and that's really important. But then from an employment perspective, if I was going to point to one industry that I don't think there's any chance it's going to have a smaller number of people working in it in five or 10 years. It's healthcare, right? Like finance, I don't know. Right. Tech, I don't know. Like we may go, we may contract employment in those, in those segments of our society. Healthcare. Are we gonna have less people working in healthcare in 5 or 10 years? No chance. Like it's gonna be more, more more. Therefore the needs to hire in healthcare are gonna be greater. Therefore the innovation required to be able to help people in healthcare are gonna be higher, the needs are gonna be higher. So I think it's a really sweet spot, frankly. And I also think that one thing that has Happened is going back to what we were talking about prior with LinkedIn and a lot of recruiting. LinkedIn has sucked the oxygen out of the room right there is such a dominant company and look, they've executed incredibly well over the years. They are not dominant in healthcare and I do not see that changing anytime soon because again, they're beholden to do people join their network and there's just a lot of people in healthcare, they're like, don't really need to be on LinkedIn, therefore I'm not gonna have a LinkedIn profile. I'm not gonna keep it up to date at least. So I actually think healthcare for us, it's an area of focus for us here at Zoom Info Talent Solutions. It's gonna be an area that we're gonna innovate in. We see other people that are innovating in the. I think if you're in TA in healthcare, you are going to see some really great advances in the coming years because almost more than any other industry, this is a great place to build technology for. It's a more stable industry than a lot of other industries right now. So we're definitely investing it from our side. I'm investing a lot of my own energy and thought processes into it. And I think you'll see some really cool stuff from our front on our front for health here soon. Wonderful. Thank you for that, Ryan. Don't disappoint here. You bet. This better be good, man. So I'm just saying, all good. Sorry, I was going over. We really covered everything. You've built it up to be something. It just better be very, very special, Ryan. So. Right, right. We covered pretty much everything on the, on the agenda. So I was going a little off there, but I wanted to kind of go back to John and your acquisition and integration of comparably data. I think that's really interesting. I've been a fan of comparably used comparably in my recruitment marketing agency leader days across several brands. So maybe could we, as a final thought, kind of lead into how data quality and kind of the completeness directly can impact candidate trust in your brand or vice versa, erode trust in your brand. So, you know, examples could be, we've seen, you know, bad reach out calling a director and associate. So is there kind of a story or example that you have? Oh, yeah, for sure. I mean I think we've seen a lot of them over the years. On the sourcing side, I think it really comes through having the right, right data, but then also a really good Outreach to a candidate. And you know, this is an area where AI is really increasingly helpful because look, everybody knows there are good recruiters and there are not so good recruiters in the world. And sometimes they're not so good recruiters. It's just they're young or inexperienced or haven't been on the job very long. And so we've seen this a lot with customers where you can tee up the best data in the world, the best, best lead, the best contact information. And if that outreach is not a good outreach to that individual, doesn't matter. And so we're helping people on that front as well. We've integrated an AI assisted emailer capability into our product so that if you're just not super confident in the messaging that you're sending, we'll give you some guidance, give you some frameworks to be able to send out messages that perform. And then I think the other side of it is generally speaking, the more customized that message, the better. Customization was very difficult pre AI, right? Very difficult to send that targeted customized message. Not going to say it's necessarily easy today, but it's way easier than it's ever been. And AI can help you to reference that information about that individual. It's really going to allow that message to stand out. So it's something that I'm passionate about. Like everybody knows there are good recruiting emails and bad recruiting emails and we want to be an enabler for good recruiting emails and really start to effectively put an end to the bad recruiting email. Because in today's day and age with ChatGPT and Gemini and Anthropic like, like the bad recruiting email should not exist. It still does. But my hope is, you know, the death of the bad recruiting email is, is dying wonderful. I think that's. Yeah. Good. Ryan, I was gonna say can the, can the messaging kind of look into like some of your, your comparable data to, to help customize that? Like it's not just reading what you may have trained it about your employer brand, but that can kind of take some of that content into consideration. For sure. For sure. And I think what's great about that is not just selling against weakness, right? Someone's at a company that has bad morale or poor pay practices or whatever, but selling strength that you have as well. And so something like comparably is great at trying to be able to figure out where are my strengths. Right. So we do like the Comparably awards every year, Best places to work, other categories, things like that, that employers, if they can use that in their messaging. Did you know that this is one of the best places to work for people of this type of things like that are really, really critical because again, it's allowing you to stand up to rise above the noise of all the other inbound that this individual might be getting. So yeah, when we made the decision to acquire comparably, that was certainly part of the thought processes. Let's be able to arm these recruiting teams with better data not just about the targets, but about their own company that then they can use in their outreach. Hey John, we know you're busy and we really appreciate you jumping in today and going down the various paths that we went down. I think it was extremely educational for myself and I'm sure here for the broader community. And so if anybody wants to engage further on you all, what's the best way to do that? John yeah, I'd say two different things. ZoomInfo.com Talent is where you can go to see all the stuff that we offer on the talent front and then probably the best way for me is just find me on LinkedIn. So grab me on LinkedIn, send me a request. Happy to connect there. Happy to be helpful to anybody on this call to the extent that I, I can't be. John, thanks for all you do and again, thanks for trenching in here today. We'll see you around. All right, sounds good. Appreciate you guys. Bye. Thanks for trenching in with us today. If a benchmark, a framework or a question from this conversation is worth taking back to your leadership team, please do that and let me know how it lands. You can always find me out on LinkedIn. For more on Rogue Hire Healthcare TA Benchmark program and Medics, the decision intelligence layer for talent acquisition, visit roguehire.com if the show is useful to you and your peers, a subscribe and a review always go a long way toward getting it in front of the leaders. Making these calls day in and day out until next time.