Recruiter Marketplace: Scout w/ CEO Ken Lazarus
One of the biggest stories from 2018 was recruiting software solution Scout getting $100 million in investment. The company is quietly becoming a force, so the boys decided to sit down with CEO Ken Lazarus to find out what's going on.
Enjoy this Uncommon exclusive.
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Announcer: Hide your kids, lock the doors. You're listening to HR's most dangerous podcast. Chad Sowash and and Joel Cheesman are here to punch the recruiting industry right where it hurts. Complete with breaking news, brash opinion, and loads of snark, buckle up boys and girls. It's time for The Chad & Cheese Podcast.
Joel: We're back, back again.
Chad: I know you missed us everybody because we don't produce podcasts every day or anything. Special guests today, we have Ken Lazarus, CEO of Scouts, all around just smart guy that has increased the IQ points of this podcast by about 300. PhD from MIT, Bachelors from Duke, tons of boards and all that good stuff. Ken, welcome to the the most knuckleheaded podcast in HR.
Ken: Thanks guys. Thanks for having me on.
Chad: You bet. So what did we miss in the intro? And feel free to give us a little bit about Scout for those who do not know.
Ken: Oh well it sounded pretty good to me, so I liked it, thank you. I have been running companies for about 25 years or founding them as a founding board member or CEO. A bunch of different types of companies, but all tech, typically trying to shake up an industry. Done similar things in kind of hardware and semiconductors. Recently in advertising, bringing all that printed media ad into the internet through demand side platforms and all those kind of algorithms that have, you know, if you'll look at a chair and it follows you around the internet, the retargeting, it's got all that fun stuff. So you're welcome for that.
Ken: And now most recently in the HR space where we've got Scout, which is really a new way to use data for recruiting. And what we do is we connect employers who've got jobs to fill with recruiters who have a track record of being really successful in filling them. We make those matches, and then those recruiters get you great candidates to make really good hires. One way to think about it, and I know it's a little bit of a tired analogy with Uber is you know Uber, you push your button, it connects you with the driver who's best able to take you from point A to point B. Essentially we connect you with the recruiter that has got that best track record of filling that position so they can help you get a great hire.
Ken: And literally like 91% of all hires made by third parties are made by specialists. So that's folks who specialize in one role, they get to know the job spec really well, the company, they know the candidate pool, they can make the match. So we match you up with the matchmaker essentially.
Chad: So Ken, one of the things I... And what you're talking about right now, Joel and I talk about all the time. We think the on-demand kind of marketplaces are definitely the way that pretty much recruiting is evolving toward. But the hardest thing about HR and recruiting is adoption is incredibly slow to adopt. Now you're coming in from an entirely different industry. Tell us a little bit about what you've been able to see within obviously Scout in itself and engaging the recruitment community with regard to adoption of new technology, new processes.
Ken: Yeah, I'd say it's a similar and different. So it's similar in that most folks in most industries don't like to adopt new things. You think about in the ad space, right? Where the old joke was 50% of the ads work, but we don't know which ones and you can't move it or anything. And the sales people, they sort of had, they knew the client, they said, I know what you need, I know how to get your stuff done. But there was no data to actually assess if any of it was right. They pushed back hard in terms of adopting this new thing. But you know, the data speaks for itself, and also actually knowing what works and not and only paying for what works, it's really kind of a really good thing, right? So even though there's pushback and it's slow and you have to get them to believe and understand and all that.
Ken: Then you have the same thing going on in kind of the HR space here where people are used to having one recruiter they like, they know, they trust, and they basically pressure them to do more. But that one person isn't necessarily an expert in the jobs that they want and so forth. So you know, they really need to use the data and get connected with the ones who know the candidate pool really good. And you have more pushback, similar adoption. Now the biggest difference is HR folks on the most part, I mean generalized, but they're more people people, not data people.
Ken: And so, you know, getting them to kind of be comfortable and deal with the data and that part it has been harder. They typically want someone to help them with that. But there has been a lot more now like analytics people placed into HR, side by side with the people people. And that's really helped. So I actually see it starting to accelerate quite a bit now. It's interesting.
Chad: What ingredients do you need to actually create a marketplace? And how is your marketplace different from some of the ones that you mentioned like the Uber's of the world? Or maybe even the Fivvers or what have you?
Joel: Is Bounty Jobs a competitor?
Ken: Yeah, well I would say a couple of different questions there. So yes, Bounty Jobs is a competitor, but it's a good kind of a compare and contrast in talking about what makes a marketplace work and what really doesn't. So one you need to actually have everyone working together kind of under the same rules. And what we've done is we've created kind of a uniform contract and getting a bunch of Fortune 200 companies to all agree on the same terms is quite a challenge. But once you do that, now we have, 600 so companies and 5,000 or so recruiters under the same terms and conditions. And therefore we can instantaneously connect any one of them to get working on any job that they're expert at being able to do whereas others like a Bounty Jobs and others they don't have that uniform contract.
Ken: If you think about your Uber, you don't want to be negotiating with each of the drivers as you go, right? It's all under one contract. The other thing that you need is you need to be able to match, right? So you need good matching. People have really sort of looked at matching resumes to jobs and that's really hard. We can get into why for a number of reasons, it isn't because of the algorithms, the machine learning can't do it, it's really a data problem. We can get into that later.
Chad: It's a human problem, right? I mean because we really write bad job descriptions.
Ken: Yeah, exactly. Job descriptions have very little useful information, as does a LinkedIn profile for that matter, right? So it was connecting kind of garbage and garbage and that becomes quite hard to match.
Joel: Did you just call LinkedIn garbage?
Chad: No, he called your profile a garbage.
Joel: Oh okay. My bad.
Ken: Well mine too, my LinkedIn profile is as much garbage as anyone else's. But what you can match to is a track record. And so if you have a recruiter track record you can really match to that. And that's one of the fundamental things that differentiates us as well, is we have all that track record data and it's all done in a way that's sort of systematic, and we can uniformly apply it across companies, across industries, across job types. It doesn't matter what the job title is because we can figure out what it means and all that.
Ken: And we have access to all these jobs and so forth because we're integrated into ATS', which is another thing our competitors aren't. So for the enterprise you can basically check a box and post the jobs to Scout, there's no redundancy. If you want to track your candidates, and I know you guys know ATS as a note, you hate them like everybody else, as we do. But when you track it in an ATS, we'll pull that information, we'll send it out to the search firms so it cuts down on the noise. Everyone can know the status of the candidates in real time and all that. So we're trying to create this marketplace that's really information rich, available to everyone, transparent, and just really easy to make those matches. If you think about trying to find like a headhunter, third party recruiter for 40 different jobs, you want a couple for each of them, like yeah, good luck with that. You got to interview like 200 head hunters, like just hang me now, right? Like I don't want to do that.
Joel: Shoot me now.
Joel: Ken, I have a question, Chad. I want to get back a little bit on the company. Last year I wrote a blog post that highlighted I believe the 10 like biggest news stories. And one of them was was you guys raising $100 million, which honestly doesn't happen a lot in our industry. Jobcase did it again fairly recently, so there must be something in the water there in Boston that people are just writing checks. But I'm curious, what have you done with that money in the past year and when are you guys filing for IPO? Well.
Ken: So first, one thing that really surprised me when I started looking at this industry is the amount of investment that's going into it. And it only makes sense considering it's literally like a half a trillion dollar market. That's the amount of money people spend worldwide on recruiting. And that doesn't even include kind of SOW, which is like consultants and stuff, which probably be $5 trillion if you added that in. So it's a huge amount of money. Companies are getting, even in the ATS space, Taleo was bought. They're getting 10x revenue on those recurring revenue streams. And there's a lot, a lot of BC, PE investment going into that. So that's number one. And it's ripe for disruption, right? So it's a great investment area. I was surprised about that to learn that, but it was very cool.
Ken: And also we have an investor, John Schwong, who's an industry veteran. He's been pioneering lots of different stuff. He's a founder and not just an investor, he's totally committed believer in this, and he's got deep pockets. So that's good. The downside is, it's just him and I, and so we don't agree, he writes a check so you know who wins that argument.
Joel: Sounds like a wife.
Ken: I spent a lot of time like raising them money with BCs and that's a whole kind of thing in itself. And I love all my BCs, and I've had great ones, but there's a certain inefficiency of having to go raise money, and spend a little, and wait, and hit some milestones, go raise some more money, get five partners from five different firms to agree on anything, all that stuff. So this is really efficient and I mean what we've been doing with that is building a fantastic crop product, building a great a service organization because that's a big part of this. Matching technology and so forth and just investing in building the marketplace.
Chad: How does the matching technology work? You have algorithms around recruiters, but is it also algorithms around the actual candidates in the system matching up against the job descriptions? Because, I mean we were just talking about how job descriptions and profiles are pretty much junk. How do you match the garbage to the garbage?
Joel: By the way, I love how you just ignored my IPO question. That was great.
Ken: I'll answer that, no idea. No, that's all right, we're here to build a big valuable company. There's a lot of different liquidity options. There's no specific, we don't have any specific time table right now.
Joel: Fair enough. So back to Chad's question, I'm sorry.
Ken: Yeah, back to the matching. So what we do is we match the job to the recruiter track record against that job type. So basically we have this machine learning system that takes any job and it doesn't really matter what the title is, it basically looks at all the words on a job description and classifies it into one of about a thousand different job types. And then we group them into subtypes and categories and things like that. So basically you can figure out it's an employment lawyer, or it's a front end developer it's a backend developer, it's a marketing manager, those types of things. There's enough information in a job description to categorize it, at least at that level.
Ken: And then we do that for every single job and we look at every single recruiter, what they work on, what jobs, what candidates they submit, do the candidates get accepted into the interview process, how far did they get, did they get hired, all that stuff. So we just look at that track record against the job type, and you have a rating, a track record against that job type, that industry, that geography, all those different things. And then it makes it really easy to match it because we have these track records and all I have to do is just figure out what kind of job it is and match it up to the one who's got the best track record. If you think about it someone's been placing Java Developers in Boston for the last six months and been pretty good at it, they're probably going to be pretty good at it the next two months as well and get you some good candidates.
Chad: And I totally understand that because you're matching against a track record. That's something that should be solid, right? But on the other side, we're talking about trying to match profiles against job descriptions. You don't believe that we're up
to that just yet because it's all garbage data. Is that what I'm hearing?
Ken: It's a lot less useful. So it's not completely useless. But, I'd say 95% of our matching comes from the track record and maybe 5% from their profile. Now you can augment those. So you can do testing, you can pull information. So for example in a resume, someone may have, it may at least list a company. Well, you can pull the information on that company and find out, hey, this person stayed five years at a company that was a hundred person company or got promoted three times, so they're probably kind of good. So you can figure out stuff, but it's a lot more complicated and difficult.
Ken: So the track record is going really, really good and over time we'll build up and just in general, you know the matching of the candidates to the jobs will get better over time, but it's going to be a long time before it is even a higher weight even then the recruiter matching. Recruiter matching is going to be the main thing at least for the next five years.
Joel: You have an incredibly strong skillset in terms of artificial intelligence, and obviously being a PhD from MIT that should come as no surprise. But AI is obviously the buzzword in the vendor space in our industry. So everything from chat bots, to automated sourcing, to scheduling, to all of it. Just kind of curious about your overall thoughts on AI. How much of it is real? How much of it is just plugging in Watson? And what should HR people and recruiters as a whole sort of appreciate about what's going on with AI in recruiting?
Ken: Yeah, great question. It's actually something I love, I'm passionate about. It's actually, it seems hot now. I think two years ago, and I've got my head of marketing sitting around here, like I would say, we got to talk about AI, she'd be like, "No one in HR wants to hear about AI." And now it has become like obviously a pretty big thing and for good reason, right? Because the algorithms have gotten over the last five years, the algorithm has gotten really good. The ability to process lots of data, even for a relatively small company like us, we're using AWS and other things like that has gotten relatively easy.
Ken: So we can use the same tools that everyone else does to do that to, to build these algorithms, and the applications of them have started to work. So for instance, our recruiter matching that really works using let's say a chat bot to ask screener questions, no problem with that, it fits pretty good. So if you try to use a chat bot to figure out who you're going to hire, it's going to be a disaster. If you're going to use a chat bot to do the screener questions, great. If you're going to use machine learning to match your resume to a job, you're going to end up with the problems Amazon had, which is arguably the best at machine learning, but they had this completely biased algorithm that didn't work. But if you are going to match your recruiter track record, it's going to work pretty well.
Ken: So the great thing that I've seen is not only the algorithms getting much, much better over the last decade, but now, especially even in HR, they've got real applications that really work. You've got to pick them carefully. I sort of think about it in terms of human decision making complexity. And you don't want to start with the hardest problems, you want to start with the easiest time consuming problems. And that's just like automation, you do it that way regardless. So in AI, now there's applications that really work and that's what's exciting. I think it was-
Announcer: It's commercial time.
Chad: Dude, we're always talking about cool new tech, but it's hard for hiring companies to change. I mean adoption's a bitch.
Chad: New tech can get them to qualified candidates so much faster.
Joel: I know man, but recruiters already have their routine in place and nobody wants to jump into another platform, especially when it's expensive and also requires hours, maybe days of training.
Chad: Exactly, but that's where Uncommon's new service comes into play. Uncommon pairs expert recruiters with in-house kick ass technology.
Joel: All right, interesting. Interesting. It sounds like Uncommon understands the problem of change.
Joel: Yeah, yeah. But I bet they're expensive and I bet it requires some kind of annual commitment or contract, right?
Chad: No, man. Uncommon is not an agency, they don't require a contract, any contingencies. All they do, they charge one flat fee per project saving, I don't know, anywhere from 50 to 80% on each hire versus the average agency cut.
Joel: Oh, snap companies could save big stacks of paper, especially if they're rapidly scaling and need hires today.
Chad: Yep. And all you have to do is reach out to Teg and the Uncommon crew at Uncommon.co. That's uncommon.co.
Joel: Change doesn't have to be a pain if you're using Uncommon.
Chad: It's show time.
Ken: What do they call it? Like the nuclear winter of AI was for about 30 years starting a decade ago where all these people were working on and none of it really worked for anything. So it was pretty cool to see it start popping up and actually working.
Joel: Yeah. You mentioned chat bots and a lot of the money that's flowing into this industry is going to chat bots. Should we believe the hype or do these services have a long way to go to really make a difference?
Ken: Oh I think a little of both. They've got a long way to go to really make a difference, but like I said, you're starting to see some initial places where it actually kind of works okay. You know, initial screenings. You know the beginning, if you think about a recruiting process as a sales process for the big funnel of the world of candidates at one end and the alpha couple or one that you want to hire at the other end. You know, at that big screening step at the beginning, or getting people to pay attention to you, or marketing to them, you don't need humans to do all that stuff. You really can do that algorithmically and then have a little bit or interaction with the people who respond. I mean, it's better.
Ken: Right now what does marketing have for that interaction? They see if you read something or clicked on something. Okay, well take it to the next step, ask them a simple question, get an answer one way or another, and now you've qualified them a little bit further before you turn it over to a human. That stuff actually is starting to work pretty well.
Chad: We were actually on stage with Holland Dombeck from a Delta Airlines a couple of weeks ago and she was talking about how her recruiting team has, they've been able to log that they're actually getting back 80 hours a week to their recruiting team. And I believe, I agree a hundred percent, chat bots have a long way to go, but because our processes are so jacked up right now as it is, these little technologies, if implemented correctly, there can be huge impact right now.
Ken: Agreed, totally.
Chad: So kind of spinning away on talking about AI, you just mentioned Amazon and that algorithm, and how it kind of went haywire. Knowing that there's a lot going on with compliance with GDPR across the pond and then obviously here pretty soon in California they're going to have a GDPR-like type of regulation that is coming out. Do you believe that black box AI is going to be able to exist? Or do you think all of that is going to have to be open and transparent so we can actually find out why the AI is making the decisions that it's making, especially when it comes to candidates and pushing them through the process?
Ken: Yeah, the compliance stuff is pretty interesting. We're already GDPR compliant here at Scout. So we kind of know how to deal with a lot of that stuff. And again, because we're looking at a track record where we can look at the track record against even let's say gender, right? We know the answer should be 50-50 that one's pretty easy. And we can see, basically the bias in the system, we can correct for it so we can show that stuff. So we have a big advantage to be able to dealing with that stuff right now.
Ken: But you asked a really... One of my favorite questions about the black box problem, right? And how do we deal with that because no, people are not going to put up with a black box for picking candidates in a world where there's known biases and things like that. So what do you do? And it's an interesting problem because if you break out the components of why you're selecting people and then use that, you're actually have now sort of handicapped your algorithms by doing that. But if you don't do that, then what are you going to do? I do think there's a solution in the middle, which is where you actually can not only output the selection, but you can output the why out of the black box. And then if you show that that why actually correlates and stacks up to the output, over time you can show that you're compliant. That's what we're doing at Scout.
Chad: Got you, got you. So when we talk about Scout itself, how many recruiters are in pretty much the database that are ready for work? And then also what segment of the population are you currently focusing on to be able to obviously provide these services?
Ken: Yeah, so we have about 5000 recruiters on the network. We probably have about 1000 kind of weekly active or so users, just kind of how we measure it, which is someone like working on a job right now. So that's kind of the population we're dealing with right now, we are focused in North America. We're not international yet, that's probably going to be next year's a task for us. But we do all industries and all job types and so forth. And we started in North America, we started with I guess what you would consider your traditional headhunter contingent firm roles. You know, your engineer, your director of this or that, even like nurse, doctor, whatever, that kind of stuff that you hire a headhunter to go help you find. And that was interesting and that's sort of how we got started.
Ken: But what we found is now this network has gotten more and more powerful and efficient because we're able to direct the jobs to the folks who are experts in them instead of them using their 10 candidate pool that they found to fill one position, they can now fill three with it. So they're getting a lot more efficient and that drives that efficiency, drives the cost down and so forth too, right? So everyone is more efficient. And what we found is we can go after not just traditional headhunter jobs, but all kinds of jobs. So we fill manufacturing operator jobs, at a couple thousand dollar a piece, we fill call centers at $1-2000 apiece. And interestingly all the way up to directors of medicine, heads of global markets, chief investment officers, $100000 fee. So the fees range from $1000 to $100000, and we really now can have a great strategic conversation with our customers about where do they need to augment their team, where do they need help hiring, and those are the jobs we can help them fill. And it could be the traditional ones, it could be executives, it could be more the volume jobs, either one. And now we started adding temp as well.
Ken: And we'll be able to optimize even between not only which firm, which recruiter works on these different jobs, but even do you want to do a temp or a temp to perm or perm, what geography you want it, and we can really advise our clients based on all the data we have. So it's a whole other set of services and information we can provide to our clients just around the huge amount of data that we're collecting. I think right now, basically a candidate is submitted like every 30 seconds or something like that. I can't remember the exact number, but it's getting pretty large.
Chad: Got you, got you. So it's interesting because as we talk about AI, and machine learning, and all these big kind of scary things, recruiters kind of feel like they're going to lose their job. And we've had conversations about how we feel about that. What are your thoughts? Let's say in the next five years, what does a recruiter's job look like? Does it look pretty similar to what it is today? Or is it much different than what it is today?
Ken: No, we won't need recruiters at all. So it's going to look quite different though. And what's going to happen is, I mentioned before kind of this human decision making complexity model, which is when you look at disruption of jobs and job functions. So the type of work that's done, that's more the mundane lower-level stuff, reaching out to candidates, scheduling, initial screenings and filters, the recruiters are not going to have to waste our time doing that anymore.
Chad: Got you.
Ken: What they're going to do is spend their time on really assessing the few that make it through all of these automated screens and so forth, or maybe finding the diamonds in the rough and really figuring out are they a fit for the company, for the job, and are really doing that actual hard work of doing that. The other thing they're going to do is sell the candidate, right? No one changes jobs and leaves a good job, but the people you want all are highly sought after.
Ken: And what's going to change because a chat bot asks them to come
take this cool new job and it really takes a human to sell somebody on something like that. And not only that, the candidates we want, they're passive. They have jobs. We don't want people who are filling out job applications online. We all want to hire people who are great employees that are currently gainfully employed and probably don't have a whole lot of time to pay attention, and certainly not pay attention to chat bots reaching out to them. So you need these recruiters and the relationships to network to the right people and to have relationships with them and convince them to even look at a job, look at a new company. That's the hard work that a recruiter is going to do, they're not going waste their time scheduling and screening and the other stuff that frankly they probably don't want to do anyway.
Chad: Right, right. So knowing that AI is getting smarter and smarter, do you believe that AI is going to be making a lot of these decisions up front and then it's just going to be the product on the back that we're really having to work through? So they're doing the candidate matching, they're doing the screening, they're doing all of these things that have pretty much been programmed into them of what we want and what we need. Is it just going to be a press the button to push the job out and then you wait until after the first interview before you actually see what pops out on the end?
Ken: I think a lot of it will be done in that way, but also you need ways to introduce randomness because you don't want to get too stuck. You're like you're never going to learn and do new things if you keep doing kind of the same thing.
Ken: And we even give that ability. So we'll give the ability of newcomers to try out and search for jobs, find them and work on them. We'll give them the ability to try new companies, new job, whatever. They'll start getting shut down if their performance stinks and kicked off if they're not good. And so if it's not working for them, they'll move onto or they'll stick with what they know or whatever. But you do need ways to make sure that your system isn't stifling in a nation, that your system isn't, blocking out certain segments, especially because of bias. So it's not that simple as just saying, oh yeah, we'll wait until we have this sort of short list of great candidates and screen them. There's a lot more work to do, but it's more of the intellectual work to make sure your system is working right. Then the mundane stuff of figuring out, oh, how am I going to get these five people scheduled on the same day for these three people I want to bring in? You don't have to spend a lot of time doing that anymore hopefully.
Chad: Last question, I don't know if you're familiar or not, but Indeed bought a platform called Sift. And Sift is really a marketplace from my standpoint, as we take a look at Indeed is owned by Recruit Holdings out of out of Japan, they are a huge recruiting/staffing company. I personally see Indeed trying to move staffing into more of a marketplace type of scenario. Do you believe that big companies like Indeed are going to start to pivot into your market? And if so, is it going to be more on the staffing side or do you really see it as more direct to an employer?
Ken: You know, I don't know if they're going to try to shift into our marketplace, but there's a huge need for kind of what they do, right? So all of the recruiters on our network need to find candidates are what we call our providers, right? The 5000 search engines out there. We're not providing the candidates for them, they're finding them through their network through other ways. And Indeed frankly is one way. There's also companies want to hire some folks directly, they don't always want to use a third party recruiter and so forth. And so again, the internal recruiters, they have limited networks for all the different job types they have to try to get and so forth. So they're always going to use things like Indeed to post their jobs and source and so forth.
Ken: So there's still a need for all of that and a big need for all of that. And it's just consolidation is good for this industry in that sense. And that for the candidate's easier, like I don't want to look at 20 different job boards or whatever. If there's one place that's serving the need or a couple that's actually not so bad. So the fact that Indeed has been buying up all these different properties and sites to do that, and that they're trying to match the candidates of the jobs they should be trying to do that, even though we all talked about, it's really hard. Whether we're going to get into tracking recruiter track records and all that stuff. And one of the problems they have is that once a candidate is submitted, they kind of lose visibility of it. So they have no way to know who's good, where we have visibility into those submissions and what happens to those candidates throughout every job, every company, and so forth. And we can normalize it, as I said, by job type and industry and so forth.
Ken: So we can build those track records because we have access to all that data and we use that to create the ratings for the recruiters. And those ratings, incorporate all the things these recruiters are good at. Like finding candidates, finding jobs, candidate experience, all that-
Chad: Sounds like an Uber rating, right?
Ken: Well, it's like the stock price, right? You know how they say all the information about a stock is included in the stock price basically? So our recruiter rating is basically that. So if you want to know who's good with candidate experience, well it's the higher rated ones because they've got people placed. And so having access to that end to end data set, is something that as a marketplace operator and us being in the middle and being integrated and ATS', we have a pretty unique view in terms of being able to track all that stuff.
Chad: Got you. So I lied, last question. We just talked about having one place to go, like where we were referencing Indeed. What about Google's foray into this market from the Google For Jobs standpoint, not to mention applicant tracking system, they are currently powering like 4000 different recruitment platforms. What do you think about Google's foray into recruitment?
Ken: I think it's great. So I mean we actually work with them quite a bit on some of this stuff, including the job categorizations and things like that. I mean they basically got into it because people were searching for jobs. And what is Google good at? Search. And we're actually using some of their capabilities in terms of job search, everyone uses Google search, right? So I think it's great that they're in that. We've collaborated on the categorization and all that stuff. I think that in terms of their ATS, if you look at the Google Suite, what do you have? You have calendar and you have mail, right? I mean that's awesome, right? So again, I was talking about chat bots doing scheduling and all that stuff, putting that in the ATS platform, that's awesome, All the ATS platforms are our partners. We're partnered and connected with IBM Connects and and Oracle, Taleo, and Workday, and Google and so forth, right?
Ken: So we don't view that as competitive at all. We actually view them as a great partner.
Chad: Excellent. Well Ken, first and foremost, I'm going to have to apologize because this is after lunch and Joel usually takes his nap after lunch and I think he actually is taking his nap right now. But I appreciate you coming on, talking to us and anything you want to leave us with?
Ken: No, just thanks so much for the opportunity. It's great chatting with you guys, even if Joel did fall asleep. And yeah, anytime you guys, I'd love to chat about Scout, and recruiting, and AI. It's like nothing I'd rather do, so thanks.
Chad: Excellent. So if somebody wants to find out more about Scout, where would the go?
Chad: Excellent. Thanks so much.
Ken: Thanks guys.
Chad: We out.
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