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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Tengai: Hi, this is Tengai, the unbiased interview robot. You're listening to the Chad and Cheese podcast. I love these guys.

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.

Chad: Right.

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.