Sponsored by

Sponsored by Jobs in Pods

TotalPicture Radio Podcast

Newsletter

HTML | Text | Mobile

Become a Sponsor

Info!

Information on becoming a Sponsor for TotalPicture Radio podcasts is available on our FAQ page, or feel free to contact us with any questions or concerns.

Suggested Podcasts

Performance Based Matching Using Scout

Ken Lazarus, CEO of Scout Describes Scout's Ability to Track Down Great Candidates

 
Ken Lazarus, CEO of Scout Exchange TotalPicture interviewKen Lazarus

Welcome to an HR Technology Channel Podcast on TotalPicture - I'm your host, Peter Clayton.

Joining me today is Ken Lazarus, CEO of Scout Exchange, a platform for marketplace recruiting. Scout, based in Boston, connects employers with search firm and third-party recruiters . Scout's recruitment marketplace identifies specialists by using data analytics - what they call 'Performance Based Matching' - all within the Applicant Tracking System (ATS) your company is already using. Scout's integrations include ATS heavyweights IBM Kenexa, Oracle Taleo, iCIMS, SAP SuccessFactors, Workday, and Bullhorn.

Ken has been a cofounder, CEO, director and advisor for more than a dozen technology-based startups during his 20-year career. He joined Scout in 2013, Lazarus holds a Ph.D. from MIT and a B.S. from Duke University. He's a World Economic Forum Technology Pioneer, has served as an MIT Visiting Committee Member and holds over 20 patents.

Complete Transcript: Ken Lazarus

Peter: Welcome to an HR Technology Channel podcast on TotalPicture. I'm your host Peter Clayton. Joining me today is Ken Lazarus, CEO of Scout Exchange, a platform for marketplace recruiting. Scout, based in Boston, connects employers with search firm and third-party recruiters. Scout Recruitment Marketplace identifies specialists by using data analytics, what they call performance-based matching, all within the applicant tracking system your company is already using. Scout's integrations include ATS heavyweights IBM Kenexa, Oracle Taleo, ICIMS, SAP SuccessFactors, Workday and Bullhorn.

Ken has been a co-founder, CEO, director and advisor for more than a dozen technology-based startups during his 20-year career. He joined Scout in 2013. He holds a PhD from MIT and a BS from Duke University. Ken is a World Economic Forum Technology pioneer and has served as an MIT visiting committee member and holds over 20 patents.

Ken, welcome to TotalPicture. Tell us a little bit more about your background and specifically what attracted you to join Scout.

Ken: Well, I think the dog's pretty cute.

Peter: The dog is cute. I agree; the dog is very cute.

Ken: Seriously, I've been running different tech startups for about 25 years in different industries and different products; but lately, I'd say the last 10 years, I've been focused way more on SaaS software and marketplaces which are with cloud computing and so forth. They're businesses that you can really scale quite quickly and are really interesting. I had some experience with that as a founding board member of DataXu, which is an internet advertising platform.

So when John Chuang, who I've known for 15 years, approached me about Scout and creating this huge disruptive marketplace company and recruiting space, I got really excited about it. And having a super successful, great guy like John who's our funder and backer - he's the CEO of Aquent, which is a large creative staffing company - the opportunity plus the backing from him just made it a no-brainer for me to join.

Peter: So when you go on to Scout's website, which is goscoutgo.com, you see this cute dog and it's split into one side for employers and the other side for search firms. So, what's the difference for the employers and the search firms?

Ken: Yeah. There are two sides of a marketplace. You can think about it as buyers or sellers or those who need a service, those who provide a service. I think one great analogy is, okay if you're on the street corner, you push your rideshare app and the algorithms figure out which is the best car to take you from point A to point B.

If you're an employer, you have a job, our application is going to figure out which search firm recruiter is best able to help you do that job. So, two sides of the marketplace, that's really the key to Scout, is that we are building this two-side of recruitment marketplace.

But at the end of the day, the objectives are the same. The objective is to hire a great employee, so everyone wins. In fact, the system's set up that way so employers are happy; they get a great hire. The search firms are happy; they've got a placement. They get paid and Scout gets a cut of the revenue.

It's a truly two-sided marketplace. We're operating the marketplace but we're all aligned with the same goal, connecting employers with great search firms so they can make awesome hires and fill their positions quickly.

Peter: All right. So, let's get into the leads a little bit and talk about machine learning, which of course is the buzzword du jour in recruiting today, right?

Ken: Yeah.

Peter: So tell us a little bit about how machine learning factors into Scout.

Ken: So if you don't mind, let me do a little background a little more on Scout and kind of what's really important.

Peter: Absolutely.

Ken: And then that'll make it clearer when we get to the machine learning, kind of how that fits in. So first of all, what we find out and look at all the data in the space is that 90+ percent of placements are made by specialists. That's a search firm recruiter or a recruiter who works on the same job type over and over again. So, if you specialize, you're literally 10 times more successful than if you don't. So that's super important.

Second is that you need a lot of these specialists if you're going to have the chance of getting the one that is a specialist for what you need. And you need all of the people in the marketplace to be able to work on sort of all the right jobs. You need liquidity, which means one contract, easy access, transparent information. Those sorts of things are super critical.

And then you need to be able to match the job to that search firm recruiter in the marketplace. So now this is one of the fundamental uses of the machine learning, is creating those successful matches. And there's a lot of factors that go into it.

Principally though, it's track record. When we say track record, we mean how well do they do at placing candidates for that job type in that geography, for that industry and that type of culture, whatever it is, there are a gazillion things that go into machine learning. Some of them we probably don't even know.

That's the beauty of machine learning. It's looking at the data. It is constantly improving itself on successful batches and we know we have the metric because we're making hires in Scout. We're submitting hundreds and thousands of candidates every day through Scout and we know the ones who are successful and we learn and we get better and better at making those matches.

The other thing that machine learning is obviously very cool about is that it's pretty unbiased and I know people worry about machines taking over and one day they're going to get smarter than humans and take over and stuff like that. I think maybe that's a legitimate concern for my kids' lifetime perhaps. But for ours, I actually worry more about humans monkeying around with the machines. In other words, there's a lot of decision bias when you look at the stuff that Danny Kahneman does around decision bias and so forth, especially the importance of diversity recruiting for large employers.

Actually, having machines help us select the right recruiters to work on the right jobs takes decision bias out and creates a better success in terms of these employer-recruiter matches and in terms of hiring.

There are other uses of machine learning. We do ratings, so people can say how they're rated, how they're doing in certain things. We look at how busy you are. We look at how urgent, how hot a job is-- not based on whether they say it's hot but actually based on the data, actions, and machine learning in the marketplace. We apply it to a lot of different things.

The future and nirvana is to be able to automatically match candidates to jobs, but that's still pretty hard. In the meantime, what we can do is we can match you to the recruiter who can sort through the candidates and find the best one for you.

Peter: So where does your candidate pool come from?

Ken: The candidate pool comes from the recruiters on the system. The experts - that's why the specialists are so good because if you focus on a job type, you really get to know not only the requirements for that job type but you really get to know the people; who's good, who's not. You can reference them. You can network. You have candidates who you're working on for one job, a developer position. If you have another one, typically a lot of them are going to be good for another job, a developer position.

The good specialists know their candidates. They have their candidate pool and all we need to know is who's good at placing them, what's your track record and we'll connect them. They'll be ready to submit very quickly.

Peter: So does predictive analytics factor into this?

Ken: Yeah. Machine learning is part of predictive analytics obviously. If you want to think about it kind of in those terms, what you can say is okay, what are we doing? We're really optimizing to get five great candidates in the interview for each of the jobs posted.

So we're going to recommend that job. We're going to suggest submissions. We're going to make matches in order to optimize that sort of across the system. That's kind of the goal. Obviously, the goal is to make a hire, but that really is still up to the company, which obviously to which person they want.

Our goal is to get them a great slate of candidates as quickly as possible. And when we see what that leads to is 30-40% faster time to hire, 30-40% higher fill rates and because of the power of the marketplace and marketplace dynamics, we typically see 30 or 40% savings.

Now that's great for the companies, maybe say that's not so good for the search firm recruiters, but we are getting the search firm recruiters more placements. And frankly, one or two extra placements makes up for any nominal differences in the fees.

Peter: Let's talk a little bit more about metrics because that has become sort of the one thing that recruiters can use to go back to their management and say, this is why this is working, if they have the correct metrics, right?

Ken: That's absolutely right. There are two things we use it for... let's say we use many things - but the metrics and analytics that we provide to both the employers and to the search firms are really critical.

One, obviously they see how they're doing. We can compare them to benchmarks, industry benchmarks, overall system-wide benchmarks. We can break it down by recruiter, by job type, by geography. We can give them advice. Let's say they're having trouble in one area, maybe they want to increase their fees to get more attention on that. If there's a ton of recruiters and applicants in another area, another geography we can suggest they lower the fees there because they're getting plenty of candidates.

We look at patterns in their recruiting behavior and we can suggest to them to replicate more successful patterns in their recruiting behaviors; for example, the time it takes for them to review a candidate. That's one of the number one things that gets a search firm excited about working with a company and if they make that extra effort to move fast, they're going to have the best search firm recruiters working for them.

Conversely, we show similar data to the search firm recruiters and what they'll say is the employers want a small slate of really good candidates as fast as possible and to make sure that all the requirements are met and so forth, there aren't any of the obvious ones that they're missing. If the person is not going to relocate and there's no relocation, they better have paid attention to that and not submitted that. So they're looking at those acceptance rates, those ratings.

We can actually tell the search firm recruiters, hey look, you're a five star in this area but you really only get two or three stars in this other area; you're obviously doing a lot... why don't you focus on these other areas and by the way, we're recommending those jobs to them, the ones that they're good at. We're going to help them really stay in their sweet spot of what they do really well.

Peter: So I'm a big fan of SourceCon and I have a lot of friends who are excellent sourcers. How would Scout benefit them and their application of your solution?

Ken: If you're a sourcer, which means you're really good at finding talent, and maybe you're not as good at the skills part about getting the clients to sign contracts which is really tough with enterprise contracting and requirements, and all that stuff. You could jump on Scout. We have a qualification process. You need recommendations, all sorts of things. But if you're good, you'll get those. You'll get on Scout and really your business development is taken care of and we're able to understand what your specialty is and get you those same jobs sort of over and over again. So you're going to be really, really successful if you're good at sourcing. There's no question.

Peter: So what's really important for potential clients to know about Scout? Let's start with the search firm side of the equation.

Ken: I think the main thing for a search firm is that you're now getting access to lots of jobs in your specialty. It used to be, it's hard to get a contract and so you would try to get - once you had a contract and you did well, let's say you're a marketing specialist which is what Aquent, our parent here, is and you do well at filling those jobs, so you want more jobs; it's easier to get the sales jobs or the engineering jobs and maybe get a contract in a different company but you're 10 times less effective at that, so you made yourself kind of unsuccessful, if you will. That's why there's a lot of dissatisfaction in the industry.

But with Scout, you're going to be able to basically sign up and based on your track record, get jobs in your sweet spot. That's one.

Two, you're going to get a lot more efficient because we're providing you the data at your fingertips that you have to work really hard to get otherwise. For example, as the employer is tracking and moving the candidate from one stage, let's say from screen to interview, they'll take care of that in their ATS and that'll just show up in your screen if you're a search firm recruiter. So you'll see you don't have to spend time calling and emailing and so forth to find out the status of candidates; it will be automatically provided to you.

Another thing is, what is the competition on the job, who else is working on it? When they look at a job on Scout, the search firm recruiter will be able to see how many candidates were submitted, where are they in the process. So it's up to you to decide, hey this one looks like it's going to get filled already so I don't want to work on it; or hey, there's not a lot of activity here and I've got some great candidates, so I'm going to focus on this job.

That transparency is sort of super critical and again, we're going to provide them the reporting analytics and the matching really to help them be successful. So those are, I think, some of the highlights for the search firm.

For the employers, especially the big enterprise employers, a lot of times they have no idea what they're spending on search firms, what they're using on search firms, which ones are successful, which ones are not, what jobs they even have out. It's a bit of a mess sometimes. And even if they do have a little bit more understood in terms of what they're spending and so forth, they really don't have a lot of data about which areas are being successful and which ones are not being successful, and how they can improve; and that's one.

And then two is, there's a new paradigm here that I think they all should know about and that's the marketplace recruiting, which they never had before. So before if they wanted to get better, they really had to go lean on their vendors, which meant pressuring them for better fees, pressuring them for more attention. You can only beat on vendors so much that you can't squeeze any more blood out of that stone.

What we found as a much better way is really let market forces do the job of letting the best search firm recruiters match up on the jobs that make sense for them to match up on and deliver the candidates, and have those sort of marketplace forces provide the right fee, provide the incentive to deliver the right candidate and it really works. We have the objective data to show that our clients really, really do get 30% better fills and 30% faster fill rates and 30% savings.

So we really offer them a lot. In fact, one of the questions we get is, can this really be true? Is it really no cost to me and I'm getting all these great results? And it is true, but the thing we have to push through here is that that leap of faith, if you will, that belief in this new paradigm of marketplace recruiting. As you know, when you're disruptive and you're trying to do something really new, it's hard. I think even with Uber or Lyft, you probably were pretty skeptical until the first time you pushed the button and you got a ride, and said hey that's pretty good. That's better than a taxi. I really like that. Or use Airbnb and you stayed somewhere and you said, wow that was great.

But everyone's a little skeptical going in and for good reason. People are taught to not trust strangers and not trust new people you don't know and things like that. We've got to design our system and our material, our information to get people over that hump and try it. Once they try it, they love it.

Peter: So what differentiates Scout from say RecruitiFi or BountyJobs which are basically doing the same thing, right?

Ken: It's sort of the same thing but in very, very different ways. First of all, one of the innovations is the liquidity of the marketplace and the contracting. We have one contract and the only thing that sort of dictates whether the search firm can work on a job, we use our machine learning analytics and so forth to kind of make those matches as opposed to having the employer pick and then have to sign a special contract and all sorts of things that get into the true liquidity.

I think the other thing is integration. We're deeply integrated into all of the major ATS's. We essentially cover 90% of the Fortune 1000 with our integrations. That means there's no duplication in data, there's no wasted time. Everything's synced. The recruiters get that information real-time. We're also SOC 1, SOC 2 qualified, passed, certified because data security and so forth is a huge big deal these days. Also, our systems are designed to be very intuitive, easy to use. Our whole philosophy about the data sort of ruling the day and the marketplace ruling the day is very different from some of these other companies.

Peter: I think it's really nice that you can integrate with ATS's. However, all of the recruiters I've spoken to hate their ATS.

Ken: Yeah, put me on the list. And so when we say integration, I want to be clear about what that means. So that means the data is being synced behind the scenes.

Peter: Got it.

Ken: So you're still using our modern consumer-oriented, easy-to-use web app that is really intuitive and great, and people love. And then behind the scenes, where appropriate, we're syncing the data.

I can't do anything about your ATS. But I'm not going to make you use it any more to use Scout. I'm just going to take advantage of the data that's in there so you don't have to do any double entry and everything works smoothly.

Peter: Ken, I really appreciate your time today. Is there anything we haven't discussed that you would like to share with the audience?

Ken: I think the cool thing is just the success of the customer. I think we're still a pretty new company, but we have some very major clients. We've got about 700 employers on the system, 50 of the Fortune 1000. We've got about a few thousand search firm firms on the system. It's nearly 10,000 search firm recruiters at this point. We're having a lot of success and growing at about 10x a year and things like that.

It is working and we have a lot of happy clients. We're trying to obviously get the word out because we think people should be taking a look at this and understanding the power of marketplace recruiting and how our platform works.

I really appreciate you taking the time today to have me on. I really enjoyed it, and thank you.

Peter: Ken Lazarus is CEO of Scout Exchange. You can learn more about Scout and check out their cute dog at goscoutgo.com.

Your comments are welcome on Ken's show page in the HR technology channel of totalpicture.com. While there, please sign up for our free newsletter. You can subscribe to TotalPicture on iTunes and Google Play, and join the conversation on our TotalPicture Radio Facebook group.

You'll find me on Twitter @peterclayton, @totalpicture and @jobsinpods. This is Peter Clayton. Thanks for tuning in.

{/slide="Interview Transcript"}
Peter Clayton

About Peter Clayton

Peter Clayton, Producer/Host, is an award-winning producer/director of radio, television, documentary, video, interactive and Web-based media who has created breakthrough media for a wide array of Fortune 100 clients.

Discussion

Posting advertisements, profanity, or personal attacks is prohibited.
ads by google
Posted in:
Interview Channels,
HR Technology Interviews