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Michael Beygelman, CEO Joberate - Predictive Analytics for HR and Recruiting

A conversation with the winner of the 2014 iTalent Competition at HRO Today Forum, Michael Beygelman

Michael Beygelman, CEO Joberate -TotalPicture Radio interviewMichael Beygelman
Welcome to a HR Technology Channel Podcast on TotalPicture Radio. This is Peter Clayton reporting. Last year I met Michael Beygelman at the HRO Today Forum. My friend and client Mark Finn interviewed Michael for TotalPicture Radio. At the time, Michael was the President of Pontoon, a successful RPO (recruitment process outsourcer) and MSP (managed service provider).

When Michael took the stage this year at the HRO Today Forum's iTalent Competition it was as CEO of Joberate, a company providing predictive analytics for HR and recruitment. His presentation was a hit with the iTalent Competition Judges -- Debbie Bolla, Andrew Gadomski, Bill Filip, Ilya Breyman, and Elaine Orler: Joberate came in first place.

Michael joined Joberate in January of this year.

TotalPicture Radio Transcript: Michael Beygelman, CEO Joberate

Welcome to an HR Technology Channel podcast here on TotalPicture Radio. This is Peter Clayton reporting. Last year, I met Michael Beygelman at the HRO Today Forum. My friend and client Mark Finn interviewed Michael for TotalPicture Radio. At the time he was RPO president of Pontoon, a successful RPO (recruitment process outsourcer) and MSP (management service provider).

When Michael took the stage this year at HRO Today's Forum in the iTalent Competition, it was as CEO of Joberate, a company providing predictive analytics for HR and recruitment. His presentation was a real hit with the iTalent Competition judges; Joberate came in first place.

Michael, congratulations on your iTalent win and welcome back to TotalPicture Radio.

Michael: Thanks so much for having me, Peter. I appreciate it.

Peter: When you joined Joberate back in January of this year, you had a pretty good job, seems to me as RPO President of Pontoon. So why did you decide to accept the CEO role at Joberate?

Michael: That's a great question, thank you. I had spent nearly 8 years with the organization. Pontoon is actually a wholly-owned subsidiary of the Adecco Group. You may be familiar with them. They're a fairly large global staffing provider, $25-28 billion, something like that.

Peter: That's pretty big!

Michael: Yeah, it's pretty big. It's one of our claims to fame at the Adecco Group is we were a top 5 employer globally in terms of how many people we employ at any given day in the world. So that was kind of interesting.

I had actually started the RPO business for the Adecco Group, which was incredible. I had joined as a consultant many years ago, stayed on, worked with the CEO directly and had the privilege of taking an idea and turning it into a business with more than 100 global clients in more than 40 countries. It was an incredible run of 8 years. It was a lot of fun, met a lot of great people. It's a great organization.

Going through that, being in the recruitment side, we really positioned ourselves as innovators. One of the reasons why I decided that it was a good idea for me to join Joberate is that I really wanted to continue down the innovation front. I really looked at recruitment and felt that it's sort of gotten a bit stagnant over the last couple of years. We had sort of a lot of innovation coming out of the gate, but really over the last couple of years, it became more difficult. There was really just point improvements.

I saw what Joberate was doing and I thought that we could disrupt the paradigm and do something different and unique and really help the industry. So for me personally, I really like building and I like innovating and I like to help solve real world problems.

I kind of viewed joining Joberate as the CEO as sort of the next step or the evolution in my journey. It wasn't a sideway step. It was just really a step in a different direction in terms of focusing more on the technology. If I told you that I don't miss working with all of my Pontoon colleagues, I would probably be a liar.

Peter: Right, yeah. In my intro, I mentioned that Joberate is into predictive analytics, which of course along with big data is one of those terms getting used a lot in HR and recruiting conferences. It's the buzzword du jour, so to speak. So explain to us exactly what predictive analytics means for specifically recruiting and HR.

Michael: You are right. If you remember when I was on the stage, I almost didn't know what to say. I said, wow I thought I was going to talk about predictive analytics but apparently everybody's doing it. So it was kind of a joke and everybody laughed.

Let's talk about this in really, really simplistic terms. If you think about HR to business function, recruiting is a business function. So any business function today has to have some level of reporting. And so the value stream of how you get the predictive analytics starts at the very fundamental rudimentary sort of level of traditional reporting. Traditional reporting basically allows the manager or the recruiter to say what happened. It usually measures results, efficiency, compliance, etc.

And then you get, let's say to the next level, then people would want to know okay, why did that happen; and then you kind of get more the dynamic reporting. You add the aggregate various pieces of data from different sources. You do some benchmarking, some validation - again, all these terms you hear a lot at different conferences. And that kind of says, okay we've benchmarked five or six things, now we know why this happened.

Then you get to the next level, really it's sort of the executive level and they kind of want to know, okay so what's happening now. What's happening now is really what I would call data analysis. You have to look at relational models of how things correlate to one another, understanding cause and effect, use some statistical analysis, and you kind of get these what I would call just in time reports - for a lack of a better term - about what's happening now. Where predictive analytics sit (and that's kind of why the joke was funny), is that what I basically described is what most businesses do at various levels of success.

What predictive analytics is really about, it's about saying what can happen or what does the future hold. And analytic is simply just a fancy term for a data point with some sort of context.

Peter: Right.

Michael: So when you talk about predictive analytics, essentially for HR and recruitment, you're talking about developing predictive models. The best example I can give you is something like a FICO score. A FICO score, for a credit sort of history says that this person will either pay their bills or they will not pay their bills, they're either a good credit risk or a bad credit risk.

Predictive analytics in terms of HR could be applied to different things. For example, if you look at predictive analytics around assessment, they could tell you how likely someone is going to be successful at a job, just in simple terms. If you look at, do you have predictive analytics around behavioral studies, they could say how likely is this person going to fit into this work group.

What we focus on is we focus on tracking job seeking behavior so that specific aspect. So our predictive analysts will tell you how likely someone is to take the job. That's really important, which is interesting because if you think about on the recruitment side, because the predictive analytics could be used for both recruitment and HR, Peter, if you think about it on the recruitment side, it really - I'm going to say something sort of stupid simple, so don't laugh. People go, 'I'm really having a hard time finding JAVA developers.' You kind of hear this. Or pick your skill set.

Peter: Exactly.

Michael: I'm having a hard time finding people that understand Hadoop, some esoteric skill set. What they're really saying is they're not having a hard time finding it; go Google them, you're going to find 10,000 of them. What they're basically saying is, I'm having a hard time finding someone with the skill set that wants to take this job. That's a very different thing.

Peter: Yeah, you're right. There are a lot of JAVA developers out there.

Michael: Of course! Don't tell me you're having a hard time finding one. I can find you 3 million, I can tell you where they are, and I can give you their numbers. That doesn't mean they're going to take your job.

I'm sorry for being so funny, but you kind of get the point.

Peter: Yeah, absolutely. Let's take this into a real world example and let's use a Valley example. I'm looking for a software engineer with mainframe, cloud integration experience, working in financial services. Could I use Joberate to go out into the Ethernet out there and find someone who may not be that excited about their current job and would at least talk to me?

Michael: That's a much more relevant question. That makes sense now. I would tell you that in order to find that person, there's lots of great companies out there that help you do that. Companies like Gild, companies like Entelo, companies like CareerBuilder and Monster that have big databases of people.

Joberate does not focus on doing semantic search or natural language processing about founding your proverbial needle in the haystack with some skill sets, because we think that there's a lot of tools that can help you do that. What we do is we tell you how likely they are to take your job, and if they were, what area and if how, then how much, etc.

What we can tell you is once you've identified an individual to say, okay this is a person of interest, kind of a little bit like that show Person of Interest on CBS. Once you've identified a person of interest or people of interest, we can then tell you how likely of those people are to be interested in your job. We could stack rank them. We could tell you who's active, semi-active, passive, active. We can tell you the level of their job seeking.

The reality is this, as a recruiter, you could probably have this sort of basket of 30-40 candidates that from a skill set would say fit a job. Now you don't want to waste your time bombarding people with spam and email and doing all this outbound marketing to people who actually are not at a place and time in their life when they're going to consider changing a job. They're just not.

Peter: Right.

Michael: So why focus on spending any time and effort on people if you know that they're not going to be interested in the job? It's a waste of time and money. So we kind of help recruiters become more effective by helping them focus on people that are in a position to change a job, and how we get to that is through the technology that we've built. We've built this sort of machine learning predictive analytics engine that tracks people's job seeking behavior and alerts recruiters, that's why our product is called Signal. It's not very sort of... it's somewhat intuitive that it would be called Signal.

Essentially what it does is alerts recruiters to people that have become in a status where they consider accepting a job. It makes a recruiter's job much easier. So essentially, as opposed to spending a lot of time sending out thousands of emails to people, they could focus on 8 to 10 people that would most likely take the job.

Peter: Focusing now on what you just talked about, how do you define machine learning?

Michael: Machine learning is the process which a computer actually makes decisions and tells you what to do. Some of the things that we have learned through their journeys - again, I'm sort of captain obvious, but the reality is -- is that if all you're doing is you're getting infographics and all these wonderful dashboards, all they're doing is presenting a human being with a lot more information that's really well organized. But at the end of the day, the human being has to process and analyze all that information to make a decision.

So in a strange way and you'll kind of hear this, people have implemented some of these tremendous analytics platforms, they'll tell you, wow I have so much data now, I don't know what to do with it.

Peter: Right. You hear that a lot.

Michael: This is a common problem, right?

Peter: Yeah, exactly.

Michael: Toxicity, it's almost like you have too much data. The challenge is that - so when you talk about machine learning in context of predictive analytics, the computer basically tells you what to do. The computer doesn't give you a lot of data for you to decide what you think you should do. This is the next evolution of how we can get better at it and also become more efficient.

So again, I'll use the credit card fraud system. Imagine American Express, if their credit card fraud system was just analytics as opposed to machine learning predictive analytics, essentially what would happen is, is we would have to have an operator sit in front of 39 million monitors and watch every single card transaction. And they would have to be so good that they could spot a credit card transaction on monitor 30,000,972. I think that's fraud, quick, quick, quick, call the merchant and tell them not to accept... It's impossible. ☺ ☺ It's impossible.

So what happens is, is that financial services companies create these machine learning predictive analytics engines that basically do not have the need for human intervention.

Peter: Right.

Michael: Now let me say, that's kind of like one example. The other example is actually quite simple, You log onto It says, Hey Mike, long time no hear, or something like that. Thanks for coming back. By the way, here's these 12 really cool books that we think you'll like.

Peter: Right. Just click on your one click and we'll send them to you this afternoon.

Michael: Amen, right. So that's machine learning and that's predictive analytics because what happens is, the computer keeps track of all your behaviors. It catalogues those behaviors. Every time you have new patterns of surfing, it catalogues those patterns of surfing. It retrains itself to know if your flavor has changed. Do I like this flavor today? Do I like that flavor tomorrow? Do I like this author today? Do I like that author tomorrow?

I could tell you that for a while there, I was going to and I was looking for a chemical compound to clean my headlights because after the years, they get all scuffed up. So then interestingly enough, the next time I logged onto, I really wasn't offered a book; I was offered five different variations of chemicals to clean headlights. So it's kind of those sort of things.

This is kind of how machine learning works and it doesn't require any human reaction. And the longer you stay a client of Amazon or Netflix, is another example, in the Joberate context, the longer we have track of the profiles and the more profiles are there, the better the system gets.

Peter: Right. And I imagine you can extend that to Facebook, to Twitter, to LinkedIn; they're gathering all this data as well.

Michael: Exactly. Actually, you bring up a good point. I kind of want to just pause here and address a certain point. I think the whole concept of a person's digital footprint today is kind of a bit of a hot potato in terms of being is it ethical, is it moral, all these things. I don't want to go down the ethics or the morality path, other than to say that there are data service providers that work with all the social media outlets and various companies that are on the internet. These data service providers actually provide a valuable function because they aggregate the data, they clean the data, they tag the data, they categorize the data, they do all these things.

Our business model is we actually work with these third-party data service providers, companies like DataSift for example, or companies like or companies like Gnip. This is what they do for a living. They basically take a lot of data, just oodles and oodles of data, a really big funnel at the top and they kind of help to categorize it. And then we basically use those data services to clean and sanitize the data to make sure that we have the right information about a person.

This way, we don't have to rely on a 1:6,000 kind of a connection. For us, it's a 1:1, and these data service providers work with the 6, 7, 8, 10, 12,000, sort of the cloud of digital information that's out there.

Peter: Right. You bring up an interesting point because there's a lot of conversation around I'm being spied on, I'm being stalked, I'm being tracked, I've not agreed to any of this. There's a lot of that kind of conversation out there.

Michael: Yeah, yeah, for sure. It's interesting because people, and this is kind of interesting, who actually sits and reads the terms and conditions of LinkedIn before they accept? No one.

Peter: Exactly. Or iTunes or Amazon or anybody else, Netflix.

Michael: If you actually took everything at the full letter of the law, no one would even own a computer.

Peter: Exactly.

Michael: But that's the thing. So what's interesting is that we seem to neglect the fact that when we sign up for all these great services, the reality is that some of these services are new and they don't really have a business model. That's just the truth. And the only way that they can subsidize their operations is really by selling their data. I mean it's just the way it is.

Peter: Right.

Michael: This is not new. Credit card companies do it all the time. I mean think about how much junk you get in the mail. I get at least five letters from either Discover or American Express or the Chase United card per week.

Peter: Right, exactly. You can throw in AT&T Uverse or whatever it is that's...

Michael: Yeah, they send them to me every week for 9 years. It's like, I haven't signed up yet. I'm probably not going to.

So this is not new and I do believe there's some sensitivity to it, but I think it's also a generational thing. If you think about it, we're entering let's say a different phase of mankind's evolution where transparency is becoming the norm. Everything used to be private. Now everything's in the public domain.

Frankly, it's like people are posting things at the public domain that shouldn't be posted. But that's a whole different conversation. But essentially, transparency is becoming the norm and if you're not above board and if you're not willing to do everything under the scrutiny of a microscope, then it's difficult to be that individual or that company.

So I think that to some extent, I'm hoping that over the next decade, over the next 15 years, this conversation will die down. But now, I think we're sort of on the adopter's curve or probably in the early adopter's phase of it, so it's almost natural for these kinds of conversations to happen.

Peter: Right. Sort of to that same point, if you look at generational differences from the standpoint of video interviews, for instance, Michael.

Michael: Yes.

Peter: The Gen-Y'ers, the Millennials, they like to do video interviews. They prefer actually doing a video interview to a live one-on-one interview, where baby boomers hate video interviewing.

Michael: Yeah, for sure. I travel a lot and it's interesting because I can look at the younger generation - this is no joke, they'll be sitting in an airport four seats apart texting each other.

Peter: Right.

Michael: It's just how it is and I think that the pace of change is accelerating. But overall, the reality is you have to go out 10 to 15 years, I remember when video interviewing first started and the biggest problem was the lawyers. The lawyers [TALKING OVER]

Peter: EEOC stuff, right?

Michael: It was EEOC compliance was like...

Peter: Oh my god, we can't do this.

Michael: People will discriminate. I'm like, hello, they're already discriminating!

Peter: Yeah.

Michael: So if anything, they'll stop discriminating because now it'll be transparent. So I think that video interviewing is actually a great business case for perception versus reality. It was a lot of perception that video interviewing would lead to more discrimination. In fact, it has actually eliminated discrimination because it has made these things much more visible. It's also enabled great people to shine and enabled companies to find great people because very early on, they're developing a relationship with an individual much better than a paper CV which really doesn't say anything about an individual.

And you know this. I laugh at - not to digress, but I laugh at the whole CV concept, how that's just really not evolved as fast as it should have. I mean I personally know people that have five LinkedIn profiles. It's like, which one do you want to see?

Peter: Right.

Michael: I could be the PhD. I could be the marketer. I could be the sales guy. I could be whoever you want me to be because it's just a piece of paper. Whereas in a video interview, it tells you a lot more about an individual because it forces them to sort of interact with you. Yeah, I agree with you.

Peter: Back to Joberate, on your website, you talk about helping your clients reduce employee attrition. How do you do that?

Michael: See, what's interesting is is we looked at our target market and our go-to market strategy, and we quickly realized that helping companies keep great people is actually a bigger problem than helping them find great people. Because if you think about it, companies they don't hire 100% of their staff every year, or at least they hope not to.

The real key here, and if you look at all the research that was done, whether it's by PWC or The Corporate Executive or whoever, you kind of see that retention is really a top three priority for every CEO, if not number one. So we kind of, how should I say, structured our technology to naturally be more, I should say have a bigger ROI for HR.

Recruitment is - how should I say? We view recruitment as an event. This year, I need 300 people. Next year I don't need 300 people. Versus from an HR context, talent management, workforce management, mobility, all these things, that's perennial. It goes on as long as the company exists.

So we wanted to focus specifically on the HR piece and help HR to retain really good people. This is kind of a 2-part question or statement, is what's really happened is if you think about up until really, let's say early '90s, at the height of when the internet was really coming to pass and just kind of coming out of its hiding. Up until that time, just about all the information that a company had about the individual represented probably 90% of the information that the individual has.

They knew your birthday. They knew where you lived. They knew your marital status. They knew about your kids, your mom, your dad, where you went to school. They knew everything about you. All that information used to sit inside the company's walls, their HRIS.

And then society was also such that - I remember my dad told me that having fun at your job was, that's not reality. You should be lucky to have a job. Right?

Peter: Right.

Michael: So society also brought you up to kind of stay in one place for a long time. So attrition and information about an individual to help companies retain them wasn't really an issue because they knew 90-95% about you anyway. And society was such that you weren't going to go anywhere anyway because it was frowned upon because you're a job hopper.

What's basically now changed is attrition is an issue because the internet made it very easy for people to look for jobs. It is just, if you have 20 minutes, you can go to the bathroom, take out your iPhone and you can find 50 jobs. So it's like you don't have to do that appointment anymore.

Attrition becomes an issue. If you look at what our technology does, it basically tracks job seeking behavior. We actually could basically deliver content information to a company about their own employees' job seeking behavior. For example, you could look at a global view and we could tell you by the country how many people you have in each country that are potentially at risk for leaving. And then you can drill down to the individual level. Then you can compare for example, one hiring manager with another.

For example, you can have a view to say, here's five hiring managers in our Murray Hill, New Jersey building. I'm just making one up. Then of these five hiring managers, we see that the 12-month potential attrition rate is let's say between 9 and 12%. But there's this one manager where their 12-month attrition rate is forecasted to be 35%. What's going on there?

Peter: Right. What's happening with that manager?

Michael: Exactly. So you can drill down and you can say, hey we found that based on the data that we have, it looks like potentially there are some people at risk and you talk to the manager and they go, yeah I know, I have people that are leaving and they're complaining. You immediately go bad manager, but then you do some interviews and you realize that they started doing construction and all the people that basically work in that one manager's department happen to live in a certain area, and now it became a 3-hour commute. So it has nothing to do with the manager but there is an external event that happened.

So we can deliver that content, that information including maps and location. It's amazing. And you can basically say, okay let's go in and let's put a new policy in place for this whole organization to say, look you can come in wherever you want as long as you work your 8-hour day or you could work from home.

Peter: Right.

Michael: And then here are the things in that, and you can put in a program in place and you could almost watch within 60 days of the target 12-month rate of attrition goes from 35% down to 15%.

Peter: There was a really interesting op-ed piece in last weekend's New York Times titled "Why You Hate Work" and this was done by The Energy Project. According to a 2013 report by Gallup, around the world across 142 countries, the proportion of employees who feel engaged at work is just 13%.

Michael: Crazy.

Peter: 13%. I mean who's paying attention to this stuff?

Michael: Fast forward to today where I would say, conservatively there's probably less than 10% of the information about an individual that sits inside the company. And 90+ percent of that information sits outside the company, in the person's digital footprint.

It's important for HR organizations to understand more information about the individual for how to engage them and retain them. This is sort of common sense, and if you think about up until now - so I'm kind of a bit of a dinosaur in this space, but up until now, what real tools has HR been able to deploy to help with retention?

Tool number 1: President's Club.

Tool number 2: watches.

Tool number 3: cars.

Tool number 4: pay raises. I mean I'm kind of joking but I'm really not.

Peter: How about the Employee of the Month plaque?

Michael: Yeah. They don't really have any insights. It's amazing because when good people leave, it's always a shock. Oh my god, I can't believe Missy left.

Peter: Yeah. And not only that, the replacement cost is astronomical.

Michael: It's huge. If you just Google attrition, what it costs companies, there's a ton of research. Let's say even if you look at median research, it's 27 to 49% of the person's salary and that's hard cost.

Peter: Right.

Michael: DDI and they're on the front end of it with the assessments, so they have a pretty good feel for it. But DDI basically estimates that on an average, it costs a company around $27 million a year for attrition. Those are just hard dollars. How about all the IP that's walking out the door?

Peter: Exactly.

Michael: Let's say that you can't completely eliminate it, but if you could chop down a few of the trees in the forest, you begin to start making progress. Because what I found that was interesting within an organization is that there tends to be a herd mentality. You don't necessarily get one or two people to go and everybody stays. It's one goes, then two goes, then four go, then 8 go, then 16 go, then 32 go. That's how it is.

Peter: Yeah because they get recruited into that company that that person went to who left.

Michael: That's exactly right. If you look at attrition curves within an organization, they tend to be exponential. They don't tend to be linear. That's just an interesting phenomena because what happens is once people start going, more people want to go. It's a herd mentality.

So the key is to get insights and analytics into the hands of HR people so they understand what's happening with sort of the mindset of the current employees, and to begin to chop away at some of the possible attrition, which could actually eliminate potential future attrition and hopefully increase the level of engagement of their current employees. That's the thesis, anyway Peter.

Peter: Interesting. I have one final question for you and this has being ripped off from one of the webinars on your website that you participated in. Actually, I have put the YouTube code on your show page here on TotalPicture Radio so people can look at it.

Michael: Thank you. I appreciate it.

Peter: What challenges is Joberate focusing on solving in the near term and in the long term?

Michael: In the short term or in the near term as you say, we're really working on developing - this is going to sound kind of funny - but we're really working on developing what I would call intuitive user interfaces. And to understand why we're kind of focusing on that for now is just kind of understanding that essentially what we've introduced is we've introduced a car 300 years ago. How do you explain it to someone? It just becomes very difficult, right? So you have to show it.

The business model that we have and that we talked about the iTalent Competition is we're basically the information services company, somewhat like a Reuters or a Dun & Bradstreet or an Equifax where we provide the streams of predictive analytics to companies. But what we've also learned that this is also sort of paradigm shifting that people kind of have a hard time getting their arms around it.

So in the short term, we're focusing on developing some proprietary user interfaces that could really train and educate people on how to use the information. We think that that's a lot of value because as they say, a picture's worth a thousand words.

So you'll see that let's say from now until the fall, we're going to come out with kind of a rapid-fire fashion some UX/UI interfaces towards a predictive analysis engine. People can start using dynamic HTML to really understand and drill down on the information. So that's kind of the short term.

In the long term, we want to move towards really providing a workforce planning exchange. What's interesting is by design, a predictive analytics engine has anonymity. And so we think that we can really provide value to companies long term because we could essentially allow companies to benchmark with pure anonymity. Because you could say, I'm in financial services and we have 200 other financial services clients; they don't know who they are. But they know that for a financial services company, here's my target rate of attrition or here's my target rate of retention.

So I think ultimately long term, I think we could really help companies with succession planning, workforce planning or planning in general. You want to open up facilities in a new area, how do you know what the new market is in that area of your talent, stuff like that.

I think that short term, it's a bit lock and tackle. Let's get these user interfaces out there that people can understand so they could really know how to learn the product. But long term, it's really doing workforce planning and benchmark based on the predictive analytics data, and I think there's just a lot of value in that.

Peter: Michael, it's really been a pleasure having you on TotalPicture Radio today. I much enjoyed this conversation.

Michael: Yeah, myself also. Thank you for having me, Peter. I really appreciate it.

Michael Beygelman is the CEO of Joberate and you will find this interview in the HR Technology Channel of TotalPicture Radio. That's

According to his LinkedIn Profile, "Michael Beygelman is an individual who is passionate about people, culture, technology and innovation," is often how my peers, colleagues and associates have described my career in HR and Recruitment."

"My career has afforded me opportunities to build business from the ground up, and also expanding maturing businesses globally. While I've been an entrepreneur, have done several mergers and acquisitions, and worked for private and public companies in executive capacities, my greatest interests are in Recruitment, Talent, Technology, and Social Media."

"One of my most memorable business successes was my recent role. I was the first employee hired by the Adecco Group in North America in 2006 to help the company define the vision for RPO and MSP. I then stayed on for nearly eight years and became the President of the RPO business segment. Under my leadership we earned the #1 industry ranking by Outsourcing Institute, and we grew that business globally to more than 100 clients, while never sacrificing high quality customer service or the interests of our shareholders. I built an amazing leadership team and company culture, which lead to a highly collaborative working environment and significant growth."

"The HR and Recruitment industry has been gracious in recognizing my contributions over the past decade. I've been featured in numerous articles, white papers, and case studies, and keynoted numerous HR and Recruitment industry conferences all over the world. I am a published author and monthly columnist for various HR publications, and most recently, Staffing Industry Analysts named me to their 100 Most Influential People list."

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.


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