You can love data, you can hate data, but you can’t argue that it’s not important to hiring. We’re going to talk about what that means on the ground – to recruiters and talent acquisition specialists – with today’s guest – Michael Fitzsimmons, the CEO of Crosschq. They provide employers with data-based insights designed to improve hiring processes and decision-making. That’s where we’re headed, on this edition of PeopleTech.
Transcript
Mark:
Welcome to PeopleTech, the podcast of the HCM Technology Report. I’m Mark Feffer.
Mark:
You can love data, or you can hate data, but you can’t argue that it’s not important to hiring. We’re going to talk about what that means on the ground to recruiters and talent acquisition specialists with today’s guest, Michael Fitzsimmons, the CEO of Crosschq. They provide employers with databased insights designed to improve hiring processes and decision-making. That’s where we’re headed on this edition of PeopleTech.
Mark:
Mike, it’s nice to see you. Thanks for coming in. We’re going to talk about recruiting and retaining around the quality of hire. That apparently is a big theme within your company, or an idea within the company. Can you tell me what you mean by it?
Mike:
Yeah, well first and foremost, what we mean by quality of hire is actually connecting hiring decisions with business outcomes in a data-driven way that enable talent leaders to get intelligence about the decisions they’re making. When we think about how the term “quality of hire” can impact our organizations, it’s really about how do we finally get a metric that the industry can all agree on, that identifies this thing, this elusive thing that we’ve talked about for decades called “quality of hire” into a kind of digestible format.
Mark:
Can you talk about the data piece of it? I think there are still people out there who are trying to put together the whole idea of applying data to hiring and such. Can you talk about that?
Mike:
It’s really interesting, when we go and audit our customers and talent leaders generally speaking, what we see is on average between eight and 13 different tech stacks that they’re using in their hiring process. As a function of that, the data with all those individual siloed systems, kind of ends up in Never Never Land. Most organizations have an applicant tracking system of some sort, and then they might have some other technology to help with interviews, and something else to help with assessments, and something else to help with scheduling, you name it.
Mike:
When you unpack all of that, you realize it’s a quagmire and we’re all really confused. Combine that with post-hire data, so when a candidate starts in our company how are they performing, how engaged are they, how long are they lasting. A lot of that is also in dis parit systems, from an HRAS, to a performance management system, to some other system they’re using to measure engagement. At the end of the day, it’s a mess.
Mike:
What we have focused on is something we have trademarked, called the “Talent Intelligence Cloud”, where we are basically creating a data lake that combines all of that pre-hire data on your talent with post-hire data around business outcomes, how they performed, and how they lasted, and giving you the ability to start to slice and dice that to get at this quality of hire metric.
Mark:
Can you put this all into the context of the business landscape today? We’ve got a lot of turnover going on, there’s still the pandemic at play, hybrid work, and all of that. How does this fit in?
Mike:
It’s amazing, because I think our talent acquisition leaders are really stressed and stretched. They’re getting mixed messages from their leaders. On one side, we’re in such a rush to fill butts and seats, the age-old challenge that we have open reqs, and right now we’re on over a decade of a bull economy where our companies are growing crazy, and we’re short on talent, and we know all of that.
Mike:
So, we have this very complex reality of our talent acquisition and recruiters being challenged to fill their open reqs quickly. Conversely, we have turnover at a peak, especially quick quits, turnover within the first 180 days of a hire. Our retention rates are going down. There’s this competing pressure on the other side to make certain that we’re hiring the right people who will last and be longterm performers for our companies.
Mike:
I think in context, it’s confusing. You’re putting stress on both sides of this equation. Without data to help guide us, I think we’re having a real hard time on optimizing for business outcomes and for productivity, and to help our companies actually accomplish their goals.
Mark:
Could you explain to me, you touched on this before but I’d like to ask a separate question, can you explain to me how the data process works? How do I gather this data? How do I know which data to compile? What are the data points out of everything that I’m looking for?
Mike:
Let me start at the end, and then come back to mapping it back. First and foremost, you can ask 10 different leaders how they define quality and quality of hire, and you might get 10 different answers. The traditional SHRM definition is, it’s a combination of performance plus tenure. We kind of say it’s 50/50 on both, and you put that into an algorithm, and it says “Hey, if Mark lasts 12 months and Mark is an above-average performer, he might have a quality of X.”
Mike:
We’ve realized it’s a lot more complex than that. Those are, of course, two very important inputs in terms of how long did you last and how did you perform, but there are other inputs. How did you engage with our culture? How did you actually help move our culture forward? How did you help engage in other areas? In addition to that, we understand that different companies have different perceptions in their organizations about the relationship between all of these dimensions.
Mike:
If I’m hiring at Starbucks for an hourly employee, frankly, admittedly or not, I might be more concerned about someone who will stay than I am about them having to be an upper decile performer. I have the exact opposite if I’m trying to hire… I can’t take that chance if I’m trying to hire for a doctor, just as an extreme example. We’ve learned that these things do need to be variable, the definition of quality of hire by the role type, but also by the organization.
Mike:
So, similarly some companies just might have an approach where they’re quite happy to over-hire, churn quickly, it’s a performance-driven organization. So they just might adjust that input where tenure is less important to them and performance is more important. Conversely, some organizations might have roles that take longer as they’re ramped to productivity, and they might be more comfortable waiting for you to achieve optimal productivity until later.
Mike:
I say all of that because although we have the baseline definition of how to understand what quality of hire is, and put a metric against it, it’s really important that we understand that that metric can vary based upon the organization and by the role type. Once you get that baseline, at least we now have a currency that we can start to connect back to our front-end hiring decisions.
Mike:
Hopping to the first part of your question, back to your other part of the question, which is all right, now how do we make sense of it? This is the fun part, is now we can connect all of that data with all this wonderful insight we got on the candidate before we made the hiring decision. This could be something as simple as… For example, we’re now correlating to interview scores. We can actually see within an organization, based on who interviewed the candidate, how they scored that candidate, how correlated that is to quality.
Mike:
What we’re finding might not surprise you, is that three out of four interviewers have no correlation to quality. Oftentimes in organizations, there are certain people that are really good, and certain people that are just not as good of talent scouts. There are some really fascinating stuff that we’re seeing. We can tie quality of hire to the source of the hire. It’s been an age-old adage around the notion that internal referrals are a really high quality source of talent. Not necessarily true.
Mike:
It’s an efficient source of talent because it saves our recruiters time. Mark’s referring someone he used to work with, but that doesn’t necessarily mean that person’s going to be high quality. There’s these other great insights we’re getting based upon the source of the hire that are really powerful. We’re connecting to pre-hire assessments, this could be a cognition assessment, this could be a personality assessment to understand how predictive are those assessments to quality.
Mike:
So, the really neat thing is, once you kind of get this machine up and going, and you have all this data in your cloud, you as a talent leader can start to ask whatever question you want of it to better understand and predict whose going to ultimately be a longterm high performer at my company.
Mark:
It sounds very complicated. I’m wondering, how are HR people expected to make this work, or are they not? The data becomes more and more important every year in the world of HR. Is this ever going to trickle down to the point where the practitioners are going to have to understand this, or will be helped by people like you to understand this?
Mike:
I think there’s a couple of things. It’s a mouthful. We understand that, and it’s complicated, and that’s part of any big challenge you’re trying to go and solve for, is it is complex. I will tell you, and you’ve been around doing this for long enough, everyone says they care about quality of hire. You’re not going to ask anyone who’s going to say, “Nope, I don’t care about quality of hire.”
Mike:
But in practice, they haven’t had the tools yet to optimize for it. We firmly believe that’s a reality. We firmly believe that recruiters and talent acquisition leaders just haven’t been properly empowered or incentivized. What we’re starting to see with organizations is that quality of hire is becoming part of the incentive structure, or compensation and bonuses are actually tied to making sure that the people we’re putting in the seats turn out to be product longterm performers. It’s really cool to see that.
Mike:
I think that’s part of it too, to your point, of dumbing it down to how’s it digestible and actionable? Well it’s first of all, we have to be able to measure it, and then we have to sort of put the incentives in place to help optimize it. Where the conversation gets complex for HR practitioners is when does of quality of hire become the business owner, or the hiring manager’s responsibility versus the recruiter’s responsibility? That is a very real and active, and fun conversation to have with our talent leaders.
Mike:
Where we see the industry going, it’s about 90 days. It’s about the first 90 days is when recruiting and TA says, “Okay, we did our thing. We got Mark in the role. We got him. He’s onboarded.” Right now, you hiring manager, it’s up to you. There’re all kinds of things that can happen after that point that might lead to Mark being productive or not productive. So, we’re absolutely seeing that the metric is not just an HR practitioner metric. It’s certainly not.
Mike:
We’re seeing buying centers in our companies now of CEOs, CEOs saying at a board level “I want to see the quality of hire metric every single board meeting.” We’re seeing CFOs say, “I want to understand the board metric because this idea of just hiring for volume and then having 35% turnover, it just doesn’t pencil. So, I need to start getting more intelligence on that.” We’re seeing buying centers’ Chief Revenue Officers, this has been a really interesting one for us where the Chief Revenue Officer is becoming the buying center where they’re saying, “Gosh, if I can optimize quality of hire, I can directly impact my sales output.”
Mike:
It’s not, I think… I say all of that because we’re not looking to put additional pressure, and complexity, and confusion to the HR practitioner. We’re really not. We are trying to bring the organization the ability to get their hands on additional data to ultimately optimize their outcomes and their productivity. I might have over-complicated it, but that’s kind of how we see it and how it’s been playing out.
Mark:
Last question is, where do you think all of this is going? How do you see the technology evolving? How will that impact you as a solutions provider, but also HR and HCM in general?
Mike:
We can never forget how the “human” in human resources, the word, “human”, because this is all human. It’s never going to be perfectly predictive. It’s never going to be all data-driven. Careers, and professions, it’s human stuff. So, we have to understand that we’re not going to be the end-all-be-all, or nor is technology is going to be the end-all-be-all. But we’re going to help. We think we can just help, and if we can make incremental improvement we think we’re helping.
Mike:
We view a world at Crosschq where when you think about workforce planning, and you think about ultimately getting your head wrapped around how am I planning to build my teams, to optimize productivity and output, and connecting all of those dots in a more predictive fashion, I think that’s a state that the world is headed to. It’s amazing how black box that is today. We’re so unfair to our talent acquisition and recruiting leaders. We build these hiring plans over here on the right side, and then we throw them over to our talent acquisition teams, “Hey, go hire for this.”
Mike:
There’s no correlation, collaboration, or frankly connectivity between a lot of the pieces in between there. It’s a big black box. I do think that that is all in play to be optimizing, and using data to help optimize, because we want to understand should we be hiring to fill butts and seats, or should we be hiring to optimize output on the other side. Those are important inputs.
Mike:
Those are important inputs for our recruiters to understand, “Hey for this role, it might be okay to take six months to fill it, by the way, because we know the cost of attrition on the other side is X, and we know the cost of getting it right is Y, so we’re willing to take that time,” whereas for the Starbucks barista, the formula is just very different, where our expectation is we’re going to fill the role in X days from the post, that sort of thing.
Mike:
We do really imagine and view a world where at the end of the day, using data to help our organizations optimize their planning all the way through enhancing and driving productivity is kind of the end state here.
Mark:
Mike, thanks for talking to me.
Mike:
I appreciate you having us on, and being interested in the topic.
Mark:
(music playing)
Mark:
My guest today has been Mike Fitzsimmons, the CEO of Crosschq. This has been PeopleTech, the podcast of the HCM Technology Report. We’re a publication of Recruiting Daily. We’re also a part of Evergreen Podcasts. To see all of their programs, visit www.EvergreenPodcasts.com. To keep up with HR technology, visit the HCM Technology Report every day. We’re the most trusted source of news in the HR technology industry. Find us at www.HCMTechnologyReport.com. I’m Mark Feffer.
Mark:
(music playing)
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