Let’s face it. If we liked math (or were any good at it), there’s a good chance we wouldn’t be in recruiting. Crunching data has to be one of the least sexy concept this side of HR Girls Gone Wild: SHRM Summer Vacation.
Numbers, unlike people, are black and white, inherently uninteresting and almost as boring as that candidate who won’t shut up when asked to tell you about himself. You know the one.
When you consider the possibilities, however, it quickly becomes obvious that data gets a pretty bad rap. You don’t have to look far to see some example of how something as mundane as analytics can create an impact on something as meaningful as helping cancer charities smash fundraising records, using crowdsourced data to cut down on traffic and improve commute times, not to mention promising pilots for finally cracking the mysteries of space and time.
Not so boring anymore, right?
The Lead Off: Agency Recruiting and Analytics.
One of the most commonly cited and well known applications of applying a statistical approach to decision making, team dynamics and formerly unmeasurable outcomes is the concept of Sabermetrics. simply defined as using quantitative analysis to baseball situations. Winning pennants with brain over brawn has become something of a touch point for the geek chic crowd, particularly after the release of the Oscar nominated film Moneyball.
Based on Michael Lewis’ behind the scenes look at the mechanics of one small market team’s rise to the top, the film cast Brad Pitt as a kind of antihero who, like some after school special, must outsmart the old boys club through data analysis. Of course, being Brad Pitt, GM Billy Beane wins big in the end, overcoming a lackluster budget to use evidence instead of instinct when evaluating players – effectively maximizing worker productivity while minimizing associated costs related to headcount.
Of course, the A’s never actually won the World Series, which was the ultimate goal, but other teams have won since sabermetrics first replaced the formerly unreliable, largely inaccurate approach to evaluating talent that had been in place since last time the Red Sox won the World Series prior to 2006, where the Curse of the Bambino was ended by Bill James, the father of sabermetrics, a laptop computer and a complex algorithm crunching numbers nonstop. The Boston bean counter proved that the key to knowing what’s going to work lies in your ability to analyze what has – and hasn’t – in the past.
This requires taking a deep dive into big data. Don’t worry. It’s not as scary as it sounds.
Middle of the Order: Supply, Demand And Agency Recruiting.
Yeah, I know. You’ve heard this whole moneyball for HR thing before, and while it seems pretty obvious that there’s some lesson in there, leadership still seems unsure exactly how to apply this analytic approach to talent acquisition and management.
This whole data thing is a whole lot harder than it looks, and by and large people people tend to have difficulty with applying an often amorphous concept into actual action.
As much as every HR Director would like to be the talent version of Billy Beane, and as much as we’re all seemingly obsessed with this amorphous idea of “big data,” the fact is that we’re still striking out – forget Moneyball, this is more like rookie ball.
For all our intentions, talent acquisition and management are still stuck in the minor leagues when it comes to analytics.
That 4 out of 5 employers last year admit to using an outside agency or contingency firm, willing to pay an inordinately high fee for candidates as a matter of last resort that most resort to way too much speaks to the fact that we’re not the most number savvy of functions. With the trend decidedly moving towards enterprise employers building search and sourcing functions in house or moving towards the embedded RPO model, why are so many employers still spending so much money on external recruiters to fill requisitions?
This capability gap seems pretty glaring – and it comes down to the fact that most organizations have no way of actually figuring out how well their recruiters are performing.
This means that when their recruiters don’t perform, the only math they have to do is calculate the percentage of a placement fee. This is not only reactive, it’s also bad business. While you can’t measure opportunity costs, you also can’t afford not to measure the bottom line impact and individual productivity of the recruiters responsible for requisition management within your organization. Not every recruiter is created alike – it’s just that few have any way to statistically measure, monitor or manage performance in a really meaningful manner.
Not knowing this information can be dangerous for any organization, because the best performing recruiters are the ones, inevitably, who are finding and hiring the best talent. Those who aren’t cutting it aren’t just costing your organization longer time to fill and higher costs per higher – they’re probably costing you the kinds of candidates your organization needs to succeed today (and tomorrow). A bad recruiter will bring in bad candidates, and no employer can afford that.
That’s why every recruiter needs to be accountable. Analytics are the obvious answer, but it’s the question of what, exactly, we should be measuring in the first place that’s the much bigger question we’ve got to answer if we’re ever really going to see a big impact from big data.
Setting Up The Save: Analytics & Agency Recruiting.
This isn’t to say that external recruiters aren’t still a valuable, viable or often cost effective option; it’s just that as the space becomes more clustered and competitive, the pressure on recruiters to fill roles and make placement fees has skyrocketed.
This has led, largely, to a vicious cycle of too many recruiters going after too few candidates, leading to what’s largely a “throw shit and see what sticks” approach to staffing.
External recruiters don’t care about volume – they don’t want to work on a ton of reqs. They want to work on the reqs they’re the likeliest to fill, because that’s how they get paid. Which is a pretty powerful incentive to make a placement.
Thing is, without a solid approach to analytics, they’re playing the numbers, not playing the odds – which, odds are, never really works in recruiting.
So how do both corporate and agency recruiters make better hires faster with less resources and more competition without going completely insane in the process? Really, recruiters?
The obvious first step, as any recovering alcoholic will tell you, is admitting you have a problem, and it’s time for all of us to get real. That means instead of casting a net for every candidate out there, you need to make sure you’re strategically targeting the best candidates out there and building relationships for the future instead of candidate slates for the present.
The most placeable candidates, like the most desireable reqs, are always going to be in demand – which is why if you can’t invest your time in maintaining high touch relationships with high potential talent, then make sure you find an agency partner who can – and has plenty of great candidates already warm in the pipeline. Remember, speed kills.
Of course, no recruiting firm is right for every req. That’s why, in my experience, choosing the right external recruiting partner is one of the most strategic decisions any in-house talent pro can make. Since it’s statistically clear that at least 80% of you are forced to go to agencies (like it or not), that means someone else is going to be representing your employer brand and representing careers at your company to an extremely fixed market for top talent. This means you’re staking your credibility and reputation on them – and the importance of choosing the right partner can’t be overstated.
The right external recruiter has an established track record with proven results (and verifiable references, preferably placements) and an understanding not only of recruiting, but your industry, product and players. The best search professionals generally have experience in the specific vertical they’re recruiting for, meaning that they know what the job order entails and exactly what kind of candidate is needed.
Having a network built as a practicing professional helps, and is generally the best choice for the sorts of specialty roles that require going out to search in the first place. They possess an innate ability to spot top talent, and to sell them on an opportunity by connecting to them as professional peers instead of as third party recruiters looking for a placement. Forget social media. These are your real brand ambassadors.
It goes without saying: choose these external recruiters wisely. Of course, the only way to do that is through big data.
Big Data’s Bottom of the Ninth: Closing Out the Numbers Game
While you may have a difficult time measuring the performance and relative efficacy or efficiency of individual recruiters, the good news is that with external recruiters, the proof is in the placement – and that’s fairly easy to analyze.
For example, Fortune 500 companies such as United Labor Bank, Monsanto and Whirlpool – all multinational employers responsible for recruiting a very wide range of very specialty and niche positions – have always relied heavily on third party recruiters.
The problem was, without knowing which firm produced results or were the best suited for a specialized or senior level search, this required the third party equivalent of spray and pray.
Let’s just say, with every agency getting a crack at every req, without respect to whether or not they had any related experience or expertise in some pretty niche positions ended up without those prayers being answered – although with enough typewriters and enough monkeys, eventually, these companies were still stuck paying out a percentage for whomever was lucky enough to stumble across a qualified, interested and available candidate. Collateral damage be damned.
These firms know that to really close out a search, you’ve got to make the right call to the bullpen – and it’s all about managing for the situation. United Labor Bank Monsanto and Whirlpool were all able to score the save – and savings on both time and cost per hire – by using big data to accelerate the talent acquisition process. By applying software capable of tracking external recruiters’ submissions, accepted candidates, offers extended and placements made – and every other output metric in between – these firms were finally able to see which recruiters were actually statistically performing the best on each respective search.
This helped them realize which agencies, and which recruiters, were the best performers for searches related to specialized roles or highly niche skill sets, allowing them to cut down the amount of firms they were working with, reducing fill times and cutting recruiting costs. For external recruiters, they were no longer pressured to work on roles that were either completely outside their area of expertise or had so much competition for those placement fees that it was easier (and better odds) simply playing Scratchers instead of sourcing.
This led to them actually prioritizing these searches and producing the kind of results that have led to recurring business – and a knowledge and trust between the two parties that’s more or less the recruiting version of a walk off home run. That’s not Moneyball, of course – that’s just money. Which is pretty much the point (and opportunity) of big data and agency recruiting in the first place.
Remember: if you have the right solution, there’s no such thing as a math problem. Period.
About the Author: Ken Lazarus is the CEO of Scout Exchange, a fast growing software company that connects employers with the best search firm and third party recruiters for every job using a sophisticated Performance Based Matching engine to harness sophisticated analytics – all within the applicant tracking system recruiters are already using.
Ken has extensive experience as an executive leader or CEO for a number of technology companies and continues to serve as an advisor and investor to a wide range of high growth, high performing startups in the space.
A PhD graduate in Aeronautics and Astronautics from the Massachusetts Institute of Technology (MIT), Ken is a lifelong Boston resident and baseball fan, where he can be seen in Fenway cheering for the love of his life: the Red Sox.