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6 Big Data Metrics That Drive Quality of Hire

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The magic and enormous value of big data is that it reveals information that was never seen before.

Over the years I’ve been using a series of metrics that indicate individual recruiter performance as they relate to productivity, sourcing effectiveness and quality of hire. By drilling down into the data it’s possible to spot root cause problems and minimize their negative impact much sooner than before. Even better, by eliminating these problems, a company is able to maximize quality of hire while minimizing cost and reducing time to fill.

The underlying idea is that if you can track and improve the quality of candidates in the top of your recruiting funnel, you’ll improve the quality of every person hired. Given the enormous opportunity big data has to offer, here’s my top six list of what I suggest every recruiting leader should be now tracking as they make the shift to big data to maximize quality of hire.

Adler’s Big Six Big Data Recruiting Metrics

1. Candidates per hire by recruiter and by hiring manager.

This is the Holy Grail for recruiting process control. Four candidates per hire is a good starting benchmark. When it goes above this it means something is wrong with the quality of the candidates being seen. Typically this is an inherent problem with the job, some company constraints, weaknesses on the part of the recruiter or hiring manager, or ineffective sourcing. Figuring out the problem is the first step in finding a solution, not sending out more candidates.

2. Sourcing mix by job.

You must know where your best candidates are coming from in order to allocate resources appropriately. When the demand for strong candidates is greater than the supply, this mix is typically one-third passive, one-third referred, and one-third from a robust pipeline of just-in-time candidates. Getting the right sourcing mix will reduce candidates per hire by improving the overall quality of all candidates presented.

3. Candidate quality by sourcing channel.

You’re not even in the big data metrics game if you’re not measuring the quality of the candidates in your pipeline by sourcing channel. One way to measure initial candidate quality is by combining general fit with evidence of the Achiever pattern. This indicates if the person’s in the top 25% of his or her peer group. When this information is used with a supply vs. demand analysis, recruiters are able to do a much better job of focusing their sourcing efforts by project. Big data then allows them to see how well they’re recruiting these candidates in real time.

4. Passive candidate conversion rates.

There’s a series of metrics in this category that allow you to track end-to-end yield from first contact to final close. Some of these include first contact response rates, prospect- to-candidate conversion rate, voluntary opt-out rate and passive candidate hire rate. All of these need to be tracked by recruiter and hiring manager to identify weak links. As a general rule, recruiters need to be above 50-60% end-to-end yield. Less than this means that too many talented passive candidates aren’t being recruited properly.

5. Referral rates by recruiter.

The best candidates are typically referred and, generally speaking, the recruiters who make the most high-quality hires obtain the most high-quality referrals. Referrals generated by week also predict the recruiters who will make the most placements in the next 30-60 days. Start tracking this by figuring out how to get at least two passive candidate referrals from well-connected employees and from every passive candidate contacted. Once you’ve mastered how your team gets referrals you’ll discover that every other metric will fall right into place. This is the value of networking.

6. Quality of pre- and post-hire.

Everyone talks about the importance of quality of hire (QoH), but few measure it. Most make excuses about why it can’t be done. Hogwash. I’ve been measuring quality of hire using aPerformance-based Interview in combination with this Quality of Hire Talent Scorecard for years. The idea is to compare the candidate’s past performance doing the work described in theperformance-based job description prepared during the intake meeting. If you get all of the other metrics tracked properly, you’ll discover that Quality of Hire will soar.

Big data represents big opportunity. Properly used, it allows recruiting leaders to pinpoint the specific source of any hiring problem long before it impacts quality of hire. Even better, it allows recruiting leaders to predict quality of hire for any recruiting campaign 30-60 days before the people are actually hired. Properly used, big data will be the engine that drives the Internet of Everything for Hiring. It’s time to get onboard.

* image by Beverley Goodwin