Another recruitment tech dunk
Measure dumb things. Get dumb results.
Seems pretty straightforward. If you collect and measure data and work to improve those metrics, but doing so doesn’t get you closer to your goal? You’re wasting time on the wrong stuff.
Vanity metrics, as the kids call it.
This applies to any aspect of business. In the sales and marketing realm, you can hit your MQL and impression goals and trick yourself into believing you’re crushing it. But did it have an impact on revenue? And if not, why?
Funny thing about metrics: some are easy to calculate, some are not. Some are useful, some are not.
And there’s no correlation between what’s easy and what’s useful. And when it’s time to bake them into a software platform, guess which one wins out?
(Do I need to say “the easy ones?” Ok.)
👉The easy ones.
It’s the same with recruiting metrics.
Time to Fill is super simple to calculate. Date of job opened -> date of start. Done. Put in in next release.
But there’s obvious problems with it:
1. It measures the entire hiring process. But gets applied to talent identification, which is a miniscule part of it.
2. It measures speed, not quality. If your hiring process is fast but you make the wrong hires, it’s only telling you how quickly you suck.
And you don’t have to look very far to find other easy-to-measure-but-not-that-useful recruiting metrics. It never ceases to amaze me how the *best* recruiters can have the *worst* activity metrics. (Because they don’t need as much outreach/conversations/candidates to paint the target.)
But, these are the things that are easy to measure. The things every recruiting software bakes into their platform. So these are things we follow.
I’ll get excited about AI when it makes qualitative measurements. How “good” an interview process, an email pitch, a candidate’s background, your interview questions, etc. really are.
Until then: remember that dubious data is everywhere. Don’t base every decision on it.