Why Predictive Hiring Algorithms Are a Corporate Recruiter’s Best Friend

Why Predictive Hiring Algorithms Are a Corporate Recruiter’s Best Friend

Why Predictive Hiring Algorithms Are a Corporate Recruiter’s Best Friend

Corporate recruiters have a very important and difficult job.  They predict who will be a top performer in certain roles and protect against non-performers getting inside the business ecosystem. We rely on their ability to make constant snap judgments to move a candidate into the interview process or not.  A single decision in either direction can cost or make a company $ millions.

Dr. John Sullivan, an internationally known HR expert, estimates that recruiters in larger organizations might carry an open requisition load of 15 – 60 open requisitions at a time.    According to CareerBuilder and Inc. Magazine  , every open position receives between 75 and 250 applications respectively.

A 2012 study by the Ladders, titled “Keeping an Eye on Recruiter Behavior” shows that corporate recruiters spend an average of 6 seconds on every resume.

In that time they make a decision about whether the candidate can 1) perform well in the role 2) last long enough in the role to make a positive impact on the business 3) Be in a role the job candidate will find satisfying for a long time.  .  (Click this link for the PDF download of the full study)

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Let’s estimate 35 open positions with an average of 100 applications per open position.  At any given time, each recruiter is screening approximately 3,500 candidates. During the 6 seconds when they are screening the candidate’s resume they need to 1) keep the “requirements” clear for each of these roles; 2) make sure their decision is unbiased, 3) try to remember if characteristics they are reading on the resume were some they remember from other candidates that worked out – or didn’t, and more.

“Get Me More Candidates Like Her”.

Sometimes a hiring manager will comment – “she was a great hire.  Get me more candidates like her.”  It’s so frustrating to not know what it was about the prior successful candidate that made them successful. You can guess.  (Was it their experience, where they went to school, their references?  How do you know, for sure, so you can consistently replicate success and avoid failure?

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You get the point; today’s candidate screening process is a losing battle.  It’s not scalable.  It’s not repeatable.  The process can’t learn from past successes and mistakes.  In 6 seconds, or less, current recruiters aren’t giving candidates a fair chance.  They’re juggling 3,500 other things.

Naysayers of using AI or predictive analytics in the candidate screening process talk about how they don’t want to be treated as a number, or how they are afraid of being misunderstood.


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