Predicting the Best Candidates: Big Data v. Crystal Ball
Last month I was intrigued by a blog published in Harvard Business Review (“In Hiring, Algorithms Beat Instinct”) that declared, “Humans are very good at specifying what’s needed for a position and eliciting information from candidates – but they’re very bad at weighing the results.”
This conclusion was based on analysis conducted by Brian S. Connelly, Nathan R. Kuncel, David M. Klieger and Deniz S. Ones. They performed 17 different studies of applicant evaluations. It turned out that a simple equation beat gut feel by a significant 25% – whether the candidates were being interviewed for front-line positions, middle management or the C suite.
Obviously there’s need for a person’s judgment at some point in a fair and objective recruitment process. But I’m not surprised that the evaluation picture is clouded by human emotions, inconsistencies, overt biases, and maybe even subconscious reactions to a particular applicant.
So given the fact that data/equations is the right path to predicting great hires, why is that some companies are still hesitant to make the move away from the “gut feel”? Some would probably argue that the data/equations price tag is too expensive. Not so. Significant advances have been made in this area to package up analytic solutions that will help companies more cost-effectively delve into the traits of excellent employees and translate those best qualities into criteria by which future staff is selected. What operations or recruiting team wouldn’t get excited about that — the ability to predict which candidates are most likely to perform better and stay longer!
When you weigh the cost of a good hiring decision against a choice that was woefully wrong, the technology expense is hardly a factor. In 2012, Forbescontributor David K. Williams wrote about the experiences of 2,700 employers surveyed by his company. No less than 41% of the respondents estimated that a single bad hire had cost them $25,000. A fourth of the employers put that number at $50,000.
Given those statistics, backing up a crystal ball with proven technology seems like a pretty good instinct.
(Shameless plug: If you haven’t already, take a look at HireIQ’s patent-pending predictive analytics software, Candidate Optimizer™, to see how we’re leading the charge to eliminate the crystal ball.)