Gut feelings have guided much of human behavior throughout our history. Sometimes they help us make decisions about trivial matters, such as what to order for lunch. And sometimes they help us make much more important decisions, such as those that affect our survival. In fact, it’s possible that you’re reading this right now because one of your ancestors said, “I have a bad feeling about this” and decided not to drink from that nearby pool of foul-smelling water.

“Going with your gut” can be a useful decision-making tactic in the absence of information. But when a full set of facts is available, data-driven decision making invariably yields the best results. Unfortunately, even with “metrics” and “Big Data” being front and center in many organizations’ practices, hiring decisions are still far more “gut-driven”—and far less “data-driven”—than they should be:

Surveys suggest that when assessing individuals, 85% to 97% of professionals rely to some degree on intuition or a mental synthesis of information. Many managers clearly believe they can make the best decision by pondering an applicant’s folder and looking into his or her eyes. . .

The truth is, though, that when it comes to hiring decisions, an algorithm usually “outperforms human decisions by at least 25%.” Apparently, a manager having a positive “gut feeling” about someone isn’t a very reliable indicator that he or she will be a successful hire.

In light of such findings, organizations are increasingly coming to the realization that they should prioritize data over feelings when making hiring decisions. After all, companies regularly do research and crunch the numbers before investing large sums of money in equipment or real estate. Why shouldn’t they adopt the same approach when investing in a new hire whose long-term salary costs can outweigh many of those one-time structural expenses—and whose work can have a huge positive (or negative) impact on the company’s bottom line?

Consider the experience of the software development firm Appster. After a rash of departures forced the company to rebuild its sales team, management took the opportunity to reevaluate its hiring process, which had a “75% failure rate.” Drawing on past experience (i.e., what had and hadn’t worked at the company so far) and heeding the recommendations of some prominent staffing experts, Appster management developed a rigorous, data-based methodology for evaluating and categorizing applicants. When it comes to hiring, the company now enjoys a “success rate . . around 90%.” By shifting the focus away from the “gut feeling” that had previously led to “[hiring] anyone who seemed personable and had the right credentials” and toward a comprehensive evaluation of each candidate’s accomplishments, skills, and demonstrated ability to work well with others, Appster not only solved its staffing problem but also created a hiring model that, recognizing its success, many other organizations have copied.

The HR industry has been paying close attention to this shift, and SHRM now calls data-driven analysis “a reliable, objective way to make the best hiring decisions.” A simple Internet search for “gut feeling hiring” yields page after page of links that lead to cautionary tales about how this approach does not work in the long term. All of those tales agree: “gut feelings” are out, and hard-data analysis is in.

Organizations that cling to hiring processes that rely on “gut” decisions and fail to optimize the many ways to gather and analyze candidate data are courting disaster. Sure, they might end up with a few good hires, but they are unlikely to make enough good hires to offset the hiring, onboarding, and training costs of the many (many!) bad hires that a “gut”-based process produces. And with so much candidate data (and tools for analyzing it) readily available, there really aren’t any good reasons not to adopt a data-driven approach for hiring—and plenty of great reasons to embrace it wholeheartedly!

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