How Machine Learning is Changing Recruiting

How Machine Learning is Changing Recruiting

Companies approach recruiting in different ways. Facebook’s method, for example, involves acquisitions for human capital and a six-week onboarding boot camp. Of course, that’s not the norm: On average, a recent study found it takes 42 days—and a cost-per-hire of $4,129—to fill an open position. That adds up, especially since Bloomberg recently reported that approximately 10,000 members of the baby boomer generation reach retirement age every day. With numbers like that, it’s no wonder efficiency-boosting advancements in machine learning are beginning to challenge the HR status quo. Let’s explore how machine learning is changing recruiting.

How Businesses Use Algorithmic Assessments

Spotify. Waze. Netflix. Amazon. These are just a handful of the many companies that have opted-in to some form of AI or machine learning. Why the boom? There are lots of reasons, of course, but there’s a primary one that comes to mind: Using algorithmic assessments—i.e., a kind of machine learning—can speed existing processes for consumers and even predict their needs ahead of schedule. In other words, these companies know that to get ahead in this digital marketplace, they’ll need to compete on customer experience (CX). They’re using technology to get there.

The same principle holds true for HR and, more specifically, recruiting. Algorithmic assessments use statistics and historical data to predict whether or not a potential employee will perform well. The benefits of this process are numerous: Recruiters can consider more applicants in less time and with less effort. In addition, they have the added peace of mind that their decisions are backed by more data than subjectivity.

Let’s examine an international startup harnessing the power of algorithmic assessments.

From Reviewing Resumes to Reviewing Social Behavior

It’s impossible to tell everything about potential hires from resumes alone. What if you could incorporate that resume data with information about who applicants really are—their values, likes and dislikes, etc.—and then use machine learning to plug that information into an algorithm that could predict how well they fit within your corporate culture? That’s the premise behind the Indian startup Belong, a brand recently profiled by Forbes as one company leading the machine learning and recruiting charge.

Belong’s algorithms do two things: First, they analyze behavioral data from social networks, blogs, and other digital platforms in addition to the standard information like education, experience, and stated objectives found in CVs to gather “a more holistic and accurate” profile of each candidate. They also use machine learning to better understand the preferences and patterns of each hiring company. From all that data, Belong generates a tailored, SERP-esque database of matches, each unique to the company and the position.

Does it work? Belong’s method reportedly gets a minimum of a threefold increase in company-candidate engagement rates. With numbers like that and customers like Cisco and Amazon, I’d say they’re onto something.

Now, Belong was by no means the first to dip its toes into social when it comes to recruiting. Their application of machine learning to their solution, though, is their secret sauce. Note that my friend and colleague, Megan Biro, CEO of TalentCulture, has written extensively about the power of social media, especially in passive recruiting. The trifecta of relationships, conversation, and unprecedented access to digital tools is a veritable hotbed of HR potential—Meghan has even referred to it as the “tech meets HR marriage,” and I couldn’t agree more. We’ve both got eyes on what’s next.

Speaking of what’s next, are there any plans to shake up what recruiting looks like at your organization in the next year? If you’re looking for a change, what potential do you see in machine learning and HR? What challenges? Let me know in the comments.

Photo Credit: martinlouis2212 Flickr via Compfight cc

 This article was first published on FOW Media.