Machine Learning and the Magic of Recruiting

Sean Little —  April 12, 2017 — Leave a comment

Machine learning in recruitingIf you haven’t heard about how machine learning is changing the entire landscape of recruiting, it might be time to call your real estate agent and get them to put “For Sale” sign on the rock you’ve been living under the past three months. Just kidding, but seriously: everyone is talking about it. If you want to do some catching up, here are a few good places to start:

How Machine Learning is Revolutionizing Recruiting: “Recruiters can start to recognize pure data points of candidates’ contact information, their profile, their work history, etc. and be able to match those with opportunities. Machine learning does not automatically select the best candidate, instead it narrows the field of search and allows us to focus on analyzing the intangibles.”

What Machine Learning Can Bring to Corporate Recruiting: “So using historical data to predict what a human being will do or like isn’t that new but it is only now that the world’s HR departments are realizing how valuable it can be. Combining employee and candidate data in the right way can help companies get more out of their most important assets: human capital.”

With to all of the hysteria surrounding the topic, I considered titling this blog “This Millennial just used Machine Learning to destroy the Fake News about Recruiting.” I surely would have had more clicks. Machine Learning is the buzzword flying around in the recruiting world today. It’s the mystical, magical solution to all of your problems. The fact of the matter is, like all buzzwords, the idea behind machine learning in recruiting comes from a place of relative truth and good science. Using a computer to analyze the processes and outcomes of recruiting will allow you to make better recruiting decision, given a quality dataset and a well-engineered analysis.

Unfortunately (you knew a “but” was coming), the blog-o-sphere got a hold of this idea, and now you need a facial recognition software, a Google-sized recruiting budget, and an in-depth understanding of quantum mechanics in order to have a shot at hiring the right person for a job. While one of those things was a joke that no blogger has ever recommended for hiring better, all three are equally unnecessary for success in recruiting. Recruiting and hiring is hard, and any blog claiming that a robot is going to make it less hard is peddling you the same rubbish that applicant tracking systems have been pushing for years.

How you can replicate machine learning in your recruiting process

The fundamental idea behind machine learning in recruiting is a rock solid one. Instead of relying on your shortcuts – reading resumes, throwing out the ones with goofy names, throwing out the ones who misspelled something, keeping all of the ones who went to the same college as you – you are forced to rely on a computer’s analysis of a candidate. The computer has a quality dataset with information about the candidates who have already been successful at your company. Essentially, it is benchmarking your set of candidates against the criteria it thinks has led to success in the past.

A computer’s benchmarking, given a quality data set and a complex algorithm, will be better than your resume search. That’s a given. But it will never be without mistakes. If you learn nothing else from this blog post, learn this: recruiting is hard. There is no magic pill.

There is, however, good process. Take anybody at your company who has been in the position you are trying to hire for and had success and ask them as many questions as you can think of. Ask them things you might ask a candidate who is coming to work at your company. How many years of experience did you have when you started? What skills did you come to us with? What work behaviors do you possess that you think lead to successful outcomes for yourself and for our business? What motivates you?

Once you have their answers, figure out which ones you can identify during your recruiting process. If your best sales representatives came into the company with zero sales experience, you’ve just learned something about what makes a person successful at your company. If your best customer service representatives are motivated by the positive feedback they receive from your clients, you know how your best future CSR’s ought to be motivated.

Here’s the important part: once you’ve got some criteria set aside, ask your candidates these questions while they apply. Don’t sort through your candidate pool using their resumes. Don’t sort through them based on who submitted a .pdf resume and who submitted a .doc resume (trust me, it has happened.) Ask them the questions that you asked your current employees and use their answers to decide who to talk to. If all of your best sales reps are motivated by money, you should be molding your recruiting process around hiring candidates who love making money. Ask a multiple choice question about what motivates them to be successful in sales, and interview the ones who chose “Money!”

There! You did it. You’re a recruiting machine. Again, this is not the magic pill. In order to reap the benefits of this system, you have to commit to it. Contact everyone who answered your questions the right way. No shortcuts. By setting up our process this way, you will be eliminating 90% of the fluff – those candidates who answer every employment ad and aren’t qualified for your position; they won’t answer your questions the way you want them to. The rest is up to you. Happy hiring.

Sean Little

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Sean is an Account Manager here at NewHire. When he isn't catering to the hiring needs of small businesses nationwide, Sean is busy campaigning for the Oxford comma, playing sports, and doing comedy. He takes his coffee with 2 creams, no sugar.

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