No matter how economic conditions ebb and flow, companies are always seeking an increased supply of job candidates. In fact, it's top of mind for most talent acquisition professionals and likely is for you as well.
The truth is, job candidates are a great source of value to your company. Still, many companies don't maximize the full potential of this value. Achieving maximum value requires thinking differently about the talent acquisition process. Let's explore further.
Think about this question—what would you do with more job candidates? You may feel a little more comfortable and secure in filling your open roles, and you might even get some recognition for a job well done. But given that the objective is to make a quality hire for the business, do those extra candidates really add value to the stack of applications you already have?
It's indisputable that a steady flow of qualified candidates is an asset for any company, but is an overload of candidates an asset? I have a finance background, so I like to evaluate the return on company assets. To figure the return, you must begin with the cost.
It's expensive to acquire a job candidate. We recently wrote about this in an article titled, The Dirty Little Secrets Job Boards Don't Want You to Know. In that article, we documented that the cost of a job candidate obtained through a public job board costs a minimum of $300 per applicant (on average, it's likely much more). For the sake of argument, let's say the cost of a job candidate (just a candidate and not a hire) is $500. Take a journey with me to follow a fictional candidate through the process. Let's see if we get a reasonable return on that $500 investment.
Meet our applicant. Her name is Jean. Jean is one of many applicants who have applied for our open sales associate position in one of our retail stores. She is officially in our ATS and ready to be evaluated.
From here, one of the following things will likely happen:
Thanks, but no thanks. In responding to our qualifications, Jean hit a trigger that knocked her out of the running. She may have indicated that she's not able to work weekends, she's not available during the hours we need, doesn't have reliable transportation, is not legally qualified to work in the U.S., or did not meet one of our other qualifications. You send Jean a very nice response saying that we're sorry, but there are currently no positions for her. Please try again soon.
The invisible candidate. Jean met all the qualifications, but she was one of a hundred other applicants. The reality is that our recruiters or hiring managers just couldn't review her application. We're glad she applied, but it's not realistic for us to properly consider everyone.
Possibly, but then again, no. Jean's qualifications and experience look good to our hiring team, so it's decided she will move ahead in the hiring process. The next step is to interview or phone screen her and decide whether to move forward with her. After her interview, we didn't think she would be a good fit, so we moved on to another candidate.
You're hired. Everyone thought Jean was great. She passed her background check and drug screen, and you extended an offer. She accepted.
In a typical organization, the "You're hired" scenario happens for about one out of every ten job candidates. That means you spend $500 per candidate and hire one, which adds up to $500 to attract Jean and $4,500 to get the other nine candidates. Is there any value in the other candidates, or do you have to get a return on the entire $5,000 based on the success of Jean's employment? That would be a heavy lift for Jean! She would need to stay on the job for a very long time and perform really well to provide a return on that investment.
I can already hear you. "I can't even evaluate all the applications I receive for any particular position, and now you suggest that I evaluate every applicant for every position. That's not possible." But it is possible. To make it possible, you need a machine learning decision support system that automatically analyzes and identifies the top candidates for each position in your company.
Armed with a candidate data model, a machine learning algorithm can analyze all your candidates in seconds and make recommendations on which candidates will make the best employees in each job. You may be wondering, "What is a candidate data model, anyway?" A candidate data model is a set of characteristics and traits that your best employees possess on a position-by-position basis. It could be the proximity of residence to job location, education, work history, personality traits, situational judgment, or any number of non-discriminatory factors.
You can use a candidate data model to match candidates against the criteria that predict a good employee and know, for certain, which candidates are worth your time. You can do this for all candidates and for every job. And even better, all are reviewed in a consistent manner free from bias to select a candidate who will be a great employee and feel a sense of satisfaction and fulfillment in their job.
Don't give up on a candidate because they weren't a great fit for the job to which they applied. It cost a lot to acquire those candidates. When a person applies for a job at your company, use all the tools available to you to get the absolute most out of your investment. The candidate made an investment also—they made an investment in time and effort to apply to your job. Use machine learning to maximize the return on both parties' investment. Jean will thank you for not making her shoulder the entire load.
You may be interested in our article, "The Real Cost of Employee Turnover."