These days, the hiring process seems to run the gamut, from haphazard to extremely stringent; yet, all with pretty much the same risk of hiring the wrong candidate. Hiring best practices have organically changed over time, but at the core, the process remains the same: resume scan, interview, interview, interview and, finally, tedious reference checking (if that).
Even prolific companies like Google, who recently changed their average “29 interviews prior to hiring to 4 to 9,” have had to tweak hiring protocol upon realizing that their process offers little insight into how the candidate will actually perform.
But what if organizations did have the ability to predict a candidate’s success rate? With automated pre-hire assessments, it’s not impossible.
Picture this: you arrive at work 20 minutes early to get settled in. You grab some coffee, log on to your computer, check email (if it hasn’t already haunted you the previous night and that morning) and start hammering away at responses. Then a meeting, another meeting, a follow-up from the meeting and… the 2:00 interview you totally forgot about! You scramble to find the candidate’s resume and mentally review your standard interview questions while walking to greet the candidate.
What’s wrong with this picture? Are the interview questions even geared towards the job role? What about understanding what talents work best in the role? Are the same questions being asked to each candidate across the board? Or, really, is the process just a subjective, off-the-cuff mess? Even the best hiring managers can get caught up in this cycle, relying on subjective, unstructured screening to identify their employee investment opportunities.
The average hiring cycle can take up to 2-3 months, which includes the average manual reference checking time of up to 2 weeks. That’s a lot of time spent, or, rather, wasted, when the new hire subsequently performs poorly or leaves. In this age of information, there is a better, more efficient way to predict a candidate’s viability: predictive talent selection.
Predictive talent selection is a method for continuously reducing uncertainty about a candidate’s ability to perform well. It removes the gut instinct and disorganization that lead to bad hires.
So, how does the predictive talent selection process work? Through structured, job-relevant activities that incorporate science to formulate a predictive structure. The outline of the process is provided below:
Predictive talent selection software streamlines these activities, developing a structured benchmark and inherently leveraging that benchmark through initial personality assessments, organized interviewing and automated reference checking. The basis in behavioral science ensures that hiring managers are accessing accurate, job-relevant information. In layering such predictive solutions, hiring managers continuously reduce uncertainty about a candidate.
What’s more, with an automated solution in place, data gathering and manipulation is not only instantaneous, but the results are more easily shared with stakeholders. In layering such predictive solutions, hiring managers continuously reduce uncertainty about a candidate.
Taking a more objective approach to hiring seems like a no brainer-and it is. Using predictive talent selection software not only streamlines the process by mapping a succinct plan for both the hiring manager and other stakeholders, it better aligns the selection process with key business objectives, such as improved customer service, sales or increased retention rates. The scientifically grounded software covers the process from pre-screening all the way to automated reference checking, offering up to 90-percent timesavings in certain activities.
The new hiring frontier is here, and it has arrived in the form of behavioral science based predictive talent selection software.
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