From Human Screen to Machine: Predictive Analytics Helps Avoid a Major Point of Hiring Failure

August 11, 2014
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By Greta Roberts, CEO, Talent Analytics, Corp.,

 

Program Chair, Predictive Analytics World for Workforce

 

By Greta Roberts, CEO, Talent Analytics, Corp.,

 

Program Chair, Predictive Analytics World for Workforce

ImageWhat is an employer’s most business-critical corporate process? At or near the top of this list has to be hiring employees that deliver more value to their role and company than they cost to their employer. Employees bring in revenue, rescue a customer, make your products, deliver goods, and sustain your profitability going forward. Identifying the right people, and avoiding the wrong ones, is an imperative to business sustainability.

But how is initial candidate screening handled at your organization? Many employers take an approach that isn’t at all what we expected.

Talent Analytics uses predictive modeling to help organizations reduce attrition and increase on the job performance within high volume job roles. Our projects give us insight into the entire hiring process.

We’ve discovered that a surprising number of companies, typically midsize and large enterprises who screen a large volume of candidates, often relegate this single most critical task to individuals far removed from the line of business.  At times this task is given to contractors, interns, temps or external part-time employees.

What a huge point of failure leading to massive hiring errors and missed opportunities.

Organizations need to replace this manual, error-prone screening process with predictive analytics processes and technologies that are unbiased, trained to look for a specific combination of predictive factors.  Predictive models don’t get tired; they learn and get better over time; they equally weight candidate.

Predictive Analytics in the Hiring Process Can Take Some Getting Used to

We’ve uncovered two truths when recommending hiring processes including talent analytics. First, some people seem to inherently distrust the analytics-based process. And second, those who distrust it are often the same people who consign candidate screening to under qualified screeners.

We understand hesitation. Including predictive modeling in the hiring cycle is new to many organizations. It can be difficult for many people to get their head around the models and what they’re doing.

But while skepticism of new approaches may be logical, blind reliance on old, ineffective methods is not logical and is not fair. And that’s certainly the case when depending on part-timers or interns to screen job candidates.

These screeners typically need to review a large number of résumés in a short period of time for a wide variety of roles. Even if they’ve had weeks of intense training on how to screen candidates—which is almost never the case—it’s impossible for a single individual to keep all relevant variables in mind as they scan résumés or conduct 10-minute screening phone calls. As a consequence, the initial decision about one of your most important corporate processes is made in what’s clearly an error-prone manner.

Adding Scientific Methods Lead to Fair, Repeatable, Decisions

A far more effective method is to include talent analytics to ensure your hiring processes are based on relevant data. Analytics technology can process high volumes of data, without bias, without excluding important variables, without growing tired—and with consistency, from the first candidate to the last.

The result is a hiring process that’s accurate, that’s repeatable and that’s far more likely to surface the best candidates and eliminate those you want to avoid.

So there doesn’t have to be a battle between traditional screening processes and talent analytics. Just be sure you’re applying the two approaches with the right balance that will deliver the results you want: a workforce that truly supports your business objectives.

For more great insight, follow Greta Roberts on twitter @gretaroberts