Hiring Analytics - 1 Bad Hire Wipes Out the Value of 3 Great Ones

Hiring Analytics - 1 Bad Hire Wipes Out the Value of 3 Great Ones

Whether you are hiring analytics professionals, customer service representatives, and retail or outside sales consultants, one proven variable is always watching your hiring decisions: One bad hire will wipe out the value of three great ones and it was just reported that ‘1 in 10 recruits are regarded as a ‘poor hiring decision’.

The profit killers of lost productivity, training costs, administrative expenses, decreased morale and the disruption of team dynamics all contribute to the irrelevance of three great hiring decisions by making one bad one.

Recruiting, interviewing, on-boarding, training and the retention of top talent is the life-blood of any organization and decreasing wrong hiring decisions, even incrementally, has proven to have an overwhelming impact to a company's bottom line. Company's interview processes have evolved from paper resumes and a single interview to requiring candidates to do group interviews and presentations, cognitive testing, peer-to-peer interviews and happy hours to gauge team dynamics have all been implemented for one reason, to avoid making a bad hire.

The next step in making the right hiring decision is moving away from just interviewing and embracing analytics into the hiring decision to eliminate or confirm personal opinions. Many large organizations were early adopters of analytics in the hiring process but small and medium companies are also recognizing the value.

"In high volume employee areas, even marginal performance or attrition improvements results in significant bottom line results," said CEO of Talent Analytics Greta Roberts. "When predictive models show that one bad hire wipes out the value of 3 great hires, hiring decisions modify instantly to add analytics to the hiring process."

Analytics companies have been developing ‘Flight Risk Score’ products to predict when an employee will leave an organization but a Flight Risk Score for existing employees is too late. Greta states that, “A better approach would be to predict how long a job candidate will stay, before you hire them, avoiding hiring those with a high flight risk altogether.”

Calculating an employee’s Flight Risk Score is just one component of ‘hiring analytics’ but combining it with other calculated indicators helps organizations analytically assess candidates and customize their hiring analytics around their industry and specific roles within the organization.

So what is the most accurate term for analytics being implemented into the interviewing, hiring, on-boarding and the career life-cycle of employees?

Hiring Analytics, Talent Analytics, HR Analytics, Recruiting Analytics, Employee Analytics, People Analytics…

Recruiting is past the goal of getting as many applications to a job opening, manually determining the right applicants to phone interview, bringing the select few in for interviews and hiring one. Adding an analytics layer into the decision now allows companies to focus on the quality of applicant and hire regardless of how many people are in the applicant pool.

Hiring Analytics, Interviewing Analytics, or whatever the correct term is, analytics needs to be a primary factor in the hiring process to decrease the chances of that 1 bad hire and eliminate the dreaded term “poor hiring decision”.

Article written by icrunchdata
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