When it comes time for employers to fill jobs again, time will be of the essence.
“But this is definitely a case where haste makes waste, costing companies enormous amounts of time and money if that fast choice becomes a bad hire,” says Denise Leaser, President of GreatBizTools, developer of pre-hire assessment tool WebAssess. “And that affects the bottom line, especially in a tight labor market where high attrition adds tremendous costs.”
All employment decisions essentially are predictions about how well people will perform their jobs. Organizations typically choose one of the following major approaches for hiring employees:
The “Speedy” approach — Companies that use this quantity-oriented method simply want to fill job openings as quickly as possible. Since expediency is valued more than anything else, minimal time and money are spent evaluating an applicant’s suitability for the job.
The “Smart” approach — Companies that use this quality-oriented strategy are interested in identifying individuals who will be able to meet all job expectations. Since the ultimate outcomes of the employment process are valued most, a minimal investment upfront to assess applicants will save you a lot of time and money in the long run.
“The ‘speedy’ approach is no better than picking names out of a hat and betting against the odds that you will get good results,” says Leaser. “The ‘smart’ approach uses scientific assessment techniques to identify top performers and eliminate the costs associated with hiring poor performers.”
For example, an independent study conducted by IBM Digital Workplace Services indicated that pre-hire assessment tools, like WebAssess, can save as much as 20% of an employee’s first year’s salary in faster onboarding and reduced turnover costs alone. That doesn’t even include the time and cost savings of having better performers, fewer errors and better service.
A science-based assessment tool like WebAssess can evaluate candidates based on their skills, abilities and personality traits – taking the guesswork out of the selection process.
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