How Analytics is Helping Federal Agencies Avoid Costly Bad Hires


A single bad hire can cost a bundle: The Labor Department estimates that, on average, organizations spend one-third of a new employee’s annual salary to replace him or her. And research from the Society for Human Resource Management (SHRM) indicates that the financial burden is far worse, amounting to five times a bad hire’s annual salary.

Additional findings from SHRM illustrate the extent of damage that one unfortunate hire can inflict upon an organization:

  • 95 percent of managers say a poor hiring decision at least “somewhat” impacts team morale, and 35 percent say morale is “greatly” affected.
  • Supervisors spend 17 percent of their time managing poor performers.
  • Three of five hiring managers say bad hires don’t get along with co-workers.

That’s an awful lot of negativity from just one “bad egg,” isn’t it? Given the numbers, it comes as no surprise that two of five talent acquisition managers say quality of hire has emerged as the most valuable performance metric, according to findings from LinkedIn. More than ever, there is intense pressure now to derive as much ROI out of recruitment/onboarding/training and development processes as possible. Across the board, the potential for strong ROI begins with the quality of the candidate.

The federal retirement tsunami further complicates the issue. Baby Boomer professionals are poised to leave agencies in droves. Time is running out, folks – creating far less “wiggle room” to risk a “swing and a miss” on a candidate.

So how do we reduce risk here? By investing into proven analytics tools which can help your agency maximize its effectiveness in collecting and reporting talent management metrics – specifically those related to quality of hire.

Analytics tools capture the broad, high-view numbers to distinguish candidates who are top performers from underachievers: You can begin with prior performance review scores and length of tenure. In addition, you want to include both promotion potential and promotion potential achieved. Why? Because we’re looking at so many near and indefinite-future vacancies created by Boomer retirements, you need to know which Generation X and Millennial staffers convey the greatest potential for advancement. And you also want to find out which of these staffers actually made successful transitions to jobs requiring more responsibility and leadership.

Beyond the “big picture” metrics, however, analytics can drill deeper into your talent management data to lend more specific, actionable – and even eye-opening – insights. Here are a few examples:

Geo-mapping. Through analytics, you can pinpoint where you’re getting the best “bang for your buck” in terms of regional recruitment investment. Let’s say you’ve spent $10,000 on job fairs, advertised postings, etc. in a particular Midwest metro area, and then the same amount of budget on job fairs and ads in another city in the South. Yet, you discover through your talent management solution that 90 percent of recruits in the southern city are rated as “good” or “great” on quality of hire, compared to just 50 percent of recruits in the Midwest town. With this level of intelligence, you then adjust regional investment strategies to favor the southern city.

Learning and development impact. Similarly, you can tap into analytics to evaluate your learning/development spend. What kind of training brings out the best in new hires, therefore boosting their chances of being high quality contributors? Should you increase available budgeting for “skills-specific” training (such as cybersecurity classes for tech teams)? Or do “soft skills” sessions (addressing communications and other general areas of professional comportment) make more impact? Through analytics, you can also weigh in on which training methods/formats work best (i.e., in-person training or online classes.)

Hiring manager breakdowns. Analytics will show you which hiring teams excel on quality of hire, and which lag behind. With this, you can examine how they go about hiring, onboarding and training/development, to hopefully extract and transfer best practices from the “winning” teams to the ones in need of improvement.

Time to hire assessment. Sure, in a perfect world, we achieve short time-to-hire periods with high quality of hire outcomes. But we don’t live in a perfect world. Again, analytics can distinguish which agency areas have the shortest time-to-hire metrics, to assess whether they are subsequently suffering with lower quality of hire scores. You could end up directing this area to spend a bit more time on interviewing, vetting, etc., to reap the longer-term rewards of quality of hire.

There are so many other examples. In fact, the extent to which you can break down metrics that contribute to quality of hire is limited only by your imagination. This is the beauty of talent management analytics solutions – they are evolving tools that HR teams can develop and refine to respond to requirements and goals. What’s more, you can easily align associated costs with every metric you pursue, to develop ROI reporting with astonishing accuracy. This separates agencies that command a firm grasp over the quality of their hires from those which simply “go with their gut” – and pay dearly as a result.

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