Basically, quality of hire is the value provided by the new employee. In psychological terms, it’s a multidimensional non-compensatory construct: a reflection of multiple factors or dimensions which include retention, performance, extra-role performance, and self-development. What has essentially changed is the ability to use technology to collect data on these factors, interpret, use, and leverage it for hiring quality evaluations. But the challenge lies in accessing this data from different systems and the fact that most recruiting models are trained on pre-hire data, not post-hire.  

Unlike the common belief, a bad hire is not entirely the employee’s fault. Steven explains using a simple story: 

“If you see a turtle sitting on a fence post, it’s not the turtle that is silly, thinking it could climb a fence, but the person who put it there,” says Steve. 

Likewise, if there is a bad hire, the person who hired and put that employee in the role is at fault. And the impact of it affects both the new hire and the person who hired them. 

Candidates are blameworthy only when they intentionally misrepresent their qualifications or are fake. Otherwise, it’s the fault of the person who hired them. Anyway, there are certain ways to dodge such bullets: source from the right places, select people the right way, engage well with selected ones, optimize the onboarding process, and change the design of the job itself if needed. 

You never hire applicants who don’t apply, hence you never know their quality unless you track the quality of your hires. If you look into hired data, you’ll figure out that most times it’s not the renowned university that gives the best candidates, but the less-renowned ones. And then some never apply to companies with long-term workforces that never accept new employees. Measuring the quality of hires is always critical. 

Talent marketing is one way to ensure that the right people apply, so you get better hiring quality. Most AI tools are not trained to track the quality of hire either, which could be a challenge. Talent marketing should be based on hiring data, so you know which candidates to avoid targeting. Data will reveal candidates who are looking for money and those who actually want to build a career with your company. With AI and ML, there are so many insights you can get based on quality-of-hire data, and more, if you learn how to leverage it right.