Quality of hire evaluates what happens after someone starts. Not how quickly you filled the role. Not what it cost. Whether the person you chose actually performs, contributes, and fits.
Most organisations struggle to track it. It requires performance data from months after the hire, connected back to the decisions made before. And "quality" doesn't mean the same thing across every role, team, or organisation. There's no single formula.
That's exactly why it matters. The organisations that figure out how to measure it gain a compounding advantage. Every hire informs the next.
Time to hire tells you about process efficiency. It says nothing about whether you filled the role with the right person. You can have excellent time-to-hire numbers and still make consistently poor hiring decisions.
Quality of hire measures the one thing that actually matters to the business: did this person make the impact we needed? When it improves, retention goes up, performance rises, and the cost of bad hires (estimated at 30% of annual salary) goes down.
The challenge: it's a lagging indicator. Months before meaningful data exists. That's why most teams default to measuring speed. But speed without quality is just fast failure.
“You can have excellent time-to-hire numbers and still make consistently poor hiring decisions.”
There's no universal formula. A high-performing customer service agent looks completely different from a high-performing construction engineer. Applying the same metric to both misses the point.
Quality of hire is a framework you adapt to your context. The most effective approaches draw on constructs research has shown to predict meaningful work outcomes:
How well does the hire perform across the broad expectations of the role? Captured through structured reviews at 3, 6, and 12 months against predefined success criteria.
How effectively do they complete their specific responsibilities? Code quality for an engineer. Close rate for a sales rep. Patient outcomes for a clinician.
Do they contribute beyond their formal role? Helping colleagues, supporting culture, adapting to change. Research shows this is a significant predictor of long-term value. Often under-measured.
Did they stay past the first year? And stay engaged? Early attrition signals poor decisions. But a disengaged employee who stays isn't a quality hire either.
The most valuable measure. Tracks whether pre-hire data actually predicted post-hire performance. Over time, this reveals which methods and criteria work best for each role type. This is how quality of hire becomes a lever for continuous improvement.
Knowing what to measure is one thing. Having the data is another. Most organisations never close the loop because the information lives in different systems: scores in one tool, performance in another, feedback in a third.
Set up evaluations at 30, 90, and 180 days. Measure against the success criteria defined before hiring. Not a general satisfaction check. A structured review against predefined benchmarks.
Track quality per role, team, and recruiter. An org-wide average hides the patterns. You need to see where structured hiring is working and where it needs attention.
The metric that makes the whole system smarter. Which dimensions predicted real performance? Which weightings need adjustment? This is where the feedback loop lives.
Leadership doesn't care about assessment scores. They care about fewer failed hires, faster ramp, higher retention, and lower turnover cost. Translate quality data into business impact. That's how TA moves from cost centre to strategic advantage.
73% of Alva's customers report fewer low-performing hires. That's the number that reaches the board.
Research consistently shows that structured hiring produces significantly higher quality of hire. Sackett et al. (2022,2023) confirmed that combining validated methods can roughly double the number of high performers per cohort: ~60 per 100 hires with structured methods, versus ~31 with unstructured.
The key drivers: clear criteria defined before hiring, validated assessments with proven predictive validity, and a feedback loop that connects outcomes back to the process.
That's the Improve step in Alva's DAADI framework. Define, Attract, Assess, Decide, Improve. Without Improve, every hire starts from zero. With it, every hire makes the next one more accurate.
Quality of hire connects directly to time to hire (a fast process only matters if it produces good outcomes) and cost of hire (bad hires are the most expensive line item in any TA budget).