Only 30% of companies can quantitatively measure Quality of Hire. The other 70% either guess, use proxy metrics, or give up and optimize for speed instead.
That’s not a data problem. It’s an interview problem.
Why the metric is hard to measure
Quality of Hire is typically defined as a composite score: new hire performance rating at 90 days or six months, ramp time, retention at one year, and sometimes hiring manager satisfaction. The math isn’t complicated.
The problem is that these outputs are hard to trace back to interview inputs. If your interviews aren’t producing structured, comparable signal — scores, competency ratings, specific behavioral evidence — there’s nothing to correlate against later performance. You’re left with vibes and a performance review that happened months after anyone remembers why they hired the person.
LinkedIn’s Global Talent Trends data consistently shows QoH topping the list of metrics TA leaders want to track. It also tops the list of metrics they admit they can’t track reliably. That gap has stayed stubbornly wide for years.
The 46% problem
SHRM research puts the new-hire failure rate at 46% within 18 months. Nearly half of hires underperform or churn before the two-year mark. The top causes aren’t shocking: poor skills assessment before hire, wrong culture fit, and job requirements that were unclear going in.
Two of those three are interview problems.
A bad hire costs roughly 30% of that employee’s first-year salary according to the U.S. Department of Labor. For a $90,000 role, that’s $27,000 — before you account for team disruption, recruiter time, and the months of lost productivity while the seat is empty again.
The irony is that most teams are laser-focused on time-to-fill and cost-per-hire because those numbers are easy to pull. Quality of Hire, the metric that actually matters for the business, gets tracked informally if at all.
What structured interviews actually change
The Schmidt & Hunter meta-analysis — the most comprehensive study of hiring method validity — found that structured interviews predict job performance at r=0.51. Unstructured interviews come in at r=0.38. That 34% improvement in predictive validity isn’t marginal. Applied across hundreds of hires, it’s the difference between building a high-performing team and running a permanent remediation program.
Structured interviews work because they produce data. Every candidate answers the same core questions. Every response gets scored against a defined rubric. Every interviewer’s score is recorded independently before the debrief. The result is comparable, auditable signal — the kind you can actually run against 90-day performance data six months from now.
Most companies claim they do structured interviews. In practice, “structured” often means the same job title has a shared doc with five suggested questions and no scoring rubric. That’s not structure. That’s a checklist with vibes.
The correlation loop
Here’s what changes when you actually measure it:
You run structured interviews with scored competency ratings. Six months later, you pull performance data on those hires. You correlate: which interview scores predicted which outcomes? Which competencies were strong leading indicators? Which questions weren’t predictive of anything?
Now you have a feedback loop. You update your rubric. Your interview quality improves. Your QoH score improves. Your hiring manager trust in TA improves.
This is not a novel idea. It’s how high-performing hiring teams operate. The barrier has always been the tooling: getting consistent, structured data out of the interview process at scale.
Casuro runs structured AI interviews that produce scored, comparable data for every candidate — the foundation you need to start closing the QoH gap. Book a demo →
