How to Track Hiring Performance with Data
A comprehensive guide to measuring and improving your hiring performance with data. Which metrics matter, how to collect them, and how to turn data into better decisions.
Door Ingmar van Maurik · Founder & CEO, Making Moves
Data Is the Key to Better Hiring
Most recruitment teams operate on feeling. They know it is busy, that some vacancies are difficult to fill, and that the hiring manager is getting impatient. But when you ask for concrete numbers such as average cycle time, cost per hire, or quality of hires after six months, silence follows.
That is not a criticism of recruiters. It is a systems problem. The tools most teams use are not designed to measure hiring performance. They are designed to move candidates through a pipeline. The difference between those two is fundamental.
In this article, we show which metrics matter, how to collect them, and most importantly: how to translate them into better hiring decisions. Because data without action is nothing more than a pretty dashboard.
The Three Pillars of Hiring Performance
Hiring performance can be divided into three categories: efficiency, quality, and experience. Each measures a different aspect of your hiring operation, and you need all three for a complete picture.
Pillar 1: Efficiency
Efficiency metrics tell you how fast and inexpensive your hiring process is. They are the easiest to measure and often the first thing organizations look at.
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Time-to-fill is the most commonly used metric, but also the most misleading. A short time-to-fill says nothing about the quality of the hire. You could hire someone tomorrow, but if that person leaves after three months, you have lost more than you gained.
That is why it is crucial to always view efficiency metrics in conjunction with quality metrics. Also read our article on what a bad hire really costs for a complete cost overview.
Pillar 2: Quality
Quality metrics are harder to measure because they often only become visible months after the hire. But they are by far the most important indicators of the effectiveness of your hiring process.
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Quality of hire is the holy grail of hiring metrics, but also the most difficult to measure. There are multiple approaches:
1. Performance reviews at 6 and 12 months, compared to the team average
2. Goal attainment in the first 90 days versus expected goals
3. Manager feedback via structured surveys
4. Retention as a proxy: if someone leaves within a year, it probably was not a good hire
The power lies in the combination. No single metric tells the whole story, but together they provide a reliable picture of the quality of your hires.
Pillar 3: Experience
Candidate experience is becoming increasingly important, especially in sectors with talent scarcity. A bad experience costs you not only the candidate it happens to, but also everyone that candidate shares their experience with.
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Measure the candidate NPS for both hired and rejected candidates. The experience of rejected candidates tells you the most about the quality of your process.
How to Collect Hiring Data
Collecting data sounds simpler than it is. Most organizations have their hiring data scattered across multiple systems: the ATS, an assessment tool, an HRIS, Excel spreadsheets, and the recruiter's inbox.
Step 1: Centralize Your Data Sources
The first step is getting all your hiring data in one place. This can be through an integrated platform that centralizes all your hiring data, or through a data warehouse where you synchronize the various sources.
Without centralization, your data is unreliable. If you calculate time-to-hire based on the ATS, but the first contact happened via LinkedIn and was never registered, your metric is wrong.
Step 2: Define Your Measurement Moments
Determine when you capture which data. This prevents having to reconstruct after the fact what happened.
Step 3: Automate Where Possible
Manual data collection is unreliable. Recruiters under pressure forget to enter scores or log feedback. Automate data collection as much as possible by making it part of the workflow, not an extra step alongside it.
From Data to Insights
Collecting data is step one. The real value lies in the analysis: recognizing patterns, formulating hypotheses, and defining actions.
Example 1: Analyzing Cycle Time
Suppose your average time-to-hire is 35 days. That tells you little. But when you break the cycle time down by stage, you see for example:
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Now you know where the problems are. The hiring manager interview and the offer process together account for 19 of the 35 days. That is where your improvement lies.
Example 2: Measuring Sourcing Quality
When you combine source-of-hire with quality-of-hire, you get a much richer picture than either metric alone:
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This analysis makes it clear: referrals deliver the best candidates at the lowest cost. Indeed delivers volume but low quality and retention. The agency delivers reasonable quality but at enormous cost. Based on this, you can lower your cost per hire by shifting your budget.
Example 3: Assessment Validation
Perhaps the most powerful analysis is validating your assessments against subsequent performance. If candidates who score high on your assessment also perform well in practice, then your assessment is valid. If not, you are measuring the wrong thing.
This requires linking assessment scores to performance data at 6 and 12 months. It is an investment in data collection that pays for itself many times over. A validated assessment is the difference between a structured guess and a data-driven decision.
Read more about how continuous validation improves your hiring.
Dashboards That Work
A dashboard is only useful if it prompts action. Many dashboards are beautiful but functionally useless: they display numbers nobody uses to make decisions.
The Strategic Dashboard (for Leadership)
The Operational Dashboard (for Recruiters)
The Analytical Dashboard (for HR Analytics)
The Technology You Need
To truly measure hiring performance in a data-driven way, you need more than an ATS with a reporting module. You need a system that collects, centralizes, and analyzes data across the entire hiring lifecycle.
An AI-powered hiring platform goes beyond reporting. It automatically identifies patterns, signals bottlenecks, and makes recommendations. It learns from your data and becomes smarter with every hire.
The investment in data-driven hiring pays for itself quickly. Companies that structurally measure their hiring performance achieve on average 25 to 30 percent lower cost per hire and 20 percent higher retention in the first year. Get in touch to discuss what this looks like for your organization.