Many companies today collect more recruiting metrics than ever before... and yet they are not making better decisions.
In our last Insight, we presented the most important classic KPIs that reveal efficiency and quality (Foxio 2025). However, figures such as time-to-hire or cost-per-hire often remain isolated. Without a connection to performance and experience data, the result is only apparent transparency. Deloitte shows that companies with integrated HR analytics make 2.5 times better talent decisions (Talentbusinesspartners 2023). The message is clear: it's not the quantity of numbers that counts, but which data is linked and interpreted.
From measurement to control: the next step after traditional KPIs
Traditional recruiting metrics such as time-to-hire, cost-per-hire, and applicant-to-hire ratio are now established in many companies. They create transparency about speed, costs, and process quality—and they were the topic of our last article. But if you stop at reporting, you'll get stuck in operations. The crucial step is to understand metrics as control instruments and link them to other data sources.
McKinsey refers to this as “return on talent”: organizations that actively use talent data achieve significantly better business results (McKinsey 2024). So it is not enough to know how long a position remains vacant – what matters is how much value is lost during this time. This is exactly where data-driven recruiting approaches come in: they combine efficiency metrics with performance and productivity data to show which hires deliver real added value.
A change is also evident in Germany. The DGfP benchmark study from 2023 shows that companies are collecting more and more recruiting data – but its use remains heavily focused on basic KPIs (DGfP 2023). In many places, the step of linking this data to quality and experience data has yet to be taken. To ensure that companies miss out on the opportunity to derive real recommendations for action from the figures.
The key message: Data-driven recruiting does not mean collecting more numbers, but asking better questions of the data. Which channels deliver high-performing candidates in the long term? Which process steps lead to dropouts? And what role does the candidate experience play in retention and employer branding? Those who not only measure key figures but also interpret them systematically build a bridge between efficiency, quality, and sustainability.
New data levels in recruiting
If you want to manage recruiting in a data-driven way today, you can't limit yourself to basic metrics such as time and cost. These provide an initial orientation, but they ignore crucial factors. Modern HR analytics open up additional data levels that provide deeper insight – and are thus to ensure much closer to the reality of recruiting success.
One example is funnel data. It shows at which points in the application process candidates drop out. If a large proportion drop out between application and interview, this indicates a lack of communication or excessively long waiting times. High drop-off rates shortly before the contract is signed, on the other hand, indicate a lack of fit or unattractive offers. Such information reveals where processes really fall short – and provides starting points for concrete optimizations.
A second lever is the offer acceptance rate. It measures how many signed contracts follow offers that have been made. If the figures remain low, this is a clear signal: either the employer value proposition is not right, or competing offers are more convincing. Deloitte points out that companies with integrated HR data make significantly better hiring decisions and use such metrics in a targeted manner to sharpen their offer strategies (Talentbusinesspartners 2023).
Another factor that is often underestimated is the cost of vacancy. It reveals how expensive unfilled positions actually are – not only in terms of opportunity costs, but also in terms of the increasing burden on existing teams. McKinsey emphasizes that companies that systematically track this data can avoid productivity losses and take more targeted countermeasures (McKinsey 2024).
These additional data layers make it clear that recruiting should not be measured not only in terms of speed. Only when efficiency and funnel metrics, offer rates, and the costs of open positions are considered together does a clear picture emerge of which levers really matter in recruiting.
Quality and experience KPIs: The second data layer
Efficiency is only half the story. Those who measure recruiting purely in terms of speed and cost continue to ignore key success factors: the quality of the people hired and their experiences in the process. This is exactly where the second data layer comes in – quality and experience KPIs.
One key indicator is quality of hire. It shows whether new employees actually deliver the desired performance and stay in the long term.
This can be assessed by target achievement, feedback from executives, or retention after six to twelve months. It has been shown that companies that systematically collect this data are significantly more productive (McKinsey 2024). Quality, to to ensure not only cultural fit, but also tangible economic value. Closely related to this is the retention rate.
Early turnover—i.e., departures in the first few months—particularly reveals whether expectations were communicated realistically in the recruiting process. CareerTeam refers to Gallup (2024) in pointing out that candidates with a positive candidate experience are three times more likely to be satisfied in their job in the long term (CareerTeam 2025). Quality and experience are therefore inextricably linked.
The candidate experience itself is particularly important. The BCG study from 2023 shows that over 50% of applicants reject an offer if they have a negative experience during the process (BCG 2023). Feedback loops, communication, and transparency are therefore not just “soft factors,” but hard metrics with a measurable impact on recruiting success.
This quality and experience data broadens the perspective: it shows that recruiting does not end when the contract is signed. Satisfying candidates and ensuring cultural fit reduces turnover, strengthens the employer brand, and improves performance at the same time. The second level of data reveals whether efficiency is also translated into sustainable success.
HR analytics in practice: from reporting to predictive insights
The key to data-driven recruiting is how information is used to improve future decisions. This is exactly where HR analytics comes in: from backward-looking evaluation to forward-looking forecasts.
As mentioned above, Deloitte shows that companies with integrated HR data make 2.5 times better talent decisions than organizations without these analytical capabilities (Talent Business Partners 2023). Instead of simply looking at past values such as time-to-hire, they use predictive models that forecast dropout rates in the funnel, early turnover, or the effectiveness of individual channels. This is to ensure that recruiting is transformed from a reactive function into a strategic management tool.
A practical example is the analysis of drop-off rates along the candidate journey. If patterns become apparent—such as high drop-off rates after the initial interview—companies can make targeted adjustments to their processes. The prediction of retention through the combination of candidate experience data and performance indicators is also gaining in importance. This allows risks to be identified early on, before bad hires become costly.
Analytics should not only be anchored in reporting, but must also be integrated into the decision-making process itself. This means that HR and specialist departments need dashboards that present key figures in an understandable way, run through scenarios, and make alternative decisions visible.
The result: companies that use HR analytics are shifting their spotlight from pure efficiency reports to strategic predictions. They recognize early on which channels deliver sustainable quality, where costs are getting out of hand, and how culture and experience affect productivity. To ensure recruiting not only becomes faster and cheaper, but also more predictable.
Best practices 2025: Using data instead of collecting it
Many companies today face the same challenge: they have a wealth of recruiting metrics at their disposal, but few manage to systematically translate this data into better decisions. Best practices from 2025 show how a data-driven approach can succeed in practice.
A key element is the linking of efficiency and quality data. Those who view time-to-hire and cost-per-hire in isolation quickly overlook the fact that speed is not synonymous with sustainability. Successful companies combine these basic KPIs with retention data or candidate experience scores to understand whether fast placements are also sustainable in the long term.
A second success factor is the systematic measurement of candidate experience. The BCG study shows that more than half of candidates reject an offer if they have a bad experience during the process (BCG 2023). Leading companies therefore continuously collect feedback—for example, via candidate NPS or structured interviews—and use this data to consistently adapt their processes.
Thirdly, pioneers rely on predictive analytics. Companies that evaluate talent data proactively are significantly more productive (McKinsey 2024). This includes models that calculate the probability of early turnover or reveal the ROI of different channels. To ensure recruiting from a cost factor into a clearly measurable value driver.
Finally, integration into the corporate culture plays a crucial role. CareerTeam refers to Gallup data, according to which candidates with a positive experience are three times more likely to be satisfied in their job in the long term (CareerTeam 2025). Successful organizations use this insight to view recruiting data not in isolation, but in conjunction with culture and engagement data. Recruiting thus becomes an early indicator of employer attractiveness.
The pattern is clear: successful companies don't just collect data, they act on it. They establish clear responsibilities, provide tools for HR and specialist departments, and anchor a data-driven approach.
Conclusion
The discussion about recruiting metrics usually starts with efficiency: How quickly and how cheaply can vacancies be filled? But if you stop there, you overlook the real power of data-driven management. Data-driven recruiting combines classic KPIs with new data layers – from funnel analyses and candidate experience scores to predictive models.
The studies lay a clear picture: companies that systematically use their recruiting data are more productive, make better hiring decisions, and secure more satisfied employees in the long term. Recruiting is to be ensured not only to be faster and more cost-efficient, but also more predictable and culturally meaningful.
For decision-makers, this means that the next step is not to collect even more metrics, but to link the right data in a targeted manner – and translate it into concrete management tools. Those who succeed in doing so will establish recruiting as a strategic function. And that is precisely what makes the difference in times of skills shortages and tight budgets.
Would you like to manage your recruiting processes more precisely – while ensuring both quality and candidate experience? Foxio supports you in setting up a suitable set of KPIs and putting data-based decisions into practice. With analytical understanding, technical expertise, and a keen eye for modern recruiting processes.
Let's work together to identify the key levers – and unlock the full potential of your recruiting data. Get in touch – we look forward to hearing from you.
Sources
- BCG (2023): What Job Seekers Wish Employers Knew.
- CareerTeam (2025): Candidate Experience = Corporate Culture in Real Time – Why many fail
- DGfP (2023): Recruiting Benchmark Study.
- Talentbusinesspartners (2023): How Top Companies Use Recruitment Analytics.
- Foxio (2025): When efficiency meets quality – the KPIs that really drive your recruiting forward
- McKinsey (2024): Increasing your return on talent: The moves and metrics that matter.
Further reading
We show you how to set up your recruiting processes based on data and optimize them technically in the previous insight “Data-driven recruiting – The key strategy for reducing costs in human resources”. It deals with tools, automation, and building a real data strategy.
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