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Transforming Data into Recruiting Intelligence

NU Nurses90 · 📅 February 13, 2026 · ⏱ 2 min read
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Arguably, the bottom line of recruiting success is the number of successful new hires. Organizations know that it is important to measure the outcomes of recruitment, but, unfortunately, most focus on measures of efficiency: time to fill an open job or cost per hire. These are helpful but by no means indicate the impact that hiring decisions have on a company’s ability to realize its business goals. For example, it’s easy to say, “Our time-to-fill is 42 days.” Wouldn’t it be better to say, “We reduced our time-to-fill by 20 percent, which got our technology and engineering teams to finish their project a quarter earlier, resulting in $500,000 of savings, which contributed to $250,000 of revenue”?

Experts agree that to measure “quality of hire” effectively, it’s important to calculate metrics both pre- and post-hire. Pre-hire, recruitment-focused quality measures include such things as candidate quality (education, experience, training), new-hire attrition, and candidate assessment scores. Consider questions such as the following: Do we have enough qualified candidates in the pipeline? Is our interview process effective? Are we closing deals with the candidates we want to hire? Measures of post-hire quality include outcomes such as time to become fully productive, rank among peers, and supervisory assessments of culture fit.

ATS enable firms to collect a wide range of data. The challenge is to distill them in ways that will convey business-critical information to decision-makers.

Questions:

You have just been named manager of recruiting at your firm. The senior executive team asks you to develop a “scorecard” that will enable it to assess the costs and benefits of recruitment efforts. What might such a scorecard look like?
The team wants you to distinguish measures of efficiency from measures of business impact. What kinds of metrics will you put in each column?
A recent survey found that nearly two-thirds of iob seekers who felt they were treated poorly during the recruitment process were less likelv to purchase goods and services from those emplovers. If 10 percent of iob seekers at vour firm are dissatisfied. how might vou tallv those costs?

The post Transforming Data into Recruiting Intelligence first appeared on Ehomeworker.

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