We Put Data-Driven Recruitment to the Test and Here’s What We Found

As organizations venture to capture more revenue and tackle transformative initiatives in 2019, one thing is certain – exceptional talent will be key to their success. In the midst of talent shortages, finding talented candidates is a challenge for organizations still using traditional hiring practices. The solution? Data-driven recruitment. When hiring for our offshore recruitment team, PSG Global Solutions set out to test the efficacy of predictive analytics using a combination of data about the source of candidates, their performance with previous employers, and our own hiring process. Here’s what we found:

Past performance was inconclusive. Our assumption going into the analysis was that past performance would predict future performance. We thought “No Issues” candidates who did not have any disciplinary actions for absences (health, accessibility, or otherwise), non-performance (failing key metrics), or tardiness would deliver at a higher level. However, our predictive analytics found that “No Issues” candidates were only 2% more likely to graduate our training program than the regular candidate pool and 28% more likely to be an A-player. Looking at past performance failed to give us any competitive edge.

One incredible source was overlooked. Before we evaluated our data, we already suspected that employee referrals were an excellent source for our top performers. SHRM reported that employee referrals delivered more than 30% of hires. Employee referrals proved to be a dependable source of candidates, but we were surprised by a near completely overlooked candidate source: applicant referrals. Retention rates for candidates sourced through applicant referrals were 133% higher than those sourced through job boards and 30% higher than employee referrals. It even turned out that job boards, our biggest source of hires, were our worst source in terms of results. Once we realized the disparity, we drastically decreased our budget for job boards and refocused our attention on getting applicant referrals.

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Alma mater made a counter-intuitive difference. Before we started to pursue data-driven recruiting strategies, we assumed that more prestigious universities would provide our most successful hires. However, the data told a different story. Our most successful recruiters did not come from the most prestigious universities, but instead attended schools outside of the top echelon. While the data told us what happened (i.e., the correlation between schools attended and success on our team), it didn’t tell us why. One hypothesis is that graduates of the most prestigious universities are less likely to persevere through the ups and downs of being a recruiter because it is easy for them to find another good job (given their prestigious degree). Regardless of the reasons, we now know the correlation, and can adjust our hiring process based on that information.

Applying the Benefits to Your Business

What does this mean for your business? While the factors you analyze to predict your top performers will vary, here are the key takeaways:

Do not disqualify any data. Data is now less cumbersome to leverage because of the increasing sophistication of data analytics platforms. This widens the range of data that your organization is capable of effectively evaluating. Comparing different data sets from external sources and different departments might provide your business with insight that can impact your productivity and performance.

Be open to your findings. Though we did have assumptions going in, we never let them influence what data we tested and the hiring strategies we implemented to improve our data-driven recruiting. Keep a similar open mind and your business will not only improve your predictions of top performers, but will increase the quality of your team.

Monica Maralit

Monica Maralit
Monica Maralit is VP of program management at PSG Global Solutions.

Monica Maralit

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