How We Used Analytics to Hire More A-Talent

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May 1, 2018

Top performers drive a tremendous percentage of an organization’s revenue, yet finding these talented candidates proves a challenge to traditional hiring processes. But organizations using a data-driven recruitment approach find higher performing candidates and, as a bonus, get lower attrition.

By using analytics in recruiting my employer, PSG Global Solutions, was able to target two benchmarks of recruiting success – graduation from our one month, full-time training program and recognition as an A-player (consistently meeting or exceeding client KPI goals) after a year of employment – to determine the quality of our hires. Using a combination of data about the source of candidates, their performance with previous employers, and our own hiring process we enhanced how we source and hire for our offshore recruiting services team.

Here are some of our findings and how your business can achieve similar results.

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, 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 though they were 28% more likely to be an A-player. Looking at past performance failed to give us the kind of improvement we were looking for.

Our internal team made a difference

Do some of our internal recruiters outperform their peers? We tested that hypothesis and found that two of our hiring managers delivered superior results when seeking out future top performers. The candidates they hired were 80% more likely to graduate from our full-time training than the average candidate and 82% more likely to be considered an A-player in a year’s time. Accepting the findings from our data-driven recruitment approach, we determined there were two courses to improve our hiring: Let those two hiring managers conduct all of the final interviews, or determine their best practices and use HR to improve our team’s performance. Either change in our process had the potential to make considerable improvements to our workforce.

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 in 2016. 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 performance results. Once we realized the disparity, we drastically decreased our budget for job boards and refocused our attention on getting applicant referrals.

Alma mater made a counterintuitive 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 attended schools outside of the top echelon. While the data told us what happened (i.e., the correlation between schools attended and success at PSG), 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.

Apply data-driven recruitment to your business

How do you duplicate our success? Though we anticipate the factors used to predict your top performers will vary from those we used to hire for our Philippines RPO services, there are two takeaways we suspect apply universally:

1. 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 insights that can impact your productivity and performance.

2. Be open to your findings –Though we did have assumptions going in, we never let them influence what data we tested or the hiring strategies we implemented to improve our data-driven recruiting. Keep a similarly open mind and your business will not only improve the predictability of who is likely to become a top performers, but will increase the overall quality of your team.


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