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Blinded by Star Gazing and the A-Player Myth

Writing in the recruiting space has brought much attention to strategies for hiring A-players, top talent, and star performers. While that sounds great, I think all that star gazing has blinded a few recruiters. In part, having a poorly calibrated candidate evaluation process in place is to a fault.

There is no team roster filled with the likes of Lebron James, Michael Jordan, or Barry Bonds (though Miami tried). There is no company executive committee entirely staffed with Warren Buffets. The reason can be explained mainly by population statistics. A-players or bright stars only make up a small percent of the available population.

As such, it is more of a myth to make all A-player hires. The size of the candidate population might have to be enlarged exponentially to create a finalist pool of only A- players to choose among. That could be a monumental task. The organization might not have the appetite for the time requirements nor the budget to complete such an undertaking. There is another approach to contributing to organization performance with each hiring decision.

Dim Stars Get Hired Too

Making the right hiring decision requires complex reasoning. To put this into perspective requires that you wrestle with another concept within population statistics known as variation. This can be best understood by looking at your hiring track record. You hired your best, and you hired your worst. When you examine the performance differences between those hired into one job, the variation in decision quality is revealed. Using a process improvement tool called Pareto Analysis (80-20) the impact of low-end variation can be explained.

Obtain a data set of performance variables from a group. Sales performance is a comfortable place to look. Obtain territory revenue per sales rep in a spreadsheet. Calculate the average sales per territory. Next, calculate the average for the top 80% and the bottom 20%. Look at the gap. After you stop shaking your head, you have to admit that your process hired those bottom 20% folks too. You can explore the impact of this with our ROI Calculators. You can perform this same form of HR analytics on any dimension of performance. It is pretty revealing.

This analysis reflects the current nature of the population from which you draw, and the decision quality variation that allows in your poorest performers. In your shining star hiring program, dim stars get hired too.

Scale of Magnitude

Hipparchus, the ancient Greek astronomer created a the six-point magnitude scale to calibrate the relative brightness of stars. Since then the scale has been expanded, revised and refined to describe better the difference observed in the intensity of heavenly bodies. Hipparchus uses analytical models to improve his conclusions. Your process hired the dim stars because of the calibration of your brightness scale. Shining stars and faint stars looked more alike than different. The evaluation process was unable to see the difference. Using HR analytics, your candidate evaluation can be refined and your hiring decisions improved. Better candidate data can enhance the yield of your staffing process. Maybe your recruiter was blinded by star gazing.

With a well-calibrated candidate evaluation process, you get better data, which can support more effective hiring decisions.

Here are a series of examples of on-the-job performance differences that were identified by score ranges during validation analysis of a Virtual Job Tryout. Each chart depicts the performance gap between individuals who scored in the top 80% versus those who scored in the bottom 20% of a Virtual Job Tryout explicitly created for the job they hold

.Individuals who scored in the bottom 20% on the Virtual Job Tryout on average produced $1.6 million less in sales revenue. In other words, about $1.6 million of performance variation was at risk with every hiring decisions. Recruiters were letting dim lights among their stars.

Individuals who scored in the bottom 20% on the Virtual Job Tryout on average closed the deal on 8% fewer opportunities. In this case, millions of dollars of revenue are at stake with a lower closing ratio. Candidates with less effective skills and attributes for bringing in new business were entering the sales organization.

Individuals who scored in the bottom 20% on the Virtual Job Tryout, on average produced 70% fewer transaction per month. The previous candidate evaluation process confidently advanced less capable individuals into the sales organization.

Individuals who scored in the bottom 20% on the Virtual Job Tryout, on average earned 21% less in sales commissions. The Virtual Job Tryout is equipped to discern underlying traits and characteristics that drive performance differences.

When you see the order of magnitude and the insight into performance provided by candidate results, ask yourself: What would my workforce look like if I could hire from the top 80%? Or even the top 50% of the candidate pool?

When you calibrate your candidate assessment process to on-the-job performance, you can better distinguish the difference between stars and black holes.

Call us for more information on calibrating your candidate assessment process to reduce low-end performance variation.

Also, remember, the sun is a star. If you stare at it, you can go blind.