Analytics in Recruiting: I’m Starting with the Man in the Mirror

March 16, 2016 | Carl Rhodes | HCI
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There are many potential villains in the “analytics in talent acquisition” story. There are incompatible systems and data sets. There are strong silos and limited collaboration within acquisition and outside it. There are the ever-present boogeymen of budget and bandwidth.

But in watching my colleagues research and develop HCI’s new course on analytics, I would argue that all those characters are supporting actors at best, not the protagonist of this tale.  The real stars (or villains, as the case may be) are the people we see when we look in the mirror: those who lead, manage, and work in the acquisition function itself.

I have come to believe this because the most important determinants of a company’s ability to produce cutting-edge talent acquisition metrics are not the usual suspects listed above, but rather it is whether and how well its leaders and staff believe in and act upon a few key principles.

Let’s look at just one very simple key principle: determining what is (and what is not) the goal.   The goal is not a single-variable dashboard, list of metrics, or chart reporting what did or didn’t happen previously. Most HR practitioners know this goal is flawed conceptually even if it’s a fairly accurate description of most of the analytical projects currently done in acquisition.

The real goal is prediction: leveraging retrospective metrics to inform future probabilities and effectively anticipate talent acquisition outcomes – and determine what you need to do about them. I predict that the real winners in acquisition teams using analytics will be those that not only accept that the goal is to forecast business-relevant outcomes, but also those teams that believe that it’s possible to do so, even with clunky technology systems, or imperfect datasets, or a lack of data scientists. Why? Because if you can predict it, you can change it. If you can’t, you’re simply stuck hoping that last year’s pipeline, acceptance rate, or retention rate simply repeats itself (if it was a good year) or somehow fixes itself on its own (if not).

Analytics for Talent Management (ATM) contains not only instructional manuals for how to apply data and analyses to talent management outcomes and drive prediction, but also provides real-world examples of companies that are already doing it well. In talent acquisition, for example, it describes how the hardware and software production company NCR used data and analytics to improve its pre-hire assessment, producing big returns in acquisition costs and customer satisfaction ratings, and resulting in the ability to predict post-hire performance from performance on that pre-hire assessment.

The new world of analytics is indeed here, and you need to make sure you’re ready.