Applications of Predictive Analytics in your Recruitment Journey

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Elanor is an HR executive at Unicorn marketer. She’s been involved in the recruitment process for six years now. Every year they do a campus drive at the most prestigious college in Chicago. They’re always on the look for a promising candidate for a challenging role as a Digital Marketer. Elanor has been maintaining a spreadsheet of rejected candidates for the same post and logging the reasons for rejection as well.

The reasons read as follows – lack of analytical and writing skills, poor communication, showed no scope for management abilities, and so on. 

So, the above scenario is a representation of how there’s enough scope for the HR department at Unicorn marketer to make data-backed decisions with predictive analytics. Before we jump into predictive analytics in hiring, let’s quickly define predictive analytics in general.

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.Wikipedia.

Let’s get back to the example we began with. For instance, let’s say, 31 candidates, have been rejected in the last six years – future hiring process can certainly be streamlined by taking the right steps to attract the right talent possibly by mentioning it in their hiring announcements – posters, display ads, first calls, interactions, and so on. How do we conclude so? It’s by adopting predictive analytics in the recruitment process i.e utilizing historical data to make predictions about the future.

Sourcing the right talent

“A study by WCN solutions  52% of recruiters say finding the right candidates from a large applicant pool is the toughest part of their job. “

Predictive HR analytics can play a crucial role right from the time of sourcing a candidate. So. let’s say Elanor has recruited qualified digital marketers in the past. A year after their joining, there was a performance evaluation. Some of them were tagged to be good performers and some, mediocre. So let’s say in the past six years, the digital marketing head has data about the reasons for mediocrity in performance and based on predictive analytics, they identify a trend.

To read further, head to Hackernoon where this article was originally published.


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