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HR Analytics: The trend of the recruitment approach to the data-driven world (Part 2)

What can you measure and improve using predictive analytics?

The data that you can measure using predictive analytics in recruitment is broad as your company’s unique inputs and sources of information. Essentially, any variable associated with a candidate or process can be collected, analyzed and measured.

To give you an idea of what these possibilities look like, here are some of the categories and questions that predictive analytics can provide insights into:  

  • Candidate sources: which are your most effective sourcing platforms (job boards, social media, referrals, and so on).
  • Candidate screening: how long the process takes, which candidate screening techniques are effective, and which aren’t.
  • Lead times: how long it takes to go from application to offer and what effect that has on drop off rates.
  • Future employment needs: what positions are likely to be needed or become vacant in the future and what will the hiring manager’s needs be.
  • Future employee performance: how likely a candidate or new hire is to perform well on the job.
  • Retention rates: how long a new hire will stay will the company or how likely it is that other candidates will leave.
  • Hiring bottlenecks: where roadblocks in the hiring process regularly occur, what their impact is, and how to fix them.
  • The urgency of hiring: which roles and skills are needed most urgently to meet company needs.

Predictive analytics can answer these questions by leveraging complex technologies and your data inputs to find trends and indicators of future behavior and results. It should be noted that predictive analytics is only as effective as the data you provide to your tech stack, and how well you measure and respond to the data that it provides.


In an era of big data, predictive analytics in recruitment is inherently a game of change and incremental improvement. It predicts outcomes based on the data it has in front of it and the outcomes of your actions. To make the most out of these platforms, you should regularly act on your platform’s recommendations, make your own changes and measure the results, and ensure that your data pool is reflective of the outside world.

A predictive analytics model will only be as effective as the data you put into it, and the actions you take with the outputs. Done right, and regularly, predictive analytics in recruitment can yield dramatic results for the quality of your hires and your recruitment processes in general.


How do you think about this article? Please share it with us via the comment section below.

>>> Go back and read Part 1 to have a clear understanding of HR Analytics.

According to AIHR + Recruitee


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