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Computer as Recruiter: How Predictive Modeling Has Changed Recruiting

by Joseph Murphy

The time of the hybrid computer-recruiter has arrived. Big data, talent analytics, and predictive modeling have superseded gut instinct and best guesses to transform the very nature of talent acquisition. I recently conducted a webcast as part of HR.com’s Talent Acquisition virtual conference to discuss the ways data-enabled recruiters are the way of the future for ensuring a better quality of hire.

What’s your other half?

As recruiter, you’re half something—is it instinct, luck, heartfelt optimism? Or are you powered by objective data and predictive analytics?

Most recruiters have confidence in their ability—and may even claim an instinct—for judging talent. But with new-hire failure rates reportedly ranging from 45 percent to as high as 80 percent, the we’ve-always-done-it-this-way hiring routines seem to have some serious shortcomings. Recruiters today must be part computer, armed with evidence-based methods to capture, analyze, and interpret the applicant data they need to optimize their hiring practices.

Data, data everywhere

Understanding the data continuum is essential to making meaningful use of candidate information.

Though it’s long been the heart of the hiring process, passively collected, random data—like resumes and social media footprints—can be an equally arbitrary way of evaluating a candidate’s fit for your position and organization. While past job titles and educational information can be useful for preliminary ranking, they ultimately will not help you predict on-the-job performance and career stability.

No standard rules exist for creating resumes, so they are not an appropriate foundation upon which to establish a standard candidate screening process. To change your hiring outcomes, you have to change the way and what you learn about candidates. The basis of candidate evaluation should be built on the rigor and discipline necessary for establishing performance criteria and developing methods for intentionally capturing and extracting relevant data that reliably predicts outcomes.

Job performance is a complex concept, an irregular shape. The only way to define an irregular shape is to take multiple measures. Multimethod candidate assessment, consisting of multiple psychometric measures, can turn the abstract shape of performance potential into an objective, whole-person evaluation.

Simplify, simplify, simplify

Meaningful data collection will help you streamline your hiring pool and connect you to the right candidates faster, with less cost, effort, and waste.

High-volume hiring especially benefits from focusing recruiting efforts on high-value candidates. Case study data shows how screening call center applicants with a multimethod evaluation reduced recruiting effort. This particular call center operator needed to locate 1,300 good hires among more than 14,000 applicants. By refining the candidate pool with a multi-dimension measuring tool—in this case, a custom Virtual Job Tryout (VJT)—recruiters performed 34 percent fewer phone screens and 26 percent fewer interviews to fill positions with best-fit talent. Better still, recruiters were able to make a higher number of job offers to qualified candidates and found a resulting better overall pass rate on background and drug screenings.

Predicting career stability and quality of hire helps reduce the organizational time, cost, and waste associated with hiring, training, and onboarding people who are not capable of doing the job. Use our interactive staffing waste ROI calculator to estimate the economic impact of false starts and rework on your organization.

How can you do better?

Consider how the human-computer collaboration can optimize how you acquire talent. Complete this brief audit of your current practices against a talent analytics maturity model. Do you see opportunities within your hiring process to improve:

  • The relevance of the data you collect?
  • Your data structure and analytic capabilities?
  • Measuring quality of hire?
  • Your time to fill or interview-to-hire ratio?
  • New-hire learning processes or time to proficiency?

Evidence-based methods to capture, analyze, and interpret applicant data are essential for fortifying recruiters with the competitive advantage they need to locate, attract, and keep the best talent. Contact us to learn more about what action you can take to optimize your talent acquisition decision making.

View the replay of my webcast for more explanations of case study statistics, the value of random versus structured data, and a talent analytics maturity model.

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