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…So I recently came across an interesting press release from a firm called Talent Analytics. The release says that the Company can feed its cloud platform into your ATS/HRIS/LMS etc., assessing how likely each applicant for any given position is to be a flight risk by looking at its current employee population and historical turnover data. For specifics around what data the predictive model needs to work you can request the white paper here, but I’m sharing because I thought it was an interesting approach to using predictive analytics to improve talent outcomes.

Companies like Google have used predictive analytics to identify triggers correlated with employee turnover and to improve its hiring and retention outcomes with women, and on a much (much) smaller scale I’ve leveraged ATS data to make predictive insights on how to lower fill times in the past… but leveraging applicant data to predict the likelihood that an applicant would stay in a role (if hired) before even interviewing them? The benefits are obvious in that an employer that can do this effectively saves on training costs, recruiting costs, yields a higher performing workforce, and spends less on administrative items such as onboarding (freeing HR up to do more strategic work).

^I don’t know how viable the model is, but it got me to thinking… if we can predict an applicant’s long-term viability based on their application alone… what else could we predict with the right data?

I’d love to get insights on this question from anyone leveraging similar platforms (internally designed or via external vendors) in the comments section below.