The Next Level Of Search: Gauging Probability Of Changing Jobs

It’s great to be able find whoever you want, whenever you want. There’s more data than ever to go on when doing these searches, too. And there are more places searchable than ever before as well.

All of that is important of course but what if, during your searches, you could flag up those prospects who may be more likely to be willing to change jobs. Maybe they are thinking about their career or updating profiles due to a pending interview but if you could talk to them as they are in that process, you could be more successful, right?

That’s what Entelo is attempting to do with their Sonar product.

Of course, predictive analytics isn’t a new concept. Bullhorn Reach’s Radar platform allows you to see who might be looking to move soon. From what I’ve heard anecdotally from recruiters, it does a pretty good job of that. Or at least as well as one can expect it to run. It has helped spark conversations and bring to light people who might have gone under your personal radar.

The biggest difference Entelo co-founder Jon Bischke says is that it analyzes candidates that might not be in your network and it takes into account a range of other factors (such as layoff announcements or the length of time a person has been at a job). And like Bullhorn, it incorporates machine learning in order to improve with time.

Bischke calls it predictive sourcing and the results have been positive. According to a release, Entelo sent out email alerts for approximately 400 professionals and monitored their job activity over 90 days. At the end of that period, 24 percent had taken a new job, compared to the 3.1 percent of the general population of passive candidates who did in the same time frame.

Bischke is excited about some of the stories he’s heard from some of their early users. “A customer based in LA was looking for a candidate to fill a software engineer requisition, ” he said. “A candidate in our system moved from Boston to LA (Marina Del Rey) and had updated their Twitter location field to state that they now lived in Marina Del Rey.” The customer ended up reaching out to the candidate at the perfect time because he had yet to update his LinkedIn profile to his new location and ended up hiring him.

This sort of pre-qualification for sourcing candidates could help your recruiter clients but only if they move quickly. As all of these predictive tools get better, sourcers will have more tools at their disposal to make better and more productive searches.