If you make a living at sourcing candidates, chances are you’ve researched, reached out to, and engaged a candidate only to find out they have no intention of leaving their current position. While it’s possible to predict which candidates are most likely to leave, the odds are typically not in your favor.
Sourcing technology has made it easier to understand where the best sources of hire come from, whether that’s the corporate career site, paid job boards or through social connecting. So, recruiters and sourcing professionals know which sources they should be focusing their attention. But with a tsunami of resumes for nearly every open position, it is impossible to efficiently sort through and analyze candidates that are not only qualified, but likely to be interested in your job or company.
How can you get better at predicting the candidates most likely to be interested in the position? Big data provides a snapshot into the ever-elusive passive candidate, enabling sourcing professionals to use predictive analytics and improve the probability of predicting which candidates will be most likely to proceed from candidate to hire.
Predictive sourcing data can uncover patterns that help you focus on candidates ready to leave their current position. Analyzing different factors can also reveal eight archetypes that can affect your sourcing investment:
Research suggests most employees make a decision to stay with a company in the first six months of employment, but at three months, it’s usually still the honeymoon stage. Bureau of Labor Statisticsdata shows that the length of time at a job also increases with age. So, spending too much effort on a newbie may slow your search.
There comes a point in nearly everyone’s career where change is welcome, but it’s unlikely that someone with a long tenure, 10 years or longer, will be interested in leaving the comfort of their current role for a technology start up. Predictive analysis may indicate passing on this candidate.
The Geographic Undesirable
Some candidates are willing to relocate for career advancement opportunities, but an extreme commute is acceptable to less than three percent of the population. Predictive data can draw a correlation between a candidate’s hometown and their current workplace and ensure you’re not taking a long shot by focusing on someone who would need to commute more than an hour.
The Wall Street Winner
Your start up firm might be the next Facebook, but if an individual is employed at a company that is posting profits, they may not be discontent with the current state. Predictive analytics can take into account information such as employer stock performance, shedding light on potential incentives and rewards. It can also indicate candidates working for a company recently acquired or in the process of being sold – two indicators that it might be time for a new beginning.
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The Big Fish
Working for a small company has its appeal – close-knit workgroups, ability to make a bigger difference in business success and more flexibility of rules. Individuals that thrive on the higher visibility and camaraderie of a small office may be less enthused at working for prestigious Fortune 500 firm. Predictive analytics looks at the size of employer to gauge comparison.
The Blue Chipper
For every person who enjoys working for a small to midsize business, there are others who want the cache of a name brand. Looking at the type of employer – start up, multinational, regional business – can also reveal statistics about a candidate’s interest in exploring their options.
One school of thought that says don’t consider a candidate who has recently earned a promotion – they’re being recognized and possibly rewarded and may want to bask in the glory. However, overlooking The Reacher can be a mistake. With promotion plastered on their LinkedIn profile, this person may be ready for the next opportunity to propel their career forward.
Predictive analytics can look at social activity – not just what’s being updated on the profile, but with what frequency. If the profile has remained stagnant, it could be less about an individual’s interest in social media and more about a statement on where they are in their career. Conversely, a job seeker regularly updating their profile may be ready to apply.
An automated approach to predictive sourcing can filter these and other factors for more informed candidate readiness assessment. Rather than casting a wide net to see what comes back, leverage big data to more accurately determine which candidates are most likely to make it to the hiring stage for your next search.