Recruiting Passive Candidates

Recruiting new employees is a difficult task. Especially when the best talent may already be employed elsewhere and only passively
looking, if at all. Finding and attracting these passive job seekers is easier said than done since they are often happy atPassive-Candidates their current job and may only leave if the right opportunity is presented. In order to win over this talent, hiring managers and recruiters should be aware of what these best-fit candidates are most concerned about. According to LinkedIn’s Talent Trends 2014 report, the most important factors for passive job seekers in considering a new job are better compensation and benefits, better work/life balance, greater opportunities for advancement, more challenging work, and better fit for their skill set. However, they are least concerned with an improved job title, better office location, increased job security, stronger relationships with their manager, and having a more impactful role.

But, how can these passive job seekers be found? Online talent platforms have developed algorithms to find candidates to speed up the hiring process. However, one model, CareersUnbound’s instaTalent talent discovery and recommendation platform, goes beyond simply finding candidates – it uses advanced cognitive technology to actually recommend the best-fit candidates, reducing the average hiring time of 40 hours to a few minutes. Through web data extraction, text analytics, graph modeling, hierarchical taxonomies, and cognitive behavioral analytics, instaTalent finds all of the best-fit candidates out there, not just those actively searching for a job.

There is some debate on whether algorithms can hire better than humans. Some argue that algorithms remove the subjective experience of hiring, allowing for certain candidate characteristics (e.g. political correctness, communication style, abstract thinking etc.) to be overlooked. It also dehumanizes the workplace and allows incapable managers to avoid taking responsibility for the growth and development of new employees as well as acknowledging when they make a bad hire.

However, using algorithms in hiring allows for increased transparency and helps employers combine different “people analytics” data in an optimal fashion. By emphasizing more of “what you can do” rather than “who you know”, algorithms reduce the decision making power from hiring managers who may be biased and guarantee a more even playing field. That way, employees are being hired based on who would do the best job rather than who the manager would prefer to play golf with.

But it is more that “what you can do”. instaTalent Culture Fit has now developed a cognitive analytics system that actually scores candidates on how well their personality fits the position for which they have either applied or been found. With this innovative hiring model, employees are more likely to have better performance and lower turnover. Now, both active and passive job seekers can be found and recommended based on their skill set and cultural fit within a company.

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