New Solutions Make It Possible to Keep Candidate Data Fresh

Roy Maurer By Roy Maurer October 31, 2017

It's a common problem—companies store their information about job candidates in their applicant tracking system (ATS), but then fail to use it when filling open positions in the future.

"Companies are historically bad at engaging with prospects who don't immediately fill a need," said Joel Cheesman, a recruiting technology industry veteran and the founder of Ratedly, which monitors anonymous employee feedback websites. "It's easy to argue that leveraging this funnel of contacts is something more companies should be doing."

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But that candidate information is vast, hard to search and often outdated, and that's why recruiters don't use it—despite the time and cost incurred to gather the data, said Howard Schwartz, CEO and co-founder of Crowded, a New York City-based recruiting technology company.

"The recruiting process is like [the movie] 'Groundhog Day,' " said Mark Roth, Crowded co-founder and chief operating officer. "A new requisition is opened and recruiters post job ads on Indeed or Dice and source on LinkedIn. They add 150 candidates to their database and hire one of them. And then do it all over again."

There are two main problems with effectively sourcing candidates through a traditional ATS, Roth explained—the systems aren't good at conducting fast and accurate searches, and the data is progressively aging and therefore not reliable after a certain point.

"Let's say you have a job for a programmer required to know a certain coding language," he said. "That skill didn't exist two years ago. The applicants in your ATS didn't have that skill when their data was collected. Some of them may have it now. That's the value of an updated candidate profile."

Crowded received a lot of attention on the exposition floor at HR Tech in Las Vegas earlier this month with its latest software platform release, Crowded:Refresh, which updates a company's hibernating candidate data, matches profiles to open jobs and presents recruiters with a scored list of qualified candidates.

The tool integrates with an organization's ATS or candidate relationship management (CRM) system, identifies potential leads and then searches the Internet for public data about those individuals. It updates their existing profiles with the candidates' new skills, employers and locations to ensure they remain a good fit for the role.

"Using machine learning and artificial intelligence, candidates tagged as ideal are updated, then they are contacted via the Crowded chatbot," Cheesman said. Reigniting dormant candidate relationships not only saves employers time, money and effort, but also provides valuable employer branding, he added.    

Tony Le, senior director of global recruiting for IAC Publishing in Oakland, Calif., uses software from data science technology firm Brilent to sift through the company's database to match past candidates to new requisitions and deliver a short list of possible hires ranked by qualifications, interest level and availability.

Le said his use of Brilent saves him the time and cost of posting openings to job boards and searching across social media sites. "Use of artificial intelligence has helped us reduce at least a week's worth of time on the front end of the cycle," he said. "Instead of taking a week or more to get candidates in front of a hiring manager, it's now often days."

Pete Radloff, comScore Inc. principal technical recruiter, regularly searches his ATS database for past applicants who might be a good match for open jobs. When a new role opens up at the Reston, Va.-based company that measures consumer behavior, Radloff first uses technology from San Francisco-based recruiting platform Restless Bandit to scour his ATS for potential candidates. "I don't want to overlook a great candidate we might already have in-house," he said.

The platform matches open jobs to top candidates, sends an e-mail to each of them inviting them to apply and follows up with those top candidates by targeting ads to them on Facebook and Google.

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