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LinkedIn connects big data, human resources

Sarah Halzack (via Washington Post/ SMH) | Aug. 13, 2013
As the network grows moment by moment, LinkedIn's rich trove of information also grows more detailed and more comprehensive.

To engage members, the company has deployed new strategies on all fronts: a redesigned site; stuff to read from the likes of Bill Gates, Jack Welch and Richard Branson; new mobile applications; status updates; targeted aggregated news stories and more.

By throwing more and more at users, of course, LinkedIn risks undermining the very thing that's made it the go-to site for recruiters: a mass of high-quality candidates, sorted and evaluated and offered up.

"I think there's a chance of people getting tired of it and checking out of it," said Chris Collins, director of Cornell University's Center for Advanced Human Resource Studies.

LinkedIn trolls a variety of sources for member data. There's the information users put into their profiles, listing current employers, past employers, certifications and skill sets. And then there's everything users do on the site. LinkedIn notes what kind of job postings they view or which company pages they visit.

In building 2016 on LinkedIn's Mountain View campus, its scientists pound away on keyboards, surrounded by walls covered in colourful data maps and windows scrawled with equations that look like something out of A Beautiful Mind.

In real time, they study the site's every detail and move with an eye towards making their product more useful for members and recruiters.

For members, the data influence the suggestions that show up in a module on one's home page called "Jobs you might be interested in," with information on how to apply.

The algorithm that powers the module takes into account an exhaustive range of factors that go far beyond one's current field and city of residence.

For example, from analysing the migration patterns of its users, LinkedIn has determined that a worker in San Francisco is more likely to move to New York for a job than to Fresno.

LinkedIn's algorithm also factors in how often a user has changed jobs.

"Are they being promoted very quickly? In that case, maybe we should recommend jobs that are a step up for them," said Parker Barrile, senior director of product for the talent solutions division. "Or have they been stable in their career for the past several years? In that case, maybe we should present simply new opportunities at the same level."

LinkedIn says that more than 50 per cent of job seeker engagement on its site comes from the "Jobs you might be interested in" feature.

For corporate clients, LinkedIn mines its member data to assist them with a variety of strategic planning decisions. If a company is trying to decide where to open a new office, LinkedIn can inform that evaluation.

"For example, we could tell you what's the best city to hire voice-over IP engineers, based on both the supply of people available and the demand for people available," said Dan Shapero, vice president of talent solutions and insights.

 

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