(Illustration by Mike Byers/ )
Every second, more than two people join LinkedIn’s network of 238 million members.
They are head hunters in search of talent. They are the talent in search of a job. And sometimes, the career site for the professional class is just a hangout for the well-connected worker.
LinkedIn, using complex, carefully concocted algorithms, analyzes their profiles and site behavior to steer them to opportunity. And corporations parse that data to set business strategy. As the network grows moment by moment, LinkedIn’s rich trove of information also grows more detailed and more comprehensive.
It’s big data meeting human resources. And that data, core to LinkedIn’s potential, could catapult the company beyond building careers and into the realms of education, urban development and economic policy.
Chief executive Jeff Weiner put it this way in a recent blog post: “Our ultimate dream is to develop the world’s first economic graph,” a sort of digital map of skills, workers and jobs across the global economy.
Ambitions, in other words, that are a far cry from the industry’s early stabs at modernizing the old-fashioned jobs board (think Monster.com and CareerBuilder).
So far, LinkedIn’s data-driven strategy appears to be working: It turned its highest-ever profit in the second quarter, $364 million, and its stock price has grown sixfold since its 2011 initial public offering. Because its workforce has doubled in a year, it’s fast outgrowing its Mountain View headquarters, just down the street from Google. In 2014, it’ll move into Yahoo’s neighborhood with a new campus in Sunnyvale.
The company makes money three ways: members who pay for premium access; ad sales; and its gold mine, a suite of products created by its talent solutions division and sold to corporate clients, which accounted for $205 million in revenue last quarter.
When LinkedIn staffers talk about their network and products, they often refer to an “ecosystem.” It’s an apt metaphor, because the value of their offerings would seem to rely heavily on equilibrium.
LinkedIn’s usefulness to recruiters is deeply contingent on the quality and depth of its membership base. And its usefulness to members depends on the quality of their experience on the site. LinkedIn’s success, then, depends largely on its ability to do more than just amass new members. The company must get its users to maintain comprehensive, up-to-date profiles, and it must give them a reason to visit the site frequently.
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 colorful 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 toward 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 analyzing 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 percent of job seeker engagement on its site comes from the “Jobs you might be interested in” feature.