What a hiring suggestion feature can teach us about LinkedIn and corporate recruiting

If LinkedIn (s lnkd) has become the quiet success of the major social web companies, LinkedIn’s Recruiter page is the quiet success — and cash machine — within the company. And a brand new feature shows how the company is turning your professional data into a gold mine for recruiters.

LinkedIn people you might want to hire recommendation tool

LinkedIn’s “talent solutions” service, which gives recruiters and hiring managers the ability to post job ads and hunt for candidates, is the fastest-growing and most profitable portion of LinkedIn’s business. The talent solutions homepage saw an update last week that involved several design changes, but it was the addition of the “People You Might Want to Hire” tool that’s sheds the most light on how LinkedIn makes money.

At this point, most professional people are at least aware of LinkedIn, and with the company boasting 200 million registered users, a good number of people now have profile pages listing their work experience and other relevant information on the social networking site. But the consumer-facing side of LinkedIn that you might see is not the portion of the company that brings in the most revenue — or, at least not directly.

The talent solutions portion of LinkedIn brought in 53 percent of the company’s revenue last quarter. The basic service allows corporate recruiters and human resources employeees to post job ads for the company, search for relevant candidates, track responses, and monitor people they might like to hire (yes, a recruiter can put an alert on your profile and know when you make updates or switch jobs.) Companies pay for access to those recruiting features, and the more accurate the data LinkedIn can provide, the more value the companies will derive from the service.

The new feature on the Recruiter page is an intelligent recommendation engine that suggests to recruiters people they might want to hire. It sounds simple enough, and with similar features all over sites like Facebook and Twitter, maybe not so novel. But the feature is huge for LinkedIn on the consumer side already, where the company reports that 50 percent of job seeker engagement comes from the recommended jobs tool, with the other 50 percent coming from people typing into the search bar. Users who are actively looking for jobs might be willing to put in search terms. But people who aren’t actively looking to switch jobs — which LinkedIn estimates is about 80 percent of the current workforce —  are far less likely to search.

“Recommendations have fundamentally changed the trajectory of Linkedin.com,” said Parker Barrile, head of product for talent solutions, in an interview this week. “The recommendations technology that suggests things for members totally change the game. Because we’ve realized how important it is not to expect consumers to actively search for things.”

linkedinMy colleague Derrick Harris recently wrote about the engineering shift at LinkedIn over the past five years that now allows the company to put significant resources behind engineering projects like recommendations, and Barrile said that refining and perfecting the suggestions has become critically important.

“We’ve invested a lot into the technology that works on those recommendations,” he said. “All of these recommendation technologies learn from the way users interact with them. And LinkedIn’s recommendation technologies have become especiallly responsive because they’re so important to the business.”

The “People You Might Want To Hire” tool takes into account past behavior on the part of the recruiter, as well as data signals from within the job ad, to put together a list of candidates who might be a fit based on a variety of signals. For instance, let’s say you’re an tech company looking to hire for a PR position. The tool won’t just surface people working in technology PR. Instead, it might surface people working in technology who list writing and editing as their skills, or have a number of endorsements for communication or working with a team. Or, let’s say you’re hiring for a venture capital position in San Francisco — the system might also suggest relevant candidates from New York, since it knows people in venture capital are likely to move between the two cities.

Potentially creepy if you start getting a lot of messages from recruiters asking you to move to New York? Maybe. But for people who end up with job offers out of the process, there’s a strong upside to that technology.