The change is so subtle that unless you’re a regular LinkedIn searcher, you might not even notice the addition of Dynamic Refinements (the official product name) to LinkedIn Recruiter. But now that we’ve alerted you, take a look at that right-hand rail. There’s a screenshot with this story that has the names obscured, but is otherwise what the results look like.
The drilldown choices have been there since LinkedIn Recruiter was introduced a year ago. What’s new is the deeper decision tree for the top-level filters. As you refine your search, adding more and more filters, watch the numbers in the rail change. These are the number of LinkedIn profiles matching your search criteria.
Now, there is nothing that LinkedIn Recruiter Dynamic Refinements does that a sharp sourcer with good Boolean skills couldn’t do. But what a snap to simply check and uncheck criteria boxes to see how the candidate universe changes.
During a demo last week, Esteban Kozak, LinkedIn’s senior product manager, took us on a search for candidates with “merger” in their profile. Starting with tens of thousands of possibles, in seconds Kozak narrowed the field to a manageable 100 or so financial professionals, working in China, who speak Cantonese, are ready to make a change, and graduated from specific business schools.
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The guided search isn’t just quicker; also, it’s more accurate. LinkedIn has bolstered its backend vocabulary so it will find USC alums even if they’ve described themselves as a University of Southern California grad and you search for USC. (No word on what happens if you say “Trojan” instead.)
Data normalization, says Francois Dufour, senior director, enterprise marketing, is a valuable enhancement that eliminates the need for a recruiter and a candidate to use the same words to describe the same thing. A Boolean string, on the other hand, will find exactly what you ask it for and not more.
About 1,000 companies use LinkedIn Recruiter. There’s an annual, per seat charge for the service. Pricing is not publicly disclosed.