Generate high quality potential candidate leads

Good hiring is one of (if not the most) important drivers of success for any company, as great people will find creative solutions to hard problems, form a strong culture and propel a company forward. Great talent is scarce in any environment and to find the best talent you need to start with a wide and well-qualified funnel of potential candidates. This is where front-end sourcers like Mr. Linz play a critical role. They find potential candidates from many well-known and obscure sources and research them to effectively qualify their leads based on factors including technical skill and company fit.

Mr. Linz’s biggest challenge is honing in on the right leads, and this is where Mr. Linz uses HeadlessBrowserAPI to gain an edge. Mr. Linz uses HeadlessBrowserAPI, combined with the Crawlomatic WordPress plugin, to source potential candidates for a myriad of job functions in the US, Europe and the Middle East and Africa. In this example, we’ll focus on how Mr. Linz finds software engineers.

Mr. Linz started with the online OpenStack community – a community whose members are a match for the skills he was looking for in this particular case. Each online profile listed the person’s name and level of familiarity with OpenStack along with Twitter handle, LinkedIn profile, or personal blog. The URLs for these page profiles fit a pattern — they shared the same base URL, and then had a user id that incremented from 1 – 10,000. Using Crawlomatic’s URL scraper, Mr. Linz quickly generated a list of the 10,000+ URLs he cared about and triggered a crawl. From there on, he used HeadlessBrowserAPI’s API traversed all the pages in his list and returned a list of all potential candidates.

This was much more valuable than a static list, letting Mr. Linz and his recruiting team track changes over time. For instance, they would monitor identified potential candidates for signals that they might be open to new opportunities, like negative posts about their current employer or complaints about traffic on twitter. The dataset also lets Mr. Linz track growth of this talent pool vs. other pools he monitors with HeadlessBrowserAPI.

Before discovering HeadlessBrowserAPI, Mr. Linz would use a combination of Google searches and more manual scraping techniques to get the source. But, each pull would take 3 hours (excluding the time required for data cleaning) and he consistently got only a tenth of the information he wanted.

This is just one example of the various ways Mr. Linz uses HeadlessBrowserAPI to make him a super-human sourcer.

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