Greenhouse Predicts: Machine Learning-Enabled Technology Now Live

Greenhouse Predicts: Machine Learning-Enabled Technology Now Live

Predictive talent acquisition technology available for Greenhouse Recruiting customers

NEW YORK, May 9, 2018 /PRNewswire/ -- Greenhouse, the fastest-growing enterprise talent acquisition suite, has announced its predictive analytics feature, Greenhouse Predicts, is now available to Greenhouse Recruiting customers.

One of the biggest challenges for recruiting teams is accurately predicting when a role will be filled, to better anticipate and sidestep the costs of a slow hiring process. Using machine learning, Greenhouse Predicts helps forecast candidate offer acceptance and new hire start dates, allowing Talent, Operations and Finance teams to more easily see around corners, make informed decisions, and effectively communicate timelines.

"Greenhouse Predicts helped the recruiting team at Workfront provide better data to the Finance team on our time-to-fill metric," said Greenhouse customer Jana Johnson, Manager of Talent Acquisition for Workfront, which has been part of the beta since the beginning. "As a fast-growing SaaS company, it's especially important that we're able to accurately forecast headcount spend. Before we had Greenhouse Predicts, each recruiter was sharing their 'best guess' on when they thought the position would be filled; this method was far less accurate than the algorithm built in the tool."

Greenhouse's resident Data Scientist, Andrew Zirm, explains why the new feature is so important: "Recruiting is a team sport. With Greenhouse Predicts, we're able to provide more visibility across the org, especially for finance teams. We're opening up a new channel of communication that helps the HR team answer tough questions about when hires are going to be made, and when salaries are going to hit the books. This is a big win for recruiting teams."

Having accumulated a wealth of historical data around pipelines, jobs, interview stages and candidates, Greenhouse is uniquely positioned to generate and deliver insights to recruiting teams they might not be able to pinpoint on their own.

"I'm excited about this - because we have this great historical data, we can cut through the fog of hiring," said Zirm. "We know a lot about how candidates move through pipelines, so we're able to provide a bit more insight on how your company is doing at a high level."

Greenhouse Predicts and the machine learning platform it runs on has been in development for about a year, with the company's data science team running analyses and models on over 10 million data points. This platform will enable the company to continue to scale Greenhouse Predicts as the available data volume increases, as well as generally support future machine learning efforts at Greenhouse.

During the Greenhouse Predicts beta, live since November 2017, predictions generated were accurate 90% of the time within 3 weeks of true hire time. Given that Greenhouse Predicts was built using machine learning, predictions will continuously improve and become more fine-tuned as hiring patterns emerge.

To learn more about Greenhouse Predicts, read the detailed blog.

About Greenhouse
Based in New York City and San Francisco, Greenhouse Software is the fastest-growing provider of enterprise talent acquisition software. Thousands of the smartest and most successful companies like Cisco Meraki, Buzzfeed, J.D. Power, Warby Parker and Airbnb use Greenhouse's intelligent guidance to design and automate all aspects of hiring throughout their organizations, helping them compete and win for top talent. Greenhouse has won numerous awards including #1 Best Place to Work by Glassdoor, Forbes Cloud 100, and Talent Acquisition FrontRunner leader by Software Advice. Greenhouse lives its mission that hiring great employees is a strategic business goal, and is ranked #15 in Crain's New York Best Places to Work 2017. To learn more, visit greenhouse.io.

CONTACT: Dinah Alobeid, Director of Communications, Greenhouse Software, (917) 780-4130, dinah.alobeid@greenhouse.io

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SOURCE Greenhouse Software