OutMatch, a workforce predictive analytics solutions provider, has announced the use of machine learning to transform by-the-hour hiring for both candidates as well as companies.
The company claims that its pre-hire assessment tool is the fastest in the industry, and can help complete the same in half the time – helping firms expand their talent base and place the right people for the right jobs.
Further, the use of machine learning techniques also helps drive better predictability in hiring. By effectively analyzing post-hire data patterns, and integrating the same into the assessment process, general work traits, success patterns, and other personality insights – both in terms of the role as well as a fit for the organization are more sharpened and refined, thereby enhancing hiring quality.
Speaking on the ocassion, Greg Moran, CEO & President, OutMatch, shared his thoughts on the approach:
“We are breaking new ground in the assessment industry by requiring candidates to answer fewer questions but giving companies better results; Using machine learning, we are broadening what we call the OutMatch Impact: arming companies with robust predictive analytics so they can hire and develop the right teams to drive productivity and profitability.”
The streamlined assessments are targeted for the filling of hourly roles, which is critical in a complex hiring landscape.
OutMatch offers nearly 20 million scientifically-proven candidate assessments, every year across over 20,000 locations. It is powered by a robust data set, and helps companies predict candidate success, in a role – thereby aiding hiring and refining the same. Further, the company works with large, decentralized firms that have very high-volume hiring requirements in the hospitality, retail, healthcare, property management, and F&B sectors. Its clients include American Airlines, Adidas, Brinker International, and HCA Healthcare.