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OutMatch Uses Machine Learning to Transform Hourly Hiring

OutMatch, a provider of actionable predictive analytics to build thriving workforces, said it is using machine learning to transform hourly hiring for both candidates and companies. It launched a quick pre-hire assessment; candidates can complete assessments in half the usual time, enabling companies to expand the talent pool and better predict the right people for the right jobs. 

Candidates for hourly roles, such as those commonly found in the hospitality and restaurant industry, can now complete assessments in less time than it takes to brew a pot of coffee – a sea change in the assessment industry that greatly improves the candidate experience. It gives candidates a positive first impression of a potential employer, which enables companies to enhance their recruitment brand and attract more potential employees. 

Just as importantly, the use of machine learning techniques improves predictability in hiring. By analyzing post-hire data and integrating it into the assessment process, the work traits and competencies most predictive of success, both on the job and within a corporate culture, are refined and sharpened even as the number of assessment questions is reduced.

Using machine learning, OutMatch is broadening 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 initially targeted to fill hourly roles, which is particularly timely amid a tight employment market. Since the shorter assessment will not discourage candidates from applying, companies are still able to gather pre-hire data and ensure they are hiring the best person for the job.

OutMatch delivers nearly 20 million candidate assessments annually at more than 200,000 locations. Powered by a powerful data set, companies can predict candidate success on the job, measure the impact of hiring, and continually refine their hiring process. OutMatch works with large, decentralized organizations with high-volume hiring needs in the hospitality, restaurant, retail, healthcare, and property management industries. 

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