Given very large application volumes-- close to 2,000 applications annually for the Operations Associate role alone-- the University Program team within a leading American fast-food chain was seeking a solution that would allow them to achieve greater efficiency for recruiters through a more digital process, and identify best-fit hires in a fair, data-driven way. They were also seeking to provide a superior, unique experience for campus applicants that would garner greater excitement around their employer brand, and the organization more broadly.

The leading fast-food chain selected pymetrics as their trusted partner in providing a more holistic, fair, and efficient approach to selecting candidates for their University Program. pymetrics developed a success model for their Operations Associate Role in the US. Incoming candidates were asked to play the pymetrics games as part of their application, and recruiters were then provided a recommendation score, as well as overall ranking relative to other candidates, based on how closely their attributes matched to the success profile.

Using pymetrics resulted in huge efficiency gains and time savings for the leading American fast-food chain: 90% less time was spent screening candidate applications, and 86% less time was spent reviewing video interviews. Additionally, the Operations Associates in-role were extremely satisfied with pymetrics experience during the application process, with 97% reporting satisfaction and engagement with pymetrics. 96% of candidates applying to the role also reported having a positive experience, further exemplified by the 94% completion rate of the games themselves. 

‍

pymetrics-recommended candidates were more likely to accept a role with the organization, shown by a 95% offer-acceptance rate amongst pymetrics recommended candidates, indicating a stronger fit for the role and also enabling recruiters to focus their efforts on candidates who had a genuine interest in working at the organization. Finally, pymetrics positively impacted the fast-food chain’s candidate gender and ethnicity split, enabling the recruiting team to screen a 45:55 split of female and male candidates, as well as a 50:50 split of minority and Caucasian candidates.

‍

To drive similar results and more at your organization, get started with pymetrics today.