One of the world's largest food production companies was looking for an innovative solution that could help their recruitment team to process applications for their Trainee Program in a fair and consistent way.
For just 40 job openings, it was common for the small team to receive a staggering average of about 6,000 applications. Time-constrained recruiters used bias-fraught indicators like GPA, past experience or extracurricular attainment to screen applications. Frustratingly, most of the applicants never heard back from the recruiting teams.
Given that the Trainee program is used to hire the future leaders of the organization and is therefore one of their most important talent pipelines, the company could not afford to miss out on any high-quality, well-fit talent.
To provide the leading food production company with an objective understanding of the attributes that make their top-performers successful, a group of employees was carefully selected to go through the pymetrics exercises. Machine learning analyses the gameplay data to build a predictive model. This model is audited for bias before being rolled out as a benchmark against which candidate recommendations are made.
From the candidate's perspective, they were invited to complete the same set of pymetrics core exercises as well as a numerical and logical reasoning assessment, followed by a digital interview process immediately after submitting their applications. Questions for the interview were standardized prior, with pymetrics’ assistance to design behaviorally-anchored rating scales that could be used by recruiters when evaluating responses.
Every candidate who completed these steps automatically received a personalized report outlining their most unique cognitive, emotional and social traits, plus advice on how they can apply these insights at work
Candidates were extremely pleased by the organization's innovative hiring process as proven by the 92% satisfaction rate after completing pymetrics.
By moving away from CVs, the recruitment team could focus their time on attraction activities and the later stages of their selection process where human interaction matters more. The team went from needing to further qualify 44% of the applicant pool after the CV screen stage, to reviewing just 16% of the most qualified candidates as a result of pymetrics’ recommendations. Later stages like the assessment centers also reported significant cost savings as a result.
From the pool of pymetrics recommendations, offers were eventually made to 35% of the candidates. This was double the historical rate of 17.5%, which indicated a better crop of candidates being funneled into the final stages. Of those candidates who received offers, a striking 93% accepted. For the first time also, the gender split of the finalists stood at an impressive 50:50.
The leading global food production company had chosen pymetrics because of its promise to drive efficiency and quality, and level the playing field. On these metrics, the platform undoubtedly delivered.