In 2017, a U.S.-based debt collection agency began using pymetrics to identify candidates for their Account Representative role. Because the position involved having stressful conversations on a regular basis, the employer was seeing very high rates of turnover. The employer’s hope was to implement pymetrics to identify candidates who would be willing to stay in the role longer.
To establish the “success profile” for an Account Representative, 144 current employees were identified to complete the pymetrics assessment. These individuals were chosen according to their productivity ranking and compliance with company policies. Managers at each site where the incumbents worked also validated that these individuals were a strong cultural fit with the firm.
Through discussions with pymetrics’ industrial-organizational (I/O) psychologists, HR personnel at the firm opted to implement pymetrics as an initial screening tool in their hiring process. Depending on how close of a fit the model predicted, candidates would be directed to various interview channels to validate their qualifications. For example, candidates that were scored as “highly recommended” would be able to skip speaking with a recruiter and move directly to a conversation with a manager.
After one month on the job, employees that were scored “highly recommended” by pymetrics had a median collection rate that was 30% higher than employees that were scored only “recommended.” By the six-month mark, this gap increased to 53%. In terms of tenure, the “highly recommended” group was 13% less likely to leave their roles during the period studied.
The most dramatic disparities in on-the-job performance could be observed with employees that pymetrics had deemed “not recommended” for the Account Representative role. This group, defined by a “fit score” below the 60th percentile, saw significantly lower tenure and collection metrics. They tended to stay in the role for less than half the time of the “highly recommended” group. Additionally, while the median “highly recommended” employee collected nearly $3,200 in their first month, the median “not recommended” employee collected $825. By six months, this gap reflected an average difference of $8,400 collected per representative.
To drive similar results and more at your organization, get started with pymetrics today.