Differences in agent performance can negatively impact customer ratings, but AI can help identify and guide low performers on how to improve.
A company’s contact center workforce comprises a range of skills and capabilities. Good supervisors strive to create balance across a shift, so they can optimize the customer experience for any interaction with the agents they have on hand.
Doing this well means understanding and accounting for variability in agent performance. That is, supervisors must be able to assess the differences in performance—as well as efficiency—among agents who have similar roles and are working under the same conditions. With this analysis, they can discover which are the top performers and which are the low performers. The next step is using workforce engagement management (WEM) tools to reduce the gap so that all agents perform at an equally high level.
Metrigy explored this challenge in a recent global study on WEM, conducted with 316 companies. In the study, we asked participants to tell us how they’re measuring agent performance, whether they’re analyzing the differences between top and low performers, and, if so, what they’re doing to close the gap. What follows are some of our findings.
Measuring Agent Performance
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