What metrics are helpful for evaluating forecast accuracy?

Mean Absolute Percentage Error (MAPE) and Bias are key metrics for evaluating forecast accuracy. MAPE measures the average magnitude of the errors in a set of forecasts, expressed as a percentage of actual values. This allows organizations to understand how far off their predictions are compared to actual performance, helping them adjust their forecasting techniques accordingly.

Bias, on the other hand, indicates whether forecasts tend to be consistently overestimating or underestimating actual outcomes. By examining both MAPE and Bias, organizations can gain insights into the reliability and accuracy of their forecasts, enabling them to make informed adjustments to their workforce management strategies.

In contrast, average response time and call handling time focus more on operational efficiency rather than forecast accuracy. Agent attendance and punctuality metrics do not directly measure how accurate the forecasts are but rather focus on workforce participation. Lastly, customer satisfaction scores provide valuable insights into service quality but do not reflect the precision of forecasting methods. Thus, focusing on MAPE and Bias gives a clearer picture of forecast performance.