Configure workload balancing

Feature coming soon

Workload balancing is a way to ensure all agents are used in a balanced way while still considering the best possible agent-interaction matches. Workload balancing overrides predictive routing recommendations when agent idle time falls outside acceptable limits.

Predictive routing pushes calls toward those agents most likely to achieve a positive KPI outcome. It may create an ‘imbalanced distribution’ of calls when compared with the standard approach of contact centers which target the longest waiting suitable agent. Higher-scored agents can receive more calls than other agents or more calls than they would have received without predictive routing. Lower-scored agents may receive fewer interactions, which may provide them less opportunity to learn. 

routes interactions based on the new scores, and therefore occupancy values for your busier agents go down and go up for under-occupied agents.

  • Using the current occupancy rate: Predictive routing compares the existing occupancy rate with the occupancy threshold values you specify. If their occupancy for the last hour is below or above the threshold, Genesys Cloud then rescores the agents up or down, respectively. Occupancy is defined as the percentage of time agents spend handling interactions against available or idle time. Occupancy percentages appear in the Agents Status Summary view.[/hidden]
  • When you use workload balancing, predictive routing evaluates agents based on KPI outcomes and then rescores agents based on their idle time. Idle time is calculated based on the queues assigned to an agent. These queues could include interaction types other than the inbound voice interactions supported by predictive routing. Scores for agents with low idle time will be adjusted downward to increase the time between interactions. Scores for agents with high idle time will be adjusted upward to increase the frequency at which they receive interactions. After rescoring, occupancy values for your busier agents go down and go up for under-occupied agents.

    Enabling workload balancing is likely to have a trade-off in decreasing the positive KPI impact of predictive routing because it reduces the frequency at which the most suitable agent is chosen. The best way to check this impact is to run comparison tests. Manually run the comparison test for two weeks without workload balancing, then enable workload balancing and test again for two weeks. Compare the impact on your target KPI for the two tests. For more information, see Test predictive routing for your queues.

    Note: Occupancy is defined as the percentage of time agents spend handling interactions against available or idle time. Occupancy percentages appear in the Agents Status Summary view.

    To set up workload balancing, see Create and configure queues