Workforce Management

Please add columns for actual adherence and/or actual staffing to the Forecast vs Actual report. This would allow teams to compare forecasted requirements with what actually happened, making it much easier to diagnose performance gaps and improve future planning. Quickly see if low service levels were due to under-staffing or adherence issues. Provide clear, data-driven explanations to leadership and clients. Reduce manual work by aggregating this data from multiple reports. Value: Enables true side-by-side analysis of forecasted vs actual performance Saves time and increases reporting accuracy

I should be able to see the actual concurrency of agents in Reports.

In "Configure staffing", the shrinkage field refers specifically to time lost during scheduled productive periods, not total shift shrinkage (which would include breaks, PTO, meetings, training, etc.). The tooltip communicates this correctly, but the industry-standard definition of shrinkage typically implies total lost time across a shift.

I should be able to access a report that quantifies the number of schedule edits made by team leads or coaches within their respective team schedules.

I should be able to track Chat Average Handling Time (AHT) based on the agent’s actual interaction time, starting from their first response to a ticket until they either resolve it or make their final reply. This metric should include active response time and applicable wrap-up time, calculated as: Chat AHT = Total Handling Time / Number of Chats Handled

Would be nice to analyze Agents re-opened tickets in Assembled to either identify if coaching is needed or if we should start adding reopened tickets into the forecast to capture FTE needs outside of the current FTE scheduled.

the Forecasted vs Actual report often shows 0% or 100% forecasted SLA, even when actual SLA is somewhere in between. This makes it nearly impossible to model SLA risk, especially for email channels or queues with long SLA windows (e.g., 24+ hrs). Assembled should more accurately calculate forecasted SLA, including factoring in: • Backlogged cases (created prior to the day, still open and breaching SLA) • Non-zero forecasted SLA based on staffing + productivity inputs • Real-time reflection of service level performance, not just tickets created today Right now, we’re seeing mismatches between FvA and Realtime Overview, and some tickets that missed SLA still appear as met (or vice versa). This impacts our ability to triage, report accurately, and forecast requirements.

Description: It would be extremely valuable to include a “Solves per Scheduled Hour” metric in the agent scorecard. This metric would show the number of tickets/cases solved by an agent divided by their total scheduled hours for the selected period. Benefits: Normalizes productivity: Allows for fair comparison between agents with different schedules. -Highlights efficiency: Makes it easier to identify high and low performers, regardless of total hours worked. Supports coaching and staffing: Provides actionable insights for team leads and workforce managers. Complements existing metrics: Adds another dimension to agent performance tracking beyond just total solves or adherence. Example Calculation: > Solves per Scheduled Hour = Total Solves / Total Scheduled Hours Use Cases: Comparing productivity across agents and teams Identifying coaching opportunities Informing scheduling and staffing decisions Request: Please consider adding this metric to the scorecard view, either as a default column or an optional metric that can be enabled.

When you look at the Forecast vs Actual report, it still shows the original required staffing and not the required staffing for how the day ended up being. This would be great to have changed so we can have a report for how off the staffing was and to see how many extra bodies we would need to adjust for.

This could be implemented in a way where if the data from cached metrics is behind and isn't being filled, an Admin from the account could refresh it. There could also be limits put in place to ensure that users aren't refreshing metrics too frequently, etc.