Workforce Management

When Schedule Analytics is exported for a multi-day range, all daily data for each agent is summarized into a single row. In order to track trends over time, breaking out each day's data for each agent into its own row would be preferred.

Currently, the Staffing Timeline uses OR logic when filtering by multiple teams—meaning it returns all agents who belong to any of the selected teams. However, in some use cases, we want to narrow down the results further using AND logic, to only show agents who match all selected filters (e.g., agents who are part of both Team A and Team B). It would be helpful to either: Change the default logic to AND when selecting multiple filters, or Provide a toggle between AND and OR logic so users can control how filters behave. This added flexibility would make it much easier to create precise staffing views across complex team structures.

Currently, the Agent Ticket Stats API provides actual_productive_seconds_worked_all_channels, which is a useful aggregate. However, it does not expose channel-specific metrics like actual_productive_hours_worked_chat, actual_productive_hours_worked_phone, or actual_productive_hours_worked_email. These breakdowns are available in the UI, but not through the API. Adding these individual metrics would help teams build more detailed reporting and better understand agent performance by channel programmatically.

Within the Time Off Requests module, "Additional Information" does not prioritize Rules that caused the request to be Denied, causing confusion within the UI and requiring end users to open the Details menu to ensure they review all conflicting rules. Can we adjust so that rules with alerts are always at the top of the list?

Set a schedule to export any report to different file types (Excel, CSV, Google Docs, etc)

When we use the Teams filter on the staffing timeline, we expect it to show total staffing needs and actual scheduled coverage for that full team - based on all the queues assigned to that team. Instead, Team filter only adjusts the scheduled staffing and visible agents, not related volume or required staffing. This results in incomplete and confusing views. For example where filtering by one team in the Staffing Timeline showed a need for 210 agents, despite the actual team size being 44. This mismatch occurred because the system aggregates queue and channel data rather than providing a true team-level view. We’d really benefit from a more intuitive way to view aggregate staffing by team - this would speed up decision-making and reduce confusion across senior stakeholders.

Right now, only out of adherence and schedule change alerts can be sent to agents via Slack DM. Other alerts - like agent state duration thresholds - can only be sent to public channels, which makes them easy to miss for an individual agent and creates unnecessary noise for those agents or leaders not impacted. Specifically, we would like to notify an agent staying too long in a state such as “available” or “not responding.” Direct messages would make these notifications more actionable, improve agent accountability, and reduce clutter in team channels. We'd love to see more flexibility in what thresholds can be set up for notification and routed via DM.

As a user, I want to choose a selection of schedule change types when exporting from the staffing page Details: It can be difficult to export this data for analysis due to the sheer number of schedule changes that occur As a user, I want to only select the types of schedule changes I care about (in this example: event change requests, direct event editing, and time off approvals) so that I can easily export data over a wide time interval for easy analysis (ie, multiple weeks or multiple months)

When exporting the Team Performance report to Google Sheets, there are differences in: The number of rows included The number of columns included The names of the columns

Implement alerts when the percentage of agents scheduled for productive events falls below a configurable threshold (e.g., 20%) for specific days, teams, or channels. This would help managers identify understaffing situations in advance, allowing them to take proactive measures to adjust schedules and maintain appropriate service levels.