Is it possible to extract detailed performance data regarding agent-initiated shift swaps directly through the Analytics API? Our team manages a high-volume contact center in the Chicago region, and we are currently struggling to correlate schedule adherence drops with the frequency of shift trades during peak hours. We publish schedules weekly using the WFM APIs, and while the interface shows swap requests, we lack a backend report that breaks down acceptance rates by team or time slot.
We attempted to query the wfm:schedule and wfm:agent data sources using the /api/v2/analytics/query endpoint with custom date ranges spanning our last three publish cycles. However, the resulting JSON payload only provides aggregate adherence scores and total hours worked. It does not include fields related to swap_request_status, swap_initiator_id, or swap_target_id. We also checked the wfm:time-off data source, but that clearly only tracks PTO requests, not operational shift changes.
Our environment is running Genesys Cloud Release 2024.2 (US East). We are using the Python SDK version 1.25.0 to construct these queries. The goal is to identify if specific teams are over-utilizing the self-service swap feature, which seems to be causing gaps in our coverage during the 13:00-15:00 CST window. We need to present this data to leadership to justify tightening the swap approval workflow.
Has anyone successfully mapped WFM transactional data, specifically shift swaps, to the Analytics reporting engine? If direct API extraction is not supported, what is the recommended workaround? Are we expected to build a custom integration that polls the WFM swap endpoints (/api/v2/wfm/users/{userId}/schedules) and pushes that data into a separate warehouse, or is there a hidden metric in the agent:schedule data source we are missing? Any insights into the schema limitations or potential workarounds would be greatly appreciated.