My current config is completely failing as expected regarding the Service Level Agreement (SLA) breach metrics for our digital messaging queues. The Performance dashboard indicates a 92% adherence to the 30-second response target, yet the underlying conversation detail reports show a significant number of interactions exceeding this threshold. This discrepancy is causing confusion during the weekly operations review in our Paris-based support center. The environment is running the standard Genesys Cloud release, and the queue configuration utilizes the default digital engagement settings without custom routing logic.
- Verified the time zone alignment between the queue configuration and the reporting dashboard; both are set to Europe/Paris.
- Cross-referenced the ‘Response Time’ field in the conversation detail view against the aggregated metrics in the queue activity report, confirming the raw data contradicts the summary percentage.
Is there a known latency in how digital engagement timestamps are aggregated for the high-level performance views? The business stakeholders require precise metric definitions to justify resource allocation, and the current variance undermines the dashboard’s credibility for operational planning.
The official documentation states that Performance Views calculate SLA breaches based on the answer_time metric relative to the defined service level threshold, but there is a critical distinction between how digital channels and voice queues handle this timing. In digital messaging, the clock often starts upon message receipt by the platform, whereas in voice, it starts upon ring. If your queues are configured with specific skill requirements or if agents are utilizing multiple queues, the “available” status might not align perfectly with the actual start of the interaction for reporting purposes.
A common fix is to isolate the metric definitions in the analytics configuration, as the discrepancy often stems from how Genesys Cloud calculates ‘handled time’ versus ‘total flow duration’. You should verify that the service_level definition in your queue settings matches the exact threshold used in the custom report. Furthermore, ensure that the data_actions array in any Terraform or API-driven report definitions explicitly references existing actions, as the API rejects undefined dependencies during the initial POST.
Check the conversation_detail report type and filter by channel_type: DIGITAL. Compare the start_time and answer_time fields. If the delta exceeds your SLA but the aggregate view shows adherence, it is likely due to rounding or the exclusion of abandoned interactions in the summary view.
{
"data_actions": [
{
"type": "conversation_detail",
"name": "DigitalSLACheck",
"filters": [
{
"type": "equals",
"field": "channel_type",
"value": "DIGITAL"
}
],
"columns": [
"id",
"start_time",
"answer_time",
"service_level"
]
}
]
}
This approach usually resolves the mismatch by providing granular visibility into individual transaction timings rather than relying on the aggregated dashboard metrics which may apply different sampling or rounding logic.