Does anyone know why the Queue Performance dashboard displays inconsistent handle time metrics compared to the raw Conversation Detail view? We have observed this specifically for our Paris-based support queues during the last quarter.
The aggregated data in the performance view shows an average handle time of 4 minutes, whereas the detailed conversation logs indicate significantly higher durations when manually calculated. This discrepancy affects our SLA reporting accuracy for enterprise stakeholders who rely on these dashboards for operational reviews.
We have verified that the timezone settings are correctly configured to Europe/Paris and that the date filters align across both views. The issue persists despite clearing cache and refreshing the data source, suggesting a potential backend calculation mismatch rather than a display error.
We need to understand if there is a known limitation in how Genesys Cloud calculates handle time for multi-step flows versus single interactions. Any insights into whether this is a configuration oversight or a platform reporting artifact would be appreciated.
The main issue here is likely the dashboard using queue local time while api calls default to utc. check your terraform resource for time_zone and ensure it matches europe/paris to align the aggregations.
TL;DR: Align dashboard time zones with API query parameters to fix metric discrepancies.
Make sure you verify the time_zone parameter in your API requests matches the dashboard configuration. The Queue Performance dashboard aggregates metrics based on the queue’s local time setting, while raw Conversation Detail API responses often default to UTC unless explicitly overridden. This mismatch causes the handle time calculations to drift, especially when calls span across date boundaries or daylight saving transitions in the Paris region.
When pulling data via the Platform API, include the time_zone query parameter set to Europe/Paris to ensure the start and end times align with the dashboard’s aggregation logic. Here is an example of how to structure the request in a JMeter HTTP Request sampler to validate this:
GET /api/v2/analytics/queues/stats?time_zone=Europe/Paris&interval=PT1H&dateFrom=2023-10-01T00:00:00.000Z&dateTo=2023-10-02T00:00:00.000Z
If the discrepancy persists, check if the dashboard is filtering out idle time or wrap-up time differently than the raw conversation logs. The handle_time metric in the dashboard usually includes talk time plus hold time, but excludes after-call work unless configured otherwise. In contrast, the Conversation Detail view might show the total session duration from connection to disconnect.
For load testing scenarios, this alignment is critical when validating SLA compliance under high concurrency. If the API returns UTC timestamps, converting them to local time in post-processing scripts can introduce rounding errors that accumulate over thousands of concurrent sessions. Standardizing the time zone at the request level reduces this computational overhead and ensures the aggregated stats match the detailed logs.
Also, consider caching the converted timestamps if you are running high-throughput queries, as repeated timezone conversions can impact API response times during peak load tests. This approach minimizes the risk of hitting rate limits while maintaining data consistency across reporting tools.
Have you tried cross-referencing the queue settings with the specific time zone configurations in your scheduling module? The suggestion above regarding UTC vs local time is spot on, but there is often a secondary layer of complexity when dealing with European regions like Paris. During daylight saving transitions, the system might latch onto the wrong offset if the queue’s primary region isn’t explicitly set to Europe/Paris in the administrative console, not just the dashboard widget.
We often see this drift in our weekly schedule adherence reports when agents are spread across multiple time zones. The Queue Performance dashboard aggregates based on the queue’s defined time zone, but if the underlying data pipeline defaults to UTC for raw logs, the calculation window shifts. Ensure your API queries explicitly include the time_zone parameter matching the queue setting. Also, verify that no agents are manually overriding their time zone preferences in self-service, as this can sometimes bleed into aggregated metrics if not filtered out.
Warning: Check your historical data exports for any gaps during DST changes last year, as this often reveals if the issue is systemic or a recent configuration drift.
If I remember right, the drift occurs because the aggregation endpoint ignores the time_zone param for historical buckets.
"error": "TIMEZONE_MISMATCH", "message": "Aggregation uses queue local time, query uses UTC"
Force time_zone=Europe/Paris on the report definition to align the metrics.