Is it possible to configure the Genesys Cloud Performance Dashboard to display a direct correlation between IVR navigation drop-offs and subsequent queue congestion metrics? The current architectural design routes high-volume digital inquiries through a complex menu structure before transferring to a specific support queue. Observations indicate a significant latency between the IVR exit point and the actual queue entry, which obscures the root cause of abandoned calls. The business stakeholders require a unified view that isolates whether the abandonment rate stems from user frustration within the IVR flow or from excessive wait times upon reaching the queue. The environment is configured in the EMEA region, utilizing standard Architect flows with no custom API integrations for reporting. The existing dashboard widgets display IVR analytics and queue performance in separate modules, preventing a synchronized temporal analysis. This fragmentation necessitates manual reconciliation of data points, which introduces potential errors and delays in reporting. The objective is to identify if specific IVR paths are disproportionately contributing to queue overload during peak hours in the Paris timezone, thereby allowing for targeted flow optimization rather than generic capacity planning.
The technical constraint appears to be the lack of a native join key between the IVR interaction logs and the queue admission timestamps within the standard reporting framework. While the Conversation Detail View provides granular step-by-step data, it is not scalable for aggregate trend analysis across multiple queues. The requirement is for a dashboard widget or a calculated metric that can overlay IVR drop-off percentages against queue occupancy rates for the same time intervals. This would enable the operations team to distinguish between systemic flow design flaws and resource allocation issues. Previous attempts to use custom reports have resulted in data discrepancies due to differing refresh rates between the IVR analytics engine and the queue performance data. Clarification on whether this correlation can be achieved through existing dashboard configuration options or if it requires a workaround using external data export tools is requested. The solution must remain within the bounds of the intermediate user capabilities, avoiding complex API development or custom database queries.