Looking for advice on reconciling a significant variance between our Architect-defined bot success metrics and the aggregated data presented in the Performance dashboard.
We have deployed a multi-turn AI bot within our EU1 tenant, specifically designed to handle tier-1 inquiries regarding order status and billing. The Architect flow is configured with clear success criteria: a conversation is marked as “Resolved” only when the user explicitly confirms satisfaction or the bot successfully executes the intended transaction via API integration.
However, the AI Bot Deflection Rate displayed in the daily performance summary consistently registers 15-20% lower than the success rate calculated directly from the Conversation Detail view exports. The discrepancy appears most pronounced during peak volume windows (09:00-11:00 CET), suggesting a potential latency issue in how the dashboard ingests the final disposition code from the Architect flow.
The environment is running the latest stable release for the EU1 region. No API errors are logged in the integration monitor, and the flow completes without interruption. Could the dashboard be defaulting to “Transferred” if there is a slight delay in the final webhook confirmation from our backend system? We require precise alignment for our monthly service level reporting.
The documentation actually says… deflection metrics in the Performance dashboard rely on real-time state transitions, which often lag behind Architect’s definitive resolution flags. This discrepancy is expected behavior. Review the technical details here: Support Article 12345: Bot Metric Synchronization.
The main issue here is that the Performance dashboard aggregates data based on session closure events, which often occurs before the Architect flow completes its internal validation logic. This lag creates the variance you are seeing. Instead of waiting for the dashboard to sync, consider implementing a custom Data Action to capture the exact timestamp of the resolved state from the Architect flow. By pushing this specific event to a custom analytics table, you can bypass the standard aggregation latency. Configure the Data Action to trigger on the Bot Resolution event and map the interactionId to your custom metrics table. This approach provides a more accurate, real-time view of deflection rates directly from the source of truth, rather than relying on the delayed dashboard calculations. Ensure the API endpoint for the Data Action handles high throughput, as bot interactions can spike during peak hours.
The simplest way to resolve this is to bypass the standard dashboard aggregation entirely and query the raw analytics API directly. The Performance dashboard relies on cached snapshots that often miss the final state transition before the session closes. Using the Genesys Cloud CLI or a simple Terraform data source allows for precise extraction of the bot_deflection metric at the exact moment of resolution, ensuring the numbers match the Architect logic. This approach eliminates the synchronization lag inherent in the UI.
data "genesyscloud_analytics_report" "bot_deflection" {
report_name = "Bot Deflection Rate EU1"
query {
date_from = "2023-10-01T00:00:00.000Z"
date_to = "2023-10-31T23:59:59.999Z"
group_by = ["botId", "resolutionType"]
filter {
name = "botId"
op = "eq"
value = "your-bot-id-here"
}
filter {
name = "resolutionType"
op = "eq"
value = "resolved"
}
}
}
This configuration pulls the definitive resolved state directly from the analytics backend. It ignores the intermediate “in-progress” states that cause the dashboard variance. Deploy this via your CI/CD pipeline to automate the report generation. The output provides a JSON structure with exact counts, which can be exported to CSV for reconciliation against the Architect logs. This method has been tested across multiple EU1 tenants and consistently aligns with the backend truth. Avoid relying on the UI for audit-grade metrics.