# Conversations Dashboard — Core Analytics The Conversations workbook is the primary operational dashboard within the Conversations tab. It opens on a multi-section dashboard (the “Dashboard” sub-view) that consolidates three key analytical charts into a single view: conversation volume by disposition, volume by date and channel, and average handle time trends. The sub-tab bar for this workbook contains five views: Dashboard, Conversations by Disposition, Conversations by Date and Channel, Conversations by Date, Average Handle Time (secs) by..., and Details Page.

*Figure 1: Conversations Dashboard — Three-Section Overview (03/08/2026 – 04/09/2026)*

### 1. Section 1 — Conversations by Disposition (Summary) The upper section of the Conversations Dashboard displays a stacked bar chart titled “Conversations by Disposition,” plotted by Day of Conversation Date. Each bar represents a single day and is color-coded by the Connection Disposition — the reason the conversation ended as recorded by the platform (not the assistant). From the visible data (03/08/2026 – 04/09/2026, STG environment), the following disposition distribution was observed:


Connection Disposition	Avg Conversation Count (Daily)
AppDisconnect	2
Timeout	2
Transfer	1
UserDisconnect	2

Connection Disposition describes how a conversation session technically ended — whether the application terminated it (AppDisconnect), the session timed out (Timeout), it was transferred (Transfer), or the user ended it (UserDisconnect). This is distinct from the Assistant Disposition, which reflects whether the AI assistant considered its goal achieved. ⚠ Note: These are staging (STG) environment values with low conversation volumes. Production environments will show significantly higher counts while maintaining the same analytical structure. ### 2. Section 2 — Conversations by Date and Channel (Summary) The middle section shows a dual-line time-series chart of daily conversation volumes broken down by Channel. The header KPIs display: Total Contacts: 27 and Average Conversations (Daily): 2 for the selected period. Key observations from the visible data: The blue line (primary channel) shows a peak of 5 conversations on Wednesday 3/25/26, with a secondary peak of 4 on Wednesday 4/1/26. The orange line (secondary channel) becomes visible from Monday 3/23/26 onwards, peaking at 3 on Monday 3/30/26. The chart reveals that conversation activity is concentrated on mid-week days (Wednesday), with a gap over the Easter/spring break period in late March. ### 3. Section 3 — Average Handle Time (secs) by Date and Channel (Summary) The lower section displays a dual-line time-series chart of Average Handle Time (AHT) in seconds, broken down by Channel. AHT measures how long a conversation lasted on average for each day. Key data points visible in the dashboard view:


Date	Observed AHT Value
Mon 3/16/26	1,739.0 seconds (peak — approximately 29 minutes)
Mon 3/23/26	956.0 seconds (approximately 16 minutes)
Thu 3/26/26	91.5 seconds
Mon 3/30/26	43.0 seconds

The high AHT values on 3/16 (1,739 seconds) and 3/23 (956 seconds) warrant investigation. In an AI assistant context, very long handle times may indicate conversations where users were stuck in a loop, the assistant could not resolve the intent and kept prompting, or the conversation involved complex multi-step flows. The sharp decline to 43 seconds by 3/30 suggests a configuration or data change. Filtering by specific assistant and channel can isolate the cause.
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