Chat Performance
Analyze conversation quality, resolution rates, and customer satisfaction across chat channels.
Ticketing Performance
Measure AI involvement, autonomous resolution, and weekend coverage for ticketing workflows.
Choosing the Right Metrics
Chat and ticketing channels measure success differently because the role of human involvement differs.| Channel type | Primary metrics | Why |
|---|---|---|
| Chat (Web, Slack, WhatsApp) | Resolution Rate, CSAT | Conversations resolve in one session-the AI either answers or it doesn’t |
| Ticketing (Zendesk, Salesforce) | Involvement Rate, Relative Autonomous Rate | Human finishing is normal and valuable. An AI draft that a human sends is a win, not a failure. Resolution Rate penalizes this collaboration. |
Resolution Rate is imperfect
Resolution Rate uses a second LLM to judge whether a conversation reached a satisfactory conclusion. This works well for most chat conversations, but the judge can misclassify in several ways:- False positives. The judge marks a conversation as resolved because the AI gave a confident-sounding answer, even though the information was wrong or incomplete.
- False negatives. The user got what they needed but left without confirming, so the judge marks it unresolved.
- Multi-intent conversations. The user asked three questions, the AI answered two well and missed one. The judge must make a binary call on a mixed outcome.
Why ticketing uses Involvement Rate instead
For ticketing, these judgment errors matter less because you don’t need them. Involvement Rate tracks whether the AI participated in a ticket at all, which the system knows deterministically, without a second LLM. A ticket either has AI-generated content or it doesn’t. This makes Involvement Rate unambiguous, easy to reason about, and directly interpretable: every involved ticket represents work the AI took off a human agent’s plate, and every autonomous ticket needed zero human time. See Ticketing Performance for the full breakdown of involvement levels.Filters
All metrics respect your filter selections. Use the date range (top right) to control the time window-trend indicators automatically compare to the equivalent previous period. Use the channel and label filters to segment by communication channel or conversation tags.Next Steps
- Chat Performance - Deep dive into conversation quality and satisfaction
- Ticketing Performance - Detailed analysis of AI involvement in ticketing
- Conversations - Review individual conversations to understand metric context
- Topics - Segment metrics by topic to find specific improvement areas
- Improve Answers - Use metric insights to refine knowledge and guidance