For ticketing channels (Zendesk, Salesforce), see Ticketing Performance. Ticketing uses different primary metrics because human finishing is an expected part of the workflow.
Conversation Status
Every conversation has a status that indicates how it concluded.| Status | Color | Meaning |
|---|---|---|
| Resolved | Green | The AI or a human operator successfully answered the user’s question |
| Unresolved | Red | The question went unanswered or received an inadequate response |
| Escalated | Purple | The system handed the conversation off to a human agent, automatically or on request |
Metrics
| Card | Description | Interpretation |
|---|---|---|
| Messages | Total user messages exchanged in the selected timeframe | Rising messages with stable conversations means longer discussions |
| Conversations | Unique conversation threads started | Spikes may indicate product issues or marketing campaigns |
| Unique Users | Distinct users who started conversations | Compare to conversation count to gauge repeat contact rate |
| CSAT Score | Percentage of satisfied customers (4–5 star ratings) out of all rated conversations | 80%+ is excellent, below 60% needs urgent attention |
| Resolution Rate | Percentage of conversations resolved without escalation or abandonment | 80%+ indicates strong autonomous performance |
Charts and Use Cases
| Chart | Use case |
|---|---|
| Conversation Status | Track resolution trends over time; correlate dips with deployments or knowledge updates |
| Conversation Rating | Check satisfaction distribution; a healthy profile peaks at 4–5 stars with under 10% at 1–2 stars |
| Message Volume | Spot changes in conversation complexity-average messages per conversation reveals efficiency (2–4 is quick, 9+ suggests struggles) |
| AI Involvement Rate | Measure your automation mix; target 60–70% fully autonomous for a mature deployment |
| handoff | Find peak escalation times and the most common reasons the AI hands off to humans |
| Answer Completeness | Surface knowledge gaps directly-a high “no answer” percentage points to missing content |
| User Sentiment | Detect frustration trends; rising negative sentiment with low CSAT means the AI’s responses frustrate users |
| User Rating Trend | Confirm that improvements sustain over time-look for upward-sloping 4–5 star lines |
| User Language | Identify whether non-English traffic with low satisfaction signals a need for multilingual knowledge |
| Usage by Page | Find high-traffic pages that generate many conversations-candidates for better self-service content |
| Knowledge Source Usage | Detect underutilized knowledge sources and prioritize updates to frequently referenced ones |
| Conversation Length | Flag efficiency issues-many single-message conversations may mean disengagement, 9+ messages may mean the AI isn’t resolving |
| Activity heatmaps | Identify peak support hours for staffing and seasonal patterns |
| Hidden Conversations | Monitor spam detection accuracy and track abuse patterns |
Identifying Issues
Find topics with low satisfaction. Filter conversations by topic and rating 1–2 to see where the AI struggles most. Cross-reference with the Resolution Status chart on the topics dashboard to quantify the problem. Spot knowledge gaps. The Answer Completeness chart shows how often the AI has no answer at all. Open those conversations and check the source attribution-”Used Sources (0)” means the AI fell back to general knowledge instead of your data. Create snippets to fill the gap. Understand escalation patterns. The handoff chart reveals when and why the AI escalates. Filter to escalated conversations and review whether escalations were necessary (complex issue) or avoidable (missing knowledge). Reduce avoidable escalations by adding the missing information. Track improvement after changes. After updating knowledge or guidance, use the date range filter to compare the affected topic’s metrics before and after. Look for rising resolution rates and CSAT on that topic.Next Steps
- Conversations - Review individual conversations to understand metrics in context
- Ticketing Performance - Compare chat metrics to ticketing performance
- Topics - Segment performance by topic to find improvement areas
- Improve Answers - Use insights to refine knowledge and guidance