Chat Performance

The General view on your metrics dashboard covers chat channels-Web, Slack, and WhatsApp. In chat, conversations resolve in a single session, so Resolution Rate and CSAT are your primary success metrics. Use the channel filter to isolate specific chat channels.

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

Charts section showing Messages and AI Involvement Rate visualizations

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

Analyze

Data Management

Conversation Status

Metrics

Charts and Use Cases

Identifying Issues

Next Steps

​Conversation Status

​Metrics

​Charts and Use Cases

​Identifying Issues

​Next Steps

Conversation Status

Metrics

Charts and Use Cases

Identifying Issues

Next Steps