Improving your AI agent
Conversation Insights
The Conversation Insights tab helps you measure and analyze user sentiment in chats. By tracking sentiment at the message level, you can identify pain points, improve workflows, and guide agents with actionable recommendations.
Key Metrics
- Net Sentiment Score (NSS)
The average sentiment across a conversation or set of conversations. - Sentiment Shift Score (SSS)
Measures how sentiment changes throughout the chat (e.g., going from frustrated → reassured). - Lowest Sentiment Point
Highlights the most negative user message with its sentiment score and detected emotion. - Actionable Recommendations
AI-powered suggestions for next steps to resolve issues and improve customer experience.
Sentiment Scoring Guide
Each user message is scored between -5 and +5, based on the emotion expressed.
| Score | Canonical Emotion Label | Umbrella Meaning |
|---|---|---|
| +5 | Ecstatic | Peak positivity — delight, gratitude, or joy |
| +4 | Delighted | Very positive satisfaction and excitement |
| +3 | Pleased | Moderately happy, content, optimistic |
| +2 | Satisfied | Mild approval or relief; things are on track |
| +1 | Reassured | Slight positive calm after concern |
| 0 | Neutral | No strong emotion; observational |
| -1 | Uncertain | Mild doubt, hesitation, or confusion |
| -2 | Concerned | Noticeable worry or disappointment |
| -3 | Frustrated | Clear irritation or dissatisfaction |
| -4 | Angry | Strong negative feeling, indignation |
| -5 | Furious | Extreme negativity — outrage or despair |
Use Cases
- Identify at-risk customers with consistently low sentiment.
- Review Lowest Sentiment Points to detect recurring issues.
- Use Actionable Recommendations to guide agent training and automation improvements.
- Track sentiment trends across teams, workflows, and time periods to measure impact of changes.