The Analytics tab gives you insight into how your chatbot is performing. Go to any chatbot → Analytics tab.
Date Range Filter
All analytics data can be filtered by date range. Presets available:
- Today
- This week
- This month
- This year
- Last year
- Custom — pick specific from/to dates
Overview Sub-Tab
The main dashboard with key metrics and trend charts.
Metrics Cards
| Metric | Description |
|---|
| Total conversations | Number of unique chat sessions |
| Total messages | Total messages sent (user + bot) |
| Avg messages per conversation | Average conversation length |
| Avg satisfaction rating | Average feedback score |
Charts
- Conversations over time — line chart showing daily conversation counts
- Satisfaction trend — line chart tracking average rating over time
Feedback Sub-Tab
Detailed feedback analysis from the thumbs up/down buttons in the chat widget.
| Data | Description |
|---|
| Thumbs up count | Total positive ratings |
| Thumbs down count | Total negative ratings |
| Feedback distribution chart | Visual breakdown of positive vs negative |
| Feedback list | Individual feedback entries with user’s text comments (if provided) |
Regularly review negative feedback to identify knowledge gaps. If visitors consistently rate certain topics poorly, add more content to those areas in your knowledge base.
Conversations Sub-Tab
A table of all conversations with details:
| Column | Description |
|---|
| Date | When the conversation started |
| Messages | User messages and bot responses in the session |
| Duration | How long the conversation lasted |
| Satisfaction | The rating the visitor gave (if any) |
LLM Sub-Tab
Token usage and cost analysis for the AI model.
| Metric | Description |
|---|
| Input tokens | Total tokens sent to the model (questions + context) |
| Output tokens | Total tokens generated by the model (responses) |
| Token usage breakdown | Chart showing input vs output token distribution |
| Cost analysis | Estimated cost per conversation / per message |
| Model usage statistics | If you’ve changed models over time, see usage per model |
Using Analytics to Improve Your Chatbot
Identify Knowledge Gaps
- Go to Conversations tab
- Filter for conversations with low satisfaction
- Read the questions — are they about topics not in your knowledge base?
- Add missing content to the Knowledge Base
Optimize Costs
- Go to LLM tab
- Check average tokens per message
- If token usage is high, consider:
- Shortening the system prompt
- Using a cheaper model tier
- Adding more specific knowledge so the AI needs less reasoning
Monitor Engagement
- Go to Overview tab
- Track conversations over time
- Low engagement? Try:
- Adding a proactive message to the widget
- Improving welcome questions
- Placing the widget on higher-traffic pages
