Analytics Dashboard

Monitor your chatbot's performance with real-time analytics. Track engagement, understand user behavior, and optimize for better results.

After publishing your chatbot, the Analytics Dashboard becomes your command center for understanding how users interact with your AI. Track key metrics, identify trends, and make data-driven decisions to improve engagement.

Tip:
Analytics data is available immediately after your first conversation. Data refreshes in real-time as new conversations occur.

Dashboard Overview

Access analytics by clicking "Analytics" in your chatbot's header menu. The dashboard displays key performance indicators at a glance.

Analytics dashboard showing key metrics including unique users, conversations, total messages, and abandon rate
Dashboard header with key performance metrics and date range selector

Key Metrics Explained

Unique Users

Total distinct visitors who interacted with your chatbot

Conversations

Number of chat sessions started

Total Messages

Combined user and bot messages exchanged

Abandon Rate

Percentage of users who left without engaging

Avg Duration

Average length of conversations

Countries

Geographic reach of your chatbot

Additional metrics include Longest Conversation (your most engaged user session) and Messages per Conversation (average engagement depth).

The Activity Trends section reveals when your users are most active, helping you understand usage patterns and optimize your chatbot's availability.

Activity trends showing daily activity chart, peak hours, day vs night usage, and weekly patterns
Four visualizations showing conversation patterns over time

Understanding the Charts

  • Daily Activity — Line chart showing conversations and messages over your selected date range. Identify spikes and trends.
  • Peak Hours — Hourly distribution showing when users are most active. Use this to schedule live agent availability.
  • Day vs Night Usage — Compare daytime (6AM-6PM) vs nighttime engagement. See how your 24/7 availability impacts after-hours leads.
  • Weekly Pattern — Average activity by day of week. Identify your busiest days for staffing and campaign planning.
Tip:
If most conversations happen outside business hours, highlight this in your ROI reports—your chatbot is capturing leads you'd otherwise miss.

Engagement Quality

Beyond volume, engagement quality metrics tell you how well your chatbot is performing. Are users actually engaging, or dropping off immediately?

Engagement quality showing conversation length distribution and user activation patterns
Analyze conversation depth and identify engagement drop-off points

Conversation Length Distribution

Conversations are categorized by engagement depth:

  • Abandoned — User left without sending a message (optimize your greeting)
  • Short — 1-3 messages (quick questions or early drop-offs)
  • Medium — 4-10 messages (healthy engagement)
  • Long — 10+ messages (deep conversations, potential high-intent leads)

User Activation Patterns

See how quickly users start conversations after the chatbot appears:

  • Immediate — Engaged right away (strong call-to-action)
  • Quick — Started within a few seconds
  • Never Started — Saw the chatbot but didn't interact (consider A/B testing your greeting)
Note:
A high "Never Started" rate might indicate your chat button placement, greeting message, or timing needs adjustment. See Content Tab for optimization tips.

User Insights

Understand who your chatbot users are with detailed insights on top engagers and geographic distribution.

User insights showing top engaged users leaderboard and geographic breakdown by country
Identify your most engaged users and where your audience is located

Top Engaged Users

A leaderboard of users ranked by engagement score, showing:

  • User identifier — From lead form or anonymous ID
  • Total messages — Number of messages sent
  • Engagement score — Calculated from message count, session length, and return visits

Geographic Breakdown

See where your users are located by country. This helps you:

  • Identify markets with high engagement
  • Consider adding language support for top countries
  • Adjust response timing for different time zones
  • Plan localized marketing campaigns
Tip:
If you see significant traffic from non-English speaking countries, consider adding multilingual support in your system prompt.

Top Conversations

Dive into your most significant conversations to understand what drives deep engagement.

Top conversations showing most active and longest duration conversations
Review your most engaged conversations for insights

Most Active Conversations

Sorted by message count, these are your chattiest users. Review these to:

  • Understand what questions generate the most back-and-forth
  • Identify complex topics that might need better documentation
  • Find potential testimonials from highly engaged users

Longest Conversations

Sorted by duration, these sessions show sustained engagement. Long conversations often indicate:

  • High-intent leads exploring your offerings in depth
  • Users with complex needs requiring detailed assistance
  • Potential issues where the chatbot couldn't provide a satisfactory answer
Tip:
Click on any conversation to view the full transcript and understand the user journey.

Using Analytics Effectively

Weekly Review Checklist

  1. Check abandon rate — If above 20%, test different greeting messages
  2. Review peak hours — Ensure live agent coverage during busy times
  3. Analyze top conversations — Look for common questions to add to your knowledge base
  4. Monitor geographic trends — Identify emerging markets
  5. Compare week-over-week — Track improvement trends

Common Optimizations

  • High abandon rate? → Improve your greeting message and chat button visibility
  • Short conversations? → Add more engaging follow-up questions in your prompt
  • Low after-hours engagement? → Promote 24/7 availability in your marketing
  • Specific country traffic? → Consider localized content and language support

Next Steps

Now that you understand your chatbot's performance, consider exploring:

  • Sources — Improve response accuracy by adding more knowledge
  • Behaviour Tab — Refine your system prompt based on conversation patterns
  • Content Tab — Optimize lead capture based on engagement data