Chats

The Chats tab under the Stats section offers a detailed view of chat activity across several dimensions. This module is divided into multiple child tabs, each offering specific metrics to help admins and agents analyze live chat performance.

All tabs support a date range filter (calendar) allowing the user to select a custom period for which stats should be displayed.

1. Total Chats

What it shows:

• Bar chart comparing current and previous week’s total chats.
• Daily counts of chats.
• Side summary with:
  • Total chats
  • Missed chats
  • Responded chats
  • Highest & Lowest single-day chats

Example: If “Monday, Jun 30” shows 2 chats, it means 2 separate chat sessions happened on that day.

Filters:

• Date range picker – adjust analysis week-to-week.

Insights:

• Indicates growth with percentage.
• Helps assess weekly performance at a glance.

2. Missed Chats

What it shows:

• Bar chart highlighting missed chats by day.
• Comparisons between two weeks.
• Sidebar summary:
  • Total, missed, and responded chats
  • Highest/lowest single-day missed chats

Example: If “Wednesday, Jun 4” shows 2 missed chats, it means no agent responded to 2 incoming requests that day.

Insights:

• Monitor dropped or unanswered queries.
• Track improvement or decline.

3. Chat Satisfaction

What it shows:

• Breakdown of:
  • Positive feedback (thumbs up 👍)
  • Negative feedback (thumbs down 👎)
  • No feedback given
• Chart with feedback counts
• Sidebar:
  • Total rated chats
  • Highest/lowest feedback counts per day
  • Positive feedback score (excluding unrated)

Example: If 1 chat got positive feedback and 2 were not rated, the score is 100% (1/1 rated = 100%).

Use Case:

Gauge how well users feel their issues were resolved.

4. Chat Queue

What it shows:

• Line chart showing:
  • Total chat requests
  • Unassigned chats (no agent online)
  • Chats routed to agents
• Sidebar:
  • Highest and lowest activity in a day

Example: If “Jun 23” shows 3 chats routed and 1 unassigned, it means 1 chat could not be served due to unavailability.

Insights:

• Understand peak load times.
• See if routing logic or agent availability needs improvement.

5. Chat Duration

What it shows:

• Bar chart displaying average chat time split between:
  • Human agent time
  • AI chatbot time
• Sidebar:
  • Average total duration
  • Longest and shortest chats by day

Example: If June 26 has AI Chatbot: 1.39 min and Human Agent: 0.48 min, users spent more time with the bot.

Use Case:

Optimize chatbot flows and agent response efficiency.

Tips for Users:

• Use this module to assess support load.
• Improve agent scheduling based on chat volumes.
• Track feedback trends to improve agent quality.
• Ensure unassigned chats are minimized for better user experience.