Live Chat Analytics
The Live Chat Analytics section helps you track, measure, and improve the performance of your human-assisted chat conversations.
Live Chat Analytics
The Live Chat Analytics section helps you track, measure, and improve the performance of your human-assisted chat conversations. These insights enable teams to understand response efficiency, customer engagement, and overall resolution quality.
Accessing Live Chat Analytics
Go to: Analytics → Live Chat
You can filter data using:
Date range
Bots (specific chatbots)
Team (support agents or teams)
Time grouping (Daily)
Metrics Explained
1. Total Live Chat Sessions
What it shows: The total number of live chat conversations initiated by users during the selected time period.
Why it matters: Helps measure chat demand and overall customer engagement with live support.
Filter Charts:
Users can filter the data according to the selected bot , their teams, by any selected date period, monthly, weekly or Daily.
2. Live Chat Acceptance Rate
What it shows: The percentage of incoming live chat requests that were accepted by agents.
Why it matters: A high acceptance rate indicates good team availability and responsiveness.
Filter Charts:
Users can filter the data according to the selected bot , their teams, by any selected date period, monthly, weekly or Daily.
3. First Response Time
What it shows: The average time taken by an agent to send the first reply after a chat is assigned.
Why it matters: Lower first response time improves customer satisfaction and reduces abandonment.
Filter Charts:
Users can filter the data according to the selected bot , their teams, by any selected date period, monthly, weekly or Daily.
4. Average Response Time
What it shows: The average time taken by agents to respond to user messages throughout the conversation.
Why it matters: Indicates how actively agents engage during an ongoing chat.
Filter Charts:
Users can filter the data according to the selected bot , their teams, by any selected date period, monthly, weekly or Daily.
5. Average Chat Duration
What it shows: The average length of a live chat session from start to end.
Why it matters: Helps understand chat complexity and agent efficiency.
Filter Charts:
Users can filter the data according to the selected bot , their teams, by any selected date period, monthly, weekly or Daily.
6. Resolution Time
What it shows: The average time taken to fully resolve a chat issue.
Why it matters: Shorter resolution times reflect effective issue handling.
Filter Charts:
Users can filter the data according to the selected bot , their teams, by any selected date period, monthly, weekly or Daily.
7. Chat Abandonment Rate
What it shows: The percentage of chats where users left before the issue was resolved.
Why it matters: A high abandonment rate may indicate slow responses or long wait times.
Filter Charts:
Users can filter the data according to the selected bot , their teams, by any selected date period, monthly, weekly or Daily.
8. Chat Resolution Rate
What it shows: The percentage of chats that were successfully resolved by agents.
Why it matters: A higher resolution rate means better customer support outcomes.
Filter Charts:
Users can filter the data according to the selected bot , their teams, by any selected date period, monthly, weekly or Daily.
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