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.

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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)

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.