Analytics

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Overview

Analytics aggregates the activity of your AI agent across conversations. It has four pages: Chats, Topics, Sentiment, and Helpdesk. By default it shows the activity for all AI agents in your workspace for the last week.

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Chats

The top of the page shows the resolution rate, the total number of chats, total number of messages, the messages that got a thumbs up and a thumbs down, daily active users, weekly active users, and stickiness. Below the totals, a line chart plots chats over the selected period against the Previous period (the dotted line), so you can see whether volume is rising or falling. Hover any point to compare the two dates directly. Resolution rate: The percentage of conversations your agent resolved independently, without creating a ticket or escalating to live chat. Chats by country shades a world map by volume and lists the top countries beside it, with View all for the full ranking. Countries are detected from the IP of the users having conversations with the AI agent. Chats by channel breaks the same total down by where the conversations came from, such as Widget/Iframe, Playground, or Slack. Action calls ranks how often each of your actions ran over the period, with View all for the complete list. It is a quick way to see which actions your AI agent actually uses. Each panel uses the visualization that suits it: a line chart for chats over time, a shaded world map for Chats by country, and horizontal bars for Chats by channel and Action calls.

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Filtering

You can filter the data by date, using pre-defined ranges such as last 7 days, last 30 days, last 3 months, and last year.

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Topics

This page shows the topics that came up in conversations and were mentioned by users. Topics are detected automatically by the AI agent. Available actions:

• Search topics
• Add topic
• Edit topic
• Delete topic
• Freeze topics: stops the AI agent detecting any new topics automatically

You can also add topics manually, using the View All button on the right of the page.

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Sentiment

This page shows how the AI agent detects the sentiment and emotion of users during conversations. Sentiment analysis is detected automatically by the AI agent.

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Helpdesk

Track ticket volume over time to monitor your support workload and resolution trends. Every metric on this page shows its change against the previous period, and each chart plots the Selected period as a solid line against the Previous period as a dotted one.

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Team member performance

The table at the top of the page breaks the period down by team member, so you can see how the workload is spread across your human agents:

• Solved tickets: tickets that member marked solved.
• Tickets replied: tickets they replied to at least once.
• Replies sent: the total number of replies they sent.
• Median first response time: how long customers waited for that member’s first reply.
• Median response time: the median across all of their replies.
• Median time to close: how long their tickets stayed open before being solved.

Every column is sortable. Scroll the table sideways to reach the columns that don’t fit on screen. The All agents row totals the counts for the whole team, and a dash means there is no data for that member in the selected period.

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Ticket volume

• Created tickets: tickets opened during the period.
• Solved tickets: tickets marked solved during the period.
• Unsolved tickets: the outstanding backlog, plotted on its own chart so you can see whether it is growing or shrinking.
• Agent replies sent: replies sent by human agents during the period, charted beside Unsolved tickets. Internal notes, automated messages, and AI agent replies are excluded.

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Response times

• Median first response time: how long a customer waits for the first human reply.
• Median response time: the median across all replies, not just the first.
• Median time to close: how long a ticket stays open before it is solved.

Medians are used rather than averages, so a handful of long-running tickets don’t skew the figure. A chart below the three tiles plots response time across the period.

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Tickets by channel

Breaks ticket volume down by where the tickets arrived from, such as Email, Widget/Iframe, or API.

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Customer satisfaction (CSAT)

Shows how customers rated the support they received, based on the satisfaction surveys sent after tickets are closed. Each figure shows its underlying counts and its change against the previous period.

• CSAT score: answers rated Good as a share of all answers.
• Response rate: answered surveys as a share of surveys sent.
• Coverage: surveys sent as a share of closed tickets that could have been surveyed.

Figures are grouped by the day the survey went out, so a late answer counts toward that day. View all lists the individual answers with their comments, the ticket they belong to, and the teammate who handled it, along with a breakdown of tickets that were not surveyed and why.

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Filtering

Use the date range picker to set the period. The filter button beside it narrows the whole page by Assignee, Channel, Type, or Last message by. Pick a field, choose an operator such as is any of, then select the values and click Apply. Discard clears the filter you were editing.

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Limitations

• Data is updated with a 1-day delay, reflecting the previous day’s information.
• Data is only recorded once you subscribe to a plan. Topics and sentiment are only available after the upgrade, and anything from before it is not included.
• Message totals and credits are counted differently. The page shows the total number of messages, including all conversation messages, but credits are only calculated for AI-generated responses, excluding the initial messages.

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Next steps