Chatbot analytics that tell you what to fix next.

Chatbot analytics measure what your AI agent did with the conversations it handled: how many it took, how many it resolved alone, and where it stopped. Outlearn tracks that automatically from the first conversation and puts the auto-solve rate, the handoff reasons and the questions it could not answer on one customer service dashboard.

Free plan, no card. Analytics is on from the first conversation, on every plan.

Last updated 17 September 2026.

Analytics · Overview

Sample workspace

This month PDF or CSV

• Conversations: 1,284
  One per visitor who sent a first message.
• Auto-solved: 51%
  Closed with no human handoff.
• Time saved: 96 h
  Estimated, from automated resolutions.
• Satisfaction: 4.6 / 5
  Average rating left after a conversation.

Resolution Handoffs Content gaps Topics

Sample workspace. How 1,284 conversations ended: resolved by the agent, handed to a person, or closed without either.

• Auto-solved 51%
• Handed to a human 6%
• Ended without either 43%

Sample workspace. Why the agent escalated, as a share of the conversations it handed over. The green bar is the one you can close with better content.

• Asked for a person: 34%
• Billing change: 24%
• Order exception: 18%
• No confident answer: 15%
• Out of scope: 9%

Sample workspace. Questions the agent could not answer from any connected source, this month, most asked first. Green marks the two worth writing today.

• Refunds after 60 days: 41
• Bulk order pricing: 33
• Okta SSO setup: 28
• Cancel mid-cycle: 22
• EU VAT invoices: 17

Sample workspace. What people came to ask about, grouped automatically, as a share of all conversations in the range.

• Orders and shipping: 31%
• Billing: 22%
• Account access: 17%
• Returns: 14%
• Everything else: 16%

The numbers we publish about our own agent

A vendor that quotes a resolution rate without a denominator is quoting marketing. Ours comes with the arithmetic, including the conversations that ended in neither outcome, in the AI support benchmark.

• 29,003 conversations measured, Oct 2025 to Aug 2026
• 45.9% resolved automatically, no human involved (13,299 / 29,003)
• 5.7% handed to a human (1,662 / 29,003)
• 2.7 knowledge sources connected by the average workspace

Reading the dashboard

What a chatbot analytics dashboard shows, and the decision behind each number

1. Conversations
   Is anyone actually using it?
   The count of conversations the agent handled in the range you picked.

2. Auto-solved
   How independent is the agent?
   The share of conversations resolved with no human handoff.

3. Time saved
   What is it worth?
   An estimate of the hours your team did not spend.

4. Satisfaction
   Are the answers any good?
   The average rating people left in the widget's rating popup.

The vocabulary

Customer service metrics, defined once

• First response time FRT
• Average handle time AHT
• First contact resolution FCR
• Resolution rate Definition
• Cost per ticket Definition
• Deflection rate Definition
• Containment rate Definition
• Agent assist Definition

The part that changes something

Content gaps turn the dashboard into a backlog

Step 1
The agent says it doesn't know
A content gap is a question the agent could not answer from any connected source.

Step 2
The gaps arrive ranked

Step 3
You fix the source, not the bot

Step 4
Out of scope stays out of scope

The difference

Most chatbot dashboards count. This one diagnoses.

A typical chatbot dashboard:
A count of chats, and nothing about how many ended in an answer.
Auto-solved is the headline: the share of conversations resolved with no human handoff, on the same screen as the volume.

Analytics as an upgrade, priced per seat like everything else.

Connect one source and watch the first week of numbers arrive.

Point the agent at the documentation you already keep.

What support leads ask about chatbot analytics

What is chatbot analytics?

Chatbot analytics is the measurement of what an AI agent did with the conversations it handled.

What does the Outlearn analytics dashboard show?

The Overview leads with four numbers: Total Conversations, Auto-Solved, Time Saved and Satisfaction Score.

What should a customer service dashboard show?

Four things: how much came in, how much was resolved and by whom, what it cost or saved, and what to fix next.

Which chatbot KPIs belong on the dashboard?

Total conversations, the auto-solve rate, handoff volume with the reasons, the top topics, the content gaps and a satisfaction score.

What is a good auto-solve rate for an AI chatbot?

There is no single right answer.

How is auto-solve rate different from deflection rate and containment rate?

They are measured over different denominators.

What are content gaps and what do I do with them?

A content gap is logged when someone asks a question and the agent cannot find a relevant answer.

Can I read the individual conversations behind the numbers?

Yes — Conversation & Training in the Analytics tab lists every conversation your agent handled.

Can I export the analytics, and what does it cost?

You can export the dashboard for the selected range as a PDF or a CSV.

Which chatbot KPIs should a support team track?

Five chatbot metrics: the auto-solve rate, handoff volume with the reasons, content gaps closed, satisfaction score, and time saved.