AI Chatbot Feedback

Short post-chat survey: satisfaction, resolution rate, effort and whether customers would choose the bot again next time.

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AI Chatbot Feedback – questionnaire preview

Almost every company now runs an AI chatbot in customer service – but few measure systematically how good it really is. This short survey is shown right after the chat (via link, QR code or redirect) and captures the four metrics that matter: satisfaction with the conversation (bot CSAT), resolution rate, perceived effort and the crucial question of whether customers would choose the bot again next time or prefer a human. A quality matrix additionally shows where it falls short: understanding, correctness, speed or tone.

When should you use this template?

This template is a great fit for:

  • Right after every bot conversation as a standard follow-up
  • When launching or switching the model behind an AI assistant (before/after comparison)
  • To decide which requests the bot keeps and which escalate to humans

Every question in this template

  1. 1

    How satisfied are you with the conversation with our AI assistant? *

    Emoji scale
    • 😡Very dissatisfied
    • 🙁Dissatisfied
    • 😐Neutral
    • 🙂Satisfied
    • 😍Very satisfied
  2. 2

    Was your issue resolved? *

    Single choice
    • Yes, completely
    • Partly
    • No
    • No, I had to switch to a human
  3. 3

    How easy was it to reach your goal with the assistant?

    Rating
  4. 4

    How did the assistant do in detail?

    Matrix
    Strongly disagreeDisagreeNeutralAgreeStrongly agree
    It understood my request.
    Its answers were factually correct.
    It responded quickly.
    The tone was pleasant.
  5. 5

    What would you prefer for a similar issue next time?

    Single choice
    • The AI assistant again
    • The assistant first, a human if needed
    • A human agent right away
    • Depends on the issue
  6. 6

    What should our assistant learn?

    Long text

From questionnaire to dashboard

In the dashboard, individual chat ratings become a steering cockpit for your AI assistant:

  • Bot CSAT figure: The emoji rating as a KPI tile – your headline quality metric, trackable week by week.
  • Resolution funnel: From conversation via “partly resolved” to “fully resolved”: the funnel shows how many requests the bot truly closes – your deflection rate.
  • Quality profile: The quality matrix (understanding, correctness, speed, tone) as diverging bars shows which aspect to improve first.
  • Effort over time: The mean effort score as a line across weeks – ideal for objectively evaluating model or prompt changes to the bot.
  • Bot-vs-human preference: Would customers choose the bot again? The donut is your most honest acceptance indicator.
  • Learning topics from open answers: “What should the assistant learn?” – clustered via AI topic analysis, this becomes the training backlog for your bot team.

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Frequently asked questions

How do we trigger the survey after the chat?
The simplest way: the bot links to the survey URL at the end of the conversation. URL parameters can carry context such as channel or request category, which you can then filter by in the dashboard.
What is a good resolution rate for an AI chatbot?
It depends heavily on which requests you route to the bot. The trend matters more than an industry benchmark: does the rate rise after improvements? Also measure the satisfaction of those escalated to a human separately – that is where the overall experience is decided.
Can we combine this with our service CSAT?
Yes, we even recommend it: run the support and service feedback template for human contacts in parallel. In DataLion you then compare both channels in the same dashboard.

Start with this template

Load the template into DataLion, adapt it to your brand and start collecting responses — GDPR-compliant, in minutes.

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