Automation

IT Support Feedback Survey: Post-Ticket Surveys That Improve Every Resolution

IT support tickets that are closed without a satisfaction check leave the helpdesk team guessing whether the resolution actually worked. Deploy this automation and a short feedback survey fires the moment a ticket is marked resolved, confirming the fix before the user discovers it did not work.

IT helpdesks with post-resolution feedback catch 34% of incomplete resolutions that would otherwise be reopened as new tickets

How it works

  1. 1

    Trigger: support ticket marked resolved

    Fires automatically the moment a support ticket is marked resolved.

  2. 2

    Survey delivered at ticket close

    Goes out via Email or SMS, the moment the ticket closes.

  3. 3

    Resolution-confirmation gate

    Every response checked the instant it arrives:

    ✓ Confirmed resolution logged to the satisfaction record.

    → Incomplete resolution routes back to the original technician immediately.

  4. 4

    Technician performance dashboard

    Resolution-confirmation rate and technician scores, updated in real time.

IT support satisfaction has a specific metric that most helpdesk teams track manually and inaccurately: first-call resolution. The ticket system says the issue was resolved. But was it actually resolved? Did the user open a new ticket for the same problem three days later because the fix was incomplete? Or worse, did they stop submitting tickets entirely because the last interaction was frustrating, and they are just working around a problem that your team has no visibility into? Deploy FeedbackRobot's IT support feedback automation and a short survey fires the moment a ticket is marked resolved in your ticketing system. It asks whether the issue was fully resolved, how the technician handled the interaction, and how long the resolution took relative to expectation. A response flagging an incomplete resolution routes back to the original technician immediately, before the user has given up or opened a new ticket. For MSPs and internal IT teams tracking SLA performance, the satisfaction data adds the human layer that SLA metrics miss. A ticket resolved in 2 hours but leaving the user confused about what was done and why scores poorly on the satisfaction survey even though it met the SLA target. That gap between technical compliance and actual user experience is where the real service quality lives, and this automation is how you measure it.

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Frequently asked questions

Why ask if the issue was fully resolved when the ticket is already marked resolved?

A ticket marked resolved by the technician and an issue actually fixed from the user's perspective aren't always the same thing, this catches the gap, flagging cases that would otherwise silently become a new ticket days later.

What happens if someone flags an incomplete resolution?

It routes back to the original technician immediately rather than waiting for the user to open a new ticket and start over.

Does this measure against SLA targets?

It adds the human layer SLA metrics miss, a ticket can be resolved within SLA and still leave the user confused about what was done, this survey is what catches that gap.

Can this be attributed to a specific technician for coaching?

Yes, if ticket data includes the assigned technician, satisfaction and resolution-confirmation rates can be tracked by individual.