Mining Support Tickets for Your Next Training Topic

Most training calendars are built the same way. Someone decides what 'should' be taught, usually based on what's new, complex, or a roadmap priority. This seems reasonable, but it means training topics are chosen with almost no input from the people who will attend the session.

There's a better source sitting mostly unused in most organisations: the support queue. Every recurring ticket is, in effect, a customer telling you exactly what they couldn't figure out on their own. Ignoring that signal in favour of guessing what to teach is one of the more avoidable mistakes in customer education.

The value of support ticket data

A support ticket represents a real, specific moment where self-serve content failed, the docs weren't clear enough, the UI wasn't intuitive enough, or the workflow required judgement a static article couldn't provide. That's precisely the kind of gap live, instructor-led training is built to close. Product intuition can tell you what's new or impressive. Usage data tells you what's confusing, and support tickets are one of the richest forms of usage data you have.

The other advantage is that tickets are already prioritised for you by volume. If forty customers file the same question in a month, that issue becomes the clearest possible signal for what your next session should cover.

Turning ticket data into a training topic

Pull and cluster your ticket history. Even a basic tag-and-count exercise across the last quarter will surface repeat offenders; the same three or four issues driving a disproportionate share of volume.
Separate "needs a bug fix" from "needs a lesson." Distinguish between product gaps and training gaps. A recurring ticket might signal a bug that needs fixing, not a concept that needs teaching. Training can clarify how something works, but it can't fix a broken feature.
Rank by cost, not just volume. Rank by impact, considering both volume and cost. A high-volume but trivial ticket may matter less than a lower-volume one that eats an hour of a specialist's time with each occurrence.
Write the session around the actual failure point. If tickets show customers consistently getting stuck at one specific step in a workflow, focus the training on that step instead of the entire feature.

The feedback loop this creates

Once a session goes live on a ticket-sourced topic, the same data source tells you whether it worked. Watch ticket volume on that exact issue for the weeks following the session. A real drop is one of the cleanest Level 3/4 Kirkpatrick signals available. It's a direct, low-effort way to connect a training session to a measurable business outcome without needing a separate analytics project.

This also quietly reframes how training gets perceived internally. A session built from "we noticed forty of you hit the same wall, so here's how to get past it" lands very differently than a generic feature walkthrough, and it gives support and training teams a shared, ongoing reason to talk to each other.

An ongoing review process

The teams that get the most from this approach build a lightweight, recurring review process. In a monthly or even biweekly meeting, support and training look at the top ticket categories together. They can then jointly decide what deserves a live session, a doc update, or a product ticket. The training calendar stops being a guess and starts being a direct response to what customers are struggling with.