Help Desk Data for L&D
Every "how do I..." ticket records a performance gap in the employee's own words. Here's how L&D can partner with IT to turn help desk data into evidence-based learning priorities.
L&D teams spend weeks running surveys and interviews to find out where employees struggle. Meanwhile, one of the richest sources of that information sits in the IT help desk, largely unread by anyone in learning. Every "how do I..." ticket is a small, timestamped record of a performance gap, written in the employee's own words at the moment they got stuck.
Surveys ask people what they think they need. Tickets show what they actually couldn't do.
What's Hiding In The Ticket Queue
Help desk tickets generally fall into three groups:
- Defects and outages: something is broken
- Access requests: someone needs permissions or an account
- "How do I" questions: the system works, but the person doesn't know how to use it
The third group is a learning and support problem, not a technical one. In many organizations it makes up a meaningful share of ticket volume, and every one of those tickets costs time twice: once for the employee waiting for an answer, and once for the support agent providing it.
How To Run A Simple Ticket Analysis
Ask IT for three to six months of ticket data with five fields: category, application, ticket description, the requester's department, and the date. Remove names and other personal details before analysis. You don't need specialist tools; a spreadsheet and an afternoon will reveal a lot.
Look for four kinds of patterns. Hotspot tasks are specific tasks that generate repeated questions, such as submitting an expense claim in multiple currencies. Hotspot applications are systems generating far more questions than their user count would suggest. Timing patterns show spikes after a launch, an update, or a month-end close. Team patterns show one department asking about a task others handle easily, which often points to a gap in onboarding or a local variation in process.
Read a sample of ticket descriptions, not just the categories. Categories tell you where the problems are. Descriptions tell you what people were trying to do.
Match Each Pattern To The Right Fix
Not every pattern needs training. For each hotspot, decide which kind of fix fits best.
A process fix is right when confusion comes from an unclear business rule, for example when no one is sure which approval a purchase needs. A system fix is right when a confusing field label or screen layout is the real cause; that's feedback for the application owner, not a course. Contextual guidance fits when people need help with a specific task at the moment they perform it, especially infrequent tasks. Contextual tooltips and walkthroughs are designed for exactly this kind of point-of-use support. Targeted training is right when the gap is conceptual, such as understanding why a process works the way it does or how to handle judgment calls.
Being honest about which fix fits keeps L&D from building courses for problems a label change would solve.
Partner With IT
This analysis works best as a joint effort. IT gets fewer repetitive tickets and a partner who can address root causes. L&D gets evidence-based priorities and a shared metric both teams care about.
Agree on a short monthly review of the top ticket themes. Assign each theme an owner and a fix, and record the decision. Over time, this review becomes one of the most reliable inputs to L&D planning, because it's grounded in what employees actually experienced rather than what anyone assumed.
Track Whether The Fix Worked
Once fixes are in place, keep tracking "how do I" tickets for the targeted tasks. A drop suggests the fix worked. No change suggests the diagnosis was wrong, and the ticket descriptions usually reveal why.
Ticket counts alone can mislead, though. Fewer tickets might mean people solved the problem, or that they gave up asking and found workarounds. Pair ticket trends with other signals, such as task completion rates, error rates, and feature usage, to get a fuller picture. Understanding how digital adoption is measured helps L&D choose the right combination of signals for each system.
Why This Changes How L&D Is Seen
Ticket-based analysis gives L&D something surveys rarely do: objective, continuous data tied to real work. It also changes the conversation with the business. An L&D team that can say "we reduced repeat questions on the expense process" is speaking the same language as the operations and IT leaders it supports.
It works as an early warning system too. When a new system goes live, ticket patterns show within days where adoption is stalling and which groups need more help. Knowing that different groups adopt new technology at different speeds helps L&D read those early patterns correctly, rather than assuming a slow start means a failed rollout.
Partner With IT
This analysis works best when L&D and IT treat it as a shared improvement process rather than handing data from one team to another. IT has visibility into where employees are getting stuck, while L&D can help determine whether the underlying issue calls for training, guidance, process clarification, or a change to the application itself.
Start with a simple monthly review of the most common “how do I” ticket themes. IT can bring the recurring issues and examples from ticket descriptions; L&D can group them into learning or performance gaps and recommend the appropriate intervention. Where necessary, involve the application owner, HR, or the relevant business team rather than assuming every issue belongs to L&D.
Give each recurring problem an owner, a proposed fix, and a date for review. This makes the process actionable instead of turning the analysis into another report that sits unused. Over time, the review can become a shared source of evidence for L&D planning and IT improvement priorities.
It also creates a useful common metric. Instead of discussing whether employees “need more training,” both teams can look at whether a specific recurring problem is declining after an intervention.
Track Whether The Fix Worked
Once a fix is introduced, continue monitoring the ticket theme that triggered it. If L&D creates targeted training for a recurring task, compare related “how do I” tickets before and after the intervention. If IT changes a confusing interface or adds contextual guidance, track whether questions about that specific workflow decline.
Don't treat a reduction in ticket volume as automatic proof that the problem is solved. Employees may stop asking for help because they found another workaround, gave up on the task, or learned from an unofficial source. Pair ticket trends with other signals, such as task completion rates, error rates, processing time, or feature usage.
The goal is to close the loop: identify the problem, apply the right intervention, and then check whether employee behavior or performance actually changed. That turns help desk data from a passive record of problems into a continuous feedback loop for L&D and IT.
Conclusion
Start small: one application, one quarter of tickets, one afternoon of analysis. You don't need a sophisticated analytics program to find useful patterns. Repeated questions about the same task, spikes after a system change, or one team struggling with a process can quickly reveal where employees are losing time and where support is falling short.
The important part is not simply identifying the most common questions. It is deciding what those questions mean and matching them to the right response. Some problems need better training. Others need clearer processes, improved interfaces, or help delivered directly inside the application. Treating every performance gap as a training problem only creates more content without necessarily solving the underlying issue.
Help desk data also gives L&D a way to measure whether its interventions are working. If targeted guidance, a redesigned learning resource, or focused training is introduced for a recurring issue, ticket trends can provide an early signal of whether the problem is changing. Pairing that data with task performance, error rates, and feature usage makes the picture more reliable.
Help desk data won't replace conversations with employees, managers, or subject-matter experts. It adds something those conversations cannot: a continuous record of where employees actually get stuck while doing their work. Used alongside other evidence, it gives L&D a stronger basis for deciding what to fix, what to teach, and what to leave alone.
