Mining the trouble tickets

Set it up

• Export the closed tickets. One row each: date, category, the short description, what closed it, and hours to close.
• Take the names out first. Users and technicians become T01 to T40; the key that maps them stays in your spreadsheet.
• One project for the queue. Attach this quarter and the same quarter last year, so a trend has something to sit against.
• Say what a cause means here. A thing you could fix once, not the words the person happened to type.

Work it in four passes

1. Pick the careful model. Pro GPT 5.6 Terra Grok Expert Sorting hundreds of rows is reasoning; the fast tier flattens the groups.
2. Group, then count. Ask for causes with a count and two real examples quoted from the tickets in each group.
3. Rank by hours, not by volume. Forty small tickets can cost less than six long ones, so ask for both columns.
4. Ask for one fix per cause. A cheat sheet, a configuration change or a short training, with an owner named for each.

Say it like this

Group the attached closed tickets by cause, not by the words used. Return a table: cause, count, total hours, two quoted examples.

Then for the top ten, propose one fix each: cheat sheet, configuration change or training. Name an owner. Quote a ticket for every claim.