Analytics
Read your ticket analytics and turn support numbers into decisions.
Guide to Ticket Analytics
Analytics answers the questions you cannot answer by scrolling through Discord: is your team getting faster, which category eats the most time, and when do you actually need staff online.
The full Analytics page is a premium feature. Free servers see a preview with an upgrade card — and a last 7 days activity and staff chart directly on the dashboard home page.
Open the Analytics
Open your server dashboard.
Click Analytics → Overview in the side navigation.
The Quick Stats
Four cards at the top summarise the current period, each with a trend compared to the previous one.
| Card | What it tells you |
|---|---|
| Total Tickets | Your support volume |
| Avg Response Time | How long a member waits for the first answer |
| Avg Resolution | How long a ticket lives from creation to close |
| Returning Users | How many members come back with a new ticket |
Avg Response Time is the number your members feel. Avg Resolution is the number your team feels. They are different problems and need different fixes.
The Charts
Most charts have their own time range selector: 7 days, 14 days, 30 days, quarters and yearly.
| Chart | What it shows | Use it for |
|---|---|---|
| Ticket Activity | Opened and closed tickets per day | Spotting backlogs — closed should track opened |
| Category Distribution | Tickets per category, with your top category | Deciding what to document or fix at the source |
| Staff Performance | Tickets handled per staff member | Recognising your workhorses and spotting overload |
| Response Time | First response and average of all messages | Measuring the effect of a schedule change |
| Messages per Ticket | Average messages exchanged per ticket | High values often mean your steps ask too little |
| Returning Support Users | New vs returning support users | A high returning rate means problems are not really solved |
| Ticket Priority Distribution | How priorities are spread | Checking whether "Urgent" still means urgent |
| Ticket Resolution Time | Average time from creation to close | Watching the long-term trend |
| Ticket Creation Patterns | Hourly and daily distribution with peak hour and peak day | Planning when staff should be online |
If a chart says Not enough data — Come back later…, there simply are not enough closed tickets in that range yet. Nothing is broken.
Reading the Numbers
Opened vs closed drift apart
Your Ticket Activity chart shows more opened than closed tickets several days in a row. That is a backlog forming.
What helps: more staff in the peak hours from Creation Patterns, a lower global open limit as an emergency brake, or auto-close scheduling for tickets that are actually done.
One category dominates
The Category Distribution chart is 60 % one topic.
What helps: that topic belongs in an FAQ, a pinned message or a step option that closes the ticket with a link to the answer.
Messages per ticket keep climbing
Every ticket needs more and more back and forth.
What helps: ticket steps. Ask for the version, the platform or the verified account before the conversation starts.
Returning rate is high
The same members keep coming back.
What helps: read a few transcripts of returning members. Either the answers are not sticking, or the same underlying bug keeps biting.
Everything is "Urgent"
Priority distribution shows almost only high and urgent tickets.
What helps: priority set by members is a wish, not a fact. Let LunAI classify tickets, and correct it with /ticket priority.
Ticket History
Next to the charts, Tickets → Ticket History lists the individual tickets behind the numbers, with a detail page per ticket.
Analytics tells you what changed. The history and the transcripts tell you why.
Next Steps
- Ticket Transcripts (The story behind a data point)
- LunAI (Automatic prioritisation)
- Ticket Categories (Better categories, better analytics)
How is this guide?