Tickets Performance: Support Team Performance

See how the support team is doing: new and resolved volume, first response and resolution times, SLA attainment, CSAT, a daily trend, breakdowns by status, channel and priority, and one table for human agents and AI agents alike.

The ticket list tells you what is open right now; the Performance page tells you how the team actually did. It rolls every ticket in the chosen period up into team totals, shows whether SLA deadlines were met, and breaks the same numbers down per handler — human agents and AI agents in one table — so a team lead can spot an overloaded colleague, a slowing response time, or a pile of tickets nobody owns, before customers feel it.

Step 1: Open the page and pick a period#

1. Go to Tickets → Performance (/tickets/performance). The page opens on the Last 7 days view by default.
2. Use the switch at the top right to change the period: Last 24 hours, Last 7 days, Last 30 days, or Custom — with Custom, pick a start and end date (up to 92 days). Every number on the page recalculates for that period.
3. If the page shows “No ticket activity in this period”, there is simply nothing to report yet — widen the period or check the ticket list first.

Step 2: Read the summary cards#

• New tickets: how many tickets were created in the period — the workload that came in.
• Resolved in period: how many tickets were resolved in the period. This is counted by resolution time, so it is not the same group of tickets as “New tickets” — a ticket created last month and resolved yesterday counts here, not there.
• Resolution rate: of the tickets created in the period, the share that is resolved or closed by now, in percent.
• Avg first response: how long customers waited, on average, for the first reply, in hours.
• Avg resolution time: how long a ticket took, on average, from creation to resolution, in hours.
• SLA attainment: of the new tickets that carry an SLA due date, the share handled in time, in percent. The card shows how many SLA-dated tickets the rate is based on — if none of the period’s tickets has an SLA date, the card says so instead of inventing a number.
• SLA breached: how many SLA-dated tickets missed their deadline — resolved late, or still open past the due time (the card notes how many are still open).
• CSAT: the average satisfaction score out of 5, from the surveys merchants answer after a ticket is resolved or closed.

Step 3: Follow the daily trend#

The trend chart draws two lines per day: new tickets (solid) and resolved tickets (dashed). When the new-ticket line stays above the resolved line for several days, the backlog is growing even if nobody feels busy yet; when resolved catches up, the team is clearing the queue.

Step 4: Read the breakdowns by status, channel and priority#

• By status: how the period’s new tickets spread across open, in progress, pending, resolved and closed — a tall “pending” bar usually means the team is waiting on customers or on another team.
• By channel: where tickets come in — email, phone, chat, web and so on. Tickets with no channel recorded appear as “Not specified”; a large share there means the intake channels are not tagging tickets, which is worth fixing at the source.
• By priority: the urgent and high share tells you how much of the load is firefighting versus routine work.

Step 5: Compare handlers — people and AI agents in one table#

1. The “Handler breakdown” table lists one row per handler, plus an Unassigned row: tickets, resolved, resolution rate, avg first response, avg resolution time, SLA attainment, and CSAT shown as “score / 5 (number of ratings)”. Figures are attributed to whoever currently handles each ticket.
2. AI agents appear in the same table with an “AI Agent” tag next to the name, so you can compare an AI colleague’s volume, speed and SLA record with the human team’s directly — no separate report to reconcile.
3. Click a column header to sort by it — the table starts sorted by tickets, most first. Sorting by avg first response, SLA attainment or CSAT quickly surfaces who needs coaching or a lighter queue.
4. Read CSAT together with its count in brackets: a 5.0 from 2 ratings is a good start, not proof; a 4.2 from 80 ratings is a solid, trustworthy score. A dash (—) in a time, SLA or CSAT column means there is no data to average yet for that handler in this period.
5. A tall Unassigned row is work to fix: open Tickets → List (/tickets/list), filter to unassigned tickets and hand them out.

Docklify features used in this article#

• Ticket Performance page with rolling and custom periods, SLA attainment, daily trend, breakdowns and the handler table — Tickets → Performance (/tickets/performance)
• Ticket list, including the unassigned view for handing out ownerless tickets — Tickets → List (/tickets/list)
• CSAT surveys and the dashboard CSAT card — Tickets → Dashboard (/tickets/dashboard)
• Assignment — handing tickets to people or AI agents — see the assignment topics in Tickets