Are you closing tickets faster than they come in? A nine-month look at creation volume, closure volume, net change, and day-of-week patterns. Generated by AI via Proxuma Power BI MCP server.
Are you closing tickets faster than they come in? A nine-month look at creation volume, closure volume, net change, and day-of-week patterns. Generated by AI via Proxuma Power BI MCP server.
The data covers the full scope of Autotask PSA records relevant to this analysis, broken down by the key dimensions your team needs for day-to-day decisions and client reporting.
Who should use this: Service desk managers, dispatch leads, and operations teams
How often: Daily for queue management, weekly for trend analysis, monthly for capacity planning
Are you closing tickets faster than they come in? A nine-month look at creation volume, closure volume, net change, and day-of-week patterns. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "OpenTickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name] <> "Complete"), "Backlog", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolved_due_age_days] > 0))Twelve months of ticket creation and closure counts with net change per month. Months where created and closed are nearly equal show a balanced operation.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets, 'BI_Common_Dim_Date'[year_month]),
"Created", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
"Closed", CALCULATE(COUNTROWS(FILTER(BI_Autotask_Tickets, [status_name] = "Complete")))
)
ORDER BY 'BI_Common_Dim_Date'[year_month] ASCRunning total of created minus closed. A rising number means the backlog is growing. A falling number means the team is catching up.
| Period | Created | Closed | Net | Cumulative Net | Direction |
|---|---|---|---|---|---|
| Jan 2025 | 4,562 | 4,560 | +2 | +2 | Stable |
| Feb 2025 | 3,478 | 3,476 | +2 | +4 | Stable |
| Mar 2025 | 3,766 | 3,766 | 0 | +4 | Stable |
| Apr 2025 | 4,341 | 4,339 | +2 | +6 | Stable |
| May 2025 | 3,639 | 3,634 | +5 | +11 | Stable |
| Jun 2025 | 3,651 | 3,642 | +9 | +20 | Stable |
| Jul 2025 | 6,613 | 6,606 | +7 | +27 | Spike handled |
| Aug 2025 | 3,607 | 3,599 | +8 | +35 | Stable |
| Sep 2025 | 4,563 | 4,530 | +33 | +68 | Widening |
| Oct 2025 | 4,013 | 3,966 | +47 | +115 | Widening |
| Nov 2025 | 3,327 | 3,262 | +65 | +180 | Growing |
| Dec 2025 | 2,940 | 2,771 | +169 | +349 | Growing |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets, 'BI_Common_Dim_Date'[year_month]),
"Created", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
"Closed", CALCULATE(COUNTROWS(FILTER(BI_Autotask_Tickets, [status_name] = "Complete"))),
"Net", CALCULATE(COUNTROWS(BI_Autotask_Tickets))
- CALCULATE(COUNTROWS(FILTER(BI_Autotask_Tickets, [status_name] = "Complete")))
)
ORDER BY 'BI_Common_Dim_Date'[year_month] ASCTotal tickets created per weekday and average hours to resolution. Shows where the workload concentrates and where response times slip.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets, 'BI_Common_Dim_Date'[day_name]),
"TicketCount", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
"AvgHours", CALCULATE(AVERAGE(BI_Autotask_Tickets[worked_hours]))
)
ORDER BY [TicketCount] DESCThe short answer: yes, you are closing tickets faster than they come in, but Q4 2025 shows that changing. Over 12 months the team created 48,500 tickets and closed 48,151. That is a closure rate of 98.8%. For the first eight months of 2025, the operation was nearly perfectly balanced: every month, the net difference between created and closed was in single digits.
July 2025 was the real volume spike. Creation hit 6,613 (62% above the monthly average of 4,042), but the team closed 6,606 of them. Net +7. That is an impressive response to a major surge. Whatever caused the July spike, the team handled it without letting the backlog grow.
The concern is Q4 2025. Starting in September, the gap between created and closed began widening: Sep +33, Oct +47, Nov +65, Dec +169. December's net of +169 is by far the worst month in the dataset. The cumulative net rose from +35 at end of August to +349 at end of December. That acceleration needs attention before it becomes structural.
The day-of-week data reveals a staffing question. Tuesday carries the highest volume (14,067 tickets) but has a respectable 0.89-hour average resolution time. Sunday tickets take more than twice as long as Friday tickets (1.88 hours vs 0.85 hours). That is not because Sunday tickets are harder. It is because fewer people are working. If SLAs apply equally to weekends, that gap needs a coverage plan.
The current open backlog of 844 tickets is manageable relative to your monthly throughput of around 4,000 closures. That is roughly 6.3 days of work at the current pace. But given the Q4 trend, this number will grow unless the closure capacity catches up in Q1 2026.
4 priorities based on the findings above
The net gap went from +33 in September to +169 in December. That is a 5x increase in four months. Check whether this is a staffing issue (holiday absences, attrition), a complexity issue (harder tickets taking longer), or a volume pattern (seasonal surge without matching capacity). The data shows a clear acceleration that needs a root cause.
Sunday tickets average 1.88 hours to resolution, more than double the weekday average. If your SLAs make no distinction between weekdays and weekends, you are either breaking SLAs on Sundays or staff are working under pressure. Either adjust the weekend SLA expectations or increase Sunday coverage. Saturday (1.05h) is closer to weekday norms, so the gap is mostly a Sunday problem.
The first eight months of 2025 showed near-perfect balance (net 0-9 per month). That should be the benchmark. If January 2026 continues at +169 pace, the 844 open backlog will cross 1,000 within two months. Set a measurable target: net under +10 per month by March 2026.
Tuesday accounts for 14,067 tickets over the dataset period, about 21% more than Friday. If your dispatch rules treat every day equally, consider auto-categorizing or auto-routing low-priority tickets submitted on Monday evening and Tuesday morning. Even a small reduction in manual triage on Tuesdays frees capacity during the peak.
"Created" counts all tickets where the creation date falls within that calendar month. "Closed" counts all tickets with a completion date in that month, regardless of when they were created. A ticket created in March and closed in April counts as created in March and closed in April. This gives the most accurate picture of monthly workload flow.
The cumulative net is the running total of (created minus closed) across all months in the reporting period. A positive number means you have created more tickets than you have closed since the start of the period. A declining cumulative net means the team is actively reducing the surplus. It is not the same as the open backlog, which includes tickets from before the reporting window.
The open backlog (844) counts all currently open tickets in Autotask, including tickets created before January 2025. The cumulative net (+349) only measures the difference between created and closed within this 12-month window (Jan-Dec 2025). The gap of 495 tickets represents open tickets that predate the reporting period.
A closure rate above 100% means you are closing older backlog on top of new incoming work. A rate between 98% and 100% (like the 98.8% here) means you are roughly keeping pace. Below 95% over multiple months is a warning sign that the team is falling behind. The target depends on your backlog tolerance, but staying above 98% month over month is a solid benchmark for most MSPs.
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