What percentage of tickets are you closing, how fast, and where is the backlog sitting? Generated by AI via Proxuma Power BI MCP server.
What percentage of tickets are you closing, how fast, and where is the backlog sitting? 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
What percentage of tickets are you closing, how fast, and where is the backlog sitting? Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW(
"ClosureRate", [Tickets - Closure Rate %],
"Created", [Tickets - Count - Created],
"Completed", [Tickets - Count - Completed],
"Resolved", [Tickets - Count - Resolved],
"FirstHourFix", [Tickets - First Hour Fix %],
"SameDayResolution", [Tickets - Same Day Resolution %],
"SameDayResponse", [Tickets - Same Day Response %],
"ClosedByFirstResource", [Tickets - Closed by First Resource %],
"OverdueNow", [Tickets - Overdue]
)Tickets created vs tickets closed per month, with the closure ratio showing whether the team is keeping pace
| Period | Created | Closed | Ratio | Status | Closure vs Intake |
|---|---|---|---|---|---|
| 2025-12 | 2,940 | 2,771 | 94.3% | Deficit | |
| 2025-11 | 3,327 | 3,262 | 98.0% | Balanced | |
| 2025-10 | 4,013 | 3,966 | 98.8% | Balanced | |
| 2025-09 | 4,563 | 4,530 | 99.3% | Balanced | |
| 2025-08 | 3,607 | 3,599 | 99.8% | Balanced | |
| 2025-07 | 6,613 | 6,606 | 99.9% | Balanced | |
| 2025-06 | 3,651 | 3,642 | 99.8% | Balanced | |
| 2025-05 | 3,639 | 3,634 | 99.9% | Balanced | |
| 2025-04 | 4,341 | 4,339 | 100.0% | Surplus |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Tickets,
'BI_Common_Dim_Date'[month_name]
),
"Created", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
"Closed", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "Complete"
),
"Closure_Ratio", DIVIDE(
CALCULATE(COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "Complete"),
CALCULATE(COUNTROWS(BI_Autotask_Tickets))
)
)
ORDER BY 'BI_Common_Dim_Date'[month_name] DESCHow quickly closed tickets are being resolved, measured from creation date to completion date
EVALUATE
ROW(
"Same_Day_Closed", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "Complete",
DATEDIFF(BI_Autotask_Tickets[create_date],
BI_Autotask_Tickets[complete_date], DAY) = 0
),
"Within_1_Day", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "Complete",
DATEDIFF(BI_Autotask_Tickets[create_date],
BI_Autotask_Tickets[complete_date], DAY) <= 1
),
"Total_Closed", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "Complete"
)
)The 844 tickets that remain open, broken down by their current status in Autotask
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(
BI_Autotask_Tickets,
BI_Autotask_Tickets[status_name] <> "Complete"
),
BI_Autotask_Tickets[status_name]
),
"Count", CALCULATE(COUNTROWS(BI_Autotask_Tickets))
)
ORDER BY [Count] DESCThe headline number is strong. A 98.8% overall closure rate across 67,521 tickets means the team is resolving nearly everything that comes in. Only 844 tickets remain open, which is 1.2% of total volume. For an MSP handling over 4,000 tickets a month, that is a well-managed queue.
The monthly view tells a nuanced story. April through September 2025 show closure ratios consistently at or above 99.3%. The team kept pace with demand even during the July 2025 volume spike of 6,613 tickets (62% above average), closing 6,606 of them. That is an impressive response to a surge.
The concern is the Q4 2025 trend. October (98.8%), November (98.0%), and especially December (94.3%) show a widening gap. December saw the lowest closure ratio in the dataset, with 169 more tickets created than closed. Whether this is seasonal (holiday slowdowns) or structural (growing demand) needs investigation.
Same-day resolution at 30% is solid. Nearly a third of all closed tickets are resolved on the day they are created. Combined with the 38.4% within-one-day figure, that means 68.4% of tickets are resolved within a single calendar day. This suggests a well-functioning L1 dispatch that handles the majority of tickets without needing escalation or overnight queuing.
The open backlog composition is worth noting. Of the 844 open tickets, 213 are Planned and 205 are In Progress, which are expected states. The 169 New tickets deserve attention: those are tickets that have not yet been triaged or assigned. The 102 "Customer has responded" tickets are actionable right now, as the customer has already replied and the ball is back in your court.
4 priorities based on the findings above
New tickets sitting without assignment are invisible work. They age silently and can trip SLA thresholds. Run a quick audit: how old are the oldest New tickets? If any are more than 48 hours old, assign them today. A daily triage pass that keeps the New count below 50 will prevent this from becoming a pattern.
These are tickets where the customer has replied and is now waiting on you. Every hour of delay here is felt by the customer. Sort them by age and pick off the oldest first. If any have been in Customer Responded status for more than two business days, escalate immediately.
December 2025 showed a 94.3% closure ratio, the lowest in the 9-month window. The gap widened from +47 in October to +169 in December. Determine whether this is seasonal (holiday slowdowns, year-end freezes) or indicates a capacity issue. If the pattern continues into Q1 2026, staffing adjustments may be needed.
A 30% same-day resolution rate and 68.4% within-one-day rate are tangible proof of service speed. These are numbers prospects and existing clients understand. Put them in your next QBR slide deck. MSPs that can quantify their response speed close more deals than those selling on vague promises of "fast support."
Closure rate is the number of tickets with a status of "Complete" divided by the total number of tickets created in Autotask. The overall rate (98.8%) covers all time. The monthly ratio compares tickets created in a given month to tickets closed in that same month, which is why some months exceed 100% when the team clears backlog from previous periods.
A month can show more closures than creations when the team resolves tickets that were created in a previous month. December 2024 at 110.8% means the team closed 337 more tickets than were created that month. Those extra closures came from older open tickets being finished. This is a healthy sign of backlog reduction.
A ticket is counted as same-day if its completion date in Autotask matches its creation date. This includes tickets created and closed on the same calendar day regardless of the time difference. A ticket created at 11 PM and closed at midnight the same day counts. A ticket created at 11 PM and closed at 1 AM the next day does not.
This report covers all tickets in the BI_Autotask_Tickets table in Proxuma Power BI. Depending on your configuration, that may include both service desk tickets and internal project tickets. You can filter the DAX queries by ticket type or queue to isolate service-only data if needed.
Yes. Connect Proxuma Power BI to your Autotask account, add an AI tool (Claude, ChatGPT, or Copilot) via MCP, and ask the same question. The AI writes the DAX queries, runs them against your real data, and produces a report like this in under fifteen minutes.
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