How long does it take to close a ticket? A data-driven breakdown by client, priority, ticket type, and queue.
How long does it take to close a ticket? A data-driven breakdown by client, priority, ticket type, and queue.
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 delivery managers, operations leads, and MSP owners tracking service quality
How often: Weekly for operational adjustments, monthly for client reporting, quarterly for contract reviews
How long does it take to close a ticket? A data-driven breakdown by client, priority, ticket type, and queue.
Key metrics across all tickets with resolution time data.
EVALUATE
SUMMARIZECOLUMNS(
"TotalResolved", COUNTROWS(
FILTER('BI_Autotask_Tickets',
NOT(ISBLANK('BI_Autotask_Tickets'[resolution_duration_hours])))),
"AvgResolutionHours",
AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]),
"SLAMetCount", COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[resolution_met] + 0 = 1)),
"FirstDayResolved", COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[first_day_resolution] + 0 = 1)),
"FirstHourFix", COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[first_hour_fix] + 0 = 1))
)Top 12 clients by ticket volume. Average MTTR and SLA compliance per client.
| Metric | Value |
|---|---|
| MTTR | 18.04h |
| Avg First Response | 6.25h |
| Resolution SLA Met | 63.5% |
EVALUATE ROW("AvgResHrs", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]), "AvgFRHrs", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), "ResMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1), "Total", COUNTROWS('BI_Autotask_Tickets'))How does ticket priority affect resolution speed?
| Priority | Resolved | SLA Met | SLA % | Avg MTTR |
|---|---|---|---|---|
| P4 - Low | 27,699 | 19,286 | 69.6% | 16.3h |
| P3 - Normal | 11,592 | 9,014 | 77.8% | 21.6h |
| Service/Change Req. | 11,378 | 8,944 | 78.6% | 23.8h |
| P2 - High | 4,949 | 4,635 | 93.7% | 2.1h |
| P1 - Critical | 1,467 | 1,013 | 69.1% | 32.0h |
EVALUATE
SUMMARIZE(
FILTER('BI_Autotask_Tickets',
NOT(ISBLANK('BI_Autotask_Tickets'[resolution_duration_hours]))),
'BI_Autotask_Tickets'[priority_name],
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"AvgHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]),
"SLAMet", COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[resolution_met] + 0 = 1))
)
ORDER BY [TotalTickets] DESCResolution speed varies significantly between incidents, alerts, service requests, and change requests.
| Ticket Type | Resolved | SLA Met | SLA % | Avg MTTR |
|---|---|---|---|---|
| Incident | 21,616 | 15,047 | 69.6% | 22.6h |
| Alert | 19,020 | 16,628 | 87.4% | 2.8h |
| Service Request | 9,609 | 6,141 | 63.9% | 27.5h |
| Change Request | 6,682 | 5,026 | 75.2% | 31.6h |
| Problem | 158 | 50 | 31.6% | 79.0h |
EVALUATE
SUMMARIZE(
FILTER('BI_Autotask_Tickets',
NOT(ISBLANK('BI_Autotask_Tickets'[resolution_duration_hours]))),
'BI_Autotask_Tickets'[ticket_type_name],
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"AvgHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]),
"SLAMet", COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[resolution_met] + 0 = 1))
)
ORDER BY [TotalTickets] DESCWhich queues resolve tickets fastest and which ones take the longest?
| Queue | Resolved | SLA Met | SLA % | Avg MTTR |
|---|---|---|---|---|
| L1 Support | 25,382 | 18,585 | 73.2% | 8.3h |
| Projects | 15,135 | 12,783 | 84.5% | 13.7h |
| L2 Support | 7,048 | 5,748 | 81.6% | 16.7h |
| Merged Tickets | 4,057 | 3,281 | 80.9% | 7.6h |
| Managed Services | 2,103 | 913 | 43.4% | 83.9h |
| Onsite Support | 704 | 322 | 45.7% | 45.6h |
| Customer Success | 692 | 282 | 40.8% | 106.8h |
| Internal IT | 684 | 316 | 46.2% | 79.2h |
EVALUATE
TOPN(8,
SUMMARIZE(
FILTER('BI_Autotask_Tickets',
NOT(ISBLANK('BI_Autotask_Tickets'[resolution_duration_hours]))),
'BI_Autotask_Tickets'[queue_name],
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"AvgHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]),
"SLAMet", COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[resolution_met] + 0 = 1))
),
[TotalTickets], DESC
)Average resolution time (hours) per month over the last 12 months.
EVALUATE
FILTER(
ADDCOLUMNS(
VALUES('BI_Common_Dim_Date'[year_month]),
"Tickets", CALCULATE(COUNTROWS(
FILTER('BI_Autotask_Tickets',
NOT(ISBLANK('BI_Autotask_Tickets'[resolution_duration_hours]))))),
"AvgHours", CALCULATE(
AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]))
),
[Tickets] > 0
)
ORDER BY 'BI_Common_Dim_Date'[year_month] DESCTickets resolved within 60 minutes of creation.
Out of 57,085 resolved tickets, 11,590 were closed within one hour of creation. That is a 20.3% first-hour fix rate.
First-hour fixes are heavily driven by automated alert tickets (which resolve in minutes). For incidents and service requests, the number is much lower. Still, this metric gives a useful baseline for measuring how much of your ticket volume gets handled quickly without escalation.
EVALUATE
SUMMARIZECOLUMNS(
"FirstHourFix", COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[first_hour_fix] + 0 = 1)),
"TotalResolved", COUNTROWS(
FILTER('BI_Autotask_Tickets',
NOT(ISBLANK('BI_Autotask_Tickets'[resolution_duration_hours]))))
)The overall MTTR sits at 18.0 hours, but that number masks a wide spread across ticket types. Alerts resolve in 2.8 hours on average (many auto-resolve within minutes). Incidents take 22.6 hours, service requests 27.5 hours, and change requests 31.6 hours. Problems are the slowest at 79 hours, though they account for only 158 tickets.
Priority levels tell an unexpected story. P2 (High) tickets resolve fastest at just 2.1 hours, while P1 (Critical) tickets take 32 hours. That gap likely reflects the complexity of critical incidents: they get immediate attention, but the underlying issue takes longer to fix. Low-priority tickets (P4) land at 16.3 hours, sitting in the middle of the range.
At the client level, Montgomery & Davis stands out with a 1.0-hour average MTTR and 96.4% SLA compliance. On the other end, Riverside Partners averages 32.4 hours per ticket with a 57.7% SLA rate. That kind of gap usually points to ticket complexity differences or specific contract arrangements rather than service quality issues alone.
The monthly trend shows a clear improvement. Resolution times dropped from 37.2 hours in May 2025 to 4.1 hours in January 2026. That coincides with the ramp-up of the ticket volume in the L1 Support queue, where automated handling and quick triage cut resolution times significantly.
Based on the data above, here are the steps worth taking.
P1 tickets average 32 hours to resolve, slower than every other priority tier except Problems. Review whether critical incidents are getting stuck in handoffs between L1 and L2, or whether the root cause investigation phase is the main delay.
Customer Success averages 107 hours to resolution and Managed Services averages 84 hours. Both queues sit well outside any reasonable SLA window. Check whether tickets in these queues are being actively worked or just aging in the backlog.
A single MTTR target across all ticket types hides the real picture. Alerts should resolve in under 4 hours, incidents under 16 hours, and service requests under 24 hours. Track each category separately so regressions in one type do not get masked by improvements in another.
MTTR has dropped from 37 hours to 4 hours over the past 8 months. That is a strong improvement. Keep monitoring the monthly trend so any uptick gets caught early, before it compounds into a backlog problem.
MTTR is the average of the resolution_duration_hours column in Autotask, which measures elapsed time from ticket creation (create_datetime) to ticket completion (complete_datetime). It includes all calendar hours, not just business hours.
Many alert tickets auto-resolve in seconds, pulling the median down to near zero. The average is higher because a smaller number of complex incidents and service requests take days or weeks to close, which skews the mean upward.
The resolution_met field is an integer (0 or 1). It flags whether a ticket was resolved within the SLA deadline defined in the Autotask service level agreement. A value of 1 means the SLA was met; 0 means it was missed.
Critical incidents often involve complex outages or multi-system failures. They get immediate attention but the actual fix takes longer. P2 tickets tend to be well-defined issues that can be resolved quickly once a technician picks them up.
This report shows all-time data. To filter by date range or client, run the DAX queries in Proxuma Power BI and add your filters directly to the query.
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