SLA compliance, queue efficiency, priority breakdown, and monthly closure trends across 67,521 Autotask tickets. Data anonymized by Proxuma MCP server.
SLA compliance, queue efficiency, priority breakdown, and monthly closure trends across 67,521 Autotask tickets. Data anonymized by Proxuma 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 delivery managers, operations leads, and MSP owners tracking service quality
How often: Weekly for operational adjustments, monthly for client reporting, quarterly for contract reviews
SLA compliance, queue efficiency, priority breakdown, and monthly closure trends across 67,521 Autotask tickets. Data anonymized by Proxuma MCP server.
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "AvgResolutionHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]), "AvgFirstResponseHours", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), "FirstResponseMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "ResolutionMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1), "FirstDayResolution", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_day_resolution]))| Priority | Tickets | Avg Hours | 1st Response Met | Resolution Met |
|---|---|---|---|---|
| P1 - Critical | 1,788 | 0.83 | 68.6% | 71.8% |
| P2 - High | 14,715 | 0.25 | 55.2% | 83.8% |
| P3 - Medium | 5,019 | 0.07 | 82.4% | 93.9% |
| P4 - Low | 30,415 | 0.62 | 83.5% | 90.6% |
| Service/Change Req. | 15,584 | 0.57 | 97.3% | 97.5% |
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[priority_name],
"Tickets", [Tickets - Count - Created],
"Avg Hours", [Tickets - Avg Hours Per Ticket],
"First Response Met", [Tickets - First Response Met %],
"Resolution Met", [Tickets - Resolution Met %]
)| Type | Tickets | Share | Avg Hours |
|---|---|---|---|
| Incident | 27,664 | 41.0% | 0.54 |
| Alert | 19,790 | 29.3% | 0.05 |
| Service Request | 12,653 | 18.7% | 0.78 |
| Change Request | 7,247 | 10.7% | 1.00 |
| Problem | 167 | 0.2% | 2.19 |
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[ticket_type_name],
"Tickets", [Tickets - Count - Created],
"Avg Hours", [Tickets - Avg Hours Per Ticket]
)| Queue | Tickets | Avg Hours |
|---|---|---|
| L1 Support | 31,378 | 0.44 |
| Monitoring | 17,082 | 0.11 |
| L2 Support | 7,889 | 0.90 |
| Merged Tickets | 4,999 | 0.00 |
| Projects | 2,316 | 2.43 |
| Customer Success | 804 | 0.66 |
| Internal IT | 793 | 0.20 |
| Onsite Support | 705 | 1.83 |
EVALUATE
TOPN(8,
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[queue_name],
"Tickets", [Tickets - Count - Created],
"Avg Hours", [Tickets - Avg Hours Per Ticket],
"Open", [Open Tickets (Dynamic)]
),
[Tickets], DESC
)| Month | Created | Completed | Closure Rate |
|---|---|---|---|
| Mar 2025 | 3,766 | 3,766 | 100.0% |
| Apr 2025 | 4,341 | 4,339 | 100.0% |
| May 2025 | 3,639 | 3,634 | 99.9% |
| Jun 2025 | 3,651 | 3,642 | 99.8% |
| Jul 2025 | 6,613 | 6,606 | 99.9% |
| Aug 2025 | 3,607 | 3,599 | 99.8% |
| Sep 2025 | 4,563 | 4,530 | 99.3% |
| Oct 2025 | 4,013 | 3,966 | 98.8% |
| Nov 2025 | 3,327 | 3,262 | 98.0% |
| Dec 2025 | 2,940 | 2,771 | 94.3% |
| Jan 2026 | 2,164 | 1,671 | 77.2% |
EVALUATE
TOPN(12,
SUMMARIZECOLUMNS(
'BI_Common_Dim_Date'[year_month],
FILTER('BI_Common_Dim_Date',
'BI_Common_Dim_Date'[date] >= DATE(2025, 3, 1) &&
'BI_Common_Dim_Date'[date] <= DATE(2026, 3, 17)),
"Tickets Created", [Tickets - Count - Created],
"Tickets Completed", [Tickets - Count - Completed],
"Closure Rate", [Tickets - Closure Rate %]
),
'BI_Common_Dim_Date'[year_month], ASC
)The overall closure rate of 98.8% looks healthy on the surface, but the monthly trend tells a different story. From March 2025 (100%) to January 2026 (77.2%), the closure rate has dropped steadily. That means tickets are piling up faster than the team can close them. The 844 currently open tickets are all overdue, which confirms a growing backlog.
The first response SLA at 80.1% is the most visible pain point. The target should be 90%+. Looking at the priority breakdown, P2 tickets are the biggest problem: only 55.2% meet the first response SLA, despite being the second-highest volume at 14,715 tickets. P1 tickets are at 68.6%, which is a concern for your most critical issues.
Service/Change requests perform well at 97.3% first response and 97.5% resolution. This suggests the team handles planned work effectively but struggles with unplanned interruptions.
On the queue side, L1 Support handles 46% of all tickets (31,378) with an average of 0.44 hours per ticket. L2 Support sees 7,889 tickets at 0.90 hours each, while the Projects queue takes 2.43 hours per ticket. Onsite support averages 1.83 hours, which is expected for on-premises work. The Monitoring queue processes 17,082 tickets at just 0.11 hours, indicating most alerts are resolved through automation or quick triage.
Closure rate dropped from 100% to 77.2% over 10 months. At this pace, the backlog will compound. Review staffing levels against current ticket volume. The spike in July 2025 (6,613 tickets) may have introduced a backlog that never recovered. Consider a dedicated sprint to clear the 844 overdue tickets.
P1 first response is at 68.6% and P2 at 55.2%. These are the highest-impact tickets. Set up automated first-response acknowledgements for P1/P2 to buy the team time. Review queue routing rules to make sure high-priority tickets reach the right engineer within the SLA window.
14,715 P2 tickets with only 55.2% first response SLA suggests a routing or prioritization issue. P2 tickets average just 0.25 hours to resolve, so the problem is not complexity; it is getting to them on time. Check if P2 tickets are sitting unassigned in queues.
The 97.3% first response rate on service and change requests proves the team can hit SLA targets consistently when the workflow is structured. Apply similar processes (templates, auto-assignment, clear escalation paths) to incident and alert handling.
It measures the percentage of tickets where the first response (any update from a technician) was logged within the SLA deadline configured in Autotask. A ticket where the SLA says "respond within 1 hour" and the first note is logged at 45 minutes counts as met. At 75 minutes, it is missed.
The "Overdue" measure counts tickets where the resolution due date has passed and the ticket is still open. If all 844 open tickets are overdue, it means none of the currently open tickets are within their SLA window. This is the backlog that needs to be cleared.
The overall average is pulled down by the Monitoring queue (17,082 tickets at 0.05 hours) and Alerts (19,790 at 0.05 hours). These are often auto-resolved or require minimal human intervention. The real per-ticket effort is higher when you look at Incidents (0.54 hours) or Change Requests (1.0 hours).
The Proxuma Power BI semantic model refreshes daily from Autotask. You can re-run this report at any time to get the latest numbers. Each report is generated fresh from the current data, not cached.
The Proxuma MCP server includes a two-pass anonymization engine. Pass 1 replaces known entities (client names, resource names, contacts) with deterministic aliases. Pass 2 runs Presidio NLP to catch anything Pass 1 missed. This ensures no real names leak into public-facing reports. You can restore real names locally using the mapping file.
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