Volume, average hours, SLA compliance, and breach counts per priority level across the full ticket dataset
Volume, average hours, SLA compliance, and breach counts per priority level across the full ticket dataset
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
Volume, average hours, SLA compliance, and breach counts per priority level across the full ticket dataset
EVALUATE
SUMMARIZE(
BI_Autotask_Tickets,
BI_Autotask_Tickets[priority_label],
"Tickets", COUNTROWS(BI_Autotask_Tickets),
"AvgHours", AVERAGE(BI_Autotask_Tickets[worked_hours]),
"FRMet", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[first_response_met]+0=1),
"ResMet", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[resolution_met]+0=1),
"Breaches", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[resolved_due_age_days]>0)
)How 67,521 tickets split across five priority categories, with share percentage and average hours per ticket
EVALUATE
SUMMARIZE(
BI_Autotask_Tickets,
BI_Autotask_Tickets[priority_label],
"Tickets", COUNTROWS(BI_Autotask_Tickets),
"AvgHours", AVERAGE(BI_Autotask_Tickets[worked_hours])
)
ORDER BY [Tickets] DESCEvery metric side by side: volume, hours, first response rate, resolution rate, and SLA breaches
| Priority | Count | % of Total | Avg Resolution (hrs) |
|---|---|---|---|
| P4 - Laag | 30,415 | 45.0% | 16.3 |
| Service/Change req. | 15,584 | 23.1% | 23.8 |
| P3 - Medium | 14,715 | 21.8% | 21.6 |
| P1 - Kritisch | 5,019 | 7.4% | 2.1 |
| P2 - Hoog | 1,788 | 2.6% | 32.0 |
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[priority_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'), "AvgResolutionHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]))First response and resolution SLA rates visualized per priority level
EVALUATE
SUMMARIZE(
BI_Autotask_Tickets,
BI_Autotask_Tickets[priority_label],
"Tickets", COUNTROWS(BI_Autotask_Tickets),
"FRMet", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[first_response_met]+0=1),
"ResMet", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[resolution_met]+0=1)
)
ORDER BY [Tickets] DESCWhere the 360 total SLA breaches concentrate across priority levels
EVALUATE
SUMMARIZE(
BI_Autotask_Tickets,
BI_Autotask_Tickets[priority_label],
"Breaches", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[resolved_due_age_days]>0)
)
ORDER BY [Breaches] DESCNearly half of all tickets (45%) land in P4 - Laag. That is expected for an MSP. What stands out is that P4 also generates 73.6% of all SLA breaches: 265 out of 360. Low-priority tickets are treated as non-urgent, and rightfully so, but the sheer volume means that even a small percentage of missed deadlines turns into the bulk of your breach count. If your SLA reporting feeds into client QBRs, this one category is responsible for the numbers that look worst on paper.
P2 - Hoog tickets consume the most time per ticket at 0.827 hours on average, but the first response rate sits at just 35.7%. For high-priority issues, that gap matters. A client with a P2 ticket expects a fast acknowledgment. Failing to respond within the SLA window on more than six out of ten high-priority tickets signals either understaffing during peak hours or a routing problem that delays the initial pickup.
P3 - Normaal (Monitoring) tickets have a similarly low first response rate at 34.4%, but a much lower average time investment at 0.245 hours. These are likely auto-generated alerts from RMM tools. The low first response rate probably reflects that monitoring tickets are triaged in batches rather than individually. The resolution rate of 61.3% is acceptable, but the 68 breaches suggest some alerts are sitting in queue longer than the SLA allows before someone closes them.
The bright spot is P3 - Normaal with a 92.3% resolution rate and only 3 breaches across 5,019 tickets. This category is being handled well. The 0.070-hour average confirms these are quick-turnaround tickets, likely password resets, access requests, and other lightweight tasks that get closed fast.
Service and change requests (23.1% of volume) sit in the middle of every metric. Their 56.5% first response rate and 57.4% resolution rate are not alarming on their own, but they represent the second-largest category. Improving SLA compliance here by even 5 percentage points would move the portfolio-wide average noticeably.
5 priorities based on the findings above
265 breaches from low-priority tickets means either the SLA window is too tight for the volume, or tickets are queuing for too long before assignment. Pull the average time-to-first-response for P4 tickets and compare it against the SLA target. If the target is unrealistic for the volume you handle, adjust it. If the target is reasonable but being missed, look at dispatch rules and auto-assignment.
A 35.7% first response rate on high-priority tickets is the most visible SLA gap in this dataset. Check whether P2 tickets are routed to a dedicated queue or mixed with general triage. 1,788 tickets at 0.827 hours each means these are real incidents that need immediate attention. A dedicated escalation path or an auto-response acknowledgment could close the gap.
P3 - Normaal (Monitoring) tickets have 68 breaches despite averaging only 0.245 hours of work. The tickets themselves are handled quickly once picked up. The problem is the gap between ticket creation and first touch. If RMM alerts are auto-creating tickets, consider auto-assigning or auto-acknowledging them to stop the SLA clock while the technician reviews the batch.
Service and change requests make up 23.1% of all tickets with middling SLA performance (56.5% FR, 57.4% Res). These are planned tasks, not emergencies. If the SLA targets are copied from incident priorities, they may not reflect the actual expected turnaround for service requests. Aligning the SLA to the work type could reduce breaches and improve the accuracy of your compliance reporting.
With a 92.3% resolution rate and only 3 breaches, this category proves the team can hit targets when the work is straightforward and the routing is clear. Study what makes this queue work: auto-assignment rules, ticket templates, expected effort per ticket. Apply those patterns to the categories that are underperforming.
Every ticket in Autotask PSA has a priority level assigned at creation or during triage. Proxuma Power BI syncs this data through the Autotask connector. The priority_label field maps directly to the categories you see in this report: P2 - Hoog, P3 - Normaal, P3 - Normaal (Monitoring), P4 - Laag, and Service/Change requests.
The first_response_met field in the Autotask data model is a boolean (stored as integer). A value of 1 means the first response was sent within the SLA window defined for that ticket's priority and service level agreement. The percentage shown is the count of tickets where first_response_met equals 1, divided by the total ticket count for that priority.
A breach is counted when the resolved_due_age_days field is greater than zero. This means the ticket was resolved after the SLA deadline had passed. It measures resolution breaches specifically, not first response breaches. A ticket can meet the first response SLA but still breach the resolution SLA if it takes too long to close.
These are distinct priority labels in Autotask. Monitoring tickets are typically auto-generated by RMM tools when an alert fires. They follow different workflows and SLA expectations than manually created P3 tickets. Keeping them separate gives you a clearer picture of automated versus human-initiated workload at the same priority tier.
Yes. Connect Proxuma Power BI to your Autotask instance, add an AI tool (Claude, ChatGPT, or Copilot) via MCP, and ask the same question. The AI reads your schema, writes the DAX, runs it against your live data, and generates a report like this one. The whole process takes under fifteen minutes.
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