This report provides a detailed breakdown of ticket priority distribution for managed service providers.
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
With 67,521 tickets and only 844 currently open, this environment has a strong closure rate. The open rate of 1.25% is a reasonable signal for a well-functioning service desk. The P1 share of 7.4% is also healthy — well below the 15% threshold where priority inflation tends to become a real problem.
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[priority_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'))The P4 Low bucket at 45% is the largest single group. This is common in MSP environments, where many tickets represent routine maintenance, minor user requests, or scheduled tasks that do not require urgent attention. The key follow-up question is whether those 556 open P4 tickets are intentionally parked or quietly forgotten.
P2 High is notably thin at just 2.6% of all tickets. This often happens when teams skip directly from P3 to P1 for escalations, bypassing the P2 bucket entirely. If that is the case, your P2 SLA targets may need review since they apply to a segment that rarely sees use.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
'BI_Autotask_Tickets',
'BI_Autotask_Tickets'[priority_name]
),
"ticket_count", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"open_count", CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[status_name] <> "Complete"
),
"closed_count", CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[status_name] = "Complete"
)
)
ORDER BY [ticket_count] DESC| Priority | Open Tickets | Total (All Time) | Open Rate | Risk Level |
|---|---|---|---|---|
| P1 Critical | 5 | 5,019 | 0.10% | Low |
| P2 High | 19 | 1,788 | 1.06% | Low |
| P3 Medium | 90 | 14,715 | 0.61% | Low |
| P4 Low | 556 | 30,415 | 1.83% | Monitor |
| Service/Change | 174 | 15,584 | 1.12% | Low |
Only 5 P1 Critical tickets are currently open. That is an extremely low open rate of 0.10%, which suggests your team resolves critical issues quickly. The 556 open P4 Low tickets warrant a closer look. At 1.83%, this is the highest open rate across all priority bands. Some may be intentionally deferred but check whether they have SLA commitments attached.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
'BI_Autotask_Tickets',
'BI_Autotask_Tickets'[priority_name]
),
"open_tickets", CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[status_name] <> "Complete"
),
"total_tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"open_rate", DIVIDE(
CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name] <> "Complete"),
CALCULATE(COUNTROWS('BI_Autotask_Tickets'))
)
)
ORDER BY [open_tickets] DESC| Client | Dominant Priority | Tickets | Profile |
|---|---|---|---|
| Rivers, Rogers and Mitchell | P3 Medium | 4,087 | High volume, medium load |
| Craig-Huynh | P4 Low | 3,208 | Routine maintenance heavy |
| Little Group | P4 Low | 3,161 | Routine maintenance heavy |
| Blanchard-Glenn | Service/Change | 2,131 | Change-request driven |
| Martin Group | P4 Low | 1,507 | Routine maintenance heavy |
| Wall PLC | P4 Low | 1,451 | Routine maintenance heavy |
Rivers, Rogers and Mitchell stands out as a P3-dominant client, which typically indicates a more reactive environment with a higher density of user-impacting issues. Craig-Huynh and Little Group are P4-heavy, which is typical for large clients with many routine tasks and maintenance activities. Blanchard-Glenn's Service/Change profile suggests strong project activity or frequent change requests.
EVALUATE
TOPN(
10,
ADDCOLUMNS(
SUMMARIZE(
'BI_Autotask_Tickets',
'BI_Autotask_Companies'[company_name],
'BI_Autotask_Tickets'[priority_name]
),
"ticket_count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))
),
[ticket_count], DESC
)
ORDER BY [ticket_count] DESCA critical ticket rate below 10% generally indicates your team is not overusing the top priority label. The very low P1 open rate (only 5 tickets) confirms that critical issues are being resolved promptly. This pattern supports strong client trust and SLA performance at the top end.
Low-priority tickets are easy to defer and easy to forget. With 556 open P4 tickets, there is a real risk that some of them are aging past acceptable thresholds without being noticed. Set a monthly review cadence specifically for tickets older than 30 days regardless of priority.
The gap between P2 (1,788 tickets) and P3 (14,715 tickets) is unusually large. This often signals that the team bypasses P2 for escalations, going directly from P3 to P1. If so, your P2 SLA configuration may have targets that are either too easy or completely untested in practice.
Service and change requests represent planned work as opposed to reactive support. At 23%, this is a good signal that a meaningful portion of your team's time goes toward proactive work. In a well-run MSP, this ratio ideally sits between 20% and 35%.
Most MSPs aim to keep P1 below 10–12% of total tickets. Above 15% typically indicates priority inflation, where the label is being used too broadly. Below 5% can mean teams are underclassifying genuinely urgent tickets to avoid SLA clock pressure. The sweet spot is 5–10%, paired with strict definitions of what qualifies as critical.
This is normal for MSPs managing large, stable environments. Many P4 tickets are recurring tasks, scheduled maintenance, minor user requests, or proactive work. If the volume feels too high, consider whether all of those tickets actually need to run through your ticketing system, or whether some should be converted to procedures or automation.
Start by checking whether your SLA targets are set for each priority level and whether they reflect the actual service expectations your clients have. Then filter this report by SLA breach status to see if a specific priority band is systematically underperforming. Often, P2 High is the most overlooked because it sits in between the extremes.
Yes. Proxuma Power BI lets you add a date filter to this query and compare distributions month over month. A shift from P4-heavy to P3-heavy over time can indicate an environment becoming less stable. Tracking this trend helps you have proactive conversations before clients raise concerns themselves.
Look at the ratio of P1 to P2 over time. If P1 keeps growing but P2 stays flat, engineers are likely escalating directly rather than using the intermediate tier. Also check whether certain technicians or queues have systematically higher P1 rates — that can reveal individual habits rather than genuine urgency patterns.
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