A breakdown of first response and resolution SLA compliance across 67,521 tickets from Autotask PSA. This report shows where your team hits the target, which priorities fall short, and which clients need attention. PSA
A breakdown of first response and resolution SLA compliance across 67,521 tickets from Autotask PSA. This report shows where your team hits the target, which priorities fall short, and which clients need attention. PSA
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
A breakdown of first response and resolution SLA compliance across 67,521 tickets from Autotask PSA. This report shows where your team hits the target, which priorities fall short, and which clients need attention. PSA
Overall SLA metrics across all 67,521 tickets in the Autotask PSA dataset.
First response and resolution SLA rates broken down by ticket priority. Color coding: green = 70%+, amber = 50-70%, red = below 50%.
| Priority | Tickets | FR Met | FR % | Res Met | Res % | Avg Res Hrs | Avg FR Hrs |
|---|---|---|---|---|---|---|---|
| P4 - Laag | 30,415 | 18,585 | 61.1% | 19,286 | 63.4% | 16.3 | 5.3 |
| Service/Change req. | 15,584 | 8,800 | 56.5% | 8,944 | 57.4% | 23.8 | 7.7 |
| P3 - Normaal | 14,715 | 5,065 | 34.4% | 9,014 | 61.3% | 21.6 | 8.9 |
| P2 - Hoog | 5,019 | 2,626 | 52.3% | 4,635 | 92.3% | 2.1 | 0.8 |
| P1 - Kritiek | 1,788 | 639 | 35.7% | 1,013 | 56.6% | 32.0 | 9.6 |
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[priority_name],
"TicketCount", COUNTROWS('BI_Autotask_Tickets'),
"FirstRespMet", 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),
"AvgResHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]),
"AvgFirstRespHours", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours])
)
ORDER BY [TicketCount] DESCFirst Response % (teal) and Resolution % (navy) side by side for each ticket type.
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[ticket_type],
"TicketCount", COUNTROWS('BI_Autotask_Tickets'),
"FirstRespMet", 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),
"AvgResHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours])
)
ORDER BY [TicketCount] DESCFirst response and resolution SLA compliance for the 10 highest-volume clients. Color coding: green = 85%+, amber = 60-85%, red = below 60%.
| Client | Tickets | FR Met | FR % | Res Met | Res % |
|---|---|---|---|---|---|
| Client A | 6,381 | 1,837 | 43.2% | 3,216 | 79.3% |
| Client B | 5,458 | 3,837 | 88.2% | 3,642 | 91.7% |
| Client C | 5,290 | 3,361 | 87.5% | 3,423 | 93.7% |
| Client D | 2,775 | 1,099 | 73.7% | 1,921 | 88.3% |
| Client E | 2,376 | 1,748 | 86.0% | 1,723 | 92.5% |
| Client F | 2,364 | 2,132 | 98.0% | 2,174 | 99.9% |
| Client G | 2,180 | 690 | 84.9% | 1,135 | 90.9% |
| Client H | 1,803 | 554 | 75.4% | 853 | 87.1% |
| Client I | 1,758 | 859 | 68.6% | 1,187 | 85.9% |
| Client J | 1,728 | 653 | 70.1% | 1,231 | 93.1% |
Average hours to resolve a ticket, broken down by priority level. Longer bars indicate priorities that take more time to close.
EVALUATE
ROW(
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"FirstResponseMetPct", [Tickets - First Response Met %],
"ResolutionMetPct", [Tickets - Resolution Met %],
"AvgHoursPerTicket", [Tickets - Avg Hours Per Ticket]
)Distribution of tickets by SLA agreement. Tickets without an SLA have a near-zero compliance rate (0.5%) because no targets are set.
Only 35.7% of P1 tickets meet the first response target and 56.6% meet the resolution target. These are the tickets that matter most to clients. With an average resolution time of 32 hours, critical issues take nearly twice as long as the next-slowest priority. This suggests a staffing or escalation gap for urgent incidents.
Across every priority level, the first response SLA rate is lower than the resolution rate. The overall gap is 10 percentage points (80.1% vs 90.2%). This pattern points to a bottleneck in initial triage and assignment rather than in the actual fix. Faster dispatch or auto-assignment rules could close this gap.
With 6,381 tickets, Client A is the highest-volume account but has the lowest first response rate at 43.2%. Resolution compliance sits at 79.3%, which is still below the 85% target. Given the ticket volume, even small percentage improvements here would move the overall numbers significantly.
P2 tickets hit 92.3% resolution compliance with an average of just 2.1 hours. This shows that when the team treats something as urgent, the turnaround is fast. The challenge is applying that same urgency to P1 tickets, where resolution takes 15x longer despite higher severity.
Concrete steps to improve SLA compliance across priorities and clients.
Create a separate dispatch queue for P1 tickets with automatic assignment to senior engineers. Set an internal target of 15-minute first response for critical issues. Track this weekly. The current 35.7% first response rate on P1 is a client-facing risk that could drive churn.
Tickets without an SLA agreement are effectively invisible to compliance tracking. Review which clients or ticket types are missing SLA assignments and apply the Standard SLA as a baseline. This alone will improve your ability to measure and manage performance.
Client A generates more tickets than any other account but has a 43.2% first response rate. Pull the time-to-assign data for their tickets over the last 90 days. Check if specific ticket types or times of day are driving the delays. Target: bring Client A above 65% within one quarter.
First response and resolution SLA compliance are calculated using the first_response_met and resolution_met fields in the BI_Autotask_Tickets table. These are int64 fields filtered with +0=1 to identify tickets that met their target. The percentage is the count of met tickets divided by the total count of tickets where the field is not blank.
First response SLA windows are typically shorter than resolution windows. A P2 ticket might have a 1-hour first response target but a 4-hour resolution target. Missing the initial response is easier because the clock starts immediately, while the resolution window gives the team more room. This is normal in MSP environments, but the gap should not exceed 15 points.
Tickets without an SLA agreement in Autotask have no defined response or resolution targets. They still track first_response_met and resolution_met fields, but with no target set, the compliance rate is near zero. These tickets are typically from clients without an active service agreement or from internal accounts.
Yes. Copy any query from the toggles above and paste it into DAX Studio or the Power BI Desktop performance analyzer. The queries reference standard Proxuma data model tables and measures that exist in every Proxuma Power BI deployment.
Weekly for the top-level KPIs (first response %, resolution %), monthly for the full priority and client breakdown. Set automated alerts in Power BI for any priority or client dropping below a 50% compliance threshold so problems surface between reviews.
Problem tickets represent root cause investigations, not individual incidents. They tend to be long-running, complex, and often do not have strict SLA targets. With only 167 tickets total and an average resolution of 79 hours, these are outliers that should be tracked separately from incident SLA performance.
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