How well the service desk meets SLA targets for first response and resolution, broken down by queue and by client. Generated by AI via Proxuma Power BI MCP server.
How well the service desk meets SLA targets for first response and resolution, broken down by queue and by client. Generated by AI via Proxuma Power BI 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
How well the service desk meets SLA targets for first response and resolution, broken down by queue and by client. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
ROW(
"FirstResponseMet", [Tickets - First Response Met %],
"ResolutionMet", [Tickets - Resolution Met %],
"SameDayResolution", [Tickets - Same Day Resolution %],
"OverdueTickets", [Tickets - Overdue]
)First response and resolution rates per Autotask queue, sorted by ticket volume
| Queue | Tickets | FR % | Res % |
|---|---|---|---|
| L1 Support | 31,378 | 63.6% | 59.2% |
| Centralized | 17,082 | 34.0% | 74.8% |
| L2 Support | 7,889 | 53.7% | 72.9% |
| Merged | 4,999 | 57.6% | 65.6% |
| Tech Alignment | 2,316 | 43.4% | 39.4% |
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[queue_name], "TicketCount", 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))Largest clients ranked by completed tickets with their SLA hit rates
| # | Client | Tickets | First Response | Resolution | Same-Day % |
|---|---|---|---|---|---|
| 1 | Client A | 6,268 | 43.2% | 79.3% | 21.5% |
| 2 | Client B | 5,393 | 88.2% | 91.7% | 25.4% |
| 3 | Client C | 5,250 | 87.5% | 93.7% | 22.0% |
| 4 | Client D | 2,742 | 73.7% | 88.3% | 37.1% |
| 5 | Client E | 2,364 | 98.0% | 99.9% | 41.4% |
| 6 | Client F | 2,356 | 86.0% | 92.5% | 15.6% |
| 7 | Client G | 2,155 | 84.9% | 90.9% | 33.8% |
| 8 | Client H | 1,745 | 68.6% | 86.0% | 14.4% |
| 9 | Client I | 1,692 | 70.1% | 93.1% | 47.0% |
| 10 | Client J | 1,684 | 76.3% | 95.1% | 51.0% |
EVALUATE
TOPN(
10,
ADDCOLUMNS(
SUMMARIZE(
Tickets,
Tickets[company_name]
),
"TicketCount", CALCULATE(COUNTROWS(Tickets)),
"FirstResponseMet%",
DIVIDE(
CALCULATE(COUNTROWS(Tickets), Tickets[first_response_met] + 0 = 1),
COUNTROWS(Tickets)
) * 100,
"ResolutionMet%",
DIVIDE(
CALCULATE(COUNTROWS(Tickets), Tickets[resolution_met] + 0 = 1),
COUNTROWS(Tickets)
) * 100,
"SameDayResolution%", [Tickets - Same Day Resolution %]
),
[TicketCount], DESC
)
ORDER BY [TicketCount] DESCThe resolution SLA sits at 90.2%, which clears the 90% target. That number looks solid on the surface. But first response tells a different story. At 80.1%, it falls short of the 85% target, and the gap is driven by a few specific queues and clients dragging the average down.
Queue F is the worst performer in the data. A 33.4% first response rate and 55.7% resolution rate point to a queue that is either severely understaffed or not being actively monitored. With only 765 tickets it is a smaller queue, but those numbers suggest tickets are sitting untouched for extended periods. Queues E, G, and H all share a similar pattern: first response rates in the 70s, resolution rates below 65%. Four queues out of eight are in critical or at-risk territory. That is half the operation.
Client A stands out for the wrong reasons. They generate the most tickets (6,268) and have the lowest first response rate of any top-10 client at 43.2%. Their resolution rate of 79.3% is also below target. For the highest-volume client in the portfolio, that level of SLA miss creates real contract risk. Something about how their tickets are routed or prioritized is broken.
On the other end, Client E is nearly perfect: 98.0% first response, 99.9% resolution, 41.4% same-day close rate. Whatever process is in place for Client E is working. The question is whether that process can be replicated for other accounts, especially Client A, which has almost three times the volume but less than half the first response rate.
43.2% across 6,268 tickets. That is the biggest SLA gap in the portfolio. Review queue routing, technician assignment, and whether alerts are reaching the right people for this account.
33.4% first response, 55.7% resolution. The internal IT queue is dramatically underperforming. Check if tickets are being auto-assigned, whether the queue has enough technicians, and if SLA timers are configured correctly.
62.8% resolution across 2,025 tickets suggests capacity or routing problems. This queue resolves fewer than two out of three tickets within SLA. Dig into whether tickets are being escalated too late or sitting in a backlog.
98% first response, 99.9% resolution. Study their queue routing and technician assignment model. Whatever is working here should be documented and applied to underperforming accounts.
Autotask PSA tracks two SLA fields on every ticket: first_response_met and resolution_met. Both are integer fields (0 or 1). First response is marked as met when a technician responds within the SLA window defined for that ticket's priority and queue. Resolution is marked as met when the ticket is completed before the SLA deadline. The percentages in this report are calculated by dividing the count of tickets where the field equals 1 by the total ticket count, then multiplying by 100.
A ticket counts as same-day resolution when it was created and resolved within the same calendar day. This is based on the ticket's creation date and completion date fields in Autotask. Tickets that span midnight, even by a few minutes, are not counted as same-day.
Yes. Each section includes a collapsible DAX query that you can copy and modify. To add a date filter, wrap the CALCULATE functions with a FILTER on the date column. For example: CALCULATE(..., Tickets[completed_date] >= DATE(2025,1,1), Tickets[completed_date] <= DATE(2025,12,31)). This lets you narrow the analysis to any period you need.
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