First-response rates, resolution compliance, and first-hour fix percentages across all priorities and queues. Generated by AI via Proxuma Power BI MCP server.
First-response rates, resolution compliance, and first-hour fix percentages across all priorities and queues. 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
First-response rates, resolution compliance, and first-hour fix percentages across all priorities and queues. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "FirstDayRes", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_day_resolution]), "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), "AvgFirstRespHrs", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), "AvgResolutionHrs", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]))First-response and resolution compliance broken down by ticket priority
| Resource | Tickets | Avg FR (h) | FR Met % | Res Met % |
|---|---|---|---|---|
| Mr. David Cooper DDS | 21,438 | 2.67 | 42.9% | 78.4% |
| Tracy Fitzpatrick | 3,600 | 4.02 | 48.4% | 52.9% |
| Gregory Horn | 3,240 | 3.25 | 68.5% | 65.6% |
| Brandon Bishop | 2,641 | 5.04 | 57.5% | 62.9% |
| Daniel Daniels | 2,444 | 3.50 | 79.7% | 73.1% |
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Tickets'[primary_resource_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'), "AvgFirstResponseHrs", 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)), [TicketCount], DESC)Which queues meet their SLA targets and which consistently miss them
| Queue | Tickets | Avg Res (h) | First Response | Resolution SLA |
|---|---|---|---|---|
| L1 Support | 31,378 | 8.3 | 48.7% | 59.2% |
| Service Desk | 17,082 | 13.7 | 68.4% | 74.8% |
| L2 Support | 7,889 | 16.7 | 61.2% | 72.9% |
| Merged Tickets | 4,999 | 7.6 | 44.3% | 65.6% |
| Projects | 2,316 | 83.9 | 32.1% | 39.4% |
| Customer Success | 804 | 106.8 | 28.7% | 35.1% |
| Internal IT | 793 | 79.2 | 34.2% | 39.8% |
| Onsite Support | 705 | 45.6 | 41.8% | 45.7% |
| Consulting | 546 | 130.0 | 22.4% | 31.3% |
| Administration | 327 | 106.6 | 36.1% | 42.2% |
EVALUATE
TOPN(10,
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets,
BI_Autotask_Tickets[queue_name]),
"TicketCount", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])),
"AvgResHours", CALCULATE(
AVERAGE(BI_Autotask_Tickets[resolution_duration_hours])),
"FirstResponsePct", DIVIDE(
CALCULATE(SUM(BI_Autotask_Tickets[first_response_met])),
CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id]))),
"ResolutionMetPct", DIVIDE(
CALCULATE(SUM(BI_Autotask_Tickets[resolution_met])),
CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])))
),
[TicketCount], DESC
)| Client | Tickets | First Response | Resolution SLA | Avg Res (h) | Status |
|---|---|---|---|---|---|
| Client A | 6,381 | 28.8% | 50.4% | 32.4 | |
| Client B | 5,458 | 70.3% | 66.7% | 18.1 | |
| Client C | 5,290 | 63.5% | 64.7% | 9.9 | |
| Client D | 2,775 | 39.6% | 69.2% | 19.8 | |
| Client E | 2,376 | 73.6% | 72.5% | 14.3 | |
| Client F | 2,364 | 90.2% | 92.0% | 1.0 | |
| Client G | 2,180 | 31.7% | 52.1% | 11.6 | |
| Client H | 1,803 | 30.7% | 47.3% | 16.3 | |
| Client I | 1,758 | 48.9% | 67.5% | 24.3 | |
| Client K | 1,684 | 22.3% | 27.9% | 2.9 |
Month-over-month first-response and resolution SLA compliance to show direction of travel
| Month | Tickets | First Response | Resolution SLA | Avg Res (h) | Direction |
|---|---|---|---|---|---|
| Sep 2025 | 11,284 | 50.1% | 61.8% | 18.9 | |
| Oct 2025 | 11,742 | 51.3% | 62.4% | 18.4 | |
| Nov 2025 | 12,108 | 49.7% | 60.2% | 19.7 | |
| Dec 2025 | 10,487 | 53.8% | 65.1% | 17.1 | |
| Jan 2026 | 11,203 | 55.2% | 66.8% | 16.8 | |
| Feb 2026 | 10,697 | 57.4% | 68.2% | 16.2 |
Tickets that missed their resolution SLA, grouped by how far past the deadline they were resolved
| Overdue Bucket | Tickets | % of Breaches | Avg Days Over | Severity |
|---|---|---|---|---|
| 0–1 day overdue | 8,742 | 35.4% | 0.4 | |
| 1–3 days overdue | 7,218 | 29.2% | 1.8 | |
| 3–7 days overdue | 4,891 | 19.8% | 4.7 | |
| 7–14 days overdue | 2,347 | 9.5% | 9.8 | |
| 14+ days overdue | 1,502 | 6.1% | 23.4 |
The headline numbers are blunt: 52.9% first-response SLA and 63.5% resolution SLA. Both fall well below the common MSP targets of 80% and 85% respectively. Nearly half of all tickets miss the first-response window.
The priority breakdown reveals an important pattern. P2 (High) tickets achieve 78.3% resolution compliance with a 2.1-hour average. These get immediate attention and it shows. But P1 (Critical) tickets sit at just 41.2% resolution SLA with 32-hour averages. Critical tickets are either miscategorized or getting stuck in escalation chains.
The queue data tells a similar story. L1 Support handles 46.5% of all tickets but its first-response rate is only 48.7%. The Service Desk, with tighter dispatch rules, achieves 68.4%. Below those, Consulting (130 hours), Customer Success (107 hours), and Administration (107 hours) have resolution times measured in days.
The monthly trend is encouraging. SLA compliance has improved from 61.8% in September to 68.2% in February, a 6.4 percentage point gain over six months. The question is whether this trend continues as ticket volumes fluctuate.
The breach analysis shows that 35.4% of SLA misses are near-misses (under 1 day overdue). These are the easiest wins. Better dispatch rules or auto-acknowledgment could convert many of these near-misses into SLA hits.
8 priorities based on the findings above
A 32-hour average resolution for critical tickets with only 41.2% SLA compliance is a structural problem. Pull the last 50 P1 tickets and trace their lifecycle: who picked them up, when they escalated, where they sat idle.
L1 handles 31,378 tickets at 48.7% first-response SLA. The Service Desk achieves 68.4% with better dispatch. Set up auto-acknowledgment on ticket creation or configure round-robin assignment so tickets do not sit unassigned.
35.4% of all SLA breaches miss by less than one day. These are tickets that almost made it. Faster first-response, quicker L1-to-L2 handoff, or slightly wider SLA windows for specific ticket types would recover thousands of these.
Consulting, Customer Success, Administration, and Projects all average 80+ hours. These are not break-fix tickets. Applying the same SLA targets creates noise. Define realistic windows (days or weeks) so compliance data is meaningful.
Client K resolves tickets in 2.9 hours but only meets SLA 27.9% of the time. The SLA window is almost certainly misconfigured for their ticket types. This is a quick fix in Autotask that would remove a permanent red flag from your reporting.
At 28.8% first-response SLA across 6,381 tickets, Client A is the single biggest drag on your portfolio SLA. A dedicated dispatcher or auto-assignment rule for this client would have an outsized impact on the overall numbers.
SLA compliance has improved from 61.8% to 68.2% over six months. If this trajectory holds, you will cross 70% by Q2 2026. Track which process changes drove the improvement and double down on them.
The current 17.2% first-hour fix rate means roughly 1 in 6 tickets resolves within an hour. Creating runbooks for the top 20 ticket categories would push that toward 25%, eliminating thousands of hours of open ticket time per year.
First Response Met tracks whether a technician acknowledged or responded to the ticket within the SLA-defined time window for that ticket's priority level. A value of 1 means the SLA was met; 0 means it was missed. This is tracked automatically by Autotask based on the first time entry or note added to the ticket.
Resolution Met tracks whether the ticket was completed within the SLA-defined resolution window. This is measured from ticket creation to ticket completion. If the ticket is closed before the SLA deadline, the value is 1. If it is closed after the deadline or remains open past it, the value is 0.
P1 tickets are typically complex, multi-step issues that require escalation, vendor involvement, or infrastructure changes. While they get immediate attention, the actual resolution takes longer because the problems themselves are harder. If P1 resolution times are unreasonably high, the root cause is usually escalation bottlenecks rather than lack of urgency.
A near-miss is a ticket that missed its SLA deadline by less than one day. These tickets were almost compliant. They represent the easiest SLA improvements because a small process change (faster dispatch, auto-acknowledgment, slightly wider SLA windows) would convert them from misses to hits.
The Service Desk typically has tighter dispatch rules, auto-assignment, and structured triage processes. L1 Support often receives tickets into a shared queue where they wait for a technician to pick them up. That queue wait time is what causes most first-response SLA misses.
Yes. Add a date filter on BI_Autotask_Tickets[create_date] or a company filter on BI_Autotask_Tickets[company_name] to any of the DAX queries. This lets you compare SLA performance month-over-month or see which clients are driving the numbers down.
Yes. Connect Proxuma Power BI to your Autotask PSA, add an AI tool via MCP, and ask the same question. The AI writes the DAX queries, runs them against your real data, and produces a report like this in under fifteen minutes.
Connect Proxuma Power BI to your PSA, RMM, and M365 environment, use an MCP-compatible AI to ask questions, and generate custom reports - in minutes, not days.
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