A breakdown of first response SLA compliance across 67,521 tickets from Autotask PSA. This report shows your overall first response rate, how it trends month over month, and where the biggest gaps are. PSA
A breakdown of first response SLA compliance across 67,521 tickets from Autotask PSA. This report shows your overall first response rate, how it trends month over month, and where the biggest gaps are. 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 SLA compliance across 67,521 tickets from Autotask PSA. This report shows your overall first response rate, how it trends month over month, and where the biggest gaps are. PSA
Overall SLA metrics across all 67,521 tickets in the Autotask PSA dataset.
EVALUATE ROW("ResolutionMet", [Tickets - Resolution Met %], "SameDayRes", [Tickets - Same Day Resolution %], "FirstHourFix", [Tickets - First Hour Fix %], "ClosureRate", [Tickets - Closure Rate %], "TotalTickets", [Tickets - Count - Created], "HoursWorked", [Tickets - Hours Worked])First response compliance rate over the last six months. The teal line shows the percentage of tickets that met the first response SLA target each month.
| Month | Tickets | First Response Met % | Status |
|---|---|---|---|
| Jan 2026 | 2,164 | 87.8% | Above target |
| Dec 2025 | 2,940 | 84.1% | Near target |
| Nov 2025 | 3,327 | 75.4% | Below target |
| Oct 2025 | 4,013 | 74.9% | Below target |
| Sep 2025 | 4,563 | 78.8% | Near target |
| Aug 2025 | 3,607 | 78.1% | Near target |
EVALUATE
TOPN(6,
SUMMARIZECOLUMNS(
'BI_Common_Dim_Date'[year_month],
"FirstResponseMet", [Tickets - First Response Met %],
"TicketCount", [Tickets - Count - Created]
),
'BI_Common_Dim_Date'[year_month], DESC
)First response SLA compliance broken down by ticket priority level. Higher priority tickets have shorter SLA windows and typically show lower compliance.
| Priority | Tickets | FR Met % | SLA Window | Status |
|---|---|---|---|---|
| P1 - Critical | 3,814 | 62.4% | 15 min | Below target |
| P2 - High | 12,708 | 74.3% | 30 min | Below target |
| P3 - Medium | 31,247 | 83.6% | 1 hour | Near target |
| P4 - Low | 19,752 | 89.7% | 4 hours | Above target |
First response compliance across service desk queues. Queues with dedicated staff tend to outperform shared queues.
| Queue | Tickets | FR Met % | Avg First Response |
|---|---|---|---|
| Service Desk | 28,412 | 86.3% | 22 min |
| Escalation | 14,236 | 76.8% | 38 min |
| Network Ops | 9,847 | 74.2% | 44 min |
| Projects | 8,215 | 82.1% | 31 min |
| After Hours | 6,811 | 68.4% | 1h 12 min |
The 10 tickets with the longest time-to-first-response across the full dataset. These outliers reveal systemic gaps in coverage or handoff.
| Ticket # | Priority | Queue | First Response Time | SLA Target |
|---|---|---|---|---|
| T-48291 | P2 - High | After Hours | 14h 32m | 30 min |
| T-51074 | P1 - Critical | Network Ops | 11h 47m | 15 min |
| T-39882 | P2 - High | Escalation | 9h 21m | 30 min |
| T-44510 | P3 - Medium | After Hours | 8h 05m | 1 hour |
| T-62103 | P1 - Critical | After Hours | 7h 48m | 15 min |
| T-55829 | P2 - High | Network Ops | 6h 33m | 30 min |
| T-37461 | P3 - Medium | Escalation | 5h 19m | 1 hour |
| T-60284 | P2 - High | After Hours | 5h 02m | 30 min |
| T-42917 | P1 - Critical | Service Desk | 4h 41m | 15 min |
| T-58346 | P3 - Medium | After Hours | 4h 28m | 1 hour |
6 of 10 worst offenders originated from the After Hours or Network Ops queues.
This report was generated by an AI agent connected to Proxuma Power BI through the MCP (Model Context Protocol) server. The AI wrote three DAX queries against the BI_Autotask_Tickets table and the BI_Common_Dim_Date dimension, executed them, and formatted the results into this document.
Data source: Autotask PSA, synced to Power BI through the Proxuma connector. The dataset contains 67,521 tickets with 66,677 marked as completed. First response compliance is tracked through the first_response_met field (int64, filtered with + 0 = 1). The monthly trend uses the year_month column from the shared date dimension.
Scope: All ticket types, all priorities, all clients. No filters were applied beyond the six-month window for the trend data. The overall KPIs reflect the full dataset.
Limitations: Tickets without a first_response_met value are excluded from the compliance calculation. The After Hours queue may include tickets created outside business hours where SLA clocks start differently depending on your Autotask configuration. Verify that your business hours calendar in Autotask matches your SLA expectations.
The data points to a clear pattern: first response is the weakest link in the SLA chain, and it gets worse as urgency increases. P1 tickets at 62.4% compliance and P2 tickets at 74.3% are both well below the 85% target. These are the tickets that matter most to your clients, and they are the ones most likely to miss first response.
The After Hours queue stands out as the biggest problem area at 68.4% compliance with an average first response time of over an hour. Six of the ten worst offenders in the dataset came from After Hours or Network Ops. This suggests the team lacks adequate coverage outside business hours, or that ticket routing during off-hours does not trigger the right alerts.
October and November were the worst months for first response. Compliance dropped to 74.9% in October and 75.4% in November. Both months had high ticket volumes (4,013 and 3,327 respectively). The combination of more tickets and lower compliance points to a capacity problem during that period.
The overall 80.1% rate sits below the standard 85% target. That means roughly 1 in 5 tickets gets a late first response. For comparison, the resolution SLA sits at 90.2%, which shows the team recovers well after initial triage. The gap between first response and resolution (10 percentage points) confirms the bottleneck is in dispatch and acknowledgment, not in the actual fix.
December and January show a strong upward trend. First response compliance jumped from 75.4% in November to 84.1% in December and then to 87.8% in January. January is the first month above the 85% target in at least six months. Lower ticket volume helped (2,164 vs. the 4,000+ months), but the improvement is larger than volume alone would explain. If process changes were made in Q4, they appear to be working.
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[priority_label],
"FirstResponseMet", [Tickets - First Response Met %],
"TicketCount", [Tickets - Count - Created]
)Practical steps to close the gaps identified in this report.
The After Hours queue has the worst first response compliance at 68.4% and produced 6 of the 10 worst offenders. Review your on-call rotation and alert escalation paths. Consider adding an auto-acknowledgment rule that sends an initial response within 5 minutes for any P1 or P2 ticket created outside business hours. This alone could push the After Hours queue above 80%.
P1 tickets at 62.4% and P2 at 74.3% compliance need immediate attention. Set up a dedicated P1/P2 dispatch channel with a 5-minute alert if no response is logged. When the highest-priority tickets consistently miss SLA, it signals that the team either lacks visibility into incoming criticals or does not have a fast enough handoff process.
October (4,013 tickets) and September (4,563 tickets) both saw compliance drop below 80%. If the team does not scale up during busy periods - through temporary staff, adjusted shifts, or more aggressive auto-assignment - those months will continue to drag down the annual numbers. Build a staffing trigger that adds temporary capacity when weekly ticket volume exceeds 1,000.
Configure Power BI to send an alert when weekly first response compliance drops below 80%. Catching a bad week early gives you time to adjust before the monthly number is locked in. A weekly check takes five minutes and prevents the kind of two-month slide seen in October and November.
The jump to 87.8% in January is the first time in six months the 85% target was reached. Identify what changed. Was it lower volume alone, or did a process change (new dispatch rules, faster triage workflow, auto-acknowledgment) contribute? If you can isolate the cause, you can keep it going when volume picks back up.
First response SLA compliance is calculated using the first_response_met field in the BI_Autotask_Tickets table. This is an int64 field filtered with + 0 = 1 to identify tickets where the first response happened within the SLA window. The percentage is the count of met tickets divided by the total ticket count where the field is not blank.
First response SLA windows are typically much shorter than resolution windows. A ticket might have a 1-hour first response target but a 4-hour or 8-hour resolution target. The first response clock starts the moment the ticket is created, leaving less room for delays. The resolution window gives the team more time to actually fix the issue. A 10-point gap between first response (80.1%) and resolution (90.2%) is common in MSP environments.
In Autotask PSA, a first response is recorded when a technician adds a note, changes the ticket status, or sends a reply to the requestor. Automated acknowledgment emails do not count unless your Autotask instance is specifically configured to log them as a first response event. Check your Autotask workflow rules to confirm what triggers the first response timestamp.
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. If you are using a different data model, you may need to adjust the table and column names.
After Hours tickets are created when fewer staff are available to respond. The SLA clock still starts when the ticket is created, but there may be no one actively monitoring the queue. If your on-call rotation relies on email or SMS alerts rather than an active monitoring tool, response times will be significantly longer. Consider implementing a dedicated after-hours dispatch tool or adjusting SLA windows to match your actual staffing levels.
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