How quickly your team responds to new tickets, where you are meeting the SLA, and where you are falling short. Generated by AI via Proxuma Power BI MCP server.
How quickly your team responds to new tickets, where you are meeting the SLA, and where you are falling short. 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 quickly your team responds to new tickets, where you are meeting the SLA, and where you are falling short. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("Total", COUNTROWS('BI_Autotask_Tickets'), "FRMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "AvgFRHrs", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]))Compliance rate per ticket priority, with donut charts showing the met/missed split for each level
| Priority | Tickets | FR Met | FR Missed | FR % |
|---|---|---|---|---|
| P4 - Laag | 30,415 | 18,585 | 11,830 | 61.1% |
| Service/Change req. | 15,584 | 8,800 | 6,784 | 56.5% |
| P3 - Normaal (Monitoring) | 14,715 | 5,065 | 9,650 | 34.4% |
| P3 - Normaal | 5,019 | 2,626 | 2,393 | 52.3% |
| P2 - Hoog | 1,788 | 639 | 1,149 | 35.7% |
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Tickets'[priority_name]),
"tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"fr_met", CALCULATE(
COUNTROWS(FILTER('BI_Autotask_Tickets',
[first_response_met] + 0 = 1)))
)Top 5 queues by ticket volume, ranked by first response compliance rate
| Queue | Tickets | FR Met | FR Missed | FR % |
|---|---|---|---|---|
| Servicedesk | 31,378 | 19,949 | 11,429 | 63.6% |
| Monitoring | 17,082 | 5,816 | 11,266 | 34.0% |
| L2 Support | 7,889 | 4,234 | 3,655 | 53.7% |
| Merged Tickets | 4,999 | 2,878 | 2,121 | 57.6% |
| Projects | 2,316 | 1,005 | 1,311 | 43.4% |
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Tickets'[queue_name]),
"tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"fr_met", CALCULATE(
COUNTROWS(FILTER('BI_Autotask_Tickets',
[first_response_met] + 0 = 1)))
)
ORDER BY [tickets] DESCTop 10 clients by ticket volume, ranked from lowest to highest first response compliance
| # | Client | Tickets | FR Met | FR Missed | FR % | Status |
|---|---|---|---|---|---|---|
| 1 | Rivers Rogers Mitchell | 6,381 | 1,837 | 4,544 | 28.8% | Critical |
| 2 | Holt Bradley Fowler | 994 | 305 | 689 | 30.7% | Critical |
| 3 | Martinez Contreras Rios | 1,803 | 554 | 1,249 | 30.7% | Critical |
| 4 | Price-Gomez | 2,180 | 690 | 1,490 | 31.7% | Critical |
| 5 | Nelson Taylor Hicks | 1,728 | 653 | 1,075 | 37.8% | At Risk |
| 6 | Hernandez Ltd | 2,775 | 1,099 | 1,676 | 39.6% | At Risk |
| 7 | Martin Group | 1,758 | 859 | 899 | 48.9% | Watch |
| 8 | Foster Inc | 5,290 | 3,361 | 1,929 | 63.5% | Good |
| 9 | Patterson Hood Perez | 5,458 | 3,837 | 1,621 | 70.3% | Good |
| 10 | Wall PLC | 2,376 | 1,748 | 628 | 73.6% | Excellent |
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Companies'[company_name]),
"tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"fr_met", CALCULATE(
COUNTROWS(FILTER('BI_Autotask_Tickets',
[first_response_met] + 0 = 1)))
)
ORDER BY [tickets] DESCAt 52.9% overall first response compliance, your team is responding to roughly half of all tickets within the SLA window. That is below the 80% target most MSPs set for themselves. The gap is not evenly distributed. Some priorities and queues are dragging the average down more than others.
P2 - Hoog tickets are the biggest concern. These are your high-priority tickets, the ones where clients expect the fastest response. At 35.7% compliance across 1,788 tickets, nearly two-thirds of urgent tickets get a late first response. That translates to 1,149 high-priority tickets where the client waited longer than the SLA allows.
The Monitoring queue is the largest single drag on the overall rate. With 17,082 tickets and only 34.0% compliance, it accounts for 11,266 missed first responses. Monitoring tickets are often automated alerts, and many teams treat them as lower priority. But they still carry SLA targets, and missing them at this volume pulls your portfolio average down by several percentage points.
On the client side, Rivers Rogers Mitchell stands out. At 28.8% compliance across 6,381 tickets, they receive the worst first response performance of any high-volume client. That is 4,544 tickets where the first response came late. For a client generating that much volume, the experience adds up fast.
The good news: Wall PLC at 73.6% and Patterson Hood Perez at 70.3% show that your team can deliver strong first response times when the workflow allows it. The gap between your best and worst clients is 44.8 percentage points. That spread suggests the problem is structural, not a blanket capacity issue.
5 priorities based on the findings above
17,082 tickets at 34.0% compliance means 11,266 missed first responses. Investigate whether monitoring alerts need an auto-acknowledgement rule, a dedicated triage rotation, or adjusted SLA targets. If these are automated alerts that do not require a human response within the same SLA as a client-reported ticket, reconfigure the SLA policy. If they do require human triage, assign a morning and afternoon sweep to clear the backlog before it stacks up.
High-priority tickets at 35.7% compliance is a service quality problem. Pull the P2 tickets that missed SLA and look for patterns: time of day, specific queues, specific technicians. 1,149 missed P2 tickets is the number your clients will remember when they review their SLA reports. If the SLA target is too aggressive for your current staffing, adjust it. If staffing is the issue, this data justifies the headcount conversation.
At 28.8% compliance across 6,381 tickets, this client is getting a materially different service experience than your top performers. Review their ticket mix: if most of their volume is in the Monitoring queue, the fix may be queue-level. If their P4 and service request tickets are also below average, something else is going on. Come to the conversation with the data. Showing them you have identified the gap and have a plan builds more trust than waiting for them to raise it.
52.9% is your baseline. Set a target of 65% for next quarter and 75% for the quarter after. Track it weekly by queue. The Servicedesk at 63.6% is already close. The Monitoring queue at 34.0% will need structural changes. Weekly tracking keeps the improvement visible and gives you early warning if the rate starts sliding again.
These clients sit at 73.6% and 70.3% respectively. Study what is different about their tickets: queue distribution, priority mix, assigned technicians, time-of-day patterns. If the Servicedesk handles most of their volume and the Monitoring queue handles most of Rivers Rogers Mitchell's volume, you have confirmed that queue assignment is the primary driver of your compliance gap.
First response met means the technician or dispatcher sent the first reply to the client within the SLA time window defined in Autotask for that ticket's priority level. The field first_response_met in the Proxuma data model is a binary flag: 1 if the SLA was met, 0 if it was missed.
Monitoring tickets are typically generated automatically by RMM tools. They often arrive in large batches, especially during maintenance windows or network events. If the SLA target for these tickets is the same as for client-reported tickets, the volume makes it almost impossible to respond to each one individually within the window. Many MSPs either create a separate SLA policy for monitoring tickets or implement auto-acknowledgement rules.
Most MSPs target 80% or higher for first response compliance. Top performers reach 90%+ on client-reported tickets. The key is to separate automated monitoring tickets from human-generated tickets in your SLA reporting. A blended rate that includes both will always look lower than the experience your clients actually receive on their own tickets.
Yes. The DAX queries in this report run against all available data by default. You can add a date filter using FILTER('BI_Autotask_Tickets', [create_date] >= DATE(2025,1,1)) to limit the scope to a specific period. For client QBRs, filtering to the last quarter gives a more relevant picture.
Yes. Connect Proxuma Power BI to your Autotask PSA, add an AI tool (Claude, ChatGPT, or Copilot) 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.
See more reports Get started