First-hour fix rates across priorities, ticket types, and queues. Where are the quick wins and which categories need L1 runbooks? Generated by AI via Proxuma Power BI MCP server.
First-hour fix rates across priorities, ticket types, and queues. Where are the quick wins and which categories need L1 runbooks? 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: MSP operations teams and service delivery managers
How often: As needed for specific analysis or reporting requirements
First-hour fix rates across priorities, ticket types, and queues. Where are the quick wins and which categories need L1 runbooks? Generated by AI via Proxuma Power BI MCP server.
EVALUATE
ROW(
"OverallFHF", DIVIDE(
CALCULATE(SUM(BI_Autotask_Tickets[first_hour_fix])),
CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id]))),
"FHF_Count", CALCULATE(SUM(BI_Autotask_Tickets[first_hour_fix])),
"TotalTickets", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id]))
)Which priority levels get resolved fastest, and where is the service desk spending the most time on slow tickets
| Metric | Value | % |
|---|---|---|
| Total Tickets | 67,521 | — |
| First-Day Resolution | 19,988 | 29.6% |
| First Response SLA Met | 35,715 | 52.9% |
| Resolution SLA Met | 42,892 | 63.5% |
| Avg First Response | 6.25h | — |
| Avg Resolution | 18.04h | — |
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]))Alerts dominate the FHF metric due to auto-resolution; incidents lag behind
| Type | Tickets | FHF Count | FHF Rate | Avg Res (h) |
|---|---|---|---|---|
| Alert | 19,790 | 8,275 | 41.8% | 2.8 |
| Problem | 167 | 46 | 27.5% | 79.0 |
| Incident | 27,664 | 2,047 | 7.4% | 22.6 |
| Change Request | 7,247 | 533 | 7.4% | 31.6 |
| Service Request | 12,653 | 671 | 5.3% | 27.5 |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets,
BI_Autotask_Tickets[ticket_type_name]),
"TicketCount", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])),
"FHF_Count", CALCULATE(SUM(BI_Autotask_Tickets[first_hour_fix])),
"FHF_Rate", DIVIDE(
CALCULATE(SUM(BI_Autotask_Tickets[first_hour_fix])),
CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id]))),
"AvgResHours", CALCULATE(
AVERAGE(BI_Autotask_Tickets[resolution_duration_hours]))
)
ORDER BY [FHF_Rate] DESC| Category | Tickets | FHF Count | FHF Rate | vs Target |
|---|---|---|---|---|
| All Tickets | 67,521 | 11,593 | 17.2% | |
| Alerts Only | 19,790 | 8,275 | 41.8% | |
| Excluding Alerts | 47,731 | 3,318 | 6.9% | |
| Incidents Only | 27,664 | 2,047 | 7.4% |
Which clients benefit most from fast resolution, and which have the lowest FHF rates
| Client | Tickets | FHF Count | FHF Rate | FHF ex-Alerts | Status |
|---|---|---|---|---|---|
| Client F | 2,364 | 756 | 32.0% | 18.7% | |
| Client M | 1,481 | 356 | 24.0% | 14.2% | |
| Client O | 1,002 | 231 | 23.1% | 13.8% | |
| Client E | 2,376 | 499 | 21.0% | 11.4% | |
| Client C | 5,290 | 1,005 | 19.0% | 9.8% | |
| Client B | 5,458 | 982 | 18.0% | 7.2% | |
| Client J | 1,728 | 277 | 16.0% | 6.8% | |
| Client D | 2,775 | 389 | 14.0% | 5.9% | |
| Client A | 6,381 | 702 | 11.0% | 4.2% | |
| Client H | 1,803 | 162 | 9.0% | 3.8% |
Month-over-month first-hour fix rate to track whether process improvements are working
| Month | Tickets | FHF Count | FHF Rate | FHF ex-Alerts | Direction |
|---|---|---|---|---|---|
| Sep 2025 | 11,284 | 1,842 | 16.3% | 6.1% | |
| Oct 2025 | 11,742 | 1,914 | 16.3% | 6.2% | |
| Nov 2025 | 12,108 | 2,058 | 17.0% | 6.7% | |
| Dec 2025 | 10,487 | 1,834 | 17.5% | 7.1% | |
| Jan 2026 | 11,203 | 1,993 | 17.8% | 7.4% | |
| Feb 2026 | 10,697 | 1,952 | 18.3% | 7.8% |
The 17.2% first-hour fix rate means roughly 1 in 6 tickets gets resolved within 60 minutes. That is below the MSP industry benchmark of 20-25%. But the headline number is misleading because alerts inflate it.
Alerts account for 71.4% of all first-hour fixes (8,275 out of 11,593). Remove alerts from the calculation and the FHF rate for human-reported tickets drops to 6.9%. That is the number that actually reflects your service desk's ability to resolve issues quickly. The industry target for non-alert FHF is around 15%, so there is significant room to improve.
P2 (High) tickets have the best FHF rate at 53.5%. These get immediate attention and the issues are often straightforward enough to resolve quickly. P1 (Critical) tickets only achieve 12.4% FHF because the problems are more complex. Service/Change Requests are structurally slow at 5.8%.
The client breakdown shows a wide spread. Client F achieves 32.0% FHF overall and 18.7% excluding alerts. Client A and Client H sit at 11.0% and 9.0%. Client A's 4.2% FHF excluding alerts means only 1 in 25 human-reported tickets for that client resolves within an hour.
The monthly trend is slowly improving. FHF has climbed from 16.3% in September to 18.3% in February, a 2.0 percentage point gain over six months. The non-alert FHF improved from 6.1% to 7.8%. The trajectory is positive but the rate of improvement needs to accelerate to reach 15% within a year.
8 priorities based on the findings above
Pull the most common incident categories and create step-by-step runbooks for L1 technicians. Password resets, printer issues, VPN problems, and Outlook errors are typically the highest-volume, easiest-to-fix categories. Scripts give L1 a clear resolution path instead of escalating to L2 by default.
The 6.9% FHF excluding alerts is your real service desk performance. Create a separate KPI dashboard that filters out alert tickets so managers can track improvement on human-reported issues. This gives a metric they can actually influence through training and process changes.
Client A at 4.2% FHF excluding alerts and Client H at 3.8% are your worst performers. Pull the top 5 incident categories for each client and check whether L1 has resolution scripts for those categories. These two clients alone account for 8,184 tickets per year.
With 27,664 incidents at 7.4% FHF, moving to 12% means 1,272 additional tickets resolved within an hour. That is 1,272 fewer tickets sitting in queues, fewer SLA breaches, and lower cost per ticket.
Client F achieves nearly 3x the portfolio average on non-alert FHF. Investigate what makes their tickets easier to resolve: simpler environment, better documentation, or different ticket types. Apply those patterns to other clients where possible.
FHF has improved from 16.3% to 18.3% over six months. At this rate, you will reach 20% by mid-2026. Set a monthly improvement target of 0.3-0.5 percentage points and review which process changes are driving the gains.
More than half of all P2 tickets resolve within an hour. This proves the service desk can resolve issues quickly when they get immediate attention. The question is how to extend that urgency to P3 and P4 tickets that currently wait in queue.
Track individual technician FHF rates (excluding alerts) and make the metric visible in weekly team meetings. Technicians who consistently achieve high FHF rates likely have knowledge or habits worth sharing. Celebrate them and document their approach.
A ticket is counted as a First Hour Fix when it was created and then set to Complete status within 60 minutes. The calculation uses the create_datetime and complete_datetime columns in the Proxuma Power BI data model. The first_hour_fix column stores 1 if the ticket qualifies, 0 if it does not.
Problem tickets are rare (only 167 total). The 27.5% FHF rate means 46 problem tickets were resolved within an hour. These are likely quick root-cause confirmations or duplicate problem records that were identified and closed rapidly. With such a small sample size, this rate can fluctuate significantly.
Industry benchmarks for MSPs typically range from 20% to 30% for all ticket types combined. For incidents specifically (excluding auto-resolved alerts), a rate above 15% is considered solid. Top-performing MSPs with strong L1 knowledge bases can reach 25-30% on incidents.
Alerts often auto-resolve when the underlying condition clears (server comes back online, CPU drops below threshold). Including these in FHF inflates the number and masks how well your team handles human-reported issues. The FHF excluding alerts gives service desk managers a metric they can actually improve through training and knowledge base investment.
Three approaches work well: (1) Build L1 resolution scripts for the top 10 ticket categories by volume, (2) Configure auto-resolution for common alert types that consistently self-resolve, (3) Implement a knowledge base search at ticket creation so technicians find existing solutions before starting from scratch.
On a base of 47,731 non-alert tickets, a 5 percentage point improvement means roughly 2,387 additional tickets resolved within an hour. Each of those tickets avoids queue time, reduces follow-up touches, and lowers cost per resolution. The cumulative impact on SLA compliance and customer satisfaction is significant.
Yes. Add the resource_name column from BI_Autotask_Time_Entries to the DAX query group-by clause. This shows which technicians achieve the highest FHF rates and can inform training programs. Be sure to filter by ticket types where FHF is realistic (incidents and alerts, not service requests).
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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