A data-driven analysis of first hour fix rate from your Power BI environment, with breakdowns and actionable findings.
This report analyzes first hour fix rate using data from Autotask 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: MSP operations teams and service delivery managers
How often: As needed for specific analysis or reporting requirements
A data-driven analysis of first hour fix rate from your Power BI environment, with breakdowns and actionable findings.
EVALUATE ROW("Tickets", COUNTROWS('BI_Autotask_Tickets'), "FHF_Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_hour_fix] + 0 = 1), "FHF_Pct", [Tickets - First Hour Fix %], "AvgWorkedHours", AVERAGE('BI_Autotask_Tickets'[worked_hours]))Clients ranked by total ticket count from the demo dataset
| Client | Tickets | FHF Count | FHF % |
|---|---|---|---|
| Wilson-Murphy | 1.002 | 676 | 67,2% |
| Stephens-Martinez | 1.481 | 985 | 66,4% |
| Welch Inc | 888 | 469 | 51,9% |
| Anderson, Brown and Mcintosh | 769 | 391 | 49,7% |
| Smith-English | 498 | 196 | 39,4% |
| West, White and Lawson | 574 | 209 | 36,4% |
| Ramos Group | 1.728 | 616 | 34,3% |
| Leach, Cunningham and Whitehead | 271 | 102 | 32,9% |
| Jacobs-Levy | 337 | 107 | 31,8% |
| Jackson-Smith | 507 | 159 | 30,7% |
| White Ltd | 425 | 119 | 28,0% |
| Clements, Pham and Garcia | 731 | 181 | 23,9% |
| Thompson, Contreras and Rios | 1.803 | 417 | 22,3% |
| Wu-Jackson | 914 | 195 | 20,3% |
| Conway Ltd | 273 | 65 | 20,0% |
EVALUATE TOPN(15, FILTER(ADDCOLUMNS(VALUES('BI_Autotask_Tickets'[company_name]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "FHFCount", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_hour_fix] + 0 = 1), "FHFPct", [Tickets - First Hour Fix %]), [Tickets] >= 200), [FHFPct], DESC) ORDER BY [FHFPct] DESCHours logged per resource from the demo dataset
| Resource | Hours |
|---|---|
| Brandon Lynn | 1,343.7 |
| Brandon Bishop | 1,361.5 |
| Daniel Daniels | 1,418.4 |
| Gregory Horn | 1,504.5 |
| Elizabeth Ortega | 1,433.4 |
| Jennifer King | 1,584.5 |
| Jeremy White | 1,492.5 |
| Dr. Amber Ayala DVM | 2,399.8 |
| Kevin Allen | 2,060.1 |
| James Li | 2,136.0 |
| Maxwell Reed | 2,050.3 |
| Chelsea Thomas | 1,779.6 |
| David Hunt | 1,862.2 |
| Andrew Roberts | 1,887.7 |
| Jerry Mcfarland | 1,554.0 |
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[resource_name], "Hours", SUM('BI_Autotask_Time_Entries'[hours_worked])), [Hours], DESC)Monthly ticket volume over the observed period
| Month | Tickets | FHF Count | FHF % |
|---|---|---|---|
| Jan 2026 | 2.164 | 992 | 29,9% |
| Dec 2025 | 2.940 | 809 | 23,1% |
| Nov 2025 | 3.327 | 733 | 20,5% |
| Oct 2025 | 4.013 | 968 | 23,2% |
| Sep 2025 | 4.563 | 729 | 15,4% |
| Aug 2025 | 3.607 | 417 | 11,4% |
| Jul 2025 | 6.613 | 1.751 | 26,4% |
| Jun 2025 | 3.651 | 420 | 11,3% |
| May 2025 | 3.639 | 185 | 5,0% |
| Apr 2025 | 4.341 | 335 | 7,7% |
| Mar 2025 | 3.766 | 279 | 7,4% |
| Feb 2025 | 3.478 | 339 | 9,7% |
EVALUATE FILTER(ADDCOLUMNS(VALUES('BI_Common_Dim_Date'[year_month]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "FHFCount", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_hour_fix] + 0 = 1), "FHFPct", [Tickets - First Hour Fix %]), [Tickets] > 0) ORDER BY 'BI_Common_Dim_Date'[year_month] DESCRevenue breakdown by company from billing data
| Client | Tickets | FHF % | Revenue € |
|---|---|---|---|
| Craig-Huynh | 5.458 | 12,3% | 2.324.617 |
| Lewis LLC | 1.758 | 6,0% | 2.212.915 |
| Little Group | 5.290 | 12,5% | 1.431.177 |
| Martin Group | 2.775 | 17,3% | 637.092 |
| Lopez-Reyes | 1.317 | 13,0% | 589.694 |
| Wall PLC | 2.376 | 9,3% | 476.622 |
| Burke, Armstrong and Morgan | 1.629 | 11,9% | 469.660 |
| Richards, Bell and Christensen | 823 | 9,4% | 328.165 |
| Wu-Jackson | 914 | 20,3% | 321.669 |
| Thompson, Contreras and Rios | 1.803 | 22,3% | 320.832 |
| Price-Gomez | 2.180 | 18,4% | 286.926 |
| Torres-Jones | 467 | 17,6% | 255.698 |
| Hahn Group | 943 | 16,2% | 253.148 |
| Montgomery-Peck | 766 | 7,0% | 214.469 |
| Ramos Group | 1.728 | 34,3% | 205.547 |
EVALUATE TOPN(15, FILTER(ADDCOLUMNS(VALUES('BI_Autotask_Companies'[company_name]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "FHFPct", [Tickets - First Hour Fix %], "Revenue", CALCULATE(SUM('BI_Autotask_Billing_Items'[total_amount]))), [Tickets] >= 200), [Revenue], DESC) ORDER BY [Revenue] DESCWhat the data is telling us
Across 39,226 total records, the distribution is heavily concentrated. Wilson-Murphy alone accounts for 2.6% of all volume (1,002 records). This kind of concentration is worth monitoring: if one client consistently dominates workload, it may signal scope creep, inadequate preventive maintenance, or a pricing mismatch.
Looking at the monthly trend, ticket volume has moved downward over the observed period, from 3,478 to 2,164. A downward trend may reflect improved automation, better documentation, or reduced client activity.
The team logged 25,868 hours across 15 resources, averaging 1,724 hours per person. Look for outliers on both ends: engineers logging significantly more may be overloaded, while those with low hours may have logging compliance issues.
Wilson-Murphy generates the most activity. Review whether this aligns with their contract scope and SLA tier.
Set up a weekly or monthly review of first hour fix rate metrics. Trends matter more than snapshots. Use the DAX queries in this report as your starting point.
This report uses demo data. Connect Proxuma Power BI to your own Autotask PSA to generate this analysis from your real numbers.
This report pulls data from PSA through the Proxuma Power BI integration, using DAX queries against the live data model.
The underlying Power BI dataset refreshes daily. Reports can be regenerated at any time for the latest figures.
Yes. Proxuma reports are fully customizable. You can modify the DAX queries, add new sections, or adjust the analysis to match your specific MSP needs.
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