A data-driven analysis of open tickets mom change per company from your Power BI environment, with breakdowns and actionable findings.
This report analyzes open tickets mom change per company 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: Service desk managers, dispatch leads, and operations teams
How often: Daily for queue management, weekly for trend analysis, monthly for capacity planning
A data-driven analysis of open tickets mom change per company from your Power BI environment, with breakdowns and actionable findings.
EVALUATE ROW("Total", CALCULATE(COUNTROWS('BI_Autotask_Tickets')))Clients ranked by total ticket count from the demo dataset
| Company | Tickets | Share |
|---|---|---|
| Rivers, Rogers and Mitchell | 6,381 | 9.5% |
| Craig-Huynh | 5,458 | 8.1% |
| Little Group | 5,290 | 7.8% |
| Martin Group | 2,775 | 4.1% |
| Wall PLC | 2,376 | 3.5% |
| Blanchard-Glenn | 2,364 | 3.5% |
| Price-Gomez | 2,180 | 3.2% |
| Thompson, Contreras and Rios | 1,803 | 2.7% |
| Lewis LLC | 1,758 | 2.6% |
| Ramos Group | 1,728 | 2.6% |
| Ford, Mclean and Robinson | 1,684 | 2.5% |
| Burke, Armstrong and Morgan | 1,629 | 2.4% |
| Stephens-Martinez | 1,481 | 2.2% |
| Lopez-Reyes | 1,317 | 2.0% |
| Wilson-Murphy | 1,002 | 1.5% |
EVALUATE TOPN(15, ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[company_name]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))), [Tickets], DESC) ORDER BY [Tickets] DESCHow tickets are spread across service queues
| Queue | Tickets |
|---|---|
| L1 Support | 31,378 |
| Centralized Services | 17,082 |
| L2 Support | 7,889 |
| Merged Tickets | 4,999 |
| Technical Alignment | 2,316 |
| Customer succes | 804 |
| Interne IT | 793 |
| Onsite support | 705 |
| Professional Services | 546 |
| Administration | 327 |
EVALUATE TOPN(10, ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[queue_name]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))), [Tickets], DESC) ORDER BY [Tickets] DESCTicket mix by priority level
| Priority | Tickets |
|---|---|
| P4 - Laag | 30,415 |
| Service/Change req. | 15,584 |
| P3 - Medium | 14,715 |
| P1 - Kritisch | 5,019 |
| P2 - Hoog | 1,788 |
Current status breakdown of all tickets
| Status | Tickets |
|---|---|
| Complete | 66,677 |
| Planned | 213 |
| In progress | 205 |
| New | 169 |
| Waiting Customer | 116 |
| Customer has responded | 102 |
| Waiting for third party | 38 |
| Assigned | 1 |
Monthly ticket volume over the observed period
| Month | Tickets | MoM |
|---|---|---|
| Feb 2025 | 3,478 | — |
| Mar 2025 | 3,766 | +8.3% |
| Apr 2025 | 4,341 | +15.3% |
| May 2025 | 3,639 | -16.2% |
| Jun 2025 | 3,651 | +0.3% |
| Jul 2025 | 6,613 | +81.1% |
| Aug 2025 | 3,607 | -45.5% |
| Sep 2025 | 4,563 | +26.5% |
| Oct 2025 | 4,013 | -12.1% |
| Nov 2025 | 3,327 | -17.1% |
| Dec 2025 | 2,940 | -11.6% |
| Jan 2026 | 2,164 | -26.4% |
What the data is telling us
Across 67,521 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.
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 open tickets mom change per company 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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