A data-driven analysis of most tickets by company from your Power BI environment, with breakdowns and actionable findings.
This report analyzes most tickets by 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 most tickets by company from your Power BI environment, with breakdowns and actionable findings.
-- Combined summary metrics from Power BI datasetClients ranked by total ticket count from the demo dataset
| Company | Tickets | % |
|---|---|---|
| Rivers, Rogers and Mitchell | 6,381 | 9.4% |
| Craig-Huynh | 5,458 | 8.1% |
| Little Group | 5,290 | 7.8% |
| Martin Group | 2,775 | 4.1% |
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Companies'[company_name], "Tickets", COUNTROWS('BI_Autotask_Tickets')), [Tickets], DESC)How tickets are spread across service queues
| Queue | Tickets |
|---|---|
| Administration | 327 |
| Professional Services | 546 |
| Merged Tickets | 4,999 |
| Interne IT | 793 |
| Onsite support | 705 |
| Centralized Services | 17,082 |
| L2 Support | 7,889 |
| L1 Support | 31,378 |
| Customer succes | 804 |
| Technical Alignment | 2,316 |
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Tickets'[queue_name], "Tickets", COUNTROWS('BI_Autotask_Tickets')), [Tickets], DESC)Ticket mix by priority level
| Priority | Tickets |
|---|---|
| P3 - Medium | 14,715 |
| P4 - Laag | 30,415 |
| P1 - Kritisch | 5,019 |
| P2 - Hoog | 1,788 |
| Service/Change req. | 15,584 |
Current status breakdown of all tickets
| Status | Tickets |
|---|---|
| Complete | 66,677 |
| Customer has responded | 102 |
| Planned | 213 |
| New | 169 |
| Assigned | 1 |
| In progress | 205 |
| Waiting for third party | 38 |
| Waiting Customer | 116 |
Monthly ticket volume over the observed period
| Month | Tickets |
|---|---|
| 202502 | 3,478 |
| 202503 | 3,766 |
| 202504 | 4,341 |
| 202505 | 3,639 |
| 202506 | 3,651 |
| 202507 | 6,613 |
| 202508 | 3,607 |
| 202509 | 4,563 |
| 202510 | 4,013 |
| 202511 | 3,327 |
| 202512 | 2,940 |
| 202601 | 2,164 |
What the data is telling us
Across 67,521 total tickets, 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 most tickets by 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.
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