A data-driven analysis of configuration items per client from your Power BI environment, with breakdowns and actionable findings.
This report analyzes configuration items per client 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: Account managers, MSP owners, and service delivery leads
How often: Monthly for client reviews, quarterly for QBRs, on-demand when client signals change
A data-driven analysis of configuration items per client 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
| Client | CIs | Tickets | Hours | CI/Ticket |
|---|---|---|---|---|
| Martin Group | 2105 | 2775 | 2046 | 0.76 |
| Craig-Huynh | 1584 | 5458 | 3575 | 0.29 |
| Lewis LLC | 985 | 1758 | 1206 | 0.56 |
| Little Group | 720 | 5290 | 3050 | 0.14 |
| Wall PLC | 411 | 2376 | 1479 | 0.17 |
| Richards, Bell and Christensen | 271 | 823 | 660 | 0.33 |
| Lopez-Reyes | 295 | 1317 | 670 | 0.22 |
| Price-Gomez | 281 | 2180 | 823 | 0.13 |
| Wu-Jackson | 251 | 914 | 590 | 0.28 |
| Burke, Armstrong and Morgan | 246 | 1629 | 943 | 0.15 |
EVALUATE TOPN(10, ADDCOLUMNS(VALUES(BI_Autotask_Companies[company_name]), "CICount", CALCULATE(COUNTROWS(BI_Autotask_Configuration_Items)), "TicketCount", [Tickets - Count - Created], "HoursWorked", [Tickets - Hours Worked], "CIPerTicket", DIVIDE(CALCULATE(COUNTROWS(BI_Autotask_Configuration_Items)), [Tickets - Count - Created])), [CICount], DESC)Total hours logged per company
| Company | Hours |
|---|---|
| Richards, Bell and Christensen | 782.4 |
| Wu-Jackson | 962.0 |
| Price-Gomez | 864.9 |
| Martin Group | 2,217.0 |
| Thompson, Contreras and Rios | 1,006.1 |
| Doyle-Contreras | 961.9 |
| Clements, Pham and Garcia | 866.3 |
| - | 7,264.2 |
| Lewis LLC | 2,801.1 |
| Little Group | 3,791.4 |
| Craig-Huynh | 4,370.4 |
| Rivers, Rogers and Mitchell | 1,661.8 |
| Burke, Armstrong and Morgan | 1,312.3 |
| Wall PLC | 1,696.9 |
| Ramos Group | 1,170.6 |
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Companies'[company_name], "Hours", SUM('BI_Autotask_Time_Entries'[hours_worked])), [Hours], DESC)Revenue breakdown by company from billing data
| Company | Revenue |
|---|---|
| Montgomery-Peck | €214,468 |
| Hahn Group | €253,148 |
| Wu-Jackson | €321,669 |
| Torres-Jones | €255,698 |
| Thompson, Contreras and Rios | €320,831 |
| Patterson, Riley and Lawson | €416,449 |
| Richards, Bell and Christensen | €328,164 |
| Burke, Armstrong and Morgan | €469,660 |
| Price-Gomez | €286,926 |
| Little Group | €1,431,177 |
| Wall PLC | €476,622 |
| Craig-Huynh | €2,324,616 |
| Martin Group | €637,091 |
| Lopez-Reyes | €589,694 |
| Lewis LLC | €2,212,914 |
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Companies'[company_name], "Revenue", SUM('BI_Autotask_Billing_Items'[total_amount])), [Revenue], DESC)What 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.
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 configuration items per client 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.
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