A data-driven analysis of billing item analysis from your Power BI environment, with breakdowns and actionable findings.
This report analyzes billing item analysis 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 owners, finance leads, and operations managers tracking profitability
How often: Monthly for financial reviews, quarterly for strategic planning, on-demand for pricing decisions
A data-driven analysis of billing item analysis 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 |
|---|---|
| Wilson-Murphy | 1,002 |
| Burke, Armstrong and Morgan | 1,629 |
| Lopez-Reyes | 1,317 |
| Ford, Mclean and Robinson | 1,684 |
| Lewis LLC | 1,758 |
| Thompson, Contreras and Rios | 1,803 |
| Stephens-Martinez | 1,481 |
| Rivers, Rogers and Mitchell | 6,381 |
| Blanchard-Glenn | 2,364 |
| Martin Group | 2,775 |
| Price-Gomez | 2,180 |
| Little Group | 5,290 |
| Wall PLC | 2,376 |
| Craig-Huynh | 5,458 |
| Ramos Group | 1,728 |
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Tickets'[company_name], "Tickets", COUNTROWS('BI_Autotask_Tickets')), [Tickets], DESC)Hours 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 | Billing Items | Revenue |
|---|---|---|
| 2024-04 | 1 | $0 |
| 2024-05 | 7 | $1,777 |
| 2024-06 | 13 | $502 |
| 2024-07 | 132 | $19,255 |
| 2024-08 | 5,081 | $850,721 |
| 2024-09 | 5,927 | $827,356 |
| 2024-10 | 7,255 | $872,119 |
| 2024-11 | 6,954 | $846,378 |
| 2024-12 | 6,381 | $933,099 |
| 2025-01 | 7,213 | $942,444 |
| 2025-02 | 7,039 | $1,051,887 |
| 2025-03 | 7,951 | $1,106,651 |
| 2025-04 | 7,520 | $1,341,613 |
| 2025-05 | 7,605 | $1,080,822 |
| 2025-06 | 7,332 | $1,033,307 |
| 2025-07 | 7,894 | $1,045,558 |
| 2025-08 | 7,241 | $1,058,862 |
| 2025-09 | 8,447 | $1,002,352 |
| 2025-10 | 7,878 | $1,006,189 |
| 2025-11 | 7,093 | $927,813 |
| 2025-12 | 7,071 | $887,195 |
| 2026-01 | 3,716 | $770,865 |
EVALUATE
ADDCOLUMNS(
SUMMARIZE('BI_Autotask_Billing_Items','BI_Common_Dim_Date'[year_month]),
"Items", CALCULATE(COUNTROWS('BI_Autotask_Billing_Items')),
"Revenue", CALCULATE(SUM('BI_Autotask_Billing_Items'[total_amount]))
)
ORDER BY 'BI_Common_Dim_Date'[year_month] DESCRevenue breakdown by company from billing data
| # | Company | Revenue | Billing Items | Avg Line |
|---|---|---|---|---|
| 1 | Craig-Huynh | $2,324,617 | 9,606 | $242.00 |
| 2 | Lewis LLC | $2,212,915 | 3,922 | $564.23 |
| 3 | Little Group | $1,431,177 | 8,017 | $178.52 |
| 4 | Martin Group | $637,092 | 4,183 | $152.30 |
| 5 | Lopez-Reyes | $589,694 | 2,013 | $292.94 |
| 6 | Wall PLC | $476,622 | 5,495 | $86.74 |
| 7 | Burke, Armstrong and Morgan | $469,660 | 3,322 | $141.38 |
| 8 | Patterson, Riley and Lawson | $416,450 | 3,073 | $135.52 |
| 9 | Richards, Bell and Christensen | $328,165 | 1,532 | $214.21 |
| 10 | Wu-Jackson | $321,669 | 1,701 | $189.11 |
| 11 | Thompson, Contreras and Rios | $320,832 | 2,321 | $138.23 |
| 12 | Price-Gomez | $286,926 | 2,529 | $113.45 |
| 13 | Torres-Jones | $255,698 | 711 | $359.63 |
| 14 | Hahn Group | $253,148 | 1,953 | $129.62 |
| 15 | Montgomery-Peck | $214,469 | 1,570 | $136.60 |
EVALUATE
TOPN(15,
SUMMARIZECOLUMNS(
'BI_Autotask_Companies'[company_name],
"Revenue", SUM('BI_Autotask_Billing_Items'[total_amount]),
"Items", CALCULATE(COUNTROWS('BI_Autotask_Billing_Items'))
),
[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.
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 billing item analysis 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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