A data-driven analysis of crm activity dashboard from your Power BI environment, with breakdowns and actionable findings.
This report analyzes crm activity dashboard 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 crm activity dashboard 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 | 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 |
EVALUATE TOPN(12, SUMMARIZECOLUMNS('BI_Common_Dim_Date'[year_month], "Tickets", COUNTROWS('BI_Autotask_Tickets')), 'BI_Common_Dim_Date'[year_month], 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.
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 crm activity dashboard 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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