A data-driven analysis of opportunity win rate from your Power BI environment, with breakdowns and actionable findings.
This report analyzes opportunity win rate 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: Sales leads, MSP owners, and account managers tracking pipeline health
How often: Weekly for pipeline reviews, monthly for forecasting, quarterly for strategy
A data-driven analysis of opportunity win rate from your Power BI environment, with breakdowns and actionable findings.
EVALUATE ROW(
"Opportunities", COUNTROWS('BI_Autotask_Opportunities'),
"Won", CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented"}),
"Lost", CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] = "Lost"),
"Active", CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] = "Active"),
"WonAmount", CALCULATE(SUM('BI_Autotask_Opportunities'[amount]), 'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented"}),
"LostAmount", CALCULATE(SUM('BI_Autotask_Opportunities'[amount]), 'BI_Autotask_Opportunities'[status_name] = "Lost"),
"WinRate", [Pipeline - Win Rate],
"AvgDaysToClose", [Conversion - Avg Days to Close]
)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)Monthly revenue trend over the observed period
| Month | Revenue |
|---|---|
| 202502 | €1,051,887 |
| 202503 | €1,106,650 |
| 202504 | €1,341,613 |
| 202505 | €1,080,821 |
| 202506 | €1,033,307 |
| 202507 | €1,045,558 |
| 202508 | €1,058,862 |
| 202509 | €1,002,352 |
| 202510 | €1,006,188 |
| 202511 | €927,812 |
| 202512 | €887,195 |
| 202601 | €770,865 |
EVALUATE TOPN(12, SUMMARIZECOLUMNS('BI_Common_Dim_Date'[year_month], "Revenue", SUM('BI_Autotask_Billing_Items'[total_amount])), 'BI_Common_Dim_Date'[year_month], DESC)Distribution of contracts across types
| Type | Count |
|---|---|
| Recurring Service | 1,207 |
| Time & Materials | 504 |
| Block Hours | 173 |
| Fixed Price | 5 |
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Contracts'[contract_type_name], "Count", COUNTROWS('BI_Autotask_Contracts'))What the data is telling us
The data above paints a picture of opportunity win rate across your MSP operations. Look for patterns, outliers, and trends that warrant attention. Each section includes the DAX query used, so you can drill deeper into any metric that catches your eye.
Set up a weekly or monthly review of opportunity win rate 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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