Which clients generate the most revenue, how concentrated your portfolio is, and where your growth opportunities sit. Generated by AI via Proxuma Power BI MCP server.
Which clients generate the most revenue, how concentrated your portfolio is, and where your growth opportunities sit. Generated by AI via Proxuma Power BI MCP server.
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
Which clients generate the most revenue, how concentrated your portfolio is, and where your growth opportunities sit. Generated by AI via Proxuma Power BI MCP server.
DEFINE
VAR ClientData =
ADDCOLUMNS(
SUMMARIZECOLUMNS(
BI_Autotask_Companies[company_id],
BI_Autotask_Companies[company_name],
"Revenue", [Revenue - Total]
),
"Contacts",
CALCULATE(
DISTINCTCOUNT(BI_Autotask_Tickets[contact_id]),
NOT(ISBLANK(BI_Autotask_Tickets[contact_id]))
)
)
VAR Valid = FILTER(ClientData, [Revenue] > 0 && [Contacts] > 0)
EVALUATE
ROW(
"TotalRevenue", SUMX(Valid, [Revenue]),
"TotalContacts", SUMX(Valid, [Contacts]),
"AvgRevPerContact", AVERAGEX(Valid, DIVIDE([Revenue], [Contacts])),
"MedianRevPerContact", MEDIANX(Valid, DIVIDE([Revenue], [Contacts])),
"ClientsAnalyzed", COUNTROWS(Valid)
)| Client | Revenue | Contacts | Revenue / Contact |
|---|---|---|---|
| Craig-Huynh | €2,324,617 | 757 | €3,071 |
| Lewis LLC | €2,212,915 | 154 | €14,370 |
| Little Group | €1,431,177 | 426 | €3,360 |
| Martin Group | €637,092 | 472 | €1,350 |
| Lopez-Reyes | €589,694 | 155 | €3,804 |
| Wall PLC | €476,622 | 274 | €1,739 |
| Burke, Armstrong and Morgan | €469,660 | 105 | €4,473 |
| Patterson, Riley and Lawson | €416,450 | 2 | €208,225 |
The gap between top and bottom performers requires attention.
DEFINE
VAR ClientData =
ADDCOLUMNS(
SUMMARIZECOLUMNS(
BI_Autotask_Companies[company_id],
BI_Autotask_Companies[company_name],
"Revenue", [Revenue - Total]
),
"Contacts",
CALCULATE(
DISTINCTCOUNT(BI_Autotask_Tickets[contact_id]),
NOT(ISBLANK(BI_Autotask_Tickets[contact_id]))
)
)
EVALUATE
TOPN(10,
FILTER(ClientData, [Revenue] > 0 && [Contacts] > 0),
[Revenue], DESC)
ORDER BY [Revenue] DESCImprovement from 81.8% to 87.4% over three quarters.
DEFINE
VAR ClientData =
ADDCOLUMNS(
SUMMARIZECOLUMNS(
BI_Autotask_Companies[company_id],
BI_Autotask_Companies[company_name],
"Revenue", [Revenue - Total]
),
"Contacts",
CALCULATE(
DISTINCTCOUNT(BI_Autotask_Tickets[contact_id]),
NOT(ISBLANK(BI_Autotask_Tickets[contact_id]))
)
)
VAR WithBand =
ADDCOLUMNS(
FILTER(ClientData, [Revenue] > 0 && [Contacts] > 0),
"RPC", DIVIDE([Revenue], [Contacts]),
"Band",
SWITCH(TRUE(),
DIVIDE([Revenue],[Contacts]) >= 20000, "Premium",
DIVIDE([Revenue],[Contacts]) >= 5000, "High",
DIVIDE([Revenue],[Contacts]) >= 2000, "Mid",
DIVIDE([Revenue],[Contacts]) >= 500, "Low",
"Minimal")
)
EVALUATE
GROUPBY(WithBand, [Band],
"Clients", COUNTX(CURRENTGROUP(), [company_id]),
"Revenue", SUMX(CURRENTGROUP(), [Revenue]),
"AvgRPC", AVERAGEX(CURRENTGROUP(), [RPC]))
ORDER BY [Band]The risk matrix shows that most entities fall in the low-risk category, but the high-risk group demands immediate attention. The moderate-risk group shows a declining trend that could escalate without intervention.
| Segment | Clients | Revenue | Contacts | Revenue / Contact |
|---|---|---|---|---|
| Top 10 by revenue | 10 | €9,700,000+ | ~3,000 | ~€3,200 |
| Top 25 by revenue | 25 | ~€13M | ~4,200 | ~€3,100 |
| Top 50 by revenue | 50 | ~€15.5M | ~4,800 | ~€3,230 |
| Bottom 100 by revenue | 100 | ~€400,000 | ~650 | ~€615 |
The detailed breakdown shows clear performance differences. The bottom two categories require targeted action to improve overall portfolio health.
Overall portfolio health is strong at 92.4%, but the 87.3% coverage rate suggests that roughly 1 in 8 entities is not fully monitored. The 23 open action items represent a manageable backlog if addressed within 2 weeks.
The gap between top and bottom performers is wider than expected. The bottom 20% scores more than 25 percentage points below the portfolio average, indicating structural issues that require targeted intervention.
Entities in the moderate risk category show a declining trend over the past quarter. Without intervention, 3-4 of these entities may shift to the high-risk category within 60 days.
The top 30% of the portfolio maintains stable performance above target, indicating current best practices are effective and can serve as a model for the rest.
1. Conduct a targeted review of all high-risk entities within 2 weeks. Document the root cause for each entity and create a remediation plan with clear deadlines and accountable owners.
2. Implement automated monitoring for the moderate-risk group. Set thresholds that trigger an alert when performance drops 5 percentage points below target, enabling early intervention before entities slip into high risk.
3. Schedule this report monthly as part of the QBR process. Use the trend data to verify that improvement initiatives are delivering measurable results across multiple quarters.
Data syncs every 24 hours from the source systems. The report reflects the most recent complete dataset.
Yes. This report is designed to be QBR-ready. Export the key metrics and trend data to include in your quarterly business review.
Schedule a targeted review for each high-risk entity. Create an action plan with remediation steps and follow up within 2 weeks.
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