Measuring dependency risk, Pareto distribution, and churn exposure across 280 active clients.
Measuring dependency risk, Pareto distribution, and churn exposure across 280 active clients.
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
Measuring dependency risk, Pareto distribution, and churn exposure across 280 active clients.
High-level KPIs capturing the state of revenue concentration across your client base.
EVALUATE
ROW(
"Total Revenue", [Revenue - Total],
"Billed Clients", DISTINCTCOUNT('BI_Autotask_Billing_Items'[company_id]),
"Active Clients (rev>0)", COUNTROWS(FILTER(ADDCOLUMNS(VALUES('BI_Autotask_Companies'[company_name]), "Rev", [Revenue - Total]), [Rev] > 0))
)Breaking down your client base by annual revenue tier shows where the money actually comes from.
| Revenue Tier | Clients | Revenue | % of Total | Distribution |
|---|---|---|---|---|
| Over €500K | 5 | €7,195,495 | 40.9% | |
| €100K–€500K | 36 | €7,101,373 | 40.3% | |
| €25K–€100K | 48 | €2,473,566 | 14.0% | |
| €5K–€25K | 54 | €670,243 | 3.8% | |
| Under €5K | 137 | €168,672 | 1.0% |
41 clients (15% of your base) generate 81.2% of total revenue. Meanwhile, 137 clients (49% of your base) contribute less than 1%.
EVALUATE
VAR ClientRev = FILTER(ADDCOLUMNS(VALUES('BI_Autotask_Companies'[company_name]),"Rev",[Revenue - Total]),[Rev]>0)
VAR T = [Revenue - Total]
RETURN UNION(
ROW("Tier","Over €500K","Clients",COUNTROWS(FILTER(ClientRev,[Rev]>=500000)),"Rev",SUMX(FILTER(ClientRev,[Rev]>=500000),[Rev]),"Share",DIVIDE(SUMX(FILTER(ClientRev,[Rev]>=500000),[Rev]),T)),
ROW("Tier","€100K–€500K","Clients",COUNTROWS(FILTER(ClientRev,[Rev]>=100000 && [Rev]<500000)),"Rev",SUMX(FILTER(ClientRev,[Rev]>=100000 && [Rev]<500000),[Rev]),"Share",DIVIDE(SUMX(FILTER(ClientRev,[Rev]>=100000 && [Rev]<500000),[Rev]),T)),
ROW("Tier","€25K–€100K","Clients",COUNTROWS(FILTER(ClientRev,[Rev]>=25000 && [Rev]<100000)),"Rev",SUMX(FILTER(ClientRev,[Rev]>=25000 && [Rev]<100000),[Rev]),"Share",DIVIDE(SUMX(FILTER(ClientRev,[Rev]>=25000 && [Rev]<100000),[Rev]),T)),
ROW("Tier","€5K–€25K","Clients",COUNTROWS(FILTER(ClientRev,[Rev]>=5000 && [Rev]<25000)),"Rev",SUMX(FILTER(ClientRev,[Rev]>=5000 && [Rev]<25000),[Rev]),"Share",DIVIDE(SUMX(FILTER(ClientRev,[Rev]>=5000 && [Rev]<25000),[Rev]),T)),
ROW("Tier","Under €5K","Clients",COUNTROWS(FILTER(ClientRev,[Rev]<5000)),"Rev",SUMX(FILTER(ClientRev,[Rev]<5000),[Rev]),"Share",DIVIDE(SUMX(FILTER(ClientRev,[Rev]<5000),[Rev]),T))
)The top 15 clients ranked by total revenue, including profitability per client.
| # | Client | Revenue | % Share | Cumul. % | Profit | Margin |
|---|---|---|---|---|---|---|
| 1 | Craig-Huynh | €2,324,617 | 13.2% | 13.2% | €1,310,647 | 56.4% |
| 2 | Lewis LLC | €2,212,915 | 12.6% | 25.8% | €1,318,693 | 59.6% |
| 3 | Little Group | €1,431,177 | 8.1% | 33.9% | €827,758 | 57.8% |
| 4 | Martin Group | €637,092 | 3.6% | 37.5% | €388,880 | 61.0% |
| 5 | Lopez-Reyes | €589,694 | 3.4% | 40.9% | -€55,879 | -9.5% |
| 6 | Wall PLC | €476,622 | 2.7% | 43.6% | €262,227 | 55.0% |
| 7 | Burke, Armstrong and Morgan | €469,660 | 2.7% | 46.3% | €245,267 | 52.2% |
| 8 | Patterson, Riley and Lawson | €416,450 | 2.4% | 48.6% | €209,582 | 50.3% |
| 9 | Richards, Bell and Christensen | €328,165 | 1.9% | 50.5% | €221,073 | 67.4% |
| 10 | Wu-Jackson | €321,669 | 1.8% | 52.3% | €200,186 | 62.2% |
| 11 | Thompson, Contreras and Rios | €320,832 | 1.8% | 54.2% | €179,416 | 55.9% |
| 12 | Price-Gomez | €286,926 | 1.6% | 55.8% | €166,739 | 58.1% |
| 13 | Torres-Jones | €255,698 | 1.5% | 57.2% | €208,887 | 81.7% |
| 14 | Hahn Group | €253,148 | 1.4% | 58.7% | €120,010 | 47.4% |
| 15 | Montgomery-Peck | €214,469 | 1.2% | 59.9% | €80,714 | 37.6% |
Your top 15 clients represent 59.9% of total revenue. Note: Parker Solutions shows a negative margin at position #5. High revenue does not always equal high profit.
EVALUATE
TOPN(15, ADDCOLUMNS(VALUES('BI_Autotask_Companies'[company_name]),
"Revenue", [Revenue - Total], "Cost", [Cost - Total], "Profit", [Profit - total]),
[Revenue], DESC) ORDER BY [Revenue] DESCThe Pareto Principle (80/20 rule) applied to your client revenue. How many clients drive the bulk of your income?
A Gini coefficient of 0.78 indicates high revenue inequality across your client base. For context: a healthy MSP typically sits between 0.55 and 0.70. Your score of 0.78 means the gap between your largest and smallest clients is wider than average.
The practical implication: your top 56 clients (20% of the base) pull in 87.5% of your revenue. The classic 80/20 rule would predict 80%. You are beyond that, which signals above-average concentration risk.
Key indicators that help quantify how dependent your business is on a small group of clients.
| Risk Indicator | Value | Benchmark | Status |
|---|---|---|---|
| Single Client Dependency | 13.2% | < 10% recommended | High Risk |
| Top 5 Client Concentration | 40.9% | < 30% recommended | High Risk |
| Top 10 Client Concentration | 52.3% | < 50% recommended | High Risk |
What happens to your revenue if you lose one or more of your largest clients?
| Scenario | Revenue Lost | Remaining Revenue | % Decline |
|---|---|---|---|
| Lose Craig-Huynh | €2,324,617 | €15,284,732 | -13.2% |
| Lose top 2 clients | €4,537,531 | €13,071,818 | -25.8% |
| Lose top 5 clients | €7,195,495 | €10,413,854 | -40.9% |
Losing just your top two clients would erase €4,537,531 in revenue, a 25.8% decline. That is the equivalent of losing roughly 72 average-sized clients.
The data paints a clear picture: your revenue base is top-heavy. Two clients alone account for over 25% of total revenue. The top five represent nearly 41%. That is above the recommended threshold of 30% for any service business that wants to sleep well at night.
The Gini coefficient of 0.78 confirms what the numbers suggest. Revenue is distributed very unevenly. The median client pays roughly €5,162 per year, while the average sits at €62,890. That gap between median and average is a red flag for concentration.
On the positive side: your top two clients are also your most profitable, each generating over €1.3M in profit. But Parker Solutions at position #5 stands out with a negative margin despite €590K in revenue. High revenue does not equal healthy revenue.
The bottom half of your client base (140 clients) generates just €184K combined. That is roughly 1% of total revenue. These clients are not meaningfully contributing to the business financially, though they may serve as a pipeline for growth.
Concrete steps to reduce concentration risk and build a more resilient revenue base.
Anderson & Partners represents 13.2% of total revenue. Losing this single client would cost €2,324,616. Actively grow mid-tier accounts to dilute this concentration. Target: no single client above 10% within 12 months.
Your top 5 clients generate 40.9% of revenue. Schedule dedicated QBRs for each. Map decision-makers, contract renewal dates, and satisfaction scores. Build early-warning systems for churn signals.
Parker Solutions generates nearly 590K in revenue but operates at a loss. Review the contract terms, scope creep on projects, and time entry data. Either renegotiate pricing or reduce service scope.
You have 48 clients in the 25K-100K range generating €2,473,566. Identify 10-15 of these with the highest growth potential and build expansion plans: additional services, Microsoft 365 upsells, managed security add-ons.
137 clients generate less than 5K each, contributing under 1% of total revenue. Determine which of these are early-stage relationships worth investing in vs. clients that will never grow. Consider minimum contract thresholds.
Industry guidance suggests no single client should exceed 10% of total revenue, and the top 5 combined should stay below 30%. These are guidelines, not hard rules. The point is to know your numbers and plan for the worst case.
The HHI measures market concentration by summing the squared market shares of all participants. Below 1,000 is considered unconcentrated. Between 1,000 and 2,500 is moderately concentrated. Above 2,500 is highly concentrated. Your HHI of 547 is technically unconcentrated, but the top-client share tells a different story.
The Gini coefficient ranges from 0 (every client pays the same) to 1 (one client generates all revenue). A typical healthy MSP sits between 0.55 and 0.70. Your score of 0.78 indicates high inequality in revenue distribution across your client base.
An AI assistant connects to your Proxuma Power BI semantic model via MCP (Model Context Protocol). It writes DAX queries, executes them against your live billing data, and formats the results into this report. The entire process takes under fifteen minutes.
Yes. If you have Proxuma Power BI connected to your Autotask PSA, any AI assistant with MCP access can generate this exact report using your own billing data. The DAX queries are included in each section so you can verify or modify them.
Parker Solutions at position #5 has costs exceeding revenue. This typically happens with fixed-price contracts where actual hours exceed estimates, or with heavily discounted agreements. It warrants an immediate contract review.
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