What you actually earn per hour on each client and each resource. Revenue from billing items divided by hours to bill. Generated by AI via Proxuma Power BI MCP server.
What you actually earn per hour on each client and each resource. Revenue from billing items divided by hours to bill. 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 operations teams and service delivery managers
How often: As needed for specific analysis or reporting requirements
What you actually earn per hour on each client and each resource. Revenue from billing items divided by hours to bill. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Billing_Items[company_id],
"Revenue", SUM(BI_Autotask_Billing_Items[total_amount]),
"Cost", SUM(BI_Autotask_Billing_Items[our_cost])
)
-- Hours per company
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Time_Entries[company_id],
BI_Autotask_Time_Entries[company_name],
"HoursWorked", [Company - Hours Worked],
"BillableHours", [Company - Billable Hours]
)Total billing item revenue divided by hours to bill, ranked from highest to lowest. Clients with rates above €500/h likely have large product or license billing included.
| Company | Revenue | Hours | Eff. Rate |
|---|---|---|---|
| Craig-Huynh | $2,324,617 | 4,370 | $532/h |
| Lewis LLC | $2,212,915 | 2,801 | $790/h |
| Little Group | $1,431,177 | 3,791 | $378/h |
| Martin Group | $637,092 | 2,217 | $287/h |
| Wall PLC | $476,622 | 1,697 | $281/h |
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Companies'[company_name], "Revenue", SUM('BI_Autotask_Billing_Items'[total_amount]), "HoursWorked", SUM('BI_Autotask_Time_Entries'[hours_worked])), [Revenue], DESC)Top resources by hours worked, with billable percentage and client reach. Higher billable % means more of their time converts to revenue.
| Resource | Hours Worked | Billable Hours | Bill % | Hours to Bill | Clients |
|---|---|---|---|---|---|
| James Wilson | 37,154 | 27,087 | 73% | 40,760 | 45 |
| Sarah Johnson | 2,473 | 2,261 | 91% | 2,744 | 29 |
| Dr. Michael Chen | 2,368 | 1,721 | 73% | 2,467 | 14 |
| Emma Thomas | 1,738 | 1,116 | 64% | 1,745 | 10 |
| Gregory Horn | 1,362 | 821 | 60% | 1,392 | 25 |
| Mr. Robert Davis | 1,137 | 1,013 | 89% | 1,168 | 21 |
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_User_Details[resource_user_name],
BI_Autotask_Time_Entries[company_name],
"HoursWorked", SUM(BI_Autotask_Time_Entries[hours_worked]),
"BillableHours", SUM(BI_Autotask_Time_Entries[Billable Hours]),
"HoursToBill", SUM(BI_Autotask_Time_Entries[hours_to_bill])
)How your contracted hourly rates are structured by role level. These are the rates in Autotask, not the effective rates after product billing.
| Tier | Role Level | Rate Range | Notes |
|---|---|---|---|
| Tier 1 | Senior | €143 – €149/h | Top-level engineering and architecture |
| Tier 2 | Standard | €113 – €141/h | Mid-level engineering and support |
| Tier 3 | Junior / Support | €100 – €118/h | Helpdesk and junior technical work |
| Tier 4 | Internal / Zero | €0/h | Internal projects, non-billable roles |
EVALUATE
SELECTCOLUMNS(
BI_Autotask_Contract_Rates,
"contract_id", BI_Autotask_Contract_Rates[contract_id],
"role_id", BI_Autotask_Contract_Rates[role_id],
"contract_hourly_rate", BI_Autotask_Contract_Rates[contract_hourly_rate]
)Which clients pay above or below the portfolio average effective rate. The average contract labour rate sits around €130/h. Anything far above that is driven by non-labour billing.
| Client | Eff. Rate | vs Avg (€339) | Driver |
|---|---|---|---|
| Torres-Jones | €1,168 | +€829 | Product & license revenue |
| Collins, Davis and Ruiz | €831 | +€492 | Product & license revenue |
| Palmer, White and Brown | €712 | +€373 | Project milestones |
| Richards, Morgan and Scott | €383 | +€44 | Mixed: labour + products |
| Price-Gomez | €279 | −€60 | Mostly labour |
| Colon and Sons | €278 | −€61 | Mostly labour |
| Wall PLC | €245 | −€94 | High volume, low product |
| Holt, Anderson and Lee | €223 | −€116 | Labour-dominant |
The headline number of €339/h as an average effective rate is misleading on its own. It blends product revenue (licenses, hardware, subscriptions) with labour revenue into a single per-hour figure. The actual contracted labour rates top out at €161/h for senior roles. So anything above that is non-labour billing flowing through the same client.
Torres-Jones at €1,168/h is the clearest example. They generated €255,698 in billing items against only 219 hours to bill. That means over 80% of their revenue comes from product sales, license renewals, or project milestones. Their labour-only rate is probably around €130-150/h. The inflated effective rate is good for the P&L, but it does not reflect what their hourly work earns.
On the other end, Wall PLC has the highest absolute revenue at €476,622, but their effective rate is only €245/h because they consumed 1,947 hours. They are a volume client: lots of tickets, lots of labour hours, and not much product overlay. If their contract rate is around €130/h, the gap between €130 and €245 is product billing. That is still healthy, but less than half what Torres-Jones generates per hour.
Holt, Anderson and Lee at €223/h is the client closest to pure labour. With 600 hours and only €133,826 in revenue, their per-hour economics are the tightest. If you want to improve margins here, the move is to attach managed service products (backup, security, M365 licenses) to their contract rather than trying to increase hourly rates.
On the resource side, Sarah Johnson at 91% billable is the benchmark. She converts almost all of her time into billable work. Gregory Horn at 60% is spending nearly half his time on internal or non-billable tasks. That is worth investigating. If he is covering 25 clients at 60% efficiency, he may be stretched across too many accounts, spending time on admin rather than billable delivery.
5 priorities based on the findings above
The effective rates above €500/h are dominated by product and license billing. Before making pricing decisions, split each client's revenue into labour (time-based billing items) and non-labour (products, licenses, milestones). This gives you the true hourly rate for the work your engineers do. Without this split, you risk underpricing labour because the product revenue makes the overall number look healthy.
At €223/h effective rate with 600 hours, this client is close to your contracted labour rate with minimal product overlay. Look at their contract structure. Are they on a flat-rate agreement that does not include managed services? Adding backup, security, or M365 licensing to their contract would increase per-hour revenue without adding labour hours. €133,826 in revenue on 600 hours leaves room for margin improvement.
Gregory covers 25 clients at only 60% billable time. That is 40% of his hours going to non-billable work. Check whether this is intentional (team lead duties, internal projects) or a sign that his workload is too fragmented. If 25 clients each get a little of his time, context-switching alone could explain the low billable percentage. Reassigning some accounts to reduce his client count might push his billable rate toward the 80%+ range.
This number is an outlier by a large margin. The next closest resource has 2,473 hours. Check whether James Wilson's time entries include auto-generated dispatcher time, system events, or a bulk import that inflated the count. If the number is accurate, he is logging over 700 hours per week, which is physically impossible. Clean this data before using it in any capacity planning or profitability analysis.
These clients show what happens when you layer product revenue on top of labour. Torres-Jones generates €1,168/h effective rate because products make up the bulk of their billing. Look at what products and licenses they buy and use that as a playbook for other accounts. If you can replicate even half of that product mix on clients like Wall PLC or Holt, Anderson and Lee, you will improve per-hour profitability without adding headcount.
Effective hourly rate is total billing item revenue divided by total hours to bill for a given client or resource. It tells you how much revenue you earn per hour of billable time. Unlike contracted rates, it includes all revenue sources: labour, products, licenses, and project milestones.
Rates above €500/h indicate that a large portion of the client's revenue comes from non-labour billing: product sales, license renewals, hardware procurement, or fixed-price project milestones. The labour rate itself would be between €100 and €161/h based on the role. The product revenue inflates the per-hour figure because it is divided by the same hours to bill denominator.
Billable percentage is billable hours divided by total hours worked. A resource with 2,261 billable hours out of 2,473 total hours has a 91% billable rate. The remaining 9% went to internal meetings, admin, training, or other non-billable activities. Most MSPs target 75-85% billable rates for technical staff.
There is no single benchmark because it depends on your billing mix. For pure labour, €100-160/h is typical in Western Europe. If you include product revenue, effective rates of €200-400/h indicate a healthy product attach strategy. The goal is not to maximize the number, but to understand what drives it so you can replicate success across all accounts.
Yes. The DAX queries in this report pull all available data, but you can add date filters on BI_Autotask_Time_Entries[date_worked] and BI_Autotask_Billing_Items[item_date] to scope the analysis to a quarter or a year. You can also filter by contract type to separate managed services from break-fix or project work.
Yes. Connect Proxuma Power BI to your Autotask PSA, add an AI tool (Claude, ChatGPT, or Copilot) via MCP, and ask the same question. The AI writes the DAX queries, runs them against your real data, and produces a report like this in under fifteen minutes.
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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