This report provides a detailed breakdown of technician billable percentage analysis for managed service providers.
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: Operations managers, service delivery leads, and MSP owners managing capacity
How often: Weekly for scheduling, monthly for utilization reviews, quarterly for staffing decisions
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[resource_name], "TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked]), "BillableHours", SUM('BI_Autotask_Time_Entries'[Billable Hours]), "NonBillableHours", SUM('BI_Autotask_Time_Entries'[Non billable Hours])), [TotalHours], DESC)The table below shows each technician's billable hours, total hours, and their billable percentage. The target column shows how far each person sits relative to the 80% benchmark. Technicians with fewer than 100 hours logged are excluded from this view.
| Rank | Technician | Billable Hrs | Total Hrs | Billable % | vs Target |
|---|---|---|---|---|---|
| 1 | Maxwell Reed | 1,837.7 | 2,050.3 | 89.6% | +9.6% |
| 2 | Dr. Amber Ayala DVM | 1,749.2 | 2,399.8 | 72.9% | -7.1% |
| 3 | Andrew Roberts | 1,527.1 | 1,887.7 | 80.9% | +0.9% |
| 4 | David Hunt | 1,415.9 | 1,862.2 | 76.0% | -4.0% |
| 5 | Daniel Daniels | 1,343.6 | 1,418.4 | 94.7% | +14.7% |
| 6 | Brandon Bishop | 1,321.7 | 1,361.5 | 97.1% | +17.1% |
| 7 | Elizabeth Ortega | 1,308.3 | 1,433.4 | 91.3% | +11.3% |
| 8 | James Li | 1,303.4 | 2,136.0 | 61.0% | -19.0% |
| 9 | Tracy Fitzpatrick | 1,254.3 | 1,290.4 | 97.2% | +17.2% |
| 10 | Jennifer King | 1,228.0 | 1,584.5 | 77.5% | -2.5% |
| 11 | Jonathon Burton | 1,212.7 | 1,284.9 | 94.4% | +14.4% |
| 12 | Chelsea Thomas | 1,157.0 | 1,779.6 | 65.0% | -15.0% |
| 13 | Kevin Allen | 1,145.0 | 2,060.1 | 55.6% | -24.4% |
| 14 | Mr. Craig Peck | 1,122.5 | 1,232.2 | 91.1% | +11.1% |
| 15 | Jeremy White | 1,093.8 | 1,492.5 | 73.3% | -6.7% |
| 16 | Brandon Lynn | 1,087.2 | 1,343.7 | 80.9% | +0.9% |
| 17 | Gregory Horn | 957.0 | 1,504.5 | 63.6% | -16.4% |
| 18 | Darren Alexander | 943.4 | 1,224.2 | 77.1% | -2.9% |
| 19 | Paula Lewis MD | 850.1 | 1,293.8 | 65.7% | -14.3% |
| 20 | Jerry Mcfarland | 819.2 | 1,554.0 | 52.7% | -27.3% |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
'BI_Autotask_Time_Entries',
'BI_Autotask_Time_Entries'[resource_name]
),
"Billable Hours", [Billable],
"Total Hours", [Total],
"Billable Pct vs Logged", [Billable % (vs Logged)]
)
ORDER BY [Billable Hours] DESCThese technicians consistently bill the majority of their time. Their high rates suggest efficient ticket handling, well-structured work, and good time-logging habits. They are the baseline your whole team should aim for.
EVALUATE
FILTER(
ADDCOLUMNS(
SUMMARIZE(
'BI_Autotask_Time_Entries',
'BI_Autotask_Time_Entries'[resource_name]
),
"Billable Hours", [Billable],
"Total Hours", [Total],
"Billable Pct", [Billable % (vs Logged)]
),
[Billable Pct] >= 0.90 && [Total Hours] >= 500
)
ORDER BY [Billable Pct] DESCLow billable percentages on high-volume technicians are the biggest drain on revenue. It's not the person logging 50 hours per year with a poor billable rate — it's the person logging 2,000 hours at 55%. That difference is hundreds of unbilled hours every month.
EVALUATE
FILTER(
ADDCOLUMNS(
SUMMARIZE(
'BI_Autotask_Time_Entries',
'BI_Autotask_Time_Entries'[resource_name]
),
"Billable Hours", [Billable],
"Total Hours", [Total],
"Billable Pct", [Billable % (vs Logged)],
"Non Billable Hours", [Total] - [Billable]
),
[Billable Pct] < 0.70 && [Total Hours] >= 500
)
ORDER BY [Billable Pct] ASCEVALUATE
TOPN(12,
ADDCOLUMNS(
SUMMARIZE(
'BI_Autotask_Time_Entries',
'BI_Autotask_Time_Entries'[resource_name]
),
"Billable Hours", [Billable],
"Total Hours", [Total],
"Billable Pct", [Billable % (vs Logged)]
),
[Total Hours], DESC
)At 75.6% team-wide, the gap between current performance and the 80% target represents roughly 2,200 hours of unbilled time. Closing half that gap at a $90/hour rate would recover over $100,000 in revenue per year.
With 915 non-billable hours logged, Allen is the single largest opportunity for improvement. Investigating whether this is structural (assigned to internal projects) or behavioral (logging time incorrectly) should be the first step.
Tracy Fitzpatrick (97.2%), Brandon Bishop (97.1%), Daniel Daniels (94.7%), Jonathon Burton (94.4%), Elizabeth Ortega (91.3%), and Maxwell Reed (89.6%) demonstrate that hitting 90% is achievable. Their approach to ticket handling and time logging is worth studying.
Paul Hoffman has the lowest billable percentage among high-volume technicians at 10.8%. This is likely a role-based explanation (internal IT, project management) rather than a billing problem, but it should be confirmed and tracked separately.
In Autotask, billable percentage is calculated as billable hours divided by total hours logged. A time entry is considered billable when the "is non-billable" flag is false. This report uses that exact logic: it sums all time entries by resource, splits them into billable and non-billable, and divides to produce the percentage.
80% is the most common industry benchmark for MSP technicians. It accounts for the reality that some time is always internal: team meetings, training, administrative tasks, and lunch breaks. Expecting 100% billable is unrealistic. Below 70% starts to indicate systemic problems. The 80% target is configurable in Proxuma if your MSP uses a different benchmark.
Yes. Proxuma includes measures like "Billable % - This Week" and "Billable % - Last Week" that are pre-built in the dataset. You can SUMMARIZE by week number or by month using the date dimension, and track trends over time. The live dashboard version of this report updates every 15 minutes.
Billable % vs logged measures billable hours as a share of what was actually entered into Autotask. Billable % vs capacity measures billable hours against the technician's full working capacity — including hours that were never logged at all. The second metric is typically lower and reveals both billing gaps and time-logging gaps simultaneously.
Technicians near 100% billable typically have very few internal time entries, or their role is almost entirely client-facing. This can be a good sign, or it can indicate that internal time is being incorrectly flagged as billable. A quick review of their time entry types is the best way to confirm the data is accurate.
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