A multi-metric breakdown of 77 technicians across 50,752 hours from Autotask PSA time entries. This report ranks resources by billable percentage, ticket volume, hours per ticket, and client coverage to separate high performers from those who need coaching or workload rebalancing.
A multi-metric breakdown of 77 technicians across 50,752 hours from Autotask PSA time entries. This report ranks resources by billable percentage, ticket volume, hours per ticket, and client coverage to separate high performers from those who need coaching or workload rebalancing.
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
A multi-metric breakdown of 77 technicians across 50,752 hours from Autotask PSA time entries. This report ranks resources by billable percentage, ticket volume, hours per ticket, and client coverage to separate high performers from those who need coaching or workload rebalancing.
Key workforce metrics from Autotask PSA time entries across 77 active resources.
All key metrics side by side. Color-coded by billable percentage: green (75%+), amber (60-75%), red (below 60%).
| Resource | Tickets | Avg FR (h) | FR Met | Res Met |
|---|---|---|---|---|
| Mr. David Cooper DDS | 21,438 | 2.67 | 9,206 (42.9%) | 16,800 (78.4%) |
| Tracy Fitzpatrick | 3,600 | 4.02 | 1,744 (48.4%) | 1,905 (52.9%) |
| Gregory Horn | 3,240 | 3.25 | 2,219 (68.5%) | 2,125 (65.6%) |
| Brandon Bishop | 2,641 | 5.04 | 1,518 (57.5%) | 1,661 (62.9%) |
| Jane Stewart | 2,628 | 14.42 | 334 (12.7%) | 933 (35.5%) |
| Daniel Daniels | 2,444 | 3.50 | 1,947 (79.7%) | 1,786 (73.1%) |
| Maxwell Reed | 1,906 | 2.80 | 1,407 (73.8%) | 1,246 (65.4%) |
| Andrew Roberts | 1,899 | 7.53 | 1,059 (55.8%) | 788 (41.5%) |
| Jonathon Burton | 1,680 | 3.10 | 921 (54.8%) | 894 (53.2%) |
| David Collins | 1,678 | 12.20 | 352 (21.0%) | 701 (41.8%) |
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Tickets'[primary_resource_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'), "AvgFirstResponseHrs", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), "FirstResponseMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "ResolutionMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1)), [TicketCount], DESC)All 15 resources sorted by billable percentage, highest to lowest. The team average is 75.6%.
EVALUATE
ADDCOLUMNS(
SUMMARIZECOLUMNS(
'BI_Autotask_Time_Entries'[resource_name],
"TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked]),
"BillableHours", CALCULATE(
SUM('BI_Autotask_Time_Entries'[hours_worked]),
'BI_Autotask_Time_Entries'[is_non_billable] = FALSE
)
),
"BillablePct", DIVIDE([BillableHours], [TotalHours])
)
ORDER BY [BillablePct] DESCPlotting ticket volume against hours per ticket reveals four distinct resource profiles. High-ticket, low-hours resources handle quick tasks. Low-ticket, high-hours resources work on complex projects.
The data splits your technicians into clear groups. Tech N, Tech M, Tech D, Tech E, and Tech J all handle over 2,000 tickets with less than 1 hour per ticket on average. These are your rapid-response techs: password resets, quick fixes, and first-touch resolution.
On the other end, Tech L (84.32 hrs/ticket), Tech F (22.17), and Tech C (20.81) spend significantly more time per ticket. This is not necessarily a problem. These resources likely handle projects, infrastructure work, or complex escalations. The key question is whether those hours are being billed. Tech L bills 91.3% and Tech F bills 76.0%, which is healthy. Tech C at 55.6% is the concern.
Tech G sits in the middle with 11.95 hours per ticket and only 65.0% billable. That combination of moderate complexity and low billing rate deserves a closer look at how time is being categorized.
How tickets are distributed across resource tiers. Some technicians handle thousands of tickets while others work on a handful of complex items.
EVALUATE
ADDCOLUMNS(
SUMMARIZECOLUMNS(
'BI_Autotask_Time_Entries'[resource_name],
"TicketCount", DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id])
),
"Tier", SWITCH(
TRUE(),
[TicketCount] >= 2000, "High Volume",
[TicketCount] >= 400, "Mid Volume",
"Project / Low Volume"
)
)
ORDER BY [TicketCount] DESCThe number of unique clients each resource has worked with. A broad coverage means the technician touches many accounts; a narrow one suggests specialization or a dedicated assignment.
Tech C logs 2,060 hours but only bills 55.6%. That is 915 non-billable hours. Tech I is worse at 52.7% with 735 non-billable hours. Combined, that is 1,650 hours of unbilled work. At even a conservative rate of $100/hr, that represents $165,000 in lost revenue potential. These two resources need an immediate time entry audit.
With only 17 tickets and 1,433 total hours, Tech L is clearly a project resource. The 91.3% billable rate is excellent, so this is not a billing problem. But 84 hours per ticket raises the question: are these tickets scoped correctly? Are time entries being logged against too few tickets? If a project runs 200 hours, it should probably be broken into subtasks.
Tech N, Tech M, Tech D, Tech E, and Tech J collectively process 13,422 of the top 15's 17,806 tickets. That concentration creates a risk: if any of these five leave or burn out, a large share of ticket throughput disappears. Consider cross-training mid-tier resources (Tech B, Tech H, Tech K) to absorb overflow.
Tech N runs at 97.1% billable across 3,275 tickets and 137 clients. Tech M is at 94.7% across 3,220 tickets and 146 clients. Both combine high volume, high billing rates, and broad client coverage. These are your model resources. Study what they do differently and use their patterns as the training standard.
Narrow client coverage combined with 52.7% billable suggests Tech I may be spending too much time on internal tasks, training, or administrative work. Alternatively, they could be assigned to a small group of clients with heavy non-billable support obligations. Either way, this resource needs a workload review.
Concrete steps to improve team utilization and balance workloads.
Pull the full time entry breakdown for both resources. Categorize every non-billable entry: internal meetings, training, admin, travel, or miscategorized billable work. Target: identify at least 200 hours per resource that should either be reclassified as billable or eliminated through process changes. Review within 30 days.
84 hours per ticket makes it nearly impossible to track progress or spot scope creep. Work with Tech L to restructure ongoing projects into smaller, trackable tickets. This gives better visibility into where time goes and makes it easier to flag when a project is running over budget.
Tech N, M, D, E, and J handle 75% of ticket volume. Create a knowledge-sharing program where mid-tier techs (B, H, K) shadow the top performers for two weeks. Goal: increase the number of resources who can handle 1,000+ tickets per year from 5 to 8, reducing single-point-of-failure risk.
Billable percentage is calculated as Billable Hours divided by Total Hours. Billable hours are time entries where is_non_billable is FALSE in the BI_Autotask_Time_Entries table. A resource logging 1,500 billable hours out of 2,000 total hours has a 75% billable rate.
Industry benchmarks vary, but most MSPs target 70-80% for service desk technicians and 60-70% for senior engineers who also handle internal projects. Resources below 60% typically need a workload audit to understand where non-billable time is going.
This is a demo report using synthetic data. In your own deployment, Proxuma Power BI shows the actual resource names from Autotask. The anonymized labels (Tech A, Tech B, etc.) are just placeholders for public demonstration.
Tech L handles only 17 tickets with 1,433 total hours, which points to project-based work. Long-running infrastructure deployments, migrations, or consulting engagements often have few tickets but many hours each. The metric is not inherently bad but suggests the ticketing structure should be reviewed for better granularity.
Yes. Add a date filter to the DAX queries using the date_worked column in BI_Autotask_Time_Entries. You can also filter by resource_role or queue_name to segment by department. The Proxuma Power BI model supports all standard Autotask dimensions.
Yes. Copy any query from the toggles above and paste it into DAX Studio or the Power BI Desktop performance analyzer. The queries reference standard Proxuma data model tables and measures that exist in every Proxuma Power BI deployment.
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