This report provides a detailed breakdown of technician productivity: top performers vs. underperformers 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]), "EntryCount", COUNTROWS('BI_Autotask_Time_Entries')), [BillableHours], DESC)The top performers combine ticket volume with strong billable output. This leaderboard excludes one outlier account showing 21,279 completed tickets — that figure is 6x the next-highest tech and consistent with a dispatch queue or automated ticket assignment account rather than an individual contributor. The real leaderboard starts at Tracy Fitzpatrick with 3,585 completed tickets.
| # | Technician | Tickets Completed | Billable Hours | Billable % | Assessment |
|---|---|---|---|---|---|
| 1 | Tracy Fitzpatrick | 3,585 | 1,254 | 97.2% | Exceptional |
| 2 | Gregory Horn | 3,234 | 957 | 63.6% | Billable Gap |
| 3 | Brandon Bishop | 2,632 | 1,322 | 97.1% | Exceptional |
| 4 | Jane Stewart | 2,614 | — | n/a | Tickets only |
| 5 | Daniel Daniels | 2,427 | 1,344 | 94.7% | Exceptional |
| 6 | Maxwell Reed | 1,899 | 1,838 | 89.6% | Top Revenue |
| 7 | Andrew Roberts | 1,871 | 1,527 | 80.9% | Strong |
| 8 | David Collins | 1,672 | — | n/a | Tickets only |
| 9 | Jonathon Burton | 1,665 | 1,213 | 94.4% | Exceptional |
| 10 | Stephen Nelson | 1,336 | — | n/a | Partial data |
Note: “—” in billable hours means the technician did not appear in the top 25 time entries by volume. Billable hours data covers the 25 highest-activity resources in the time entries table.
EVALUATE
ADDCOLUMNS(
TOPN(25,
SUMMARIZE(
'BI_Autotask_Tickets',
'BI_Autotask_Tickets'[primary_resource_name]
),
[Tickets - Count - Completed], DESC
),
"Tickets Completed", [Tickets - Count - Completed]
)
ORDER BY [Tickets Completed] DESCFour technicians stand out as genuinely exceptional by combining high ticket completion with high billable rates. These are the resources who deliver the most value per working day because every hour they log also generates revenue.
Gregory Horn is the clearest example of a productivity gap in this dataset. He is the second-highest ticket completer at 3,234 tickets — but his billable rate is 63.6%, well below the team average of 75.6%. That gap means that despite handling a large volume of work, a substantial portion of his time is not generating client revenue. At the team's typical rate structure, recovering 10 points of billable rate on 1,505 hours would translate to roughly 150 additional billable hours per year.
Several resources show extremely low completed ticket counts. Some of these are expected — part-time roles, escalation specialists, or internal contributors who do not take primary ticket ownership. Others may indicate resources who are not actively engaged with the ticket queue. The data below shows the bottom 8 by completed tickets, excluding the resource with null completion data.
| Technician | Tickets Completed | vs. Team Avg (567) | Likely Explanation |
|---|---|---|---|
| Mr. Corey Griffin | 1 | -99.8% | Likely new hire, internal role, or data entry error |
| Michael Macdonald | 3 | -99.5% | Likely specialist or non-standard role |
| Stephen Castillo | 6 | -98.9% | Review queue access and assignment rules |
| Michael Ayers | 6 | -98.9% | Review queue access and assignment rules |
| Christopher Garcia | 8 | -98.6% | Check if dispatch is routing tickets to this resource |
| Sean Castillo | 9 | -98.4% | Very low. Likely a specialist or off-queue role |
| Jaime Weaver | 15 | -97.4% | Review workload and active assignments |
| Virginia Combs | 16 | -97.2% | Logs 932 hours in time entries — may focus on project work |
EVALUATE
ADDCOLUMNS(
TOPN(10,
SUMMARIZE(
'BI_Autotask_Tickets',
'BI_Autotask_Tickets'[primary_resource_name]
),
[Tickets - Count - Completed], ASC
),
"Tickets Completed", [Tickets - Count - Completed]
)
ORDER BY [Tickets Completed] ASCBoth combine high ticket output (3,585 and 2,632 respectively) with near-97% billable rates. That combination is rare and difficult to sustain. These two should be part of any conversation about best practices for time logging, ticket handling, and client work categorization. Understanding how they structure their day could help lift the team average.
This is the clearest productivity gap in the dataset. High ticket completion paired with a low billable rate suggests that either the tickets Horn handles are categorized as non-billable, time entries are being logged incorrectly, or there is internal work mixed into his queue. A short analysis of his time entry work types against his completed ticket categories would likely identify the root cause within one review session.
This is almost certainly a system account, dispatch queue, or auto-assignment resource rather than a human technician. Leaving it in productivity reporting without flagging it would artificially inflate averages and distort comparisons. Check the resource configuration in Autotask and ensure it is either excluded from reporting or labeled as a queue rather than an individual contributor.
Despite ranking sixth on ticket count, Maxwell Reed generates 1,838 billable hours at 89.6%. That is the highest billable output of any resource and it comes from a combination of high volume (2,050 total hours) and a strong conversion rate. He is the highest single-resource revenue contributor, which makes him worth protecting from schedule creep, over-assignment of non-billable work, or burnout risk.
Partly. Tickets completed is a strong signal of throughput — how much work a technician is moving through the system. But it does not capture complexity or time investment. A technician who resolves 3,000 simple password resets may produce less value than one who resolves 500 complex network issues. This is why this report combines ticket counts with billable hours and billable rate to give a more complete picture.
The billable hours data covers the top 25 resources by volume in the time entries table. Some technicians who close many tickets do not appear in the top 25 by time entries — this can happen when tickets are bulk-assigned or auto-closed without corresponding time logs. Checking whether these resources are logging time against their completed tickets is worth investigating in Autotask.
Start with data, not conclusions. Show the technician their specific numbers — tickets completed and billable rate — alongside the team average. Ask open questions: What types of work are you spending most of your time on? Are there ticket categories you handle that tend to be non-billable? Are there blockers in your queue or approval process that slow you down? Often, low performance has a structural cause that the technician can identify quickly when given the data.
Yes. The DAX queries in this report can be extended with CALCULATE and additional filter arguments on columns like queue_name, team_name, or ticket_category in BI_Autotask_Tickets. The live Power BI dashboard linked at the top of this page includes both filters as standard slicers.
Cross-reference this report with the utilization comparison report. If a technician is logging 2,000+ hours at 97% billable and their volume has been climbing quarter-over-quarter, that is a flag. The right response is not to reduce their assignments immediately but to review queue routing: are they the default escalation path? Can other techs with lower ticket counts and good billable rates absorb some of the load?
The numbers here come from two Autotask tables: tickets and time entries. The AI asked one question, ran five DAX queries across both tables, cross-referenced the results, and built this report in under two minutes. With your own Proxuma Power BI connection, the same analysis runs against your live data on demand.
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