This report provides a detailed breakdown of technician performance trends 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: MSP operations teams and service delivery managers
How often: As needed for specific analysis or reporting requirements
| Resource | Hours | Billable | Tickets |
|---|---|---|---|
| Dr. Amber Ayala DVM | 2,400 | 1,749 | 603 |
| James Li | 2,136 | 1,303 | 794 |
| Maxwell Reed | 2,050 | 1,838 | 2,613 |
Green = 90%+, Amber = 70–89%, Red = below 70%
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[resource_name], "TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked]), "BillableHours", SUM('BI_Autotask_Time_Entries'[Billable Hours]), "TicketCount", DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id])), [TotalHours], DESC)-- Same query as above; billable % = BillableHours / TotalHours * 100
-- Filter to a single tech for individual trend:
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(BI_Autotask_Time_Entries,
BI_Autotask_Time_Entries[resource_name] = "Gregory Horn"
),
BI_Common_Dim_Date[year],
BI_Common_Dim_Date[month]
),
"BillableHours",
CALCULATE(SUM(BI_Autotask_Time_Entries[Billable Hours])),
"TotalHours",
CALCULATE(SUM(BI_Autotask_Time_Entries[hours_worked])),
"BillablePct",
DIVIDE(
CALCULATE(SUM(BI_Autotask_Time_Entries[Billable Hours])),
CALCULATE(SUM(BI_Autotask_Time_Entries[hours_worked]))
) * 100
)
ORDER BY BI_Common_Dim_Date[year], BI_Common_Dim_Date[month]Both have logged above 92% billable every single month in the tracking window. Tracy has never dipped below 88.7% — and that was one month in March 2025. Brandon's low was 92.3% in October 2025. These are the techs to point at when setting team expectations.
Through December 2024, Horn averaged above 97%. In January 2025 the number dropped to 75.5%, then to 42.9% in February — and it never recovered. By January 2026 it was 28.9%. This is not volatility; it is a structural change in how his time is being allocated. Non-billable project work, internal tasks, or a role shift — this warrants direct investigation.
Roberts ran at 93–99% for the first 8 months, then fell to 46–57% for four consecutive months (May–Sep 2025). October 2025 bounced back to 100%, then returned to a normal range. This pattern — strong baseline with sudden dips — could indicate large internal projects, onboarding assignments, or batch non-billable work concentrated in certain months.
Reed hit 100% four times in H2 2024 and January 2025. Since then he has not exceeded 94.6% and has trended toward 80%. The drop is gradual, not sudden — suggesting a shift in work mix rather than a specific event. Worth checking if his ticket types or clients have changed.
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