How quickly tickets move from created to completed across 67,521 tickets, with first-hour fix rates and same-day resolution. Generated by AI via Proxuma Power BI MCP server.
How quickly tickets move from created to completed across 67,521 tickets, with first-hour fix rates and same-day resolution. 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: Service desk managers, dispatch leads, and operations teams
How often: Daily for queue management, weekly for trend analysis, monthly for capacity planning
How quickly tickets move from created to completed across 67,521 tickets, with first-hour fix rates and same-day resolution. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "ClosedByFirst", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]), "FirstDayRes", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_day_resolution]), "AvgResHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]))Largest clients by completed tickets with closure and same-day metrics.
| # | Client | Completed | Same-Day % | First Resp % | Status |
|---|---|---|---|---|---|
| 1 | Client A | 6,268 | 21.5% | 43.2% | |
| 2 | Client B | 5,393 | 25.4% | 88.2% | |
| 3 | Client C | 5,250 | 22.0% | 87.5% | |
| 4 | Client D | 2,742 | 37.1% | 73.7% | |
| 5 | Client E | 2,364 | 41.4% | 98.0% | |
| 6 | Client F | 2,356 | 15.6% | 86.0% | |
| 7 | Client G | 2,155 | 33.8% | 84.9% | |
| 8 | Client H | 1,745 | 14.4% | 68.6% | |
| 9 | Client I | 1,692 | 47.0% | 70.1% | |
| 10 | Client J | 1,684 | 51.0% | 76.3% |
EVALUATE
TOPN(
10,
SUMMARIZE(
dw_tickets,
dw_tickets[company_name],
"Completed", [Tickets - Count - Completed],
"SameDayPct", [Tickets - Same Day Resolution %],
"FirstRespPct", [Tickets - First Response Met %]
),
[Completed], DESC
)
ORDER BY [Completed] DESCSpeed indicators across the full ticket lifecycle.
EVALUATE
ROW(
"AvgHoursPerTicket", [Tickets - Avg Hours Per Ticket],
"FirstHourFixPct", [Tickets - First Hour Fix %],
"OverdueCount", CALCULATE(
COUNTROWS(dw_tickets),
dw_tickets[resolved_due_age_days] > 0,
dw_tickets[ticket_status] <> "Complete"
)
)A 98.8% closure rate is excellent. The service desk is keeping up with incoming volume. Of 67,521 tickets created, only 844 remain open or unresolved. That means the backlog is not growing, and new tickets are being processed at roughly the same rate they come in.
The 16.1% first-hour fix rate is low. Industry benchmarks for MSPs are typically 25-35%. The 0.49 average hours per ticket suggests many tickets are quick, but the first-hour fix specifically measures tickets fully resolved within 60 minutes of creation. There may be a classification issue where quick fixes are not being marked as resolved promptly. Technicians could be completing work but leaving tickets open for documentation or quality checks.
Client J stands out with 51% same-day resolution, the highest in the top 10. Client E has a 98% first response rate. Both are worth studying for best practices. Client A with 43.2% first response across 6,268 tickets is the most urgent problem. That is the highest-volume client with the lowest first response compliance.
43.2% first response across the highest-volume client. Every delayed response on this account is visible. Review dispatch rules and resource assignment for this client. A dedicated queue or escalation path could close the gap quickly.
16.1% is below MSP benchmarks. Check whether technicians are resolving tickets but not closing them immediately. If the work is done within the hour but the ticket stays open for notes or approval, the metric will undercount actual performance. A workflow adjustment could fix the measurement without changing the work.
The backlog is not growing. The team can focus on speed rather than volume. This is a good position to be in: the capacity is there, and now the question is how to use it more efficiently on the accounts that need it.
The percentage of tickets where the resolution happened within 60 minutes of ticket creation. It measures how many issues are fully closed in the first hour, from the moment they enter the system.
Total logged hours divided by total completed tickets. This gives a single number that represents the average effort spent per ticket across the entire dataset of 66,677 completed tickets.
Tickets that have passed their SLA due date and are still open. These are active tickets where the resolution deadline has already been missed. The count is based on resolved_due_age_days > 0 for non-completed tickets.
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