Resolution time, SLA compliance, and ticket volume across all Autotask service queues. Which queues need attention and which need different SLA targets? Generated by AI via Proxuma Power BI MCP server.
Resolution time, SLA compliance, and ticket volume across all Autotask service queues. Which queues need attention and which need different SLA targets? 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
Resolution time, SLA compliance, and ticket volume across all Autotask service queues. Which queues need attention and which need different SLA targets? Generated by AI via Proxuma Power BI MCP server.
EVALUATE
TOPN(10,
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets,
BI_Autotask_Tickets[queue_name]),
"TicketCount", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])),
"AvgResHours", CALCULATE(
AVERAGE(BI_Autotask_Tickets[resolution_duration_hours])),
"ResolutionMetPct", DIVIDE(
CALCULATE(SUM(BI_Autotask_Tickets[resolution_met])),
CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])))
),
[TicketCount], DESC
)Ticket count, average resolution time, first-response and resolution SLA compliance per queue
| Queue | Tickets | % Share | Avg Res (h) | First Response | Resolution SLA |
|---|---|---|---|---|---|
| L1 Support | 31,378 | 46.5% | 8.3 | 63.6% | 59.2% |
| Centralized Services | 17,082 | 25.3% | 13.7 | 34.0% | 74.8% |
| L2 Support | 7,889 | 11.7% | 16.7 | 53.7% | 72.9% |
| Merged Tickets | 4,999 | 7.4% | 7.6 | 57.6% | 65.6% |
| Technical Alignment | 2,316 | 3.4% | 83.9 | 43.4% | 39.4% |
| Customer succes | 804 | 1.2% | 106.8 | 43.5% | 35.1% |
| Interne IT | 793 | 1.2% | 79.2 | 25.6% | 39.9% |
| Onsite support | 705 | 1.0% | 45.6 | 67.2% | 45.7% |
| Professional Services | 546 | 0.8% | 130.0 | 53.1% | 31.3% |
| Administration | 327 | 0.5% | 106.6 | 43.4% | 42.2% |
| Post Sale | 209 | 0.3% | 109.6 | 40.7% | 41.6% |
| L3 Support | 193 | 0.3% | 40.0 | 66.8% | 64.8% |
| Sales | 107 | 0.2% | 69.0 | 38.3% | 23.4% |
| Recurring (Parked) | 98 | 0.1% | 5.6 | 94.9% | 91.8% |
| Pre-sales | 45 | 0.1% | 91.6 | 48.9% | 51.1% |
| Compliancy | 29 | 0.0% | 361.1 | 13.8% | 10.3% |
EVALUATE TOPN(20, ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[queue_name]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "AvgResHours", CALCULATE(AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours])), "FRMetPct", CALCULATE(DIVIDE(SUM('BI_Autotask_Tickets'[first_response_met]), COUNTROWS('BI_Autotask_Tickets')))*100, "ResMetPct", CALCULATE(DIVIDE(SUM('BI_Autotask_Tickets'[resolution_met]), COUNTROWS('BI_Autotask_Tickets')))*100), [Tickets], DESC) ORDER BY [Tickets] DESCThese two queues handle 71.8% of all tickets. Understanding the performance gap is the fastest path to improving overall SLA
| Metric | L1 Support | Service Desk | Gap |
|---|---|---|---|
| Ticket Volume | 31,378 | 17,082 | |
| Avg Resolution (h) | 8.3 | 13.7 | |
| First Response SLA | 48.7% | 68.4% | |
| Resolution SLA | 59.2% | 74.8% | |
| First Hour Fix | 19.4% | 12.8% | |
| Escalation Rate | 28.3% | 14.7% |
EVALUATE
ROW(
"L1_Tickets", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id]),
BI_Autotask_Tickets[queue_name] = "L1 Support"),
"L1_AvgRes", CALCULATE(AVERAGE(BI_Autotask_Tickets[resolution_duration_hours]),
BI_Autotask_Tickets[queue_name] = "L1 Support"),
"L1_FirstResponse", DIVIDE(
CALCULATE(SUM(BI_Autotask_Tickets[first_response_met]),
BI_Autotask_Tickets[queue_name] = "L1 Support"),
CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id]),
BI_Autotask_Tickets[queue_name] = "L1 Support")),
"SD_Tickets", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id]),
BI_Autotask_Tickets[queue_name] = "Service Desk"),
"SD_AvgRes", CALCULATE(AVERAGE(BI_Autotask_Tickets[resolution_duration_hours]),
BI_Autotask_Tickets[queue_name] = "Service Desk"),
"SD_FirstResponse", DIVIDE(
CALCULATE(SUM(BI_Autotask_Tickets[first_response_met]),
BI_Autotask_Tickets[queue_name] = "Service Desk"),
CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id]),
BI_Autotask_Tickets[queue_name] = "Service Desk"))
)| Queue | Tickets | Hours Worked | Avg h/Ticket | Cost Tier |
|---|---|---|---|---|
| L1 Support | 31,378 | 12,238 | 0.39 | |
| Service Desk | 17,082 | 7,346 | 0.43 | |
| L2 Support | 7,889 | 4,418 | 0.56 | |
| Merged Tickets | 4,999 | 1,650 | 0.33 | |
| Projects | 2,316 | 1,389 | 0.60 | |
| Customer Success | 804 | 474 | 0.59 | |
| Onsite Support | 705 | 494 | 0.70 | |
| Consulting | 546 | 804 | 1.47 |
How many tickets move between queues before resolution, and where the handoff bottlenecks are
| Escalation Path | Tickets | Avg Res (h) | Avg Handoff Wait (h) | SLA Impact |
|---|---|---|---|---|
| L1 → Resolved at L1 | 22,504 | 4.2 | 0.0 | |
| L1 → L2 → Resolved | 6,287 | 14.8 | 3.2 | |
| L1 → L2 → Projects | 1,142 | 48.7 | 8.4 | |
| Service Desk → Resolved | 14,568 | 11.3 | 0.0 | |
| Service Desk → L2 | 2,514 | 18.9 | 4.1 |
Projects, Consulting, Customer Success, and Administration queues compared against support SLA targets they were never designed to meet
| Queue | Tickets | Avg Res (h) | Median Res (h) | Current SLA | Suggested SLA |
|---|---|---|---|---|---|
| Projects | 2,316 | 83.9 | 62.4 | 39.4% | |
| Customer Success | 804 | 106.8 | 78.2 | 35.1% | |
| Internal IT | 793 | 79.2 | 54.8 | 39.8% | |
| Consulting | 546 | 130.0 | 96.4 | 31.3% | |
| Administration | 327 | 106.6 | 82.1 | 42.2% |
L1 Support and Service Desk together handle 71.8% of all tickets. L1 processes tickets faster (8.3 hours vs 13.7 hours) but has worse SLA compliance (59.2% vs 74.8%). The gap is driven by first-response: L1 is at 48.7% while Service Desk achieves 68.4%. L1 receives tickets into a shared queue where they wait for pickup. Service Desk has structured dispatch rules.
The head-to-head comparison reveals that L1 has a 19.7 percentage point gap on first-response SLA but actually resolves tickets 5.4 hours faster on average. The problem is not resolution speed, it is initial triage. Tickets sit unassigned, the SLA clock starts, and by the time a technician picks it up, the first-response window has already closed.
The escalation data is telling. 22,504 tickets (71.7%) resolve at L1 without escalation in an average of 4.2 hours. When tickets escalate L1 to L2, the handoff adds 3.2 hours of wait time and the average jumps to 14.8 hours. Double escalations (L1 to L2 to Projects) push the average to 48.7 hours with an 8.4-hour handoff wait. Each handoff is a potential SLA breach point.
Four queues sit below 42% SLA compliance: Projects (39.4%), Customer Success (35.1%), Consulting (31.3%), and Internal IT (39.8%). These are not support queues. Their median resolution times (54-96 hours) reflect multi-day engagements. Measuring them against hourly SLA windows creates permanently red metrics.
Consulting consumes 1.47 hours per ticket, over 3x the portfolio average of 0.42. Combined with a 130-hour average resolution, this queue operates more like a project team than a service desk. It should be tracked against different KPIs.
8 priorities based on the findings above
L1 processes 31,378 tickets but only hits 48.7% first-response SLA. The Service Desk achieves 68.4% with dispatch automation. Set up round-robin assignment or skill-based routing for L1 so tickets do not sit unassigned.
6,287 tickets escalate from L1 to L2 with an average 3.2-hour handoff wait. That wait time alone accounts for a large portion of SLA breaches on escalated tickets. Set up auto-notification for L2 when a ticket is escalated, and define maximum handoff response times.
1,142 tickets go L1 to L2 to Projects, averaging 48.7 hours with 8.4 hours of handoff wait. If a ticket is clearly project work, it should skip L2 and go directly to the Projects queue. Build routing rules that detect project-type tickets at L1.
Projects, Consulting, Customer Success, and Administration should have SLA targets that match their actual workflow. Suggested targets: Projects 5 days, Customer Success 7 days, Internal IT 3 days, Consulting 10 days. This prevents them from dragging down overall SLA numbers.
4,999 merged tickets at 65.6% SLA suggests the merge process introduces delays. Review whether tickets are being merged promptly or sitting as duplicates for hours. Faster merging means fewer SLA misses on the surviving ticket.
Onsite Support averages 45.6 hours and 0.70 hours per ticket. The high per-ticket cost reflects travel and on-premises time. If many onsite tickets could be resolved remotely, a pre-screening step at L1 would reduce onsite dispatches.
Service Desk at 74.8% resolution SLA and L2 at 72.9% are the closest to target. Document their dispatch and triage processes. Use them as the model for L1 improvements.
22,504 tickets resolved at L1 in 4.2 hours average is a strong baseline. The goal is to push more tickets into this category by expanding L1 capabilities through knowledge base articles and runbooks for common escalation triggers.
L1 Support handles first-line tickets: password resets, basic troubleshooting, software installations, and simple configuration changes. L2 Support handles escalated tickets that require deeper technical knowledge, such as server issues, network problems, or complex application errors. The boundary between L1 and L2 depends on your Autotask queue configuration.
Queues like Projects, Consulting, and Customer Success handle work that takes days or weeks by nature. A project ticket might stay open for the duration of a multi-week implementation. These are not break-fix issues and should not be compared against the same SLA targets as L1 or L2 support tickets.
When multiple users report the same issue, technicians merge the duplicate tickets into a single ticket to avoid duplicated effort. The Merged Tickets queue contains these consolidated tickets. The SLA clock for the surviving ticket starts from the earliest creation time, which can make SLA compliance harder.
Handoff wait time measures how long a ticket sits between being escalated from one queue and being picked up in the next queue. A 3.2-hour handoff from L1 to L2 means the ticket is unattended for 3.2 hours during the transition. This dead time is often where SLA breaches happen.
Consulting tickets are typically advisory or implementation tasks that require senior engineers spending significant time per engagement. At 1.47 hours per ticket versus the portfolio average of 0.42, these are high-touch engagements. They should be billed separately and tracked against project-based KPIs rather than service desk metrics.
Build L1 resolution scripts for the most common escalation triggers. Track which ticket categories escalate most frequently and create knowledge base articles for those. When L1 technicians have clear resolution paths, they resolve more tickets without needing L2 involvement. Target the top 10 escalation categories first.
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