“SLA Performance by Queue”
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SLA Performance by Queue

Built from: Autotask PSA
How this report was made
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Autotask PSA
Multiple data sources combined
2
Proxuma Power BI
Pre-built MSP semantic model, 50+ measures
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AI via MCP
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This Report
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SLA Performance by Queue

This report provides a detailed breakdown of sla performance by queue 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: Service delivery managers, operations leads, and MSP owners tracking service quality

How often: Weekly for operational adjustments, monthly for client reporting, quarterly for contract reviews

Time saved
Pulling per-client SLA data from PSA manually takes hours. This report delivers the breakdown in minutes.
Client-level clarity
Portfolio averages mask the clients getting poor service. This report surfaces the specific accounts that need attention.
Contract evidence
Concrete SLA data per client gives you proof points for renewals, pricing adjustments, or staffing conversations.
Report categorySLA & Service Performance
Data sourceAutotask PSA · Datto RMM · Datto Backup · Microsoft 365 · SmileBack · HubSpot · IT Glue
RefreshReal-time via Power BI
Generation timeUnder 15 minutes
AI requiredClaude, ChatGPT or Copilot
AudienceService delivery managers, operations leads
Where to find this in Proxuma
Power BI › SLA › SLA Performance by Queue
What you can measure in this report
AI-Generated Power BI Report
Data source: Autotask PSA · Generated March 2026
SLA Performance by Queue
First response and resolution SLA rates across 16 support queues — sorted by ticket volume
23.5%
Compliancy FR
Lowest first-response rate
64.7%
Centralized Svcs FR
17K tickets, SLA gap
95.6%
L1 Resolution
Best resolution SLA
88.5%
L1 Support FR
Best first-response rate
SLA Performance by Queue — All Queues
First Response SLA (green) and Resolution SLA (blue) per queue, sorted by ticket volume. Bars scaled to 100%. Queues with rates below 60% shown in red.
L1 Support31,378 tickets
First Response
88.5%
Resolution
95.6%
Centralized Services17,082 tickets
First Response
64.7%
Resolution
91.6%
L2 Support7,889 tickets
First Response
82.3%
Resolution
88.0%
Merged Tickets4,999 tickets
First Response
78.1%
Resolution
92.4%
Technical Alignment2,316 tickets
First Response
74.6%
Resolution
62.8%
Customer Success804 tickets
First Response
72.3%
Resolution
59.5%
Interne IT793 tickets
First Response
33.4%
Resolution
55.7%
Onsite Support705 tickets
First Response
76.6%
Resolution
56.0%
Professional Services546 tickets
First Response
71.6%
Resolution
52.0%
Compliancy29 tickets
First Response
23.5%
Resolution
18.8%
MetricValue
Resolution Met90.2%
First Hour Fix16.1%
Same-Day30.0%
Closure98.8%
View DAX Query — SLA performance by queue
EVALUATE ROW("ResolutionMet", [Tickets - Resolution Met %], "FirstHourFix", [Tickets - First Hour Fix %], "SameDayRes", [Tickets - Same Day Resolution %], "ClosureRate", [Tickets - Closure Rate %], "TotalTickets", [Tickets - Count - Created])
Key Insights
What the queue SLA data reveals about where your service desk is succeeding — and where structural issues are hiding.

Interne IT and Compliancy are in SLA crisis — below 35% first response

Internal IT queues typically operate without client SLA pressure, so they're often the last to get SLA governance. But with Interne IT at 33.4% first response and Compliancy at 23.5%, these queues have essentially no functional SLA compliance. For Compliancy (29 tickets), this may reflect misrouted work rather than capacity issues. For Interne IT (793 tickets), it's a process gap that needs attention.

Centralized Services: 17,000 tickets, 65% first-response — the biggest SLA miss by volume

Centralized Services is the second-largest queue by ticket count, handling 17,082 tickets. Its 64.7% first-response rate means roughly 6,000 tickets per year miss the initial SLA window. Interestingly, its resolution rate is 91.6% — meaning once engineers pick up a ticket, they tend to close it. The bottleneck is acknowledgement, not resolution. Auto-assignment or acknowledgement automation could close this gap.

Technical Alignment, Onsite, and Professional Services: good first response but poor resolution

Three queues show a clear split between first response (70–77%) and resolution (52–63%). These typically handle complex, multi-step work: on-site visits, professional engagements, alignment sessions. The SLA windows may not account for the inherent time requirements of this work. Either extend resolution SLA targets or segment these ticket types out of standard SLA measurement.

L1 Support at 88.5% FR and 95.6% resolution — the benchmark for all other queues

L1 Support handles 31,378 tickets — the largest volume by far — while maintaining the best SLA rates on both metrics. This shows what consistent triage, clear ownership, and appropriate SLA windows can achieve at scale. The rest of the service desk should be benchmarked against L1's operational model, not just its numbers.

Frequently Asked Questions

Why does Centralized Services have a much better resolution rate than first-response rate? +
This split pattern — low first response, high resolution — typically means tickets sit unacknowledged in the queue, but once someone picks them up, they close quickly. The SLA clock starts when the ticket arrives, not when it's assigned. If Centralized Services has a later-than-typical assignment pattern (e.g. tickets route there from another queue after triage), the first-response window may be expiring during transit. Check whether routing delays are contributing to the first-response gap before adding headcount.
Should internal queues like Interne IT have SLA targets at all? +
Yes — internal queues benefit from SLA governance even without client-facing consequences. Without targets, internal work consistently gets deprioritized in favour of billable tickets, creating a "permanent backlog" of internal tech debt. Setting internal SLAs (even lenient ones like 8-hour first response) makes internal work visible and manageable. The low compliance rate in Interne IT suggests either no SLA targets are set, or they're set but consistently ignored without enforcement.
How do I improve resolution SLA for project-oriented queues like Professional Services? +
Three approaches work for queues doing long-cycle work: (1) Exclude project-phase tickets from standard resolution SLA measurement — use milestone tracking instead of ticket-close SLAs. (2) Create sub-queues with longer SLA windows for professional engagements vs. simple service requests. (3) Add interim status updates (e.g. "In Progress" with a note) that pause the resolution clock in Autotask, provided your SLA configuration supports that. Match the SLA framework to the actual work type, rather than forcing all tickets into a one-size model.

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