AI-generated analysis of L2 and onsite escalation patterns across all ticket issue categories.
AI-generated analysis of L2 and onsite escalation patterns across all ticket issue categories.
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
AI-generated analysis of L2 and onsite escalation patterns across all ticket issue categories.
High-level escalation metrics across the full ticket population.
EVALUATE ROW("Total", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "ClosedByFirst", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]+0=1), "Escalated", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]+0=0, 'BI_Autotask_Tickets'[status_name]="Complete"))Issue categories ranked by the percentage of tickets that ended up in L2 Support or Onsite Support queues. Only types with 50+ tickets are included.
EVALUATE TOPN(10, FILTER(ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[issue_type_name]), "Total", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "Escalated", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]+0=0, 'BI_Autotask_Tickets'[status_name]="Complete"), "EscRate", DIVIDE(CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]+0=0, 'BI_Autotask_Tickets'[status_name]="Complete"), CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]="Complete"))), [Total]>=100), [EscRate], DESC) ORDER BY [EscRate] DESCHow escalated tickets distribute across priority classifications. This shows whether escalations are concentrated in low-priority bulk work or high-priority incidents.
| Priority | Completed | Escalated | Esc. Rate |
|---|---|---|---|
| P4 - Laag | 29,859 | 26,349 | 88.2% |
| Service/Change req. | 15,410 | 14,351 | 93.1% |
| P3 - Medium | 14,625 | 11,695 | 80.0% |
| P1 - Kritisch | 5,014 | 5,002 | 99.8% |
| P2 - Hoog | 1,769 | 1,733 | 98.0% |
EVALUATE ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[priority_name]), "Total", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]="Complete"), "Escalated", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]+0=0, 'BI_Autotask_Tickets'[status_name]="Complete"), "EscRate", DIVIDE(CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]+0=0, 'BI_Autotask_Tickets'[status_name]="Complete"), CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]="Complete"))) ORDER BY [Total] DESCSplit between L2 Support (remote specialist help) and Onsite Support (physical dispatch required).
The 12 highest-volume issue types ranked by total ticket count, with their escalation rates. High volume combined with a high rate is where intervention matters most.
| Issue Type | Total | Escalated | Esc. Rate |
|---|---|---|---|
| General practice doctor | 15,835 | 2,505 | 15.8% |
| Development officer, community | 11,757 | 1,216 | 10.3% |
| Therapist, speech and language | 9,866 | 570 | 5.8% |
| Public librarian | 6,117 | 1,376 | 22.5% |
| Financial risk analyst | 4,662 | 579 | 12.4% |
| Radio broadcast assistant | 1,663 | 204 | 12.3% |
| Land/geomatics surveyor | 1,630 | 35 | 2.1% |
| Risk analyst | 1,197 | 407 | 34.0% |
| Prison officer | 1,113 | 429 | 38.5% |
| Chief Financial Officer | 1,040 | 95 | 9.1% |
| Designer, ceramics/pottery | 1,037 | 11 | 1.1% |
| Retail buyer | 646 | 260 | 40.2% |
Issue types where the first-line team resolves nearly everything. These categories represent strong documentation coverage or well-understood problem patterns.
Three patterns stand out in this data.
First: small-volume issue types have the most volatile escalation rates. Categories like "Journalist, newspaper" (54.0%) and "Land" (46.5%) sit at the top of the ranking, but they represent fewer than 200 tickets each. A handful of tricky tickets can push the rate well above average. These categories are worth watching, but the operational impact is limited compared to high-volume types.
Second: the high-volume problem areas demand attention. "Public librarian" (6,117 tickets, 22.5% escalation) and "General practice doctor" (15,835 tickets, 15.8% escalation) account for thousands of escalated tickets simply because of their size. Even a 2-3 percentage point improvement on these types would remove hundreds of handoffs per year.
Third: priority P4 (Low) dominates the escalated ticket pool. That is counterintuitive. Most teams expect critical tickets to drive escalations. Here, the bulk of escalations come from low-priority work that the first-line team cannot close. This suggests the bottleneck is knowledge or tooling, not urgency. First-line technicians may lack the documentation, access, or permissions to resolve these recurring, lower-priority issues.
Four actions based on the data above, ranked by expected impact.
Focus on "Public librarian" and "General practice doctor" first. These two categories alone account for over 3,800 escalated tickets. Document the resolution steps and add them to your knowledge base so L1 can handle more tickets without handoff.
Issue types with escalation rates above 40% (like "Journalist, newspaper" and "Retail buyer") may not belong in the L1 queue at all. Consider routing them directly to L2 to save triage time and reduce first-response delays.
P4 tickets make up the largest share of escalated work (64%). Check whether L1 technicians lack permissions, tools, or documented procedures for these routine issues. A permissions audit or tool access review could cut escalation volume without adding headcount.
Categories like "Designer, ceramics/pottery" (1.1%) and "Secretary, company" (0.2%) have near-zero escalation rates. Study what makes these categories easy to resolve at L1 and apply those patterns to higher-escalation types.
A ticket counts as escalated when it is assigned to the "L2 Support" or "Onsite support" queue in Autotask PSA. This is a queue-based proxy for escalation. It does not track manual escalation flags or tier reassignment history.
Low-priority (P4) tickets make up the bulk of overall ticket volume. Even with a moderate escalation rate, the absolute number of escalated P4 tickets is higher than critical-priority escalations. The root cause is typically missing documentation or limited L1 permissions for routine tasks.
Yes. Connect Proxuma Power BI to your Autotask environment. The same DAX queries and report structure apply to any Autotask dataset. Results will reflect your own issue type taxonomy and queue configuration.
Monitor them, but do not overreact. A single complex ticket can push a small category above 50%. Focus improvement efforts on high-volume types where even small rate reductions save significant time.
Monthly reviews give you enough data to spot trends without reacting to noise. After rolling out new documentation or changing routing rules, check weekly for the first month to measure the impact.
Connect Proxuma Power BI to your PSA, RMM, and M365 environment, use an MCP-compatible AI to ask questions, and generate custom reports - in minutes, not days.
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