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What is the cost of ticket escalations by category and client?

Autotask Ticket Escalation Cost by Category and Client

Built from: Autotask PSA

How this report was made

Autotask PSA

Multiple data sources combined

Proxuma Power BI

Pre-built MSP semantic model, 50+ measures

AI via MCP

Claude or ChatGPT writes DAX queries, executes them, formats output

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Autotask Ticket Escalation Cost by Category and Client

This report provides a detailed breakdown of autotask ticket escalation cost by category and client 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 desk managers, dispatch leads, and operations teams

How often: Daily for queue management, weekly for trend analysis, monthly for capacity planning

Time saved

Manual ticket analysis requires exporting data and building pivot tables. This report does it automatically.

Queue health

Stuck tickets, aging backlogs, and escalation patterns become visible at a glance.

Process improvement

Data-driven decisions about routing, staffing, and escalation rules.

Report categoryTicketing & Helpdesk

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 desk managers, dispatch leads

Where to find this in Proxuma

Power BI › Ticketing › Autotask Ticket Escalation Cost by Ca...

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What you can measure in this report

Escalation Summary — Portfolio-Wide Metrics

Autotask Ticket Categories Ranked by Resolution Time

Autotask Priority Tiers: Resolution Time and SLA Miss Rate

High-Volume Clients Ranked by Resource Handoff Rate

First-Touch Resolution Performance

Key Findings and Recommended Actions

Total Tickets

Avg Resolution Time

Resolution SLA Met

Avg First Response

1-Hour Fix Rate

Same-Day Resolution

AUTOTASK PSA — ESCALATION ANALYSIS REPORT

Date: March 2026

Source: Autotask PSA · Proxuma Power BI

Scope: All managed clients · All priorities

Sources: Autotask PSA

Autotask Ticket Escalation Cost by Category and Client

Generated by AI via Proxuma Power BI MCP — 67,521 tickets analyzed, 8 service categories, 5 priority tiers, 8 high-volume clients

Demo data: This report uses synthetic data. Connect your own Autotask PSA via Proxuma Power BI to generate this report from your actual ticket history.

1.0 Escalation Summary — Portfolio-Wide Metrics

High-level ticket metrics across all categories and clients

Total Tickets

67,521

66,677 completed

Avg Resolution Time

18.0h

Range: 2h – 45.8h by category

Resolution SLA Met

90.2%

Among tickets with SLA targets

Avg First Response

6.3h

17.4% resolved within 1 hour

30.0%

Same-day resolution rate

17.4%

First-hour fix rate

9.8%

Overall SLA miss rate

View DAX Query — Portfolio Summary KPIs

EVALUATE
ROW(
    "TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
    "AvgResolutionHours", ROUND(AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]), 1),
    "ResolutionSLA_Pct", ROUND(DIVIDE(
        COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[resolution_met] = 1)),
        COUNTROWS(FILTER('BI_Autotask_Tickets', NOT(ISBLANK('BI_Autotask_Tickets'[resolution_met]))))
    ) * 100, 1),
    "FirstHourFix_Pct", ROUND(DIVIDE(
        COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[first_hour_fix] = 1)),
        COUNTROWS(FILTER('BI_Autotask_Tickets', NOT(ISBLANK('BI_Autotask_Tickets'[complete_datetime]))))
    ) * 100, 1),
    "FirstDayResolution_Pct", ROUND(DIVIDE(
        COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[first_day_resolution] = 1)),
        COUNTROWS(FILTER('BI_Autotask_Tickets', NOT(ISBLANK('BI_Autotask_Tickets'[complete_datetime]))))
    ) * 100, 1),
    "AvgFirstResponseHours", ROUND(AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), 1),
    "TotalCompleted", COUNTROWS(FILTER('BI_Autotask_Tickets', NOT(ISBLANK('BI_Autotask_Tickets'[complete_datetime]))))
)

What are these DAX queries? DAX (Data Analysis Expressions) is the formula language Power BI uses to query data models. The AI wrote and executed each query against a live Autotask dataset. Copy any query to run it in Power BI Desktop against your own data.

2.0 Autotask Ticket Categories Ranked by Resolution Time

Categories sorted by average hours to close, with ticket volume and resource handoff rate

Category	Tickets	Avg Hours	Resolution Speed	Handoff Rate
Cloud Services	1,784	45.8h	Slowest	83.4%
Server & Infrastructure	1,357	39.5h	Very Slow	94.6%
Software & Application Support	16,578	26.8h	Slow	94.4%
Connectivity & Networking	27,955	22.1h	Moderate	79.6%
Onboarding & Provisioning	249	18.3h	Moderate	78.3%
Security & Access Management	1,710	11.5h	Good	95.8%
Email & Communication	1,222	2.3h	Fast	97.1%
Hardware & Device Issues	13,316	2.4h	Fast	100.0%

Handoff rate = % of tickets closed by a different engineer than the one who opened the ticket. High handoff rate does not always mean poor performance — it often reflects a deliberate dispatch-then-specialist workflow.

Average resolution time (hours) by category

Cloud Services

45.8h

1,784 tickets

Server & Infrastructure

39.5h

1,357 tickets

Software & App Support

26.8h

16,578 tickets

Connectivity & Networking

22.1h

27,955 tickets

Security & Access Mgmt

11.5h

1,710 tickets

Email & Communication

2.3h

1,222 tickets

Hardware & Device Issues

2.4h

13,316 tickets

View DAX Query — Category Breakdown

EVALUATE
TOPN(12,
    ADDCOLUMNS(
        SUMMARIZE('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[ticket_category_name]),
        "TicketCount", COUNTROWS(FILTER('BI_Autotask_Tickets',
            'BI_Autotask_Tickets'[ticket_category_name] = EARLIER('BI_Autotask_Tickets'[ticket_category_name]))),
        "AvgResHours", ROUND(AVERAGEX(FILTER('BI_Autotask_Tickets',
            'BI_Autotask_Tickets'[ticket_category_name] = EARLIER('BI_Autotask_Tickets'[ticket_category_name])),
            'BI_Autotask_Tickets'[resolution_duration_hours]), 1),
        "HandoffPct", ROUND(DIVIDE(
            COUNTROWS(FILTER('BI_Autotask_Tickets',
                'BI_Autotask_Tickets'[ticket_category_name] = EARLIER('BI_Autotask_Tickets'[ticket_category_name]) &&
                'BI_Autotask_Tickets'[closed_by_first_resource] = 0)),
            COUNTROWS(FILTER('BI_Autotask_Tickets',
                'BI_Autotask_Tickets'[ticket_category_name] = EARLIER('BI_Autotask_Tickets'[ticket_category_name]) &&
                NOT(ISBLANK('BI_Autotask_Tickets'[closed_by_first_resource]))))
        ) * 100, 1)
    ),
    [AvgResHours]
)

3.0 Autotask Priority Tiers: Resolution Time and SLA Miss Rate

How priority assignment affects resolution speed and SLA compliance across 67,521 tickets

Priority	Tickets	Avg Res. Hours	SLA Miss Rate	Handoff Rate	Assessment
P1 — Critical	5,019	2.1h	7.8%	99.8%	Working
P2 — High	1,788	32.0h	54.9%	98.0%	Problem
P3 — Medium	14,715	21.6h	53.0%	80.1%	Review
P4 — Low	30,415	16.3h	52.3%	88.5%	Review
Service / Change Req.	15,584	23.8h	72.4%	93.2%	Problem

P2 High takes 15x longer than P1 Critical — P1 tickets average 2.1 hours to resolve. P2 tickets average 32.0 hours. High-priority issues are being tagged correctly but the resolution capacity for them does not match the label.

Service and Change requests sit at the bottom of every metric: 72.4% SLA miss rate, 23.8h average resolution, and 93.2% handoff rate. These tickets are often deprioritized because they are not incidents — but when they represent a third of total ticket volume, the accumulated delay has real business impact.

View DAX Query — Priority Tier Breakdown

EVALUATE
SUMMARIZECOLUMNS(
    'BI_Autotask_Tickets'[priority_name],
    "TicketCount", COUNTROWS('BI_Autotask_Tickets'),
    "AvgResHours", ROUND(AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]), 1),
    "SLAMissPct", ROUND(DIVIDE(
        COUNTROWS(FILTER('BI_Autotask_Tickets',
            NOT(ISBLANK('BI_Autotask_Tickets'[resolution_met])) &&
            'BI_Autotask_Tickets'[resolution_met] = 0)),
        COUNTROWS(FILTER('BI_Autotask_Tickets',
            NOT(ISBLANK('BI_Autotask_Tickets'[resolution_met]))))
    ) * 100, 1),
    "HandoffPct", ROUND(DIVIDE(
        COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[closed_by_first_resource] = 0)),
        COUNTROWS(FILTER('BI_Autotask_Tickets',
            NOT(ISBLANK('BI_Autotask_Tickets'[closed_by_first_resource]))))
    ) * 100, 1)
)

4.0 High-Volume Clients Ranked by Resource Handoff Rate

Clients with 50+ tickets, sorted by handoff rate — names anonymized for this public report

Metric	Value
Escalated	59,974 (88.8%)
FCR	7,547 (11.2%)
Total Hours	33,271

Client names anonymized. A 100% handoff rate at low resolution time (Client A: 1.0h, Client B: 1.5h) often points to a dispatch workflow where one team logs tickets and a separate team resolves them — intentional by design. The concern is clients like C, E, and G where the handoff rate is 100% and the resolution time is high.

View DAX Query — Client Handoff Rate (≥50 tickets)

EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "ClosedByFirst", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]), "Escalated", COUNTROWS('BI_Autotask_Tickets') - CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]), "TotalWorkedHours", SUM('BI_Autotask_Tickets'[worked_hours]))

5.0 First-Touch Resolution Performance

How many tickets are resolved quickly, without escalation or extended handling

1-Hour Fix Rate

17.4%

11,601 tickets resolved <1h

Same-Day Resolution

30.0%

20,001 tickets same-day

Avg First Response

6.3h

Client A drives this up

Only 17.4% of tickets get resolved within the first hour of being opened. For an MSP handling mostly routine issues, this number is low. It suggests that even simple tickets are going through multi-step workflows when a faster resolution path might exist for certain categories.

Hardware & Device Issues resolve in 2.4 hours on average and represent 13,316 tickets — roughly 20% of total volume. Getting this category's 1-hour fix rate up would have a meaningful impact on the portfolio average.

View DAX Query — First-Touch Resolution Metrics

EVALUATE
ROW(
    "FirstHourFix_Pct", ROUND(DIVIDE(
        COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[first_hour_fix] = 1)),
        COUNTROWS(FILTER('BI_Autotask_Tickets',
            NOT(ISBLANK('BI_Autotask_Tickets'[complete_datetime]))))
    ) * 100, 1),
    "FirstDayResolution_Pct", ROUND(DIVIDE(
        COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[first_day_resolution] = 1)),
        COUNTROWS(FILTER('BI_Autotask_Tickets',
            NOT(ISBLANK('BI_Autotask_Tickets'[complete_datetime]))))
    ) * 100, 1),
    "AvgFirstResponseHours", ROUND(AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), 1)
)

6.0 Key Findings and Recommended Actions

Four findings from this analysis, ranked by business impact

Cloud Services and Server tickets are your time sink

Cloud Services (45.8h avg) and Server & Infrastructure (39.5h avg) are the two slowest categories. Together they represent 3,141 tickets that take more than double the portfolio average to close. These categories also have high handoff rates (83–95%), which means a specialist is always involved. The fix is not faster handoffs — it is better knowledge capture and runbooks so the specialist can resolve faster once assigned.

P2 High priority takes 15x longer than P1 to resolve

P1 Critical tickets average 2.1 hours and miss their SLA just 7.8% of the time. P2 High priority tickets average 32.0 hours and miss SLA at a 54.9% rate. The triage threshold between P1 and P2 may be too loose — tickets that genuinely need fast resolution might be tagged P2 when they should be P1, or P2 SLA targets may be set tighter than your team can consistently hit.

Service and Change Requests have a 72.4% SLA miss rate

With 15,584 tickets and a 72.4% SLA miss rate, service requests are the largest source of SLA failures in the portfolio. They are not incidents, so they get deprioritized — but at this volume, the accumulated miss is visible to clients. Consider whether SLA targets for change requests are calibrated correctly, or whether a separate queue with lighter SLAs would reduce noise.

Hardware tickets are the best candidate for first-touch improvement

Hardware & Device Issues have 100% handoff but only 2.4h average resolution. This combination suggests a dispatch workflow, not a skill gap. 13,316 tickets flow through this category. If a fraction of them could be resolved at first touch with better diagnostic scripting or self-service tools, the 1-hour fix rate would improve significantly across the whole portfolio.

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