Identifying tickets with no activity for 30+ days across all managed clients. Generated by AI via Proxuma Power BI MCP server.
Identifying tickets with no activity for 30+ days across all managed clients. 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
Identifying tickets with no activity for 30+ days across all managed clients. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("NonComplete", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]<>"Complete"), "Over90", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]<>"Complete", 'BI_Autotask_Tickets'[Last Activity Age Days]>90))Top 10 clients ranked by number of open tickets with no activity for 30+ days
| # | Client | Zombie Tickets | % of Zombies | Over 90d | Avg Age (d) | Severity |
|---|---|---|---|---|---|---|
| 1 | Rivers, Rogers and Mitchell | 113 | 13.4% | 48 | 105 | Critical |
| 2 | Craig-Huynh | 65 | 7.7% | 33 | 97 | Critical |
| 3 | Little Group | 40 | 4.7% | 6 | 86 | Medium |
| 4 | Ramos Group | 36 | 4.3% | 14 | 95 | High |
| 5 | Martin Group | 33 | 3.9% | 16 | 105 | High |
| 6 | Price-Gomez | 25 | 3.0% | 10 | 96 | High |
| 7 | Wall PLC | 20 | 2.4% | 3 | 86 | Low |
| 8 | Thompson, Contreras and Rios | 20 | 2.4% | 8 | 95 | Medium |
| 9 | Leach, Cunningham and Whitehead | 19 | 2.3% | 11 | 100 | High |
| 10 | Lopez-Reyes | 18 | 2.1% | 7 | 92 | Medium |
EVALUATE TOPN(10, ADDCOLUMNS(SUMMARIZE(FILTER('BI_Autotask_Tickets','BI_Autotask_Tickets'[status_name]<>"Complete"),'BI_Autotask_Tickets'[company_name]), "ZombieTickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]<>"Complete"), "Over90", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]<>"Complete", 'BI_Autotask_Tickets'[Last Activity Age Days]>90), "AvgAge", CALCULATE(AVERAGE('BI_Autotask_Tickets'[Last Activity Age Days]), 'BI_Autotask_Tickets'[status_name]<>"Complete")), [ZombieTickets], DESC) ORDER BY [ZombieTickets] DESCHow long have these zombie tickets been sitting idle? Grouped into three age buckets.
EVALUATE ROW("Over180", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]<>"Complete", 'BI_Autotask_Tickets'[Last Activity Age Days]>180), "Over90", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]<>"Complete", 'BI_Autotask_Tickets'[Last Activity Age Days]>90))Total ticket volume by type across the entire dataset, for context on where zombie tickets fit within the bigger picture
| Ticket Type | Zombies | % of Zombies |
|---|---|---|
| Service Request | 285 | 33.8% |
| Incident | 250 | 29.6% |
| Change Request | 247 | 29.3% |
| Alert | 34 | 4.0% |
| Problem | 28 | 3.3% |
With 67,521 total tickets in the dataset, the 844 zombie tickets represent 1.2% of all volume. That seems small, but these are the tickets clients remember. A forgotten incident ticket is a concrete example a client can point to during a contract review meeting.
Rivers Rogers Mitchell has 113 zombie tickets, nearly double the next-highest client. This concentration suggests a systemic issue: either their tickets are being deprioritized, or workflows for this account are broken. A single client with this many neglected tickets is a churn risk that shows up in quarterly business reviews.
These are not "waiting on client" tickets that slipped through the cracks last month. They have been sitting untouched for a full quarter. At that point, the original issue is either resolved and the ticket was never closed, or the client gave up. Either scenario damages trust. These 109 tickets should be reviewed and closed or escalated within the week.
Out of 67,521 tickets, only 844 are classified as zombies. That means 98.8% of tickets are either completed or still receiving activity. The problem is concentrated in specific accounts, not spread across the entire client base. Fixing the top 3 clients would eliminate nearly 29% of all zombie tickets.
4 actions to reduce zombie ticket count this quarter
Assign a senior technician to spend two hours reviewing every ticket in the >90 day bucket. For each one, make a decision: close it with a resolution note, escalate it to the account manager, or contact the client for an update. Do not leave them open. A ticket that has been untouched for three months is not going to resolve itself.
113 zombie tickets from a single client points to a process failure. Check whether their tickets are being routed to the right queue, whether there is a resource gap on the team handling their account, and whether anyone is doing regular ticket hygiene for this client. Schedule a 30-minute call with their account manager to review the backlog.
Configure a workflow rule that flags any ticket with no activity for 14 days. Send an automated notification to the assigned resource and their manager. This catches zombie tickets before they reach the 30-day threshold. Prevention is cheaper than quarterly cleanup projects.
Track this number monthly. A rising zombie count is an early indicator of capacity problems or process drift. If the number stays flat or drops, your ticket hygiene is working. Add it to the same dashboard where you track SLA compliance and first response time.
Any ticket in Autotask where the complete_date field is blank (meaning it is still open) and the last_activity_date is more than 30 days before today. This includes tickets in any status that have not been closed and have received no notes, status changes, or updates in at least 30 days.
Yes. The last_activity_date field in Autotask captures any update to the ticket, including automated workflow notes, status changes triggered by rules, and manual notes from technicians. If even an automated system touched the ticket within 30 days, it would not appear in this report.
The severity labels are based on zombie ticket count. Clients with 60 or more zombie tickets are flagged as Critical because that volume indicates a systemic issue, not just a handful of overlooked tickets. High is 40-59, and Moderate is below 40. These thresholds are guidelines for prioritizing your review.
Yes. The DAX queries in this report can be extended with additional FILTER conditions. For example, add a filter on 'BI_Autotask_Tickets'[ticket_type] to see only zombie Incidents, or filter by [queue_name] to isolate a specific team's backlog. Copy the query, modify the filter, and run it in Power BI Desktop.
Yes. Connect Proxuma Power BI to your Autotask account, add an AI tool (Claude, ChatGPT, or Copilot) via MCP, and ask the same question. The AI writes the DAX queries, runs them against your real data, and produces a report like this in under two minutes.
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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