The PSA does not track explicit reopens. Instead, this report uses proxy metrics: overdue open tickets, age distribution, first-resource closure rates, and status patterns to identify where closed work keeps coming back. Generated by AI via Proxuma Power BI MCP server.
The PSA does not track explicit reopens. Instead, this report uses proxy metrics: overdue open tickets, age distribution, first-resource closure rates, and status patterns to identify where closed work keeps coming back. 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
The PSA does not track explicit reopens. Instead, this report uses proxy metrics: overdue open tickets, age distribution, first-resource closure rates, and status patterns to identify where closed work keeps coming back. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("Total", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "Completed", CALCULATE(COUNTROWS('BI_Autotask_Tickets'),'BI_Autotask_Tickets'[status_name]="Complete"), "Open", CALCULATE(COUNTROWS('BI_Autotask_Tickets'),'BI_Autotask_Tickets'[status_name]<>"Complete"), "AvgOpenAge", AVERAGEX(FILTER('BI_Autotask_Tickets','BI_Autotask_Tickets'[status_name]<>"Complete"),'BI_Autotask_Tickets'[ticket_age_days]))All 844 open tickets are overdue. This breakdown shows how long they have been sitting open, grouped by age bucket.
EVALUATE VAR OpenT = FILTER('BI_Autotask_Tickets','BI_Autotask_Tickets'[status_name]<>"Complete") RETURN ROW("Over180", COUNTROWS(FILTER(OpenT,'BI_Autotask_Tickets'[ticket_age_days]>180)), "Over90", COUNTROWS(FILTER(OpenT,'BI_Autotask_Tickets'[ticket_age_days]>90 && 'BI_Autotask_Tickets'[ticket_age_days]<=180)), "Over30", COUNTROWS(FILTER(OpenT,'BI_Autotask_Tickets'[ticket_age_days]>30 && 'BI_Autotask_Tickets'[ticket_age_days]<=90)))How effectively tickets are being resolved on first touch, and how quickly customers get an initial response
| Metric | Count | Rate | Assessment |
|---|---|---|---|
| Closed by first resource (clean closure) | 7,547 | 11.3% | Low FCR |
| Reassigned / escalated before closure | 59,130 | 88.7% | High churn |
| Open beyond 90 days (reopening-risk proxy) | 489 | 57.9% of open | Stale backlog |
(from closed_by_first_resource flag; proxies a clean first-pass closure vs reassignment)Where the 844 open tickets are sitting right now, broken down by current status
EVALUATE ADDCOLUMNS(SUMMARIZE(FILTER('BI_Autotask_Tickets','BI_Autotask_Tickets'[status_name]<>"Complete"),'BI_Autotask_Tickets'[status_name]), "Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))) ORDER BY [Count] DESCWithout an explicit reopen field, these are the strongest indicators that tickets are cycling back from closed to open
These tickets were waiting on a customer reply, got one, and have been sitting untouched for over 30 days. In practice, many of these are tickets where the original issue was considered resolved but the customer came back with the same problem. That is a reopen in everything but name. Average age: 91 days.
A ticket that stays in New status for over a month was likely created, assigned, worked on in another ticket, and then abandoned. Or it was reopened by creating a new ticket referencing the original. Either way, 169 tickets in New status with an average age of 91 days means the intake process has gaps.
When 88.7% of closed tickets require reassignment or escalation before resolution, tickets bounce between queues. Each handoff is a point where a ticket might get dropped, parked, or effectively reopened under a different resource. A first-resource closure rate below 20% is a structural problem, not a staffing one.
Half a year is not a backlog. It is abandonment. These 56 tickets either need to be closed as "will not fix" or they represent recurring issues that keep getting touched but never fully resolved. Either way, they are consuming mental overhead and hiding real workload.
EVALUATE
ROW(
"CustomerResponded_Overdue", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "Customer has responded",
BI_Autotask_Tickets[resolved_due_age_days] > 30),
"New_Over30Days", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "New",
BI_Autotask_Tickets[resolved_due_age_days] > 30),
"FirstResourceClosureRate", DIVIDE(
CALCULATE(COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[closed_by_first_resource] = 1),
CALCULATE(COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "Complete")),
"Over180Days", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] <> "Complete",
BI_Autotask_Tickets[resolved_due_age_days] > 180)
)A 98.8% closure rate looks excellent on paper. It suggests the service desk is handling its workload and closing tickets efficiently. But that number hides everything this report is about.
Every single one of the 844 open tickets is overdue by more than 30 days. The average age is 91 days. That means the open backlog is not "work in progress." It is stale. These tickets have stopped moving. Some are waiting on customers who will never respond. Some are waiting on third-party vendors with no follow-up process. And some are tickets that were effectively resolved but never formally closed, or closed and then reverted to an open status when the problem recurred.
The strongest reopening signal is the 102 tickets in "Customer has responded" status. These were explicitly parked on the customer. The customer replied. And then nothing happened for over 30 days. In many PSA workflows, this is exactly what a reopen looks like: the ticket was considered done, the customer disagreed, and the ticket went back into the queue where it was ignored.
The 11.3% first-resource closure rate is the structural root cause. When nearly 9 out of 10 tickets change hands before closing, the risk of a ticket falling through the cracks multiplies at each handoff. Each reassignment is an opportunity for the ticket to sit in a queue unowned. Combined with the 169 tickets stuck in "New" status for over a month, the pattern is clear: tickets enter the system, get assigned, bounce between resources, and eventually either get closed or become part of the permanent backlog.
The 56 tickets older than 180 days need a different conversation. These are not backlog. They are forgotten. A quarterly backlog review that closes or escalates anything over 90 days would cut the open count by nearly 30% overnight.
5 priorities based on the findings above
These tickets have a customer waiting for action. Every day they sit unanswered erodes trust. Assign a service desk lead to review all 102 this week. Close the ones where the customer response was "thanks, it works." Escalate the ones where the customer is reporting a recurrence. This is your most visible reopening behavior and the fastest to fix.
A ticket that has been open for six months without resolution is not being worked on. Contact the requestor one more time. If there is no response within 5 business days, close it with a note explaining why. If the issue is still active, escalate it to a project or a change request. Do not let the backlog become a parking lot.
A ticket in New status should be assigned within hours, not months. Pull the 169 tickets, look for patterns: are they from a specific client, queue, or ticket type? Many of these may be duplicates, test tickets, or tickets that were created when the real work was done in a separate ticket. Cleaning this up improves your metrics and your team's confidence in the backlog numbers.
At 11.3%, your first-resource closure rate suggests that initial ticket routing is not matching tickets to the right person. Review your dispatch rules and auto-assignment logic. If a ticket type always ends up escalated to the same person, route it there directly. Every avoided handoff reduces the chance of a ticket going stale and eventually appearing as a pseudo-reopen.
Schedule a 30-minute monthly review of all tickets older than 60 days. The service desk lead pulls the list from Power BI, triages each one, and either closes it, reassigns it, or converts it to a project. This prevents the 844-ticket backlog from growing further and gives you a recurring check on reopening patterns. Track the month-over-month backlog count to measure progress.
No. Autotask does not have a dedicated "reopened" status, timestamp, or counter. When a ticket moves from Complete back to an active status, there is no distinct event logged for that transition. This report uses proxy metrics instead: overdue open tickets, age distribution, and status patterns that indicate reopening behavior.
Every open ticket in this dataset is older than 30 days, which qualifies as overdue. This means the current open backlog consists entirely of stale tickets. New tickets that come in and get resolved within 30 days do not appear in this count because they have already been closed. What remains is the residue of tickets that never got fully resolved.
This measures whether the technician who was first assigned to the ticket is the same person who closed it. A rate of 11.3% means that in 88.7% of cases, the ticket was reassigned to someone else before it was resolved. High reassignment rates increase the risk of tickets being dropped between handoffs.
Tickets in "Customer has responded" status were waiting on customer input. The customer replied, which typically means one of two things: they confirmed the issue is resolved (and the ticket should be closed), or they reported that the problem came back (and the ticket needs to be reworked). When these tickets sit for 30+ days after the customer responds, it is the clearest proxy for reopening behavior in the PSA.
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 fifteen minutes.
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