Analyzing which incidents keep coming back and where to break the cycle.
Analyzing which incidents keep coming back and where to break the cycle.
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: MSP operations teams and service delivery managers
How often: As needed for specific analysis or reporting requirements
Analyzing which incidents keep coming back and where to break the cycle.
67,521 tickets analyzed across 2,623 unique client-issue combinations.
EVALUATE
VAR RecurrenceByCompanyIssue =
ADDCOLUMNS(
SUMMARIZE(
'BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name],
'BI_Autotask_Tickets'[issue_type_name]
),
"Cnt", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))
)
VAR RecurringCombos = FILTER(RecurrenceByCompanyIssue, [Cnt] > 1)
VAR TotalRecurring = SUMX(RecurringCombos, [Cnt])
VAR TotalAll = COUNTROWS('BI_Autotask_Tickets')
VAR RecurringCombosCount = COUNTROWS(RecurringCombos)
VAR AllCombos = COUNTROWS(RecurrenceByCompanyIssue)
RETURN
ROW(
"TotalTickets", TotalAll,
"RecurringTickets", TotalRecurring,
"RecurrenceRate", DIVIDE(TotalRecurring, TotalAll) * 100,
"RecurringCombinations", RecurringCombosCount,
"AllCombinations", AllCombos,
"AvgOccurrences", DIVIDE(TotalRecurring, RecurringCombosCount)
)Issue categories ranked by total ticket volume. The "Avg. per Client" column shows how many times each issue type recurs per client on average.
| Issue Type | Total Tickets | Clients Affected | Avg. per Client |
|---|---|---|---|
| Network Connectivity | 15,835 | 146 | 108.5 |
| Email / Exchange | 11,757 | 174 | 67.6 |
| Printer Issues | 9,866 | 182 | 54.2 |
| Password Reset / MFA | 6,117 | 138 | 44.3 |
| Hardware Failure | 4,662 | 151 | 30.9 |
| Software Installation | 1,663 | 132 | 12.6 |
| VPN / Remote Access | 1,630 | 122 | 13.4 |
| Backup Failure | 1,197 | 98 | 12.2 |
EVALUATE
TOPN(8,
ADDCOLUMNS(
FILTER(VALUES('BI_Autotask_Tickets'[issue_type_name]),
'BI_Autotask_Tickets'[issue_type_name] <> ""),
"TotalTickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"CompaniesAffected", CALCULATE(DISTINCTCOUNT('BI_Autotask_Tickets'[company_name])),
"AvgPerCompany", DIVIDE(
CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
CALCULATE(DISTINCTCOUNT('BI_Autotask_Tickets'[company_name]))
)
),
[TotalTickets], DESC
)Clients ranked by average tickets per issue type. A high number means the same problems keep coming back for this client.
| Client | Total Tickets | Unique Issue Types | Avg. Tickets per Issue Type |
|---|---|---|---|
| Greenfield Partners | 6,381 | 35 | 182.3 |
| Summit Healthcare | 5,458 | 32 | 170.6 |
| Coastal Logistics | 5,290 | 33 | 160.3 |
| Riverside Manufacturing | 2,364 | 15 | 157.6 |
| Pacific Ventures | 1,684 | 18 | 93.6 |
| Heritage Financial | 2,775 | 35 | 79.3 |
| Lakewood Associates | 2,376 | 32 | 74.2 |
| Northern Trust Corp | 2,180 | 31 | 70.3 |
EVALUATE
TOPN(8,
ADDCOLUMNS(
VALUES('BI_Autotask_Tickets'[company_name]),
"TotalTickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"UniqueIssueTypes", CALCULATE(DISTINCTCOUNT('BI_Autotask_Tickets'[issue_type_name])),
"AvgPerIssue", DIVIDE(
CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
CALCULATE(DISTINCTCOUNT('BI_Autotask_Tickets'[issue_type_name]))
)
),
[AvgPerIssue], DESC
)The specific client + issue type pairs that generate the most repeat tickets. These are your highest-impact targets for root cause analysis.
| Client | Issue Type | Occurrences |
|---|---|---|
| Summit Healthcare | Email / Exchange | 1,467 |
| Coastal Logistics | Email / Exchange | 1,281 |
| Heritage Financial | Network Connectivity | 1,197 |
| Eastside Technology | Network Connectivity | 1,189 |
| Greenfield Partners | Network Connectivity | 1,151 |
| Summit Healthcare | Printer Issues | 1,062 |
| Coastal Logistics | Printer Issues | 993 |
| Pinnacle Group | Network Connectivity | 972 |
How recurring tickets distribute across priority levels. High-priority recurrence is the most urgent to address.
| Priority | Tickets | Unique Issues | Clients | Avg. per Issue |
|---|---|---|---|---|
| P4 - Laag | 30,415 | 39 | 226 | 779.9 |
| Service/Change req. | 15,584 | 35 | 212 | 445.3 |
| P3 - Medium | 14,715 | 36 | 190 | 408.8 |
| P1 - Kritisch | 5,019 | 21 | 89 | 239.0 |
| P2 - Hoog | 1,788 | 30 | 119 | 59.6 |
Drilling one level deeper into sub-issue types to find the specific failure modes driving repeat tickets.
| Sub-Issue Type | Total Tickets | Clients Affected | Avg. per Client |
|---|---|---|---|
| Slow DNS Resolution | 4,318 | 146 | 29.6 |
| Outlook Sync Error | 3,338 | 62 | 53.8 |
| Toner Replacement | 3,204 | 128 | 25.0 |
| Azure AD Lockout | 2,734 | 127 | 21.5 |
| Disk Replacement | 2,523 | 119 | 21.2 |
| License Activation | 2,448 | 59 | 41.5 |
| Split Tunnel Config | 2,402 | 77 | 31.2 |
| Agent Offline | 2,056 | 66 | 31.2 |
The data shows a recurrence rate of 98.9%, meaning the vast majority of tickets belong to issue categories that have been logged more than once for the same client. That is expected in an MSP environment. The question is not whether recurrence exists, but where it concentrates.
The top issue types account for a disproportionate share of total ticket volume. The worst-performing category averages over 100 tickets per client, while healthier categories sit below 15. That spread tells you exactly where to focus root cause analysis.
On the client side, a small number of accounts generate outsized repeat volumes. The top 3 clients by recurrence ratio each average over 150 tickets per issue type. For those accounts, a targeted review meeting would likely uncover infrastructure or process issues worth addressing once rather than patching repeatedly.
Priority distribution is also revealing. Low-priority tickets (P4) make up the largest share, which is normal. But the presence of recurring P2 and P3 tickets is worth flagging. Those represent problems significant enough to escalate, yet still not being resolved permanently.
Based on the recurrence patterns above, here are the most impactful next steps.
The highest-volume recurring issue types are generating thousands of repeat tickets. Schedule a 30-minute review for each: pull up the last 10 tickets, look for common threads (same device, same config, same workaround), and document a permanent fix or automation.
Clients averaging 100+ tickets per issue type are stuck in a cycle. Add a "recurrence review" slide to their next QBR showing the data. Propose a small project to address the top 2 recurring categories permanently.
Higher-priority recurring tickets signal problems that matter enough to escalate but are still being handled as one-offs. Review the P2/P3 recurrence list and determine which need a permanent infrastructure change vs. a process change.
Sub-issue analysis shows specific failure modes that repeat across many clients. Evaluate which of the top 5 sub-issues could be resolved with scripting, monitoring alerts, or proactive maintenance instead of reactive tickets.
Track recurrence rate month-over-month in Proxuma Power BI. Set a target to reduce the top 3 recurring issue types by 20% per quarter. Use this report as the baseline.
A recurring incident is defined as any company + issue type combination that appears more than once in the ticket history. If the same client has logged the same issue category two or more times, those tickets are counted as recurring.
Not necessarily. Some recurrence is expected. Password resets and hardware replacements will always repeat. The goal is to identify issue types with unusually high recurrence that could be reduced with better tooling, automation, or root cause fixes.
Start with the top 5 recurring issue types. For each, review recent tickets to find common patterns. Then evaluate whether the fix is a process change, an automation (script, monitoring alert), or an infrastructure upgrade. Small targeted projects often cut recurrence dramatically.
This report analyzes all tickets in the BI_Autotask_Tickets table, which typically includes service desk tickets and incidents. Project tasks are stored in a separate table and are not included in this analysis.
The current report covers the full ticket history. In Proxuma Power BI, you can apply date filters interactively to see recurrence patterns for specific periods, such as the last 90 days or the last quarter.
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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