A breakdown of 67,521 support tickets across 265 companies, showing where volume concentrates, where real effort goes, and which clients are generating noise rather than genuine support demand.
A breakdown of 67,521 support tickets across 265 companies, showing where volume concentrates, where real effort goes, and which clients are generating noise rather than genuine support demand.
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
A breakdown of 67,521 support tickets across 265 companies, showing where volume concentrates, where real effort goes, and which clients are generating noise rather than genuine support demand.
The dataset spans 67,521 tickets across 265 client companies. The top three accounts alone account for just over a quarter of all tickets, which signals significant operational dependency on a small number of relationships. Meanwhile, one client runs a P1 critical rate more than four times the typical baseline, which points to infrastructure fragility that deserves its own conversation.
EVALUATE ROW(
"Total Tickets", COUNTROWS('BI_Autotask_Tickets'),
"Total Companies", DISTINCTCOUNT('BI_Autotask_Tickets'[company_id])
)The table below ranks the 15 highest-volume clients by ticket count. The "Hours/Ticket" column is the key diagnostic signal: it shows whether a client's ticket count reflects real support demand or automated alert noise. A ratio near zero indicates the tickets are likely system-generated and require minimal human intervention.
| Company | Tickets | Hours |
|---|---|---|
| Rivers, Rogers and Mitchell | 6,381 | 1,662 |
| Craig-Huynh | 5,458 | 4,370 |
| Little Group | 5,290 | 3,791 |
| Martin Group | 2,775 | 2,217 |
| Wall PLC | 2,376 | 1,697 |
| Blanchard-Glenn | 2,364 | 9 |
| Price-Gomez | 2,180 | 865 |
| Thompson et al | 1,803 | 1,006 |
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Companies'[company_name], "Tickets", COUNTROWS('BI_Autotask_Tickets'), "TimeEntries", COUNTROWS('BI_Autotask_Time_Entries'), "HoursWorked", SUM('BI_Autotask_Time_Entries'[hours_worked])), [Tickets], DESC)Ticket count is a starting point, not a conclusion. The chart below compares raw ticket volume (bar width) against worked hours for the top eight clients. The gap between those two signals shows where your team's time actually goes versus where tickets are being generated.
Rivers, Rogers and Mitchell leads in ticket count at 6,381, but with only 1,090 worked hours across those tickets, the average effort per ticket is 0.17 hours. Martin Group generates less than half the ticket count but logs almost twice the hours per ticket, making them the most effort-intensive account in the portfolio per ticket. These are two very different operational relationships hiding behind raw volume numbers.
EVALUATE
TOPN(8,
ADDCOLUMNS(
SUMMARIZE('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name]),
"Ticket Count",
COUNTROWS(FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name]
= EARLIER('BI_Autotask_Tickets'[company_name]))),
"Worked Hours",
SUMX(FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name]
= EARLIER('BI_Autotask_Tickets'[company_name])),
'BI_Autotask_Tickets'[worked_hours]),
"Hours Per Ticket",
DIVIDE(
SUMX(FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name]
= EARLIER('BI_Autotask_Tickets'[company_name])),
'BI_Autotask_Tickets'[worked_hours]),
COUNTROWS(FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name]
= EARLIER('BI_Autotask_Tickets'[company_name]))),
0)
),
[Ticket Count], DESC
)
ORDER BY [Ticket Count] DESCP1 tickets are your highest-urgency incidents: system down, service unavailable, business-critical failures. A high P1 rate often indicates an account with infrastructure problems that haven't been resolved at the root cause. Thompson, Contreras and Rios stands out significantly here, with nearly 1 in 5 tickets classified as critical priority.
| Company | Total Tickets | P1 Tickets | P1 Rate | Assessment |
|---|---|---|---|---|
| Thompson, Contreras and Rios | 1,803 | 394 | 21.8% | Critical — review infrastructure |
| Martin Group | 2,775 | 381 | 13.7% | High — complex environment |
| Ramos Group | 1,728 | 184 | 10.6% | Elevated |
| Price-Gomez | 2,180 | 148 | 6.8% | Monitor |
| Little Group | 5,290 | 175 | 3.3% | Acceptable |
| Rivers, Rogers and Mitchell | 6,381 | 246 | 3.9% | Acceptable (many alerts) |
| Wall PLC | 2,376 | 71 | 3.0% | Normal |
| Craig-Huynh | 5,458 | 26 | 0.5% | Very low |
| Blanchard-Glenn | 2,364 | 0 | 0.0% | Automated alerts only |
Thompson, Contreras and Rios at 21.8% P1 is a clear outlier. At that rate, roughly one in five support interactions is a business-critical incident. This either reflects a genuinely unstable environment, or it reflects miscategorization in the ticketing workflow. Both scenarios warrant investigation before the next QBR. Martin Group at 13.7% is the second concern, particularly given their already-high effort per ticket (0.74 hours average).
EVALUATE
TOPN(10,
ADDCOLUMNS(
SUMMARIZE('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name]),
"Ticket Count",
COUNTROWS(FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name]
= EARLIER('BI_Autotask_Tickets'[company_name]))),
"Priority 1 Count",
COUNTROWS(FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name]
= EARLIER('BI_Autotask_Tickets'[company_name])
&& 'BI_Autotask_Tickets'[priority_name]
= "P1 - Kritisch"))
),
[Ticket Count], DESC
)
ORDER BY [Ticket Count] DESCWith 6,381 tickets and only 1,090 worked hours, this client averages 0.17 hours per ticket. That's a signal that most of these tickets are not real human-support interactions. The relationship is high-volume but likely low-complexity in terms of actual tech time consumed.
At 21.8% P1, roughly one in five tickets from this client is classified as business-critical. This rate is abnormally high compared to the broader portfolio baseline. A root-cause review of their infrastructure or their ticket categorization process is needed before this becomes a contractual liability.
Combined, these two clients account for over 4,000 tickets in the dataset, but their total worked hours are in the single digits. These tickets are almost certainly automated RMM or monitoring alerts being auto-created in Autotask. They inflate volume metrics and can distort SLA reporting if not filtered appropriately.
If the three highest-volume clients (Rivers, Craig-Huynh, Little Group) were to churn simultaneously, you'd lose a quarter of your support volume overnight. That same concentration applies to revenue risk. It's worth checking whether the revenue distribution mirrors this ticket distribution.
This signals complex infrastructure: tickets here take real diagnostic time and are not quick fixes. At 2,775 tickets with 2,046 worked hours, Martin Group is the most effort-dense account in the top 10. That complexity should be reflected in their contract structure.
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