Weekly ticket creation patterns showing when support demand peaks and dips. Generated by AI via Proxuma Power BI MCP server.
Weekly ticket creation patterns showing when support demand peaks and dips. 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
Weekly ticket creation patterns showing when support demand peaks and dips. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[day_name],
"ticket_count", COUNTROWS('BI_Autotask_Tickets')
)
ORDER BY [ticket_count] DESCTicket creation volume for each day, Monday through Sunday. Tuesday carries the highest load at 14,067, followed closely by Monday at 13,580. Volume drops sharply on Friday and falls off a cliff on weekends.
EVALUATE UNION(ROW("Day", "Monday", "Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), WEEKDAY('BI_Autotask_Tickets'[create_date], 2) = 1)), ROW("Day", "Tuesday", "Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), WEEKDAY('BI_Autotask_Tickets'[create_date], 2) = 2)), ROW("Day", "Wednesday", "Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), WEEKDAY('BI_Autotask_Tickets'[create_date], 2) = 3)), ROW("Day", "Thursday", "Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), WEEKDAY('BI_Autotask_Tickets'[create_date], 2) = 4)), ROW("Day", "Friday", "Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), WEEKDAY('BI_Autotask_Tickets'[create_date], 2) = 5)), ROW("Day", "Saturday", "Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), WEEKDAY('BI_Autotask_Tickets'[create_date], 2) = 6)), ROW("Day", "Sunday", "Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), WEEKDAY('BI_Autotask_Tickets'[create_date], 2) = 7)))How total ticket volume divides between business days (Mon–Fri) and weekends (Sat–Sun)
EVALUATE
ROW(
"WeekdayTickets", CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[day_name] IN {"Monday","Tuesday","Wednesday","Thursday","Friday"}
),
"WeekendTickets", CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[day_name] IN {"Saturday","Sunday"}
),
"TotalTickets", COUNTROWS('BI_Autotask_Tickets')
)What types of tickets make up the weekly volume. Incidents and alerts account for 70% of all tickets.
| Ticket Type | Count | Share | Volume |
|---|---|---|---|
| Incident | 27,664 | 41.0% | |
| Alert | 19,790 | 29.3% | |
| Service Request | 12,653 | 18.7% | |
| Change Request | 7,247 | 10.7% | |
| Problem | 167 | 0.2% |
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[ticket_type],
"ticket_count", COUNTROWS('BI_Autotask_Tickets')
)
ORDER BY [ticket_count] DESCThese two days alone account for 27,647 tickets out of 67,521 total. If your staffing is flat across the week, you are understaffed on Tuesday and overstaffed on Friday. The gap between Tuesday (14,067) and Friday (9,181) is 4,886 tickets, a 53% difference. That is enough to warrant different shift sizes by day.
Saturday (2,791) and Sunday (3,644) tell different stories. Saturday is the quietest day of the week. Sunday is 30% busier, likely driven by scheduled scans, backup jobs, and monitoring checks that fire before Monday morning. If your weekend on-call team treats both days the same, Sunday needs more attention.
Friday at 9,181 tickets is the lightest weekday. That is a 34% drop from Tuesday. If your team is mostly reactive on Fridays, consider reserving that slack for scheduled project hours, internal documentation, or training. A structured Friday with lower reactive load and dedicated project blocks can improve both throughput and morale.
4 actions based on the weekly patterns above
These two days handle 41% of weekly volume. If you run the same team size every day, consider adding one extra technician on Monday and Tuesday, or shifting a Friday resource to the start of the week. The data supports front-loading your schedule. Even a half-day shift from Friday to Tuesday mornings would reduce queue pressure during peak hours.
Weekend volume is 9.5% of the total. The mix will skew toward automated alerts and monitoring tickets rather than user-generated requests. Your weekend on-call team should focus on triage and critical response, not full service delivery. Two technicians with alert-handling protocols will cover the volume without burning out your team on unnecessary weekend rotations.
Sunday runs 30% higher than Saturday. That gap is almost certainly automated. Review your monitoring schedules: do backup verification jobs, patch scans, or RMM health checks run on Sunday evenings? If those alerts are informational and auto-resolve, consider suppressing or batching them to reduce noise for the on-call team.
Friday carries 13.6% of weekly volume. That leaves capacity. Block two to three hours on Friday afternoons for scheduled project work, internal training sessions, or documentation catch-up. A structured approach to Friday downtime turns wasted capacity into measurable output: completed projects, updated runbooks, and upskilled technicians.
Monday tickets often include issues reported over the weekend, but users need time to discover and report problems after they start working. Tuesday picks up the backlog from Monday morning plus a full day of new user-reported issues. Many scheduled tasks and maintenance windows also fall on Monday evening, generating alert tickets that land as Tuesday entries.
All ticket types are included: Incidents (27,664), Alerts (19,790), Service Requests (12,653), Change Requests (7,247), and Problems (167). The total of 67,521 covers every ticket in the dataset. You can filter by type using the DAX queries provided to see patterns for specific categories.
Yes. Weekend tickets are 9.5% of volume but skew heavily toward automated alerts. Your weekend team should be sized for triage and critical incident response, not full helpdesk coverage. Two technicians with clear escalation paths will handle the typical Saturday/Sunday load without unnecessary overhead.
Scheduled jobs. Many MSPs run backup verifications, patch scans, and RMM health checks on Sunday evenings to prepare for Monday. These generate alert tickets automatically. Some users also submit tickets on Sunday evening as they prepare for the work week. The 30% gap between Saturday and Sunday (2,791 vs 3,644) is consistent with automated pre-Monday activity.
Yes. Connect Proxuma Power BI to your Autotask PSA, 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 ticket data, and produces a report like this in under fifteen minutes. Your numbers will reflect your actual client mix and operational patterns.
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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