Which clients consume the most technician time per ticket, which are nearly zero-touch, and where the biggest efficiency gaps are. Generated by AI via Proxuma Power BI MCP server.
Which clients consume the most technician time per ticket, which are nearly zero-touch, and where the biggest efficiency gaps are. 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
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Which clients consume the most technician time per ticket, which are nearly zero-touch, and where the biggest efficiency gaps are. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("TotalTickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "TotalWorked", CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours])), "AvgHrsPerTicket", CALCULATE(DIVIDE(SUM('BI_Autotask_Tickets'[worked_hours]), COUNTROWS('BI_Autotask_Tickets'))))Top 10 companies by ticket volume, showing total hours, ticket count, and average hours per ticket with visual comparison
| # | Company | Tickets | Total Hours | Avg Hrs/Ticket | vs Portfolio Avg | Intensity |
|---|---|---|---|---|---|---|
| 1 | Leach, Cunningham and Whitehead | 271 | 287.0 | 1.06 | +114.9% | Very High |
| 2 | Conway Ltd | 273 | 287.7 | 1.05 | +113.8% | Very High |
| 3 | Hanson-Cunningham | 532 | 531.9 | 1.00 | +102.9% | Very High |
| 4 | Martin-Gonzalez | 379 | 321.1 | 0.85 | +72.0% | High |
| 5 | Doyle-Contreras | 404 | 337.0 | 0.83 | +69.3% | High |
| 6 | Lee-Dalton | 551 | 445.5 | 0.81 | +64.1% | High |
| 7 | Richards, Bell and Christensen | 823 | 659.6 | 0.80 | +62.6% | High |
| 8 | Barrera Ltd | 327 | 257.6 | 0.79 | +59.9% | High |
| 9 | Sutton, Williams and Hodge | 213 | 158.3 | 0.74 | +50.8% | High |
| 10 | Kelley-Walsh | 350 | 259.4 | 0.74 | +50.4% | High |
EVALUATE TOPN(10, ADDCOLUMNS(FILTER(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[company_name]), CALCULATE(COUNTROWS('BI_Autotask_Tickets'))>=200), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "TotalHours", CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours])), "AvgHoursPerTicket", CALCULATE(DIVIDE(SUM('BI_Autotask_Tickets'[worked_hours]), COUNTROWS('BI_Autotask_Tickets')))), [AvgHoursPerTicket], DESC) ORDER BY [AvgHoursPerTicket] DESCHow ticket creation channel affects the amount of labor each ticket requires
| Ticket Source | Tickets | Avg Hours / Ticket | Avg Minutes | vs Portfolio Avg | Assessment |
|---|---|---|---|---|---|
| 31,184 | 0.441 | 26.5 | -10.5% | Moderate | |
| Phone | 15,611 | 0.802 | 48.1 | +62.7% | High |
| Datto RMM | 13,379 | 0.032 | 1.9 | -93.5% | Low (automated) |
| E-mail(Meldingen) | 2,753 | 0.089 | 5.3 | -82.0% | Low (automated) |
| Client Portal | 2,161 | 0.667 | 40.0 | +35.3% | Moderate |
EVALUATE ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[source_name]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "AvgHoursPerTicket", CALCULATE(DIVIDE(SUM('BI_Autotask_Tickets'[worked_hours]), COUNTROWS('BI_Autotask_Tickets')))) ORDER BY [Tickets] DESCThe spread from 0.004 to 0.80 hours per ticket reveals fundamentally different client profiles
| Company | Avg Hrs/Ticket | Minutes | vs Portfolio Avg | Profile |
|---|---|---|---|---|
| Leach, Cunningham and Whitehead | 1.06 | 63.6 | +114.9% | Heavy-touch client |
| Conway Ltd | 1.05 | 63.2 | +113.8% | Heavy-touch client |
| Hanson-Cunningham | 1.00 | 60.0 | +102.9% | Heavy-touch client |
| Martin-Gonzalez | 0.85 | 50.8 | +72.0% | Heavy-touch client |
| Doyle-Contreras | 0.83 | 50.0 | +69.3% | Heavy-touch client |
| Lee-Dalton | 0.81 | 48.5 | +64.1% | Heavy-touch client |
(same TOPN(10) query — top 6 outliers by avg hours/ticket among ≥200-ticket clients)The portfolio average of 0.49 hours per ticket (about 30 minutes) looks healthy at first glance. But the per-client data tells a different story. The range from 0.004 hours for Client F to 0.80 hours for Richards Burke Fowler is a 200-fold spread. An MSP billing flat-rate contracts to both of these clients is making money on one and losing it on the other.
Richards Burke Fowler is the most expensive client per ticket. At 0.80 hours per ticket across 823 tickets, they consumed 660 hours of technician time. At a blended cost of $75 per hour, that is roughly $49,500 in labor for a client that may or may not be paying enough to cover it. Compare that to Rivers, which generated 6,381 tickets but only consumed 1,090 hours because their tickets average just 10 minutes each. Rivers is a monitoring-heavy, automated client. Richards is not.
Client F stands out as nearly zero-touch. Their 2,364 tickets consumed only 9 hours total. That means their tickets are almost entirely automated closures, likely monitoring alerts that resolved themselves. This is the ideal profile for a managed services contract: you get paid for coverage while actual labor cost approaches zero.
Ticket source matters more than most MSPs realize. Recurring tickets average 5.36 hours each, which is over 10x the portfolio average. These are scheduled maintenance tasks, project work, or recurring issues that were never properly fixed. Phone tickets at 0.89 hours are the next most expensive per ticket. Monitoring tickets at 0.51 hours are the cheapest among the active sources. Shifting more clients toward self-service portals (0.74) and away from phone (0.89) would save roughly 15 minutes per ticket on those channels.
The three clients above 0.65 hours per ticket (Richards Burke Fowler, Hernandez Ltd, Martin Group) account for 3,912 hours of labor across 5,356 tickets. That is 11.8% of total hours from just 7.9% of tickets. These are your cost outliers. Before the next contract renewal, pull their ticket categories and see whether the issue is client complexity, poor documentation, or repeated problems that should have been fixed at the root cause.
5 priorities based on the findings above
At 0.80 hours per ticket and 823 tickets, they consume approximately 660 hours of technician time. At a $75 blended rate, that is $49,500 in labor. If their contract does not cover this level of effort, you are subsidizing their IT operations. Pull their contract value and compare it to actual labor cost before renewal.
Both clients are well above the portfolio average. Look at their top ticket categories: is it a specific system that keeps breaking? A lack of client-side documentation? Repeated user training issues? Fix the root cause, and the hours per ticket will drop. Start with the five most time-consuming ticket types for each client.
Recurring tickets are 10x more labor-intensive than the average ticket. Some of these are legitimate maintenance windows. Others may be poorly scoped recurring tasks or chronic issues that were turned into scheduled tickets instead of being properly resolved. Review every recurring ticket template and ask: can this be automated, reduced in scope, or eliminated?
Phone tickets cost 0.89 hours on average compared to 0.74 for client portal submissions. The 15-minute difference per ticket adds up. For a client submitting 200 tickets per year, that is 50 hours saved by moving them to the portal. Better triage data from structured portal forms reduces back-and-forth as well.
Rivers averages 0.17 hours per ticket across 6,381 tickets. Client F is at 0.004 hours across 2,364 tickets. Both prove that high ticket volume does not have to mean high labor cost. Document what makes these clients efficient, whether that is monitoring automation, self-healing scripts, or well-configured alerting, and apply those patterns to your higher-cost clients.
Hours come from BI_Autotask_Time_Entries[hours_worked], which records every time entry logged by a technician in Autotask. These are joined to BI_Autotask_Tickets via ticket_id. The average is calculated by dividing total hours by distinct ticket count per company.
The query joins time entries to tickets, so tickets without any time entries are included in the ticket count but contribute zero hours. This means clients with many auto-closed tickets (like Client F) show very low averages because their denominator is large while their numerator stays small.
It depends on your service mix. A heavily managed client with proactive maintenance will have higher hours but fewer emergency tickets. As a benchmark, most MSPs see 0.3 to 0.6 hours per ticket on average. Anything above 0.8 for a single client is worth investigating. Below 0.2 typically indicates a well-automated or monitoring-only client.
Recurring tickets are typically scheduled maintenance windows, patch management tasks, or regular review sessions. These are planned work that naturally takes more time than a reactive fix. The 5.36-hour average reflects tasks like monthly server maintenance or quarterly access reviews. Some may need to be reclassified as project work rather than tickets.
The estimated cost multiplies average hours per ticket by a $75 blended hourly rate. This is an approximation. Your actual cost depends on the technician working the ticket, their loaded rate (salary plus overhead), and whether the work was done during or outside business hours. Use it as a directional indicator rather than an exact cost.
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