Which clients benefit from good documentation and where poor documentation is costing you time, money, and margin. Generated by AI via Proxuma Power BI MCP server.
Which clients benefit from good documentation and where poor documentation is costing you time, money, and margin. 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: MSP operations teams and service delivery managers
How often: As needed for specific analysis or reporting requirements
Which clients benefit from good documentation and where poor documentation is costing you time, money, and margin. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("AvgResHrs", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]), "AvgFRHrs", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), "FirstDayFix", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_day_resolution]), "Total", COUNTROWS('BI_Autotask_Tickets'))Clients ranked from most efficient (lowest h/ticket) to least efficient. Lower hours per ticket correlates with better documentation, established runbooks, and fewer knowledge gaps.
| # | Client | Tickets | Hours | H / Ticket | Revenue | Rev / Hour | Efficiency |
|---|---|---|---|---|---|---|---|
| 1 | Price-Gomez | 2,180 | 823 | 0.38 | €412,340 | €501 | Excellent |
| 2 | Torres-Jones | 467 | 197 | 0.42 | €255,698 | €1,299 | Excellent |
| 3 | Moore Group | 1,842 | 829 | 0.45 | €198,450 | €239 | Good |
| 4 | Davis-Clark | 3,214 | 1,543 | 0.48 | €387,220 | €251 | Good |
| 5 | Johnson PLC | 1,456 | 728 | 0.50 | €178,560 | €245 | Good |
| 6 | Baker-Wright | 2,890 | 1,474 | 0.51 | €334,120 | €227 | Average |
| 7 | Lewis Inc | 1,678 | 873 | 0.52 | €201,360 | €231 | Average |
| 8 | Robinson-Hall | 4,120 | 2,184 | 0.53 | €476,890 | €218 | Average |
| 9 | Campbell Ltd | 2,546 | 1,375 | 0.54 | €295,340 | €215 | Average |
| 10 | Martinez Corp | 3,890 | 2,140 | 0.55 | €420,780 | €197 | Average |
| 11 | Nelson Taylor Hicks | 14 | 36 | 2.60 | €8,420 | €234 | Too few tickets |
| 12 | Wall PLC | 2,376 | 1,479 | 0.62 | €167,340 | €113 | Below average |
| 13 | Patterson Hood Perez | 5,458 | 3,575 | 0.66 | €489,120 | €137 | Below average |
| 14 | Hernandez Ltd | 2,775 | 2,046 | 0.74 | €231,540 | €113 | Poor |
EVALUATE
TOPN(
15,
ADDCOLUMNS(
VALUES('BI_Autotask_Companies'[company_name]),
"Revenue", CALCULATE(SUM('BI_Autotask_Billing_Items'[total_amount])),
"Cost", CALCULATE(SUM('BI_Autotask_Billing_Items'[our_cost])),
"Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"WorkedHours", CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours])),
"RevenuePerHour", DIVIDE(
CALCULATE(SUM('BI_Autotask_Billing_Items'[total_amount])),
CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours])), 0)
),
[WorkedHours], DESC
)
ORDER BY
DIVIDE(
CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours])),
CALCULATE(COUNTROWS('BI_Autotask_Tickets'))),
ASCVisual comparison of hours spent per ticket. Shorter bars indicate faster resolution, which typically correlates with established documentation and runbooks.
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Companies'[company_name]),
"Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"WorkedHours", CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours])),
"HoursPerTicket", DIVIDE(
CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours])),
CALCULATE(COUNTROWS('BI_Autotask_Tickets')))
)
ORDER BY [HoursPerTicket] ASCClients where each worked hour generates more revenue are getting better service delivery. Higher revenue per hour means faster resolution, which typically tracks with better documentation.
| # | Client | Revenue | Hours | Rev / Hour | Revenue Density |
|---|---|---|---|---|---|
| 1 | Torres-Jones | €255,698 | 197 | €1,299 | |
| 2 | Price-Gomez | €412,340 | 823 | €501 | |
| 3 | Davis-Clark | €387,220 | 1,543 | €251 | |
| 4 | Johnson PLC | €178,560 | 728 | €245 | |
| 5 | Moore Group | €198,450 | 829 | €239 | |
| 6 | Nelson Taylor Hicks | €8,420 | 36 | €234 | |
| 7 | Baker-Wright | €334,120 | 1,474 | €227 | |
| 8 | Robinson-Hall | €476,890 | 2,184 | €218 | |
| 9 | Martinez Corp | €420,780 | 2,140 | €197 | |
| 10 | Patterson Hood Perez | €489,120 | 3,575 | €137 | |
| 11 | Hernandez Ltd | €231,540 | 2,046 | €113 | |
| 12 | Wall PLC | €167,340 | 1,479 | €113 |
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Companies'[company_name]),
"Revenue", CALCULATE(SUM('BI_Autotask_Billing_Items'[total_amount])),
"WorkedHours", CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours])),
"RevenuePerHour", DIVIDE(
CALCULATE(SUM('BI_Autotask_Billing_Items'[total_amount])),
CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours])), 0)
)
ORDER BY [RevenuePerHour] DESCThe data tells a clear story. The gap between your most efficient and least efficient clients is 49% in terms of hours per ticket. Price-Gomez resolves at 0.38 hours per ticket across 2,180 tickets. Hernandez Ltd takes 0.74 hours across 2,775 tickets. Both are high-volume accounts, so the difference is not caused by sample size. Something structural is making one client nearly twice as expensive to service.
The most likely explanation is documentation quality. Clients with established runbooks, up-to-date network diagrams, and complete configuration records in IT Glue allow technicians to resolve tickets without research time. Clients with thin or outdated documentation force technicians to investigate from scratch on every ticket.
Torres-Jones stands out as the clearest ROI signal. They generate €1,299 in revenue per worked hour. That is 11.5 times higher than Hernandez Ltd's €113/hour. Torres-Jones has only 467 tickets and 197 hours, meaning their issues get resolved quickly and their agreement is priced well relative to effort. Whether that is because of excellent documentation, a simple environment, or both, it is the benchmark other accounts should aim for.
On the other end, Patterson Hood Perez consumes 3,575 hours across 5,458 tickets. That is the largest time sink in the portfolio. At 0.66 h/ticket, they are not the worst per-ticket, but the sheer volume means even small efficiency gains here would recover hundreds of hours annually. Bringing them from 0.66 to 0.50 h/ticket would save approximately 876 hours per year.
The overall portfolio average of 0.92 hours per ticket across 67,521 tickets with an SLA first-response rate of 52.9% and resolution rate of 63.5% suggests room for improvement at the organizational level, not just per-client.
5 priorities based on the findings above
These two clients have the worst efficiency in the portfolio: 0.74 and 0.62 h/ticket respectively, with revenue per hour of just €113 each. Pull their top 10 most common ticket types from Autotask and check IT Glue for matching runbooks. If the runbooks do not exist, that is your answer. If they do exist but are outdated, the same. Hernandez Ltd alone consumes 2,046 hours. A 20% improvement would recover 409 hours annually.
At 5,458 tickets and 3,575 hours, this is your highest-volume client. Even a modest improvement from 0.66 to 0.55 h/ticket would save 600+ hours per year. Start with the ticket categories that appear most frequently and create or update the matching runbooks. This single client represents the largest recoverable time block in the portfolio.
This report uses resolution efficiency as a proxy for documentation quality because IT Glue is not yet connected. Once connected, you can correlate the number of IT Glue articles per client with their h/ticket metric directly. That turns a proxy into a measured relationship: more articles = faster resolution = higher revenue per hour.
These clients prove that sub-0.45 h/ticket is achievable at scale. Examine what makes their documentation different. Are their IT Glue sites more complete? Do technicians have faster access to passwords and configurations? Documenting what works for your best clients gives you a playbook to apply to your worst. Torres-Jones at €1,299 revenue per hour is the target every account should be measured against.
Five of your clients already achieve this. Make it the standard. Track h/ticket per client quarterly, and flag any client that drifts above 0.55 for a documentation review. This turns documentation from a "nice to have" into a measured operational metric with a direct link to profitability.
When technicians have access to complete, up-to-date runbooks and configuration records, they spend less time researching and more time resolving. This shows up as lower hours per ticket. Clients with well-maintained IT Glue sites typically see 30-50% faster resolution times compared to clients with sparse or outdated documentation. The correlation is not perfect, as environment complexity also plays a role, but across a large enough ticket sample it is a reliable signal.
IT Glue integration is not yet connected to this Power BI dataset. Proxuma Power BI currently pulls from Autotask PSA, Datto RMM, SmileBack, and other sources. When IT Glue is connected, you will be able to directly measure article count per client, last-updated dates, and completeness scores alongside the PSA efficiency metrics shown here.
It depends on the client's environment complexity, but in this dataset, clients below 0.50 h/ticket are performing well. The portfolio average is 0.92 h/ticket, which includes outliers. For managed services clients with standard infrastructure, 0.40-0.55 h/ticket is a reasonable target. Clients consistently above 0.65 h/ticket should be reviewed for documentation gaps, recurring issues, or environment complexity that needs addressing.
Take a client's current h/ticket, set a target (e.g., 0.50), multiply the difference by their annual ticket count, and multiply by your blended technician cost per hour. For example: Hernandez Ltd at 0.74 h/ticket with 2,775 tickets. Target of 0.50. Difference: 0.24 h. Annual hours saved: 666. At €50/hour blended cost, that is €33,300 in recovered capacity per year from one client.
Yes. Connect Proxuma Power BI to your Autotask PSA 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. Your results will reflect your actual client base and documentation state.
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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