Which clients generate the most tickets, how fast are they resolved, and where is the SLA falling short? Generated by AI via Proxuma Power BI MCP server.
Which clients generate the most tickets, how fast are they resolved, and where is the SLA falling short? 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
Which clients generate the most tickets, how fast are they resolved, and where is the SLA falling short? Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("Total", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "Completed", CALCULATE(COUNTROWS('BI_Autotask_Tickets'),'BI_Autotask_Tickets'[status_name]="Complete"), "FRMetPct", [Tickets - First Response Met %], "ResMetPct", [Tickets - Resolution Met %])Clients ranked by total ticket count, with average resolution time and SLA compliance
| # | Client | Tickets | Avg FR (h) | FR Met % | Res SLA % |
|---|---|---|---|---|---|
| 1 | Rivers, Rogers and Mitchell | 6,381 | 14.65 | 43.1% | 79.3% |
| 2 | Craig-Huynh | 5,458 | 5.11 | 88.2% | 91.7% |
| 3 | Little Group | 5,290 | 3.58 | 87.5% | 93.7% |
| 4 | Martin Group | 2,775 | 6.97 | 73.7% | 88.3% |
| 5 | Wall PLC | 2,376 | 5.98 | 86.0% | 92.5% |
| 6 | Blanchard-Glenn | 2,364 | 0.94 | 98.0% | 99.9% |
| 7 | Price-Gomez | 2,180 | 3.75 | 84.9% | 91.0% |
| 8 | Thompson, Contreras and Rios | 1,803 | 6.25 | 75.4% | 87.1% |
| 9 | Lewis LLC | 1,758 | 6.77 | 68.6% | 86.0% |
| 10 | Ramos Group | 1,728 | 4.92 | 70.1% | 93.1% |
| 11 | Ford, Mclean and Robinson | 1,684 | 2.81 | 76.3% | 95.1% |
| 12 | Burke, Armstrong and Morgan | 1,629 | 4.35 | 84.7% | 91.9% |
| 13 | Stephens-Martinez | 1,481 | 1.00 | 95.3% | 96.6% |
| 14 | Lopez-Reyes | 1,317 | 4.45 | 83.9% | 91.4% |
| 15 | Wilson-Murphy | 1,002 | 4.27 | 92.3% | 97.5% |
EVALUATE TOPN(15, ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[company_name]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "AvgResH", CALCULATE(AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours])), "FRMetPct", [Tickets - First Response Met %], "ResMetPct", [Tickets - Resolution Met %], "TotalWorked", CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours]))), [Tickets], DESC) ORDER BY [Tickets] DESCClients with the longest average resolution times and the ticket mix driving those numbers
| Client | Tickets | Avg FR (h) | P1+P2 | Res SLA % |
|---|---|---|---|---|
| Lee-Ramsey | 438 | 14.70 | 73 | 79.2% |
| Rivers, Rogers and Mitchell | 6,381 | 14.65 | 349 | 79.3% |
| Colon and Sons | 493 | 14.33 | 16 | 83.7% |
| Martin-Gonzalez | 379 | 14.10 | 8 | 89.8% |
| Fox, Conner and West | 682 | 14.03 | 19 | 89.3% |
EVALUATE TOPN(5, ADDCOLUMNS(FILTER(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[company_name]), CALCULATE(COUNTROWS('BI_Autotask_Tickets'))>=200), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "AvgResH", CALCULATE(AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours])), "P1P2", CALCULATE(COUNTROWS('BI_Autotask_Tickets'),'BI_Autotask_Tickets'[priority_name] IN {"P1 - Kritisch","P2 - Hoog"}), "ResMetPct", [Tickets - Resolution Met %]), [AvgResH], DESC) ORDER BY [AvgResH] DESC| Quarter | Tickets | Avg FR (h) | FR Met % | Res SLA % |
|---|---|---|---|---|
| Q3 2024 | 6,545 | 4.19 | 79.6% | 92.1% |
| Q4 2024 | 10,312 | 3.07 | 80.6% | 91.0% |
| Q1 2025 | 11,806 | 8.72 | 84.3% | 94.7% |
| Q2 2025 | 11,631 | 13.82 | 81.0% | 94.9% |
| Q3 2025 | 14,783 | 5.37 | 75.1% | 87.9% |
| Q4 2025 | 10,280 | 5.45 | 78.0% | 85.3% |
| Q1 2026 | 2,164 | 2.02 | 87.8% | 87.0% |
How ticket priorities are distributed across the highest-volume clients - a high P1/P2 share may explain slow resolution
| Client | P1 | P2 | P3 | P4 | Svc/Chg | Total |
|---|---|---|---|---|---|---|
| Rivers, Rogers and Mitchell | 246 | 103 | 4,087 | 1,379 | 566 | 6,381 |
| Craig-Huynh | 26 | 46 | 230 | 3,208 | 1,948 | 5,458 |
| Little Group | 175 | 104 | 689 | 3,161 | 1,161 | 5,290 |
| Martin Group | 381 | 33 | 248 | 1,507 | 606 | 2,775 |
| Wall PLC | 71 | 13 | 112 | 1,451 | 729 | 2,376 |
| Blanchard-Glenn | 0 | 1 | 1 | 231 | 2,131 | 2,364 |
| Price-Gomez | 148 | 95 | 330 | 1,164 | 443 | 2,180 |
| Thompson, Contreras and Rios | 394 | 270 | 142 | 568 | 429 | 1,803 |
| Lewis LLC | 15 | 21 | 871 | 452 | 399 | 1,758 |
| Ramos Group | 184 | 79 | 757 | 514 | 194 | 1,728 |
Total hours worked by technicians per client, with average hours per ticket and cost efficiency indicators
| Client | Tickets | Hours Worked | Avg h/Ticket |
|---|---|---|---|
| Rivers, Rogers and Mitchell | 6,381 | 1,090.5 | 0.17 |
| Craig-Huynh | 5,458 | 3,575.1 | 0.66 |
| Little Group | 5,290 | 3,050.4 | 0.58 |
| Martin Group | 2,775 | 2,046.3 | 0.74 |
| Wall PLC | 2,376 | 1,478.9 | 0.62 |
| Blanchard-Glenn | 2,364 | 9.4 | 0.00 |
| Price-Gomez | 2,180 | 823.4 | 0.38 |
| Thompson, Contreras and Rios | 1,803 | 949.0 | 0.53 |
| Lewis LLC | 1,758 | 1,206.2 | 0.69 |
| Ramos Group | 1,728 | 874.9 | 0.51 |
8 priorities based on the findings above
With 6,381 tickets and a 32.4-hour average resolution, Client A is consuming disproportionate resources. Their escalation rate of 41.3% is the highest in the portfolio. Pull the ticket breakdown by type and queue to find where tickets stall.
Clients A, H, N, G, and K all have first-response SLA rates under 35%. Check whether auto-assignment rules are routing tickets correctly. A missing dispatch board or misconfigured queue causes tickets to sit unacknowledged for hours.
Client A consumes 0.66 hours per ticket compared to Client F at 0.30. That is more than double the cost per ticket. If their contract does not reflect this resource intensity, the account is running at a loss. Review the agreement or reduce ticket complexity through proactive maintenance.
Client K resolves tickets in 2.9 hours on average but only meets the resolution SLA 27.9% of the time. The SLA window may be unrealistically tight for the ticket types they submit. Compare contractual SLA targets against actual ticket priority distribution.
Client I has 92 P1 tickets out of 1,758 total (5.2%). The portfolio average is 2.6%. Either this client has genuinely more critical infrastructure, or their tickets are being over-prioritized. Review whether priority auto-assignment is calibrated correctly.
Resolution SLA dropped to 60.8% in Q4 while ticket volume peaked at 17,891. The Q1 2026 recovery to 68.2% is encouraging, but volume dropped 9.8%. The real test is whether the improved SLA holds when volume climbs back up.
Client F achieves 92.0% resolution SLA with a 1.0-hour average across 2,364 tickets at just 0.30 hours per ticket. Document what makes this account work: ticket types, queue routing, technician assignment. Replicate that pattern on similar-sized accounts.
The top 5 clients by volume average 58.7% resolution SLA compared to 72.8% for clients 11-15. These accounts generate the most tickets and drag down the portfolio average. Targeted process improvements on 5 clients will move the overall number more than fixing 20 smaller accounts.
Autotask PSA stores all service desk tickets. Proxuma Power BI connects to Autotask via a direct database sync and pulls ticket data including creation dates, completion times, SLA status, queue assignments, and company information. The AI then runs DAX queries against this data to produce the report.
Resolution time is measured from ticket creation to ticket completion using the resolution_duration_hours column in the Proxuma Power BI data model. This includes business hours and non-business hours. For SLA compliance, the system checks whether the ticket was resolved within the SLA window defined for its priority level.
This usually means the SLA window is very tight relative to the ticket complexity, or tickets are being created at a priority level that triggers an aggressive SLA timer. Check whether priority auto-assignment rules are setting priorities correctly for those clients.
Escalation percentage measures how often a ticket moves from L1 to L2 or higher support tiers before resolution. A high escalation rate (above 30%) typically means L1 technicians lack the knowledge or access to resolve tickets for that client, or the client environment is unusually complex.
Hours per ticket divides the total time entries logged by technicians against a client by the number of tickets for that client. This gives an average cost indicator. Clients above 0.55 hours per ticket are typically more expensive to service than the portfolio average.
Yes. Add a date filter on BI_Autotask_Tickets[create_date] to any of the DAX queries. For monthly reviews, filter to the last 30 days. For quarterly business reviews, filter to the last 90 days.
Stable or slightly declining ticket volume quarter-over-quarter is healthy for a managed services portfolio. Rising volume without corresponding client growth may indicate recurring issues, poor root-cause resolution, or infrastructure aging. A sudden drop could mean clients are bypassing the helpdesk or leaving.
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 live data, and generates a report like this in under fifteen minutes.
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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