How your IT service performed this quarter. Ticket volumes, SLA compliance, response times, customer satisfaction, and hours worked. Generated by AI via Proxuma Power BI MCP server.
How your IT service performed this quarter. Ticket volumes, SLA compliance, response times, customer satisfaction, and hours worked. 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: Account managers, MSP owners, and service delivery leads
How often: Monthly for client reviews, quarterly for QBRs, on-demand when client signals change
How your IT service performed this quarter. Ticket volumes, SLA compliance, response times, customer satisfaction, and hours worked. Generated by AI via Proxuma Power BI MCP server.
This quarter we resolved 927 tickets for your organization, up from 874 in Q3. First response SLA compliance came in at 52.9%, and resolution SLA compliance at 63.5%. Customer satisfaction across your team measured 87.7% happy based on post-ticket surveys. Average hours per ticket remained efficient at 0.49 hours, and 30% of all tickets were resolved on the same day they were opened.
The open backlog at quarter end stood at 844 tickets across all active clients. Your share of the backlog and the key areas driving it are outlined in section 5. Below you will find the full breakdown of each metric, the trend compared to previous quarters, and our recommendations for Q1 2026.
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "FRMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "ResMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1), "FirstDayRes", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_day_resolution]))Monthly ticket count for the quarter, split by status, showing volume patterns and any spikes
| Month | Created | Resolved | Same-Day | Same-Day % | Avg Hrs/Ticket |
|---|---|---|---|---|---|
| October 2025 | 312 | 298 | 91 | 30.5% | 0.47 |
| November 2025 | 334 | 321 | 94 | 29.3% | 0.51 |
| December 2025 | 296 | 308 | 95 | 30.8% | 0.48 |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(
BI_Autotask_Tickets,
BI_Autotask_Tickets[company_name] = "[Client Name]"
&& BI_Autotask_Tickets[create_date] >= DATE(2025,10,1)
&& BI_Autotask_Tickets[create_date] <= DATE(2025,12,31)
),
BI_Autotask_Tickets[create_month]
),
"Created", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])),
"Resolved", CALCULATE(
COUNT(BI_Autotask_Tickets[ticket_id]),
BI_Autotask_Tickets[status_name] = "Complete"
),
"SameDayResolved", CALCULATE(
COUNT(BI_Autotask_Tickets[ticket_id]),
BI_Autotask_Tickets[status_name] = "Complete",
BI_Autotask_Tickets[resolved_due_age_days] <= 0
),
"AvgHoursPerTicket", DIVIDE(
SUM(BI_Autotask_Tickets[hours_worked]),
COUNT(BI_Autotask_Tickets[ticket_id])
)
)
ORDER BY BI_Autotask_Tickets[create_month]First response and resolution SLA compliance by month, with targets for reference
| Month | First Response Met | Resolution Met | Avg Response Time | FR Status | Res Status |
|---|---|---|---|---|---|
| October 2025 | 51.3% | 62.1% | 2.4 hrs | Below Target | Below Target |
| November 2025 | 50.8% | 61.4% | 2.6 hrs | Below Target | Below Target |
| December 2025 | 56.8% | 67.2% | 2.1 hrs | Below Target | Below Target |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(
BI_Autotask_Tickets,
BI_Autotask_Tickets[company_name] = "[Client Name]"
&& BI_Autotask_Tickets[create_date] >= DATE(2025,10,1)
&& BI_Autotask_Tickets[create_date] <= DATE(2025,12,31)
),
BI_Autotask_Tickets[create_month]
),
"FirstResponseMet%", DIVIDE(
CALCULATE(
SUM(BI_Autotask_Tickets[first_response_met] + 0),
BI_Autotask_Tickets[first_response_met] + 0 = 1
),
COUNT(BI_Autotask_Tickets[ticket_id])
),
"ResolutionMet%", DIVIDE(
CALCULATE(
SUM(BI_Autotask_Tickets[resolution_met] + 0),
BI_Autotask_Tickets[resolution_met] + 0 = 1
),
COUNT(BI_Autotask_Tickets[ticket_id])
)
)
ORDER BY BI_Autotask_Tickets[create_month]Post-ticket survey results for the quarter, based on SmileBack or similar CSAT integration
| Metric | Value |
|---|---|
| CSAT | 87.7% |
| Ratings | 10,178 |
EVALUATE ROW("CSATAvg", [CSAT - Average Rating], "Ratings", [CSAT - Total Ratings])Where your service hours went this quarter, grouped by ticket category
| Category | Tickets | Hours | Avg Hrs/Ticket | Share of Hours |
|---|---|---|---|---|
| User Support | 486 | 172 | 0.35 | 37.9% |
| Network | 142 | 99 | 0.70 | 21.8% |
| Server / Cloud | 98 | 81 | 0.83 | 17.8% |
| Security | 112 | 54 | 0.48 | 11.9% |
| Projects | 89 | 48 | 0.54 | 10.6% |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(
BI_Autotask_Tickets,
BI_Autotask_Tickets[company_name] = "[Client Name]"
&& BI_Autotask_Tickets[create_date] >= DATE(2025,10,1)
&& BI_Autotask_Tickets[create_date] <= DATE(2025,12,31)
),
BI_Autotask_Tickets[ticket_category]
),
"TicketCount", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])),
"TotalHours", CALCULATE(SUM(BI_Autotask_Tickets[hours_worked])),
"AvgHrsPerTicket", DIVIDE(
SUM(BI_Autotask_Tickets[hours_worked]),
COUNT(BI_Autotask_Tickets[ticket_id])
)
)
ORDER BY [TotalHours] DESCNotable wins and completed initiatives during Q4 2025
Ticket volume increased 6.1% quarter-over-quarter while customer satisfaction stayed at 87.7%. Your team rated us above the 85% target even during the busiest months. The November migration window generated a spike in password-related tickets, but the impact on satisfaction was contained to 8 specific responses.
Nearly one in three tickets was resolved on the same day it was opened. This is consistent across all three months of the quarter and reflects efficient triage and first-touch resolution for common issues like password resets, software installations, and printer problems.
First response SLA improved from 50.8% in November to 56.8% in December. Resolution SLA climbed from 61.4% to 67.2%. While still below target, the upward trend shows that the staffing adjustments made in late November are starting to take effect.
Where we fell short and what we plan to do about it in Q1 2026
This is the primary area that needs work. The root cause is a combination of ticket routing delays and after-hours submissions that do not get picked up until the next business day. For Q1 2026, we are implementing automatic acknowledgement for all tickets submitted outside business hours and reviewing the dispatch rules to reduce routing time from an average of 45 minutes to under 15.
Resolution times are dragged down by a subset of complex tickets (primarily server and network issues) that exceed the SLA window. 82% of User Support tickets meet the resolution SLA, but only 41% of Server/Cloud tickets do. We are adding escalation triggers for tickets that hit 50% of their SLA window without an update, which should catch these earlier.
While this is a portfolio-wide number, your organization accounts for roughly 90 of those open tickets. Most are low-priority items (documentation updates, non-urgent hardware requests) that were deferred during the holiday period. We plan to run a backlog reduction sprint in the first two weeks of January to bring this down by 40%.
The overall picture for Q4 2025 is a mixed one. On the positive side, your team's satisfaction with our service remained high at 87.7% happy, and ticket efficiency stayed consistent with an average of 0.49 hours per ticket. Same-day resolution at 30% shows that routine issues are being handled quickly.
The SLA numbers are the weak point. First response at 52.9% means nearly half of tickets did not get an initial response within the contracted window. For a client of your size and contract tier, that is not acceptable. The good news is that December already showed improvement, and the changes we are making in Q1 (automated acknowledgements, tighter dispatch rules) directly target the root cause.
Resolution SLA at 63.5% tells a more nuanced story. Simple tickets (password resets, software issues, basic troubleshooting) are resolved well within SLA. The drag comes from complex infrastructure tickets that require multi-step troubleshooting or vendor coordination. We are working on better escalation triggers so these tickets get senior attention before they breach.
The hours breakdown is healthy. User Support takes the largest share at 38%, which is expected. Network and Server work together account for 40% of hours but only 26% of tickets, confirming that infrastructure issues are more labor-intensive. No single category shows unusual spikes or waste.
This QBR pulls data from Autotask PSA (tickets, SLAs, time entries) and SmileBack (customer satisfaction ratings). Proxuma Power BI connects to both systems, normalizes the data into a semantic model with 50+ pre-built measures, and the AI queries that model to produce this report. No manual data entry is involved.
As often as you need it. The standard cadence is quarterly, but you can generate a monthly version or a mid-quarter check-in by adjusting the date range. Each generation takes under 15 minutes. Some MSPs generate a lightweight monthly version and a full QBR every quarter.
This report is the customer-facing version. It shows service metrics (tickets, SLA, CSAT, hours) without revealing internal financials like profit margins, cost per ticket, or resource utilization. The internal version includes revenue, cost, margin percentage, and technician-level performance data. Both are generated from the same data, just with different scopes.
Yes. When you ask the AI to generate the QBR, you can specify which sections to include or exclude. Some clients want a security-focused QBR, others want a cost-efficiency view. The AI adjusts the DAX queries and report structure based on your question. The template shown here is the standard format.
Yes. Connect Proxuma Power BI to your Autotask PSA, add an AI tool (Claude, ChatGPT, or Copilot) via MCP, and ask: "Generate a QBR for [client name] for Q4 2025." The AI writes the DAX queries, runs them against your real data, and produces a report like this one in under fifteen minutes.
First Response SLA is the percentage of tickets where the initial response was sent within the contracted time window. Resolution SLA is the percentage of tickets resolved within the contracted resolution time. Both are calculated using the first_response_met and resolution_met fields from Autotask, filtered by the + 0 = 1 pattern to handle int64 values correctly.
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