Month-by-month SLA compliance across 46,102 tickets. First response met rate, resolution met rate, ticket volume, and breach count per month with trend analysis.
Month-by-month SLA compliance across 46,102 tickets. First response met rate, resolution met rate, ticket volume, and breach count per month with trend analysis.
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 delivery managers, operations leads, and MSP owners tracking service quality
How often: Weekly for operational adjustments, monthly for client reporting, quarterly for contract reviews
Month-by-month SLA compliance across 46,102 tickets. First response met rate, resolution met rate, ticket volume, and breach count per month with trend analysis.
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), "AvgFR", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), "AvgRes", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]))First response and resolution met rates per month with ticket volume and breach count. Months below 75% FR are flagged.
| Month | Tickets | FR Met % | FR Breaches | Res Met % | Status |
|---|---|---|---|---|---|
| Feb 2025 | 3,478 | 82.7% | 414 | 94.3% | Good |
| Mar 2025 | 3,766 | 78.5% | 574 | 94.5% | Watch |
| Apr 2025 | 4,341 | 86.1% | 404 | 95.6% | Good |
| May 2025 | 3,639 | 83.1% | 292 | 90.5% | Good |
| Jun 2025 | 3,651 | 69.2% | 478 | 81.7% | Critical |
| Jul 2025 | 6,613 | 68.7% | 840 | 89.1% | Critical |
| Aug 2025 | 3,607 | 78.1% | 514 | 87.3% | Watch |
| Sep 2025 | 4,563 | 78.8% | 601 | 88.4% | Watch |
| Oct 2025 | 4,013 | 75.0% | 665 | 86.1% | Watch |
| Nov 2025 | 3,327 | 75.4% | 555 | 87.4% | Watch |
| Dec 2025 | 2,940 | 84.1% | 355 | 87.3% | Good |
| Jan 2026 | 2,164 | 87.8% | 189 | 94.4% | Good |
EVALUATE
SUMMARIZECOLUMNS(
'BI_Common_Dim_Date'[year_month],
"FR_Met_Pct", DIVIDE(
COUNTX(FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[first_response_met] + 0 = 1
&& NOT(ISBLANK('BI_Autotask_Tickets'[first_response_met]))), 1),
COUNTX(FILTER('BI_Autotask_Tickets',
NOT(ISBLANK('BI_Autotask_Tickets'[first_response_met]))), 1), 0),
"Res_Met_Pct", DIVIDE(
COUNTX(FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[resolution_met] + 0 = 1
&& NOT(ISBLANK('BI_Autotask_Tickets'[resolution_met]))), 1),
COUNTX(FILTER('BI_Autotask_Tickets',
NOT(ISBLANK('BI_Autotask_Tickets'[resolution_met]))), 1), 0),
"Total_Tickets", COUNTROWS('BI_Autotask_Tickets'),
"FR_Breaches", COUNTX(FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[first_response_met] + 0 = 0
&& NOT(ISBLANK('BI_Autotask_Tickets'[first_response_met]))), 1)
)
ORDER BY 'BI_Common_Dim_Date'[year_month] ASCFirst response consistently underperforms resolution across every month. The gap widens when ticket volume spikes.
The twelve-month trend has three distinct phases. Early 2025 (Feb–Apr) showed strong performance, with first response rates ranging from 78.5% to 86.1%. April 2025 was the best-performing month of the year at 86.1% FR and 95.6% resolution. Something worked in Q1 that stopped working in Q2.
June and July 2025 were the low point. FR dropped to 69.2% in June, then July added a volume spike of 6,613 tickets, the highest month in the dataset, with only 68.7% FR compliance and 840 breaches. The volume surge overwhelmed capacity. Resolution held up better in July (89.1%), which suggests the team handled closure well once they caught up, but initial response was consistently late.
The recovery from August through January 2026 was gradual. FR climbed from 78% in August–September to 87.8% in January, the best month in the trailing year. Resolution has been consistently strong throughout, ranging from 86% to 95%, which tells you the underlying capability is there. The first response bottleneck is a triage and routing problem, not a capacity problem.
Three actions based on the twelve-month pattern
April was your best month at 86.1% FR. June was your worst at 69.2%. That 17-point drop in sixty days points to a specific event: a process change, a staffing shift, a queue configuration update, or a volume spike in a specific account. Pull the May and June ticket data and compare queue routing and account mix. Finding that cause tells you exactly what to protect against going forward.
July 2025 at 6,613 tickets produced 840 FR breaches. Your team handled resolution well (89.1%) but not initial response. Build a surge protocol: automatic queue escalation when hourly volume exceeds a threshold, temporary cross-queue assignments, and pre-approved overtime triggers. The data tells you the volume level that breaks first response. Use it to set the threshold.
At 87.8% FR and 94.4% resolution, January 2026 is your best recent month. Identify what is different now compared to the July–October trough. Lower volume is part of it, but if routing changes or queue assignments also changed, document them. January's performance is the benchmark you want to hold through the next high-volume period.
First response windows are much tighter, typically one to four hours. Once a technician engages, resolution deadlines allow significantly more time. The data shows this pattern holds across every month: resolution is always 8 to 16 percentage points higher than first response.
July 2025 shows the relationship clearly: 6,613 tickets produced only 68.7% FR compliance. But May 2025 had 3,639 tickets at 83.1% FR, and October had 4,013 at 75.0%. Volume alone does not explain everything. Routing efficiency and staffing coverage matter as much as raw ticket count.
A single month dropping 5 percentage points is worth noting. A two-consecutive-month decline of that size is a signal worth investigating. The Jun–Jul 2025 double dip was the clearest signal in this dataset and coincided with the highest breach count period.
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