A visual breakdown of first response and resolution SLA compliance across 67,521 tickets from Autotask PSA. Two donut charts show the met-vs-unmet split at a glance, with monthly trend data and the exact DAX queries behind each visual. PSA
A visual breakdown of first response and resolution SLA compliance across 67,521 tickets from Autotask PSA. Two donut charts show the met-vs-unmet split at a glance, with monthly trend data and the exact DAX queries behind each visual. PSA
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
A visual breakdown of first response and resolution SLA compliance across 67,521 tickets from Autotask PSA. Two donut charts show the met-vs-unmet split at a glance, with monthly trend data and the exact DAX queries behind each visual. PSA
Overall SLA metrics across all 67,521 tickets in the Autotask PSA dataset.
EVALUATE ROW("ResolutionMet", [Tickets - Resolution Met %], "FirstHourFix", [Tickets - First Hour Fix %], "SameDayRes", [Tickets - Same Day Resolution %], "ClosureRate", [Tickets - Closure Rate %], "TotalTickets", [Tickets - Count - Created])Of the 67,521 tickets with a first response SLA value, 54,080 met the target and 13,441 did not.
The first response SLA sits at 80.1%, which means roughly one in five tickets does not get an initial reply within the agreed window. For MSPs that sell on responsiveness, this number is the one clients notice first. The gap between 80.1% and the typical 85% industry target translates to about 3,300 extra missed responses over this dataset.
EVALUATE
ROW(
"MetCount", CALCULATE(
COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[first_response_met] + 0 = 1)
),
NOT(ISBLANK('BI_Autotask_Tickets'[first_response_met]))
),
"NotMetCount", CALCULATE(
COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[first_response_met] + 0 = 0)
),
NOT(ISBLANK('BI_Autotask_Tickets'[first_response_met]))
)
)Of the 67,521 tickets with a resolution SLA value, 60,907 met the target and 6,614 did not.
Resolution SLA tells a better story at 90.2%. Once the team picks up a ticket, they close it within the agreed window nine times out of ten. The 10-point gap between first response and resolution confirms that the bottleneck sits in triage and initial assignment, not in the actual fix. That is the single biggest lever for improvement.
Six-month trend of first response SLA compliance. The upward curve from October onward shows recent process improvements taking effect.
October marked the low point at 74.9%. Since then, each month has improved. January 2026 hit 87.8%, clearing the 85% target for the first time in this window. Whatever changed in the dispatch process in late November is working. The question now is whether the team can hold above 85% through Q1.
EVALUATE
TOPN(6,
SUMMARIZECOLUMNS(
'BI_Common_Dim_Date'[year_month],
"FirstResponseMet", [Tickets - First Response Met %]
),
'BI_Common_Dim_Date'[year_month], DESC
)At 80.1%, first response SLA is 5 points below the standard 85% target. That translates to roughly 13,400 tickets where the client waited longer than promised for an initial reply. In client-facing SLA reviews, this is the number that gets flagged first.
90.2% resolution compliance clears the 85% bar with room to spare. Once a ticket is assigned and acknowledged, the team closes it within the window nine out of ten times. This is a solid foundation to build on.
From the October low of 74.9% to January's 87.8%, first response compliance has climbed 12.9 points in three months. That is not a fluke. Something in the dispatch or triage process shifted, and the data confirms it is holding. Sustaining this above 85% through Q1 would bring the rolling average in line with the target.
Autotask PSA has a built-in reporting engine, but it is limited to tabular views and basic bar charts. There is no donut or pie chart option, and the SLA fields (first_response_met, resolution_met) are stored as integer flags that require calculated aggregations. Power BI reads these fields through the Proxuma data model and builds any visual you need, including the donut charts in this report.
The first_response_met and resolution_met fields in the BI_Autotask_Tickets table are int64 columns. A value of 1 means the target was met, 0 means it was missed. Because they are stored as integers rather than booleans, the DAX filter uses the pattern [field] + 0 = 1 to count met tickets. Tickets where the field is blank are excluded from both the numerator and denominator.
Yes. Copy any query from the toggles above and paste it into DAX Studio or the Power BI Desktop performance analyzer. The queries reference standard Proxuma data model tables and measures that exist in every Proxuma Power BI deployment. If your dataset uses the same schema, the queries will return your own numbers.
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