This report provides a detailed breakdown of first-contact resolution: how many tickets are closed by the first assigned resource? for managed service providers.
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
The 11.3% FCR rate means that for every 100 tickets completed, only about 11 are handled start-to-finish by the same technician. The escalation rate of 88.7% is the inverse: the vast majority of tickets pass through at least one reassignment. Whether that reflects necessary specialist routing or avoidable churn depends on what you find when you break it down by technician and category.
The first-hour fix rate of 16.1% is worth watching separately. Tickets resolved within an hour almost always stay with the first tech. This means the pool of tickets that could realistically improve FCR is not the full 88.7% of escalations: it is the subset where resolution was fast enough that a handoff added zero value.
EVALUATE ROW("FirstHourFix", [Tickets - First Hour Fix %], "SameDayRes", [Tickets - Same Day Resolution %], "ResolutionMet", [Tickets - Resolution Met %], "ClosureRate", [Tickets - Closure Rate %])| Technician | Tickets Closed | FCR Count | FCR % | FCR Bar |
|---|---|---|---|---|
| David Collins | 1,672 | 607 | 36.3% | |
| Jane Stewart | 2,614 | 896 | 34.3% | |
| Brandon Bishop | 2,632 | 399 | 15.2% | |
| Jonathon Burton | 1,665 | 225 | 13.5% | |
| Daniel Daniels | 2,427 | 311 | 12.8% | |
| Gregory Horn | 3,234 | 328 | 10.1% | |
| Andrew Roberts | 1,871 | 152 | 8.1% | |
| Tracy Fitzpatrick | 3,585 | 265 | 7.4% | |
| Mr. David Cooper DDS | 21,279 | 1,298 | 6.1% | |
| Maxwell Reed | 1,899 | 63 | 3.3% |
The FCR gap between David Collins (36.3%) and Maxwell Reed (3.3%) is not a rounding error — it is a factor of eleven. That kind of spread typically points to one of three things: technician skill depth, ticket type distribution by person, or a routing system that does not account for who can actually close what. Collins and Stewart are resolving tickets themselves at rates that are more than three times the team average.
Mr. David Cooper DDS handles more tickets than anyone else on this list (21,279 closed) but sits at 6.1% FCR. Volume alone does not build first-contact resolution. Specialization and routing precision do.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(Tickets, Tickets[Status] = "Completed"),
Tickets[AssignedTechnician],
"Total_Closed", COUNTROWS(Tickets)
),
"FCR_Count", CALCULATE(
COUNTROWS(Tickets),
Tickets[FirstAssignedResource] = Tickets[ClosingResource]
),
"FCR_Pct", DIVIDE(
CALCULATE(COUNTROWS(Tickets), Tickets[FirstAssignedResource] = Tickets[ClosingResource]),
COUNTROWS(Tickets)
)
)
ORDER BY [FCR_Pct] DESC| Client | Total Tickets | FCR % | Assessment |
|---|---|---|---|
| Rivers, Rogers and Mitchell | 6,268 | 24.5% | Above average |
| Price-Gomez | 2,155 | 17.8% | Above average |
| Ramos Group | 1,692 | 17.1% | Above average |
| Little Group | 5,250 | 9.1% | Below average |
| Craig-Huynh | 5,393 | 9.0% | Below average |
| Wall PLC | 2,356 | 6.0% | Below average |
| Lewis LLC | 1,745 | 4.8% | Low FCR |
| Thompson, Contreras and Rios | 1,783 | 4.4% | Low FCR |
| Martin Group | 2,742 | 5.3% | Low FCR |
| Blanchard-Glenn | 2,364 | 0.04% | Near-zero FCR |
Blanchard-Glenn stands out immediately. With 2,364 tickets and a near-zero FCR of 0.04%, virtually every ticket gets reassigned. This could reflect a dedicated account team structure where the first touch is always a dispatcher, or it could signal a routing problem specific to this client. Either way, it warrants a closer look at how tickets from Blanchard-Glenn are being categorized and assigned.
Rivers, Rogers and Mitchell sits at 24.5% with over 6,000 tickets. That combination of scale and relatively high FCR suggests the routing for this client is more deliberate. The contrast with Craig-Huynh (9.0%, 5,393 tickets) at similar volume is a useful comparison point.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(Tickets, Tickets[Status] = "Completed"),
Tickets[CompanyName],
"Total_Tickets", COUNTROWS(Tickets)
),
"FCR_Count", CALCULATE(
COUNTROWS(Tickets),
Tickets[FirstAssignedResource] = Tickets[ClosingResource]
),
"FCR_Pct", DIVIDE(
CALCULATE(COUNTROWS(Tickets), Tickets[FirstAssignedResource] = Tickets[ClosingResource]),
COUNTROWS(Tickets)
)
)
ORDER BY [Total_Tickets] DESC| Category | Total Tickets | FCR % | FCR Visual |
|---|---|---|---|
| Research scientist (life sciences) | 27,738 | 20.6% | |
| Naval architect | 1,687 | 17.5% | |
| Oceanographer | 3,089 | 8.1% | |
| Product manager | 16,390 | 5.7% | |
| Airline pilot | 13,307 | 0.02% |
Airline pilot tickets have a near-zero FCR rate across 13,307 tickets. This is the most concentrated routing failure in the dataset. A category that large with essentially no first-contact resolution is almost certainly being funneled through a dispatcher or first-line team that has no authority or tooling to close tickets themselves. The fix is not to train everyone on airline pilot issues — it is to either route those tickets directly to the right person the first time, or to create a specialist queue.
Research scientist tickets, which make up the largest single category at 27,738, achieve 20.6% FCR. That is nearly twice the overall average. Whatever routing logic applies to this category is working better than the rest and worth studying for what can be replicated elsewhere.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(Tickets, Tickets[Status] = "Completed"),
Tickets[Category],
"Total_Tickets", COUNTROWS(Tickets)
),
"FCR_Count", CALCULATE(
COUNTROWS(Tickets),
Tickets[FirstAssignedResource] = Tickets[ClosingResource]
),
"FCR_Pct", DIVIDE(
CALCULATE(COUNTROWS(Tickets), Tickets[FirstAssignedResource] = Tickets[ClosingResource]),
COUNTROWS(Tickets)
)
)
ORDER BY [Total_Tickets] DESCThe escalation rate is the defining number here. Nearly 9 in 10 tickets change hands. Some of that is appropriate specialist routing, but at this scale the probability of significant avoidable churn is high. The cost shows up in longer resolution times, technician context-switching, and client-facing delays that erode trust.
A category this large with a 0.02% FCR is not a skill problem — it is a structural routing problem. Every ticket in this category passes through at least one reassignment. Fixing the first-assignment logic for this single category could improve your overall FCR meaningfully.
David Collins closes 36.3% of his tickets himself. Maxwell Reed closes 3.3%. If you can identify what Collins and Stewart are doing differently — whether that is ticket type, client familiarity, or a different approach to triage — you have a coaching opportunity that does not require additional headcount.
This client combination is a flag for a specific account-level investigation. The near-zero FCR at that ticket volume almost certainly reflects a process issue rather than inherent complexity. It may be as simple as all tickets being logged by a dispatcher before assignment, but it deserves an explicit answer.
This is the category that is working. At 27,738 tickets (the largest single category), maintaining a 20.6% FCR shows that scale and first-contact resolution are not mutually exclusive. The routing logic for this category is worth documenting and applying elsewhere.
With 6,268 tickets and a 24.5% FCR, this is the highest-performing large client. The combination of volume and above-average resolution quality suggests a client-specific routing strategy is in place. Understanding what that looks like could improve FCR for other high-volume clients at similar complexity levels.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(Tickets, Tickets[Status] = "Completed"),
Tickets[CompanyName],
Tickets[Category],
Tickets[AssignedTechnician],
"Total_Tickets", COUNTROWS(Tickets)
),
"FCR_Count", CALCULATE(
COUNTROWS(Tickets),
Tickets[FirstAssignedResource] = Tickets[ClosingResource]
),
"FCR_Pct", DIVIDE(
CALCULATE(COUNTROWS(Tickets), Tickets[FirstAssignedResource] = Tickets[ClosingResource]),
COUNTROWS(Tickets)
),
"Avg_Hours_To_Close", AVERAGEX(Tickets, Tickets[HoursToClose]),
"Same_Day_Pct", DIVIDE(
CALCULATE(COUNTROWS(Tickets), Tickets[DaysToClose] = 0),
COUNTROWS(Tickets)
)
)
WHERE [Total_Tickets] >= 10
ORDER BY [FCR_Pct] DESCThe first assigned resource is the technician who was assigned to the ticket at the point of creation or first assignment, before any reassignment occurred. The closing resource is whoever was assigned when the ticket status changed to "Completed." If those two are the same person, the ticket counts as an FCR. Tickets that were never reassigned but were closed by someone other than the original assignee (for example, if the ticket was auto-assigned to a queue) are handled according to your PSA's assignment logging, and the exact behaviour may vary by configuration.
It depends on your service model. If you operate a tiered support structure where first-line staff intentionally escalate to specialists, a low FCR is by design. The number becomes a problem when escalations are avoidable: when the first-assigned technician had the skills to close the ticket but passed it on anyway, or when routing logic consistently sends tickets to the wrong person first. The more useful question is not whether 11.3% is "bad" but whether your FCR by category and by technician shows patterns that indicate routing or skill gaps you can address.
The fastest wins usually come from routing rules, not training programs. If airline pilot category tickets have near-zero FCR and 13,000 ticket volume, the first question is whether those tickets are being routed to a technician who can actually resolve them at first touch. Updating your PSA's dispatch rules to match ticket categories to technician skill profiles can lift FCR without any additional hiring or training. Second, look at the technicians with high FCR rates: identify what ticket types they handle well and make sure those tickets preferentially route to them first.
Yes. The DAX queries in this report can be extended with a date filter using your ticket close date or creation date dimension. Once you add a time filter, you can track FCR month over month, identify whether changes to your routing rules or team composition have improved the rate, and spot seasonal patterns. A useful format is a monthly FCR % line chart broken out by category or technician, which makes routing changes visible as inflection points in the trend.
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