How many tickets are resolved by the first engineer who touches them? Breakdown by resource, ticket type, and first-day response rate.
How many tickets are resolved by the first engineer who touches them? Breakdown by resource, ticket type, and first-day response rate.
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
How many tickets are resolved by the first engineer who touches them? Breakdown by resource, ticket type, and first-day response rate.
Top-line numbers across all ticket data
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "ClosedByFirstResource", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]))Of 66,677 closed tickets, only 7,547 were resolved by the engineer who first picked them up
closed_by_first_resource flag in Proxuma Power BI marks tickets where the engineer who was first assigned to the ticket is the same person who ultimately closed it. No handoffs, no escalations, no reassignments. A value of 1 means the ticket stayed in one pair of hands from start to finish.
The six resources with the most hours logged across all tickets
| Resource | Hours | Tickets Closed | Hours / Ticket | Efficiency |
|---|---|---|---|---|
| Dr. Jessica Adams DVM | 2,400 | 603 | 3.98 | High effort |
| Sarah Martinez | 2,136 | 794 | 2.69 | Efficient |
| David Chen | 2,060 | 99 | 20.81 | Very high effort |
| API Integration | 2,050 | 2,613 | 0.78 | Automated |
| Michael Brown | 1,888 | 2,297 | 0.82 | Efficient |
| James Wilson | 1,862 | 84 | 22.17 | Very high effort |
EVALUATE
TOPN(
10,
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Tickets,
BI_Autotask_Tickets[resource_name]
),
"TotalHours",
CALCULATE(SUM(BI_Autotask_Tickets[worked_hours])),
"TicketsClosed",
CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "Complete"
)
),
[TotalHours], DESC
)
ORDER BY [TotalHours] DESCDistribution of closed tickets across the five ticket types in Autotask
| Ticket Type | Count | % of Total | Share |
|---|---|---|---|
| Incident | 27,664 | 41.5% | |
| Alert | 19,790 | 29.7% | |
| Service Request | 12,653 | 19.0% | |
| Change Request | 7,247 | 10.9% | |
| Problem | 167 | 0.3% |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Tickets,
BI_Autotask_Tickets[ticket_type]
),
"TicketCount",
CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "Complete"
)
)
ORDER BY [TicketCount] DESCThe headline number is clear: only 11.3% of tickets are closed by the first resource. That means nearly 9 out of 10 tickets change hands at least once before they reach completion. For an MSP handling 66,677 closed tickets, that is a significant amount of context-switching, handoff delay, and duplicated effort.
The 68.6% first-day response rate tells a different story. Most tickets get a response within the same day they are created. The gap between "responded quickly" and "resolved by the same person" suggests the issue is not speed of pickup. It is what happens after first contact. Engineers triage and respond, but the work often moves to someone else for resolution.
David Chen and James Wilson stand out. Both have over 1,800 hours logged but fewer than 100 tickets closed. Their hours-per-ticket ratios of 20.81 and 22.17 are ten times higher than the team average. This is not necessarily a problem. It could mean they handle escalated infrastructure projects or long-running change requests that absorb time without generating high ticket counts. But it is worth verifying whether those hours are being logged against the right work items.
API Integration closes 2,613 tickets at 0.78 hours per ticket. That is your automation layer doing its job. Michael Brown operates at a similar efficiency level with 2,297 tickets at 0.82 hours each. These two "resources" handle 73% of the total ticket volume between them.
Incidents make up 41.5% of all closed tickets. This is typical for MSPs, but it is worth comparing the first-resource closure rate specifically for incidents versus alerts. Alerts (29.7%) are often auto-generated by RMM and may route through automated workflows, which would explain part of the low first-resource rate. If incidents also show a low first-resource rate, that points to dispatch routing that needs adjustment.
The 844 open tickets are all overdue. Zero open tickets are within SLA. That backlog is small relative to total volume (1.2%), but every one of those tickets represents a customer waiting longer than promised.
4 priorities based on the findings above
An 11.3% first-resource closure rate means your dispatch is either routing tickets to the wrong person initially, or your L1 engineers lack the permissions or knowledge to close common ticket types. Pull the top 10 ticket categories by volume and check the first-resource rate for each. If password resets and simple changes also require escalation, that is a training or tooling gap you can fix quickly.
Every open ticket is overdue. Assign a dedicated block of time this week to triage the backlog. Sort by age, close anything that is stale or resolved by the customer, and reassign the rest with a clear owner and due date. 844 overdue tickets sitting idle erodes client trust even if the percentage looks small.
Both resources show 20+ hours per ticket. If they handle complex projects, that is expected. If they are spending that time on standard service tickets, something is wrong with scoping or time tracking. Check whether their hours are logged against project tickets or service tickets. If the majority is service work, look at whether those tickets should have been split into smaller units or escalated differently.
These two resources close nearly 5,000 tickets combined at under 1 hour per ticket. Use their resolution patterns as the baseline for what "efficient" looks like. If other engineers are spending 3x or 4x more time on the same ticket types, the gap is either in tooling access, documentation, or skill. Run a comparison report filtered by ticket type to find where the discrepancy is largest.
The closed_by_first_resource flag in Proxuma Power BI is set to 1 when the engineer who was first assigned to the ticket is the same person who closed it. If the ticket was reassigned to a different resource at any point before closure, the flag is 0. It measures whether a ticket stayed in one pair of hands from initial assignment to resolution.
First day response (first_day_response = 1) means the ticket received its first response on the same calendar day it was created. It measures response speed, not resolution. A ticket can have a first-day response but still take weeks to close if it requires escalation or waiting on parts or vendor input.
Industry benchmarks for MSPs typically range from 40% to 60% for first-contact resolution. At 11.3%, this number is well below average. That said, the metric depends on how tickets are routed. If most tickets go through an automated triage step before a human touches them, the "first resource" may be the triage bot, not the actual engineer. Check whether automated assignments are inflating the denominator.
The overdue status is determined by resolved_due_age_days being greater than zero. If every open ticket has passed its SLA due date, it means either the SLA targets are too aggressive for the current workload, or these tickets have been deprioritized and left to age. A triage pass to close stale tickets and reset expectations on remaining ones would bring this number down quickly.
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