Zendesk solved tickets per agent hour report

Tickets per agent tells you how much work each person closed in a period. That is useful, but it hides one of the biggest variables in support: available time. An agent who solved 80 tickets while working 20 support hours is very different from an agent who solved 80 tickets across 40 hours. Solved tickets per agent hour gives you the rate, not just the count.

This guide covers what the metric means, how to build it with Zendesk data, and how to interpret it without punishing agents who take harder work. For the broader dashboard context, start with the support metrics dashboard.

What solved tickets per agent hour means

Solved tickets per agent hour is a throughput metric:

Solved tickets per agent hour = Solved tickets / staffed support hours

It answers a practical question: how much resolved work did the team produce for the time it had available?

This is different from agent utilization. Utilization asks whether agents were busy. Solved tickets per hour asks whether that busy time produced completed work. A team can look busy and still have low throughput if tickets bounce, escalations rise, or queues fill with complex issues.

Why this metric is useful

The main value is normalization. It helps you compare periods with different staffing levels.

Counts become comparable - 500 solved tickets means something very different with 6 agents than with 10.
Schedule changes are visible - Time off, part-time coverage, and training weeks stop distorting the story.
Staffing conversations improve - You can tell the difference between “the team had less capacity” and “the team had the same capacity but cleared less work.”

Used with queue velocity and resolution time, this metric helps answer whether low output came from insufficient hours, lower efficiency, or harder ticket mix.

How to report solved tickets per agent hour in Zendesk

Zendesk Explore gives you the solved-ticket side of the formula easily. The staffed-hours side usually comes from outside Zendesk.

Step 1: Build the solved tickets metric

In Explore, start with the Support: Tickets dataset.

Use a metric that counts solved or closed tickets.
Break it down by assignee and by week or month.
Filter out tickets that should not count toward agent throughput, such as spam, merged duplicates, or deleted records.

If your team solves tickets under one group and another team closes them administratively, stay consistent about whether the metric uses solved tickets or closed tickets. Most support teams should use solved.

Step 2: Add staffed hours

Zendesk does not usually know scheduled support hours natively, so most teams bring this from:

a workforce management tool
a scheduling spreadsheet
payroll or time-tracking exports
a simple weekly headcount x expected support hours table

At minimum, build a table with:

agent name
week or month
staffed support hours

If you do not have exact schedules yet, start with a reasonable approximation. A rough hours denominator is still better than pretending every agent worked the same amount of time.

Step 3: Calculate the rate

Once you have solved tickets and staffed hours by the same time period:

Solved tickets per agent hour = solved tickets / staffed support hours

You can do this in a spreadsheet, a BI layer, or a purpose-built support analytics tool. If you want a simpler setup, see Zendesk analytics dashboard.

Step 4: Trend it by team and segment

The most useful views are:

Weekly team trend - Is throughput rising or falling over time?
By group - Which queues convert hours into solved work most efficiently?
By channel - Email, chat, and messaging often behave differently. See Zendesk channel performance report.
By issue type or form - Low throughput may reflect hard work, not low effort.

Business hours vs calendar hours

This metric is not about business hours vs calendar hours in the usual SLA sense. It is about staffed hours. Still, the same idea applies: use the time basis that reflects how work actually happens.

If agents split time between support and projects, count only support hours.
If some agents cover weekends and others do not, include the actual staffed hours in the denominator.
If your chat team handles concurrent conversations, do not compare them directly to an email queue without noting channel mix.

The goal is not mathematical perfection. The goal is a rate that matches reality better than simple ticket counts.

How to interpret changes

If solved tickets per hour falls

Do not jump straight to performance conclusions. Check:

Ticket complexity - A new product issue may increase handle time and reduce solved-per-hour without any agent problem.
Routing quality - Bad assignment creates extra touches and handoffs. Review Zendesk auto-assignment accuracy.
Backlog pressure - A growing backlog can slow completions as agents triage more work than they finish.
Escalations or reopens - More follow-up work often lowers throughput.

If solved tickets per hour rises

That can be good, but verify why:

Did automation remove low-value work?
Did the ticket mix get easier?
Did agents close tickets faster but increase reopen rate?

Throughput should improve alongside stable quality, not instead of it.

Common mistakes

Using solved tickets per agent instead of per hour - Counts alone punish teams during vacation periods and make staffing changes invisible.
Comparing unlike work - Chat, email, enterprise escalations, and billing cases do not produce the same solve rate.
Ignoring quality - Higher throughput with worse CSAT or reopens is not healthy improvement.
Using total logged time instead of support time - Meetings, projects, onboarding, and QA work can distort the denominator if you are trying to compare support capacity.

When to put this on your dashboard

Add solved tickets per agent hour when your team is asking one of these questions:

Are we getting more productive or just working more hours?
Did staffing cuts actually hurt throughput?
Is one queue under-performing after we account for capacity?

For small teams, this is usually a second-layer metric, not a top-line executive KPI. Put it behind the core dashboard of volume, backlog, first response time, resolution, and quality. Then use it during staffing and workflow reviews.

FAQ

Can I calculate this entirely inside Zendesk Explore?
Usually not, because staffed hours are not a standard Zendesk metric. Explore handles the solved-ticket numerator well, but most teams need a second data source for the hours denominator.

Should I use solved tickets or closed tickets?
Use solved tickets unless your workflow depends on final closed status. Solved aligns better with when support work is functionally complete.

Is a higher number always better?
No. A higher rate is only healthy if resolution time and quality stay stable. Fast closes that create more reopens are not a win.

What is a good benchmark?
There is no universal benchmark because channel mix and ticket complexity vary too much. Track your own baseline and compare like-for-like teams over time.

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