Zendesk After-Hours Ticket Report
Tickets don’t stop arriving when your team clocks out. For most support teams, anywhere from 20% to 50% of ticket volume lands outside business hours—evenings, weekends, holidays. Those tickets sit untouched until the next shift, inflating first reply time, aging the backlog, and starting the day with a hole to dig out of.
An after-hours ticket report isolates this off-hours volume so you can quantify the gap, decide whether to staff it, and set SLA targets that reflect reality.
Why after-hours tickets matter
They inflate your metrics
If 30% of tickets arrive between 6 PM and 9 AM, those tickets accumulate hours of wait before anyone touches them. Your median first reply time in calendar hours will be significantly higher than in business hours—and if you’re reporting calendar hours to leadership, the number looks worse than your team’s actual speed during working hours.
They create morning backlog spikes
After-hours tickets stack up overnight and hit the queue at shift start. If you don’t know the volume, you can’t plan for it. Teams that ignore this pattern frequently start every day behind. See backlog aging report for how stale tickets compound the problem.
They reveal coverage gaps
A high percentage of after-hours tickets in a specific category (e.g. billing, outages) may justify an on-call rotation, a follow-the-sun model, or AI agent deployment for those hours.
How to build the report in Zendesk Explore
Step 1: Define “after hours”
Use the business-hours schedule already configured in Zendesk Admin (Business Rules → Schedules). After-hours is everything outside that schedule—nights, weekends, and holidays you’ve defined.
If you serve multiple time zones or brands, you may have multiple schedules. Decide whether to report per schedule or roll up to a single definition.
Step 2: Create a custom attribute for time-of-day
In Explore, you can use the Ticket created – Hour attribute to bucket tickets by creation hour. Create a calculated attribute:
IF [Ticket created - Hour of day] >= 9 AND [Ticket created - Hour of day] < 18
THEN "Business hours"
ELSE "After hours"
Adjust the hours to match your schedule. For more precise definitions (excluding weekends), add day-of-week logic:
IF ([Ticket created - Day of week] = "Saturday" OR [Ticket created - Day of week] = "Sunday")
THEN "After hours"
ELIF [Ticket created - Hour of day] >= 9 AND [Ticket created - Hour of day] < 18
THEN "Business hours"
ELSE "After hours"
Step 3: Build the report
- Dataset: Support – Tickets.
- Metric: Count of tickets (for volume), median first reply time (for response speed).
- Rows: Your custom “Business hours / After hours” attribute.
- Columns: Ticket created – Date (by week or day).
This gives you a table showing after-hours vs business-hours volume and FRT side by side.
Step 4: Add channel and category breakdowns
After-hours tickets aren’t uniform. Add channel (email, chat, web form) and tag/category as additional rows to see:
- Which channels generate the most after-hours volume?
- Which topics arrive after hours—and are they the kind AI agents could handle?
What to measure
| Metric | Definition | Why it matters |
|---|---|---|
| After-hours volume | Tickets created outside business-hours schedule | Quantifies the gap |
| After-hours % of total | After-hours volume ÷ total volume | Shows proportion |
| After-hours FRT (calendar) | First reply time for after-hours tickets | Customer wait experience |
| Morning backlog | Unsolved tickets at shift start | Operational pressure |
| After-hours by channel | Volume breakdown by channel | Coverage planning |
| After-hours by category | Volume breakdown by tag/category | AI agent or on-call targeting |
Common mistakes
- Reporting only in business hours — Business-hours metrics are useful for evaluating agent speed, but they hide the customer experience. A customer who emails at 10 PM and gets a reply at 9 AM waited 11 hours. Reporting in business hours makes that look like 0 minutes. Report both time bases.
- Treating after-hours as a monolith — Saturday afternoon and 2 AM Tuesday are both “after hours” but have very different volumes and expectations. Break down by hour-of-day or day-of-week to find the actual peaks.
- Ignoring time zone mismatch — If your business-hours schedule is US Pacific but a significant chunk of customers are in Europe, their morning tickets land in your “after hours.” The issue isn’t coverage—it’s time zone alignment. See peak hours report for traffic patterns.
- Not connecting to SLAs — If your SLA targets are defined in business hours, after-hours tickets don’t breach until the clock starts the next day. That’s technically correct but may not match customer expectations. Consider whether after-hours SLA targets need to be different.
What to do with the data
Low after-hours volume (< 15% of total)
Auto-replies are usually sufficient. Set up an auto-responder that acknowledges the ticket, sets expectations (“We’ll respond within X hours when our team is back”), and links to help center articles for the most common after-hours topics.
Moderate after-hours volume (15–35% of total)
Consider deploying AI agents for the top 3–5 after-hours contact reasons. An AI agent that resolves even 30% of after-hours tickets significantly reduces the morning backlog.
High after-hours volume (> 35% of total)
Evaluate staffing options:
- Staggered shifts — Shift some agents to start earlier or end later to cover peak after-hours windows.
- Follow-the-sun — If you have team members in other time zones, route after-hours tickets to them.
- On-call rotation — For urgent categories (outages, billing), set up an on-call agent who handles high-priority tickets only.
Track the impact of any change by comparing after-hours FRT before and after implementation.
Dashboard template: after-hours
Panel 1 — After-hours volume trend Stacked area chart: business-hours vs after-hours ticket creation by week. Shows whether the after-hours proportion is growing.
Panel 2 — After-hours FRT comparison Two-column chart: median FRT for business-hours tickets vs after-hours tickets (in calendar hours). Highlights the experience gap.
Panel 3 — After-hours by hour of day Heatmap: ticket creation count by hour-of-day × day-of-week. Identifies the real peak windows. See peak hours report for more on traffic patterns.
Panel 4 — Top after-hours categories Bar chart: top 10 tags or categories for after-hours tickets. Targets for AI agents or on-call coverage.
For an overall dashboard layout, see support metrics dashboard.
FAQ
Should I use business hours or calendar hours for SLAs on after-hours tickets? It depends on customer expectations. If customers know you’re closed evenings and weekends, business-hours SLAs are fair. If you serve a global customer base that expects 24/7 response, calendar-hours SLAs are more honest. Many teams use business-hours SLAs but track calendar-hours FRT separately to understand the full customer experience.
How do I know if AI agents are worth deploying for after-hours? Start by looking at your top 5 after-hours ticket categories. If they’re repetitive and well-documented in your help center (password resets, order status, known issues), AI agents can handle them effectively. If they’re complex or require account-level investigation, AI agents may not help much. Run a 30-day pilot and measure automated resolution rate.
Can TicketBoard break down metrics by time of day? Yes. TicketBoard lets you filter and segment by creation time, making it easy to isolate after-hours tickets and compare their metrics against business-hours tickets without building custom Explore formulas.