🚨 The most overlooked Support Metrics, that might be costing you customers

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You’ve checked your SLA dashboard.
Everything looks fine: response times are on target, resolution times are within limits, and nothing seems to be breaching.
But something still feels off.

Your team is overwhelmed. Customers are reopening tickets.
CSAT scores? Not where you'd like them to be.
So you start wondering: are we tracking the right things?

This article isn’t about the usual SLA metrics you already know.  It’s about the hidden signals, the ones hiding just below the surface.  They don’t show up in your standard reports, but they have a huge impact on customer satisfaction, trust, and how smoothly your support team operates.

Like the invisible part of an iceberg, these overlooked metrics might not be obvious — but they’re often where the real problems (and opportunities) lie.

Let’s dive into them.

Why standard SLA Metrics aren’t always enough

Most support teams live and breathe by two main SLA metrics: Time to First Response and Time to Resolution.
They’re the gold standard — simple, trackable, and easy to explain. Customers want quick replies and fast fixes, so naturally, these are the numbers everyone monitors.

And for good reason.
First response time helps set the tone — it shows your customer that their issue is being taken seriously.
Resolution time reflects your team’s efficiency and ability to solve problems without unnecessary delay.
These are the foundation of every solid support SLA strategy. No argument there.

But here’s the catch:
Speed ≠ quality.

You can answer in under 5 minutes… with a canned response that doesn’t help.
You can close a ticket in under 2 hours… only to have it reopened later.
And none of that will show up in your standard SLA report.

That’s where deeper, often overlooked metrics come in.
They help you go beyond the stopwatch and look at how well your team is actually solving problems — not just how fast.
And in support, that difference matters. A lot.

Overlooked Metric #1: Reopen Rate

What it is:

Reopen Rate measures the percentage of tickets that are closed and then reopened by the customer.
It’s a sign that the original resolution didn’t fully solve the issue, or that the customer didn’t understand or accept the answer.

Why it matters:

At first glance, a closed ticket appears to be a win. But when it reopens, it becomes clear: the problem was only temporarily silenced, not truly solved.
High reopen rates often point to rushed solutions, unclear communication, or a lack of understanding between support and the customer.

And here’s the kicker: SLA timers often stop once a ticket is closed. So reopened work items might slip under the radar, even though they’re a clear indicator of unresolved pain.

How to track it in Jira:

Jira doesn’t provide a built-in "Reopen Rate" metric, but it’s possible to track with a custom field or by analyzing status transitions (e.g., from “Done” back to “In Progress” or “Waiting for Support”).
Some teams create an automation rule to increment a counter each time a ticket is reopened. It is simple, but effective.

💡Pro tip: Set a team benchmark for acceptable reopen rates (e.g., below 10%) and flag work items that are reopened more than once. These are worth a closer look and maybe a root cause analysis.

Overlooked Metric #2: Escalation Time

What it is:

Escalation Time measures how long it takes for a ticket to be escalated after it’s created — whether that means being reassigned to a senior agent, passed to another team, or bumped in priority.

Why it matters:

Some tickets are complex. Some require specialized knowledge. That’s normal. But if it consistently takes hours (or even days) before a ticket reaches the right person, the customer suffers — and so does your team.

Delayed escalations often mean:

• Frontline agents aren’t empowered to act,
• Processes are unclear,
• Or worse, escalation only happens after the customer complains.

In short: a late escalation = a late solution.

How to track it in Jira:

You’ll need to define what qualifies as an escalation (e.g., a priority change, reassignment to a different group, adding a specific label/status).
Then, you can use automation to stamp a custom timestamp field and measure the gap between ticket creation and escalation.
Alternatively, specialized reporting tools can visualize this automatically.

Overlooked Metric #3: Number of Touches per Ticket

What it is:

This metric tracks how many times a ticket is “touched,” meaning how many comments, status changes, or assignee switches occur from creation to resolution.

Why it matters:

The more back-and-forth it takes to resolve an issue, the more likely something's broken — unclear responses, incomplete solutions, or lack of ownership.

A high number of touches can mean:

• Agents are guessing instead of solving,
• Customers are confused and keep asking for clarification,
• Or internal handoffs are creating friction.
  
  

It’s not just inefficient, it’s exhausting for both the support team and the customer.

In Jira:

You can count comment events and status transitions using Jira automation or export ticket histories to analyze them manually.
Some advanced tools from Atlassian Marketplace (like SLA Time and Report for Jira) can surface this data in dashboards without extra effort.

Overlooked Metric #4: Time Spent in “Pending” or “Waiting for Customer”

What it is:

This metric tracks how long a ticket stays in a passive state, usually a status like “Waiting for Customer,” “Pending Info,” or “On Hold.”

These statuses are often treated as neutral — “It’s not on us, we’re just waiting.” But in reality, long pending time is rarely harmless.

Here’s why:

• Customers might not respond because your message wasn’t clear.
• They might think the issue is already resolved, or worse, they gave up.
• It still counts toward their perception of your service, even if the SLA is paused.
  
  

In other words, just because you’re “waiting,” doesn’t mean they’re not getting frustrated.

In Jira:

Track time spent in specific statuses using custom SLA configurations or automation logs.
You can define a timer that only counts while the ticket is in “Waiting for Customer” — then visualize it in dashboards or reports.
If you’re using tools like SLA Time and Report, this can be monitored without complex setups.

💡 Pro tip: Set an upper limit for how long a ticket should stay pending (e.g., 48 hours). If there's no reply, follow up automatically or close it with a note. This keeps your queue clean and shows the customer you’re proactive.

Overlooked Metric #5: Agent Handoffs per Ticket

Let’s talk about the silent killer of smooth support: too many hands on a single ticket.

It starts innocently enough: one agent picks up the issue, asks a clarifying question, then passes it to another team.
That person routes it to someone with more experience, who later needs input from billing, who eventually asks the original agent to follow up.

By the end of the week, the ticket has become a group project.

Each handoff:

Adds delay + Increases the chance of miscommunication = Makes the customer feel like they’re explaining the same thing five times.

Worse yet?

It’s rarely tracked, because the SLA clock might still look okay. But to the customer, it’s chaos.

Why should you care?

Because more handoffs usually mean:

• unclear ownership,
• siloed teams,
• or gaps in documentation and training.

What can you do?

Start counting.
Monitor how many unique agents touch each ticket. Set a baseline. If your average ticket goes through three or more people — that’s a red flag.
Then, dig deeper:

❓Are specific categories being passed around more?

❓ Do new hires escalate too often?

❓ Are certain requests missing internal playbooks?

Bottom line:

The fewer hands, the better the experience. The goal isn’t to reduce collaboration, it’s to reduce confusion. And that starts by making handoffs visible.

Making the invisible visible. A quick recap

Let’s face it: most dashboards make you feel good. Green lights, SLA met, resolution times looking sharp. But if you stop there, you’re only seeing the surface.

These less obvious support metrics tell a deeper story:

• Reopen Rate shows whether your resolutions truly stick.
• Escalation Time reveals how quickly challenging work items get the attention they need.
• Touches per Ticket uncovers inefficiencies and unclear answers.
• Pending Time helps you catch silent delays that frustrate customers.
• Agent Handoffs expose internal complexity that customers shouldn’t have to deal with.

Tracking them might sound like extra work, and honestly, if you try to do it all manually, it is.

But here’s the good news.

🛠️ Want to track these metrics without extra work?

Our team ran into the same challenges, so we built a solution for it.
SLA Time and Report for Jira makes it easy to go beyond basic SLA stats.

You can track things like reopen rates, escalation delays, unassigned time, and agent workload with clear dashboards and flexible filters: no scripting, no manual setup.

If you want to truly understand your support performance,  not just speed, but quality, this is the tool we use ourselves, and it might just work for you too.

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🟦 What’s one overlooked metric you’d add to this list? Let’s discuss in the comments.