Benchmark Report: Email Resolution Rates by Ticket Category (2027)

"What's your resolution rate?" is the first question in almost every evaluation call, and it is close to useless on its own. A vendor quoting 71% and a vendor quoting 71% can be describing completely different products, because the number is a weighted average of thirteen very different jobs.

Order status emails resolve at 91%. Complaint escalations resolve at 29%. If your queue skews one way or the other, the blended figure you were quoted has almost no predictive value for you.

So here is the breakdown.

How to read this benchmark

These figures are modelled composites rather than a measured study. They are built from deployment patterns across a reference profile of companies running 12,000 to 400,000 email tickets a year in e-commerce, SaaS, fintech, logistics, telecom, travel, insurance, edtech and marketplaces, with at least twelve months of maturity.

Every category shows a median and a quartile range, because the range is frequently the more interesting number. Resolution means the customer's issue was closed without a human touching the thread, measured at 14 days to allow reopens to surface. Tickets that a human reviewed before sending do not count as autonomous, which is a stricter definition than some vendors use and it moves the numbers down by several points.

Resolution rate by category

Ordered by volume share of a typical mixed queue, with median autonomous resolution rate and quartile range:

• Order status / WISMO — 22% of volume: 91% resolution (84–96%)
• Product and how-to questions — 11%: 72% (60–82%)
• Account access and login — 9%: 86% (78–92%)
• Technical troubleshooting — 9%: 44% (30–58%)
• Shipping and delivery exceptions — 8%: 74% (62–83%)
• Refunds — 8%: 63% (48–77%)
• Returns and exchanges — 7%: 79% (70–88%)

And the second half of the distribution, where the difficulty concentrates:

• Subscription and plan changes — 6%: 77% (68–86%)
• Billing disputes — 6%: 51% (38–64%)
• Invoice and receipt requests — 5%: 88% (81–94%)
• Complaints and escalations — 4%: 29% (18–41%)
• Cancellations and churn saves — 3%: 34% (22–47%)
• Miscellaneous — 2%: 40%

Volume-weighted, that blends to 71%. Which is a fine headline number and tells you nothing about your own queue until you apply your own mix.

Three tiers, roughly

The categories cluster more cleanly than the list suggests. Above 85%, you have lookup work: WISMO, invoice copies, account access. The agent retrieves a fact from a system and formats it. These resolve nearly automatically once the integration exists, and the failure mode is a data gap rather than a reasoning gap.

Between 60 and 85% sits policy work. Returns, subscription changes, shipping exceptions, refunds, how-to questions. The agent has to apply a rule to a situation, and the rule has exceptions. Performance here tracks how well your policies are actually written down.

Below 60% is judgment work. Billing disputes, technical troubleshooting, complaints, cancellations. These need a decision that carries risk, or access to state nobody has granted, or a human being to absorb somebody's anger. Some of this will improve. A meaningful slice of it should not be automated at all, which we get into in our piece on when to resolve versus route to a human.

Resolution is not the same as staying resolved

A ticket that closes and reopens four days later was never resolved. Reopen rate by category, measured at 14 days:

• Order status: 3%
• Invoice requests: 4%
• Account access: 6%
• Refunds: 11%
• Billing disputes: 14%
• Technical troubleshooting: 19%
• Complaints: 21%

Technical troubleshooting is the category to watch. It already has the second-lowest resolution rate, and nearly a fifth of what does close comes back. Effective resolution is therefore closer to 36% than the headline 44%, which changes the automation case substantially.

The pattern holds generally: categories that are hard to resolve are also the ones where apparent resolution is least trustworthy. Any dashboard reporting resolution without reopen rate next to it is flattering itself. We covered why in our rundown of the email support metrics that actually matter.

How long each category takes to get good

Categories do not mature at the same speed, and knowing the order helps you sequence a rollout instead of switching everything on at once and drowning in review.

Weeks to reach 70% autonomous resolution within the category:

• Order status: 3 weeks
• Invoice and receipt requests: 4 weeks
• Account access: 6 weeks
• Returns and exchanges: 9 weeks
• Subscription changes: 11 weeks
• Refunds: 16 weeks
• Billing disputes: 24 weeks
• Technical troubleshooting: does not reach 70%

Order status hitting 70% in three weeks is not because the model is clever about shipping. It's because the answer lives in one API call and the question is asked the same way every time. Our walkthrough of automating WISMO tickets covers why this category is nearly always the right place to start.

Billing disputes taking twenty-four weeks is not a model limitation either. It's an approval limitation. Most of that timeline is spent getting sign-off on how much credit the agent may issue without a human.

Why each category fails

The dominant escalation reason differs by category, and it points at what you would need to fix:

• Order status: carrier data gap. The tracking system has nothing useful, so neither does the agent.
• Refunds: policy edge case outside the configured guardrails.
• Billing disputes: needs a credit decision above the approval threshold.
• Technical troubleshooting: needs log or account state the agent cannot read.
• Complaints: tone-shift detection fires and hands off deliberately.

Only one of those five is a knowledge problem. The others are access problems, permission problems, or intentional design. That distinction matters when you're deciding where to spend the next quarter of effort, because writing more help articles will not fix four of them.

The finding that should change how you evaluate vendors

Look at the refunds range again: 48% to 77%. That is a 29-point spread inside one category, across companies buying broadly similar software.

Now compare categories. Refunds median 63%, shipping exceptions median 74%. An 11-point gap.

The variation between companies handling the same category is nearly three times the variation between categories. Which means the honest answer to "what resolution rate will we get on refunds" is not a number, it's a question about your implementation: are your refund policies written down including the exceptions, and is the agent permitted to actually issue the refund?

Teams that answer yes to both land near 77%. Teams where refund policy lives in a senior agent's memory and the agent can only draft a reply land near 48%. Same category. Same product. Our breakdown of how AI handles refund request emails gets into what separates the two setups.

So when a vendor quotes you a category number, the useful follow-up isn't "is that verified?" It's "what did the top quartile do differently?"

High resolution rate does not mean high value

There's a trap in optimising for this chart. The categories that resolve best are also the cheapest tickets to handle manually, and the ones that resolve worst are frequently the most expensive.

An order status email takes a human about four minutes. A technical troubleshooting thread takes twenty-five, often across three replies and sometimes a second-line handoff. So a point of resolution gained on technical troubleshooting is worth roughly six times a point gained on WISMO, ticket for ticket.

Volume still wins overall. WISMO is 22% of the queue and resolves at 91%, which makes it the largest single block of deflected work by a wide margin, and no serious rollout starts anywhere else. But once the easy categories are done, the ranking of what to work on next should be resolution headroom multiplied by handling cost, not resolution rate alone.

Run that calculation on a typical mixed queue and technical troubleshooting usually comes out first, despite sitting near the bottom of the resolution table. Refunds and billing disputes follow. Nobody's instinct points there, because the dashboard makes those categories look like failures.

The categories worth leaving alone

Complaints resolve at 29% and reopen at 21%, and our view is that this is roughly where it should stay. A customer writing in angry is telling you something a deflection metric cannot price. Cancellations sit in similar territory at 34%, since a churn save is a commercial conversation rather than a support ticket.

Automating those two harder would move the blended number up by about two points and cost more than two points are worth.

Applying this to your own queue

Pull your last 90 days of email tickets and classify them into these thirteen buckets. Rough is fine; you're looking for shape, not precision. Multiply each category's volume share by the median above and sum it.

That number is a far better forecast than any blended benchmark, and it's usually a surprise in one direction or the other. A fashion e-commerce brand with 40% WISMO and heavy returns will model out around 80%. A B2B infrastructure company with 30% technical troubleshooting and long multi-issue threads will model out near 55%, and should plan accordingly rather than being disappointed at month six.

Two cautions on the arithmetic. First, categories overlap in real inboxes, since one email routinely contains a WISMO question and a refund request, and multi-issue threads resolve lower than either category alone. Second, these medians assume a mature deployment. Month three will look nothing like this, which is the point of tracking category-level maturity in an email support agent rather than staring at a single blended figure that mixes a three-week-old category with a six-month-old one.

The blended number is a summary statistic for a board slide. The category breakdown is the thing you actually operate on.

Frequently Asked Questions

Which email ticket types have the highest AI resolution rates?

Lookup-type tickets perform best. Order status and WISMO reach 91%, invoice and receipt requests 88%, and account access and login 86%. These share a common shape: the answer exists as a fact in a connected system, the question is phrased consistently, and no judgment call is required. The main failure mode is missing upstream data, such as a carrier that has not scanned a parcel, rather than the agent misunderstanding the request.

What is a realistic blended AI email resolution rate?

Around 71% for a typical mixed queue at maturity, which is the volume-weighted average across all thirteen categories in this benchmark. Your own figure depends almost entirely on ticket mix. A queue heavy in order status and returns models toward 80%; one dominated by technical troubleshooting and billing disputes models closer to 55%. Calculate it from your own category distribution rather than trusting any single industry average.

Why do refund emails resolve at such different rates?

Refunds show a 48% to 77% quartile spread, wider than the gap between most categories. Two factors explain nearly all of it. Whether refund policy including its exceptions is written down in retrievable form, rather than living in an experienced agent's head. And whether the agent can actually issue the refund or is limited to drafting a reply for review. Implementation depth matters more here than ticket type.

Should reopen rate change how I read resolution benchmarks?

Yes, particularly for harder categories. Technical troubleshooting reopens at 19% and complaints at 21%, against 3% for order status. That means a 44% technical resolution rate is effectively closer to 36% once reopens are subtracted. Categories that are difficult to resolve are also the ones where apparent resolution is least reliable, so any benchmark quoting resolution without a reopen figure alongside it is overstating performance.

Which ticket categories should I automate first?

Sequence by time to maturity, not by volume. Order status reaches 70% resolution in about three weeks, invoices in four, account access in six. Returns and subscription changes follow at nine to eleven weeks. Leave billing disputes and technical troubleshooting until later, since the first is gated on internal approval thresholds rather than model capability, and the second often needs system access that takes time to provision safely.

Ready to see what your own ticket mix would resolve at? Robylon AI resolves 60–80% of customer emails autonomously with agents that take action across Shopify, Zendesk, Stripe, Salesforce and 60+ other integrations. See how email agents work at robylon.ai