# Email bounce rate benchmarks and what "acceptable" means

Published averages put hard bounces near 0.2% and soft bounces under 1% for opted-in lists. What mailbox providers enforce, and how to set your own target.

Date: 2026-09-03

For opted-in marketing lists, the published averages sit around 0.2% hard bounces and 0.7% soft bounces per send, with the worst industries still under 1% hard. Anything above 2% total bounces on a regular send is a list problem, and above 5% a reputation problem in progress. The mailbox providers do not publish a bounce threshold, but they publish a spam-complaint threshold of 0.3%, and the two move together: dead addresses, recycled traps and unengaged recipients are the same list.

## What do the published benchmarks say?

The most widely cited figures come from Mailchimp's platform data as analysed by Mailerio in 2025: across billions of emails the average hard bounce rate was [0.21% and the average soft bounce rate 0.70%](https://mailerio.com/blog/email-bounce-rate-benchmark/). By industry, the same analysis reports the lowest bounce rates for daily deals and e-coupon senders at 0.17% and the highest for software and web apps at 0.93%, as of the June 2025 publication.

| Figure | Value | Source |
| --- | --- | --- |
| Average hard bounce rate | 0.21% | [Mailerio's analysis of Mailchimp data, 2025](https://mailerio.com/blog/email-bounce-rate-benchmark/) |
| Average soft bounce rate | 0.70% | same |
| Lowest industry (daily deals and e-coupons) | 0.17% | same |
| Highest industry (software and web apps) | 0.93% | same |

Those numbers describe senders on a platform that already suppresses hard bounces automatically and enforces opt-in. A list that has never been verified, was collected years ago, or came from a partner will bounce at many times these rates on its first send, which is exactly the situation a benchmark is least useful for.

Other benchmark reports circulate with higher overall bounce figures, often in the 2% to 3% range across all industries. They are quoted widely but the source pages were not reachable for verification while this guide was written, so they are described here only qualitatively: they generally count all bounces together and include less strictly opted-in traffic, which explains most of the gap.

## What do the mailbox providers enforce?

Gmail and Yahoo publish requirements rather than benchmarks, and the requirements are what get mail blocked.

Google's [sender guidelines](https://support.google.com/a/answer/81126), in force since February 1, 2024, apply to anyone sending more than 5,000 messages a day to Gmail accounts: authenticate with SPF and DKIM, publish a DMARC policy, support one-click unsubscribe, and keep the spam rate reported in Postmaster Tools below 0.10%, never reaching 0.30% or higher. Yahoo's [sender requirements](https://senders.yahooinc.com/best-practices/) match: SPF and DKIM, a DMARC policy of at least `p=none` that passes, one-click unsubscribe, a spam rate below 0.3%, and, in their words, monitoring hard and soft bounces and removing invalid recipients promptly.

Neither publishes a numeric bounce threshold, but both treat bounce behaviour as a reputation input. A sender that keeps hitting `550 5.1.1` replies at a provider is doing what an address harvester does, and providers throttle or defer such senders long before any published number is crossed.

## Which tiers are used in practice?

Deliverability teams tend to work with tiers rather than a single number. One commonly quoted framing, published by [verified.email](https://verified.email/blog/email-deliverability/email-bounce-rate-benchmark) as its own synthesis of several reports, puts total bounces under 1% as excellent, 1% to 2% as acceptable, 2% to 5% as concerning, and above 5% as dangerous. Treat the tiers as a working convention rather than a measured fact; the measured facts are the platform averages above and your own history.

| Total bounce rate on a regular send | Reading |
| --- | --- |
| Under 1% | Healthy, opted-in, verified list |
| 1% to 2% | Normal for a list that has not been verified in a while |
| 2% to 5% | Clean the list before the next send |
| Above 5% | Stop, verify everything, and expect reputation repair to take weeks |

## Why do bounce rates rise?

Addresses die at a steady rate: people change jobs, companies rename domains, consumers abandon inboxes. A list that was perfect at collection decays every month it is not checked, and the decay is invisible until the next send turns it into bounces. Three sources dominate.

Typos at signup, which a form-time check catches before they enter the list. Job changes on B2B lists, which turn a confirmed corporate mailbox into a `550 5.1.1` within a year. And old consumer addresses that mailbox providers first disable and then, in some cases, recycle into spam traps, so that the address stops bouncing and starts hurting you instead.

## How do you measure your own rate correctly?

Count hard and soft bounces separately, per send, over the delivered attempts of that send. Exclude `5.7.x` policy rejections from the address side of the ledger, since those are about your authentication and reputation rather than the recipient; they belong in a different chart. Track the rate per segment and per acquisition source, because a 0.3% overall figure can hide a partner list bouncing at 8%.

Spaw's feedback loop makes the measurement part of the verification data. Post delivered and bounced outcomes from your sending platform, or connect its bounce webhook, and the feedback summary endpoint reports your outcomes crossed with the verdicts on file over the last 90 days: how many addresses verified as deliverable actually delivered, how many bounced anyway, and where the risky segment landed.

```bash
curl https://spaw.co/api/v1/email/feedback/summary \
  -H "Authorization: Bearer sk_live_…"
```

The same reports change future answers. A bounce joins your suppression list, a delivery turns later lookups of that address into a confirmed mailbox, and the per-domain bounce rate feeds the confidence estimate for catch-all addresses at that domain.

## How do you get under the benchmark?

Verify before the first send and on a schedule after it. A bulk verification run removes the addresses that would hard bounce, and undeliverable answers cost nothing, so the price of the run is the share of the list that is still good. Segment the risky verdicts rather than sending to them blindly, warm the catch-all segment in small batches, and keep a monitor on the list so decay shows up in a report instead of in a send.

Set your own target from your own history rather than from a published average. A B2B list that turns over 20% of contacts a year will never match a consumer newsletter's 0.2%, and does not need to; it needs a verification cadence that keeps each send under the tiers above.

## What to do next

- Run the list through the [cleaning checklist](/guides/email-list-cleaning-checklist) before the next campaign.
- Verify it with one [bulk job](/docs/api/create-bulk-job); undeliverable answers are free.
- Connect your bounce webhook to the [feedback endpoint](/docs/api/report-delivery-feedback) and read your measured accuracy from the [summary endpoint](/docs/api/feedback-summary).
- Learn which replies count as hard bounces in [SMTP bounce codes explained](/guides/smtp-bounce-codes-explained).

Reference: https://spaw.co/guides/email-bounce-rate-benchmarks