What Is a Good Email Open Rate? By Industry and List Size

A good email open rate is generally 20 to 35 percent, but the honest answer depends more on your industry and list size than most benchmark posts admit. A B2B SaaS product sending to 500 engaged trial users should expect a very different number than a 200,000-subscriber ecommerce newsletter, and comparing the two tells you nothing useful.

Open rate benchmarks also carry a caveat that most articles bury: Apple's Mail Privacy Protection has been inflating reported open rates since 2021, so the "average" you read on any single blog may not reflect real human engagement at all. This guide gives you real, sourced ranges by industry and list size, explains why the numbers you see vary so wildly from one benchmark report to the next, and tells you what to actually track instead.

Key takeaways

• A good open rate for most industries sits between 20 and 35 percent, with government, nonprofit, and education consistently at the top and retail, ecommerce, and travel at the bottom.
• List size changes the baseline more than most people expect: smaller, more engaged lists routinely post higher open rates than large ones, independent of subject line quality.
• Apple Mail Privacy Protection (MPP) has been shown to inflate reported open rates by roughly 15 to 20 percentage points for Apple-heavy audiences, so raw open rate alone is not a reliable engagement signal anymore.
• Click-to-open rate (CTOR), the percentage of openers who clicked, is a cleaner read on content relevance than open rate alone because it is less distorted by automated opens.
• Benchmark reports disagree with each other by 10 to 20 points because they use different denominators, different time windows, and different Apple MPP filtering. Track your own trend over time rather than chasing one external number.
• Segmented sends outperform blanket sends: Mailchimp's own benchmark data shows segmented campaigns get meaningfully higher open rates than unsegmented ones (Mailchimp).

What counts as a good email open rate

Open rate is the percentage of delivered emails that were recorded as opened: unique opens divided by emails delivered, times 100. Most industry reports put the all-industry average somewhere between 19 and 22 percent when they exclude the Apple MPP inflation, and closer to 35 to 45 percent when they do not (WebFX; Mailchimp).

A workable, source-triangulated framework:

• Below 15 percent: worth investigating. Could be a list hygiene problem, a sender reputation issue, or a subject line that is not landing.
• 15 to 20 percent: typical for large B2B lists and retail/ecommerce sends.
• 20 to 35 percent: the broad "good" band most benchmark aggregators converge on across mixed industries.
• Above 35 percent: strong, and common for smaller, highly engaged lists, or industries like nonprofit and religion where subscribers opt in for a specific relationship, not a transaction.

The reason you will see wildly different "average" numbers cited elsewhere (some sources say 19 percent, others say over 40 percent) comes down to methodology: whether Apple MPP opens are filtered out, whether the dataset skews B2B or B2C, and whether the figure is a mean or median across millions of campaigns. Treat any single cited average with some skepticism and prioritize the range, not the decimal point.

Average email open rate by industry

Industry benchmarks are the most useful comparison point, because your competitors are fighting for the same inbox attention as you. Numbers below are drawn from Mailchimp's own by-industry benchmark data and corroborating aggregator reports; treat them as directional ranges, not exact targets (Mailchimp; WebFX).

Two things worth flagging. First, SaaS and B2B open rates specifically skew wide (15 to 30 percent is a realistic band) because "B2B" covers everything from a five-person tool with a hyper-engaged trial list to an enterprise platform blasting a 100,000-contact newsletter. Second, ecommerce and retail consistently underperform not because their content is worse, but because their subscribers are used to constant promotional volume from every store they have ever bought from once.

Why list size changes what "good" means

This is the part most benchmark posts skip, and it matters as much as industry. List size and open rate have a well-documented inverse relationship: smaller lists post higher open rates, largely independent of send quality (BizGenius; historical ExactTarget research cited via Business Wire).

If you run a 2,000-contact SaaS trial list and you are seeing 22 percent opens, that is not "good," it is below where a list that size should sit. If you run a 150,000-contact newsletter at 16 percent, that is roughly in line with expectations for that scale. The fix for a large list is not shrinking it, it is segmenting it: Mailchimp's benchmark data shows segmented campaigns post meaningfully higher open rates than one blast to the whole list (Mailchimp).

Why open rate numbers do not agree with each other

If you have compared two benchmark reports and gotten confused by how different the numbers are, you are not imagining it. Four things drive the gap:

1. Apple Mail Privacy Protection (MPP). Since 2021, Apple pre-fetches images (including the tracking pixel) for Mail app users regardless of whether a human opens the email. Research cites inflation of roughly 15 to 20 percentage points on Apple-heavy audiences, with some Apple-dominant lists seeing even larger swings (Paubox). A list that is 60 percent Apple Mail users can show a reported open rate 15 or more points higher than actual human engagement.
2. Mean vs. median. Some reports average every campaign equally; others report the median across millions of sends. A handful of extremely high-performing niche newsletters can pull a mean well above what a typical sender experiences.
3. B2B vs. B2C mix. A dataset weighted toward SaaS and services will report differently than one weighted toward ecommerce and retail.
4. Time window and filtering. Some cited benchmarks reuse older data (Campaign Monitor's widely cited 21.5 percent figure traces back to 2021), while others reflect the last 12 months.

The practical takeaway: pick one methodology (ideally your own historical data) and track your trend over time rather than chasing whichever external number sounds best that week.

Open rate versus the metrics that actually reflect engagement

Because of the MPP distortion, most email practitioners now treat raw open rate as a directional signal, not a precise measurement. Two metrics fill the gap:

• Click-to-open rate (CTOR): unique clicks divided by unique opens, times 100. This tells you what percentage of people who opened actually engaged with the content, and it is less affected by bot/scanner opens since a scanner rarely clicks a link. A CTOR in the 20 to 30 percent range is generally considered strong (Twilio).
• Click-through rate (CTR): unique clicks divided by emails delivered. The average sits around 2 to 2.5 percent across industries, with 3.5 percent and above considered excellent.

If your open rate looks unusually high but your CTOR is flat or falling, that is often a sign a meaningful chunk of your "opens" are automated prefetches, not people. This is exactly the gap that distinguishing human opens from scanner opens closes: rather than reporting one blended number, true open-rate analytics separates verified human opens (Apple MPP, Mimecast, Proofpoint, and Microsoft Defender scanner traffic identified and excluded) from a scanner percentage, so you can see what part of your reported number is real. Meisa's broadcast analytics report this split, alongside A/B subject-line testing and resend-to-non-openers for the segment that did not engage the first time, which is one practical way to act on the gap once you can see it.

How to calculate your own benchmark instead of guessing

1. Pull your last 90 days of sends and calculate unique opens divided by delivered, per send, not blended.
2. Separate by list segment: trial users, active customers, dormant leads. A blended number across all three hides the real signal.
3. Track CTOR alongside open rate for the same period. A stable or rising CTOR with a flat open rate usually means your content is fine and your subject lines or send times need work.
4. Re-run the calculation monthly and watch the trend line, not the single data point.

Copyable example: a subject line test worth running

If your open rate sits below your industry range, a subject-line A/B test is the fastest lever, because it isolates one variable. A simple two-variant test:

Variant A (benefit-led): "Your trial ends in 3 days: here's what to do next"
Variant B (curiosity-led): "3 days left (one thing most people miss)"

Send each variant to a small percentage of your list, measure open rate on a defined winning criterion (open rate, or click rate if you want to test past the subject line), then send the winner to the remainder. This is a standard subject-line split test, not a Meisa-specific mechanic, and it works on any platform that supports A/B sends with a winning criterion.

FAQ

What is a good open rate for email marketing?

Most benchmark sources converge on 20 to 35 percent as a good range across industries, with anything above 35 percent considered strong and anything below 15 percent worth investigating for a deliverability or list-quality problem.

What is the average email open rate in 2026?

Reported averages range from about 19 percent to over 40 percent depending on the source, mainly because of how each dataset handles Apple Mail Privacy Protection. A defensible cross-industry midpoint, factoring out heavy MPP inflation, is roughly 20 to 25 percent.

Does a smaller email list get a higher open rate?

Yes. Smaller, more recently opted-in lists consistently post higher open rates than large ones, often 30 percent or higher under 1,000 subscribers versus 12 to 18 percent for lists over 100,000, largely because smaller lists tend to be more targeted and less diluted by inactive contacts.

Why is my open rate higher than it used to be with no change in strategy?

This is almost always Apple Mail Privacy Protection, which began prefetching tracking pixels in 2021 and can inflate reported opens by 15 to 20 percentage points or more on Apple-heavy lists, with no actual change in how many people read your email.

Is click-to-open rate more reliable than open rate?

For gauging content relevance, yes. CTOR measures clicks as a share of opens rather than total sends, so it is less skewed by automated prefetch opens, since scanners rarely click links. A CTOR of 20 to 30 percent is generally considered strong.

What email open rate should a SaaS company expect?

SaaS and B2B lists typically fall in a 20 to 30 percent range, but the honest number depends heavily on list size and how targeted the send is: a small, product-qualified trial list will outperform a large blended newsletter list even within the same company.

If your dashboard number does not match any of these ranges, the fastest way to find out why is to separate verified human opens from scanner traffic and look at CTOR alongside open rate for the same period, rather than trying to interpret open rate alone. If you are building the sequences and broadcasts these numbers describe, the email sequence guide for SaaS and SaaS email marketing playbook cover the send side. Deliverability issues that suppress opens usually trace back to authentication, covered in SPF, DKIM, and DMARC explained and sender reputation.

If you want to see your own real number instead of guessing at a benchmark, Meisa's broadcast analytics split human opens from scanner opens per send, so you can compare your actual engagement to the ranges above rather than a blended figure. See how Meisa reports broadcast performance.