Two newsletters with identical readers can report open rates thirteen percentage points apart, and neither number is a lie. That gap is the most important fact in email measurement right now, and most benchmark roundups quote one side of it without telling you which.
The figures below come from Brevo’s 2026 Marketing Orchestration Benchmark, built on aggregated campaign data from more than 175,000 active customers across 2025. It is worth naming the source and the sample, because most email newsletter statistics circulate for years detached from both, and a 2019 average quoted in 2026 describes a world before privacy protection changed what an open means.
The headline open rate is two different numbers
Across marketing campaigns, the average open rate is 20.73% when automatic opens are excluded. It is 33.87% when they are included.
Both describe the same campaigns. The difference is Apple Mail Privacy Protection, which fetches the tracking pixel on the subscriber’s behalf whether or not they ever look at the message. Every one of those fetches registers as an open. This single mechanism is why email newsletter statistics from different sources can look irreconcilable.
So the first question to ask about any open rate, yours or a benchmark’s, is which of the two bases it sits on. Platforms differ. Some report the inflated figure by default, some strip it, some give you both under names that do not make the distinction obvious. Until you know, you are comparing a number whose definition you have not checked against a number whose definition you also have not checked.
Find out by reading your platform’s own documentation rather than its dashboard labels. The terms used vary and are not standardized across vendors, so a field called “unique opens” means different things in different tools. The question to answer is narrow: does this figure include opens recorded by privacy proxies that pre-fetch images? One sentence in the docs settles it, and that sentence is the foundation for every comparison you make afterwards.
The practical damage from this is larger than it sounds. A team that believed its open rate was 34% and then migrated to a platform reporting the stripped figure watches the number fall to 21% overnight and concludes the migration broke something. Quarterly reviews get built around the drop. Budget moves. Nothing changed except the definition, and nobody in the room had written down which one they were using.
What is the MPP Gap?
Call it the MPP Gap: the spread between your reported open rate and your open rate with automatic opens removed, expressed in percentage points.
For the market as a whole that gap is 13.14 points. For your list it will be different, and the difference is diagnostic. A list where 70% of subscribers read on Apple Mail carries a much larger gap than one dominated by Gmail on Android. Two senders can both report 34% and have genuinely different businesses underneath.
Measuring it takes one afternoon. Segment your last ten campaigns by mail client, calculate the open rate for the Apple Mail segment and for everything else, and take the difference. The resulting number is the correction you apply to every open rate you report internally from then on. Write it on the dashboard.
What you do with it matters more than the figure. A large gap means open rate has stopped being usable as your primary engagement signal, and you need to promote clicks, replies and conversions to the top of the report. A small gap means open rate still carries information and you can keep using it, carefully. Either way you now know which situation you are in, and most senders do not.
The gap also has a direction over time, and that trend is worth more than the absolute number. If your Apple Mail share is climbing, your reported open rate will drift upward on its own while real engagement sits flat or falls. A chart that goes up while the business goes nowhere is the most expensive kind of metric, because it buys the current strategy another two quarters it did not earn.
Click-through rate is the number that survived
The average click-through rate across marketing campaigns is 2.27%. The top 10% of senders reach 5.22%.
Clicks are harder to fake accidentally. Privacy features can inflate opens, but a click still requires a human decision, which is why click-through rate and click-to-open rate have quietly become the serious metrics. Click-to-open sits in a 10% to 20% band across industries, and the position of your number inside that band tells you whether your subject line is over-promising relative to your body copy.
The diagnostic is worth spelling out. A high open rate paired with a click-to-open rate at the bottom of that band means the subject line is doing work the email cannot cash. People came in and left. The reflex is to rewrite the body, and the better move is usually to make the subject line more honest, which lowers opens and raises everything downstream. Teams resist this because the open rate is the number that gets reported upward.
The sharper comparison is between campaign email and automated email. Automated and transactional messages average 30.63% open and 7.39% click-through, roughly triple the campaign click rate. The reason is timing rather than craft. A shipping confirmation or a password reset arrives at the moment the reader is already thinking about the thing it concerns. A Tuesday newsletter arrives when the reader is thinking about something else.
That ratio is the strongest argument in the data for moving effort from broadcast to triggered sends. If your automated flows are a welcome email and nothing else, the gap between 2.27% and 7.39% is the size of the opportunity you are leaving in place.
One caution on reading that comparison. Transactional volume is smaller and self-selecting, so the 7.39% is not a target a newsletter can hit by trying harder. Use it as a structural argument instead: build more of your program out of messages tied to something the reader just did. A send triggered by a download, a renewal date or a second visit to a pricing page inherits the timing advantage automatically, and the timing is what the number is measuring.
Stop comparing yourself to the global average
Regional variation in this dataset is wide enough to make any single worldwide figure misleading for planning.
Europe averages 22.83% open and 2.29% click-through, with a 0.51% unsubscribe rate. North America averages 17.32% and 1.78%, with unsubscribes at 0.46%. Asia-Pacific sits at 17.56% and 1.46%, the Middle East and Africa at 17.26% and 1.53%, and Latin America at 14.72% and 1.21%, with the lowest unsubscribe rate of the five at 0.23%.
A North American sender measuring against the 20.73% global average will conclude they are underperforming by three and a half points when they are sitting almost exactly on their regional norm. That misreading has consequences. It gets subject-line tests prioritized over list hygiene, and it makes teams chase a number their geography does not produce.
Latin America is the instructive case. The lowest open and click rates in the set, paired with the lowest unsubscribe rate by a wide margin. Low engagement with high tolerance is a different problem from low engagement with high churn, and it calls for a different response: more frequency, not better copy.
Europe sits at the other end and deserves a note of its own. It posts the highest open and click rates in the set and also the highest unsubscribe rate at 0.51%, which looks contradictory until you account for consent law. Lists assembled under explicit opt-in run smaller and more engaged, and the same regime makes leaving easy and visible. High churn on a high-quality list is a healthy signal, not a leak, and a European sender who optimizes that 0.51% downward will usually do it by making unsubscribing harder, which is the one change that reliably damages deliverability.
What do the top performers actually hit?
Top-decile senders reach 44.02% open and 5.22% click-through. That is roughly double the average on both measures.
What separates them is rarely the thing people expect. It is not send-time optimization or emoji in subject lines. It is list composition. A list built from a single clear value proposition, where subscribers know what they signed up for and recent additions outnumber dormant ones, produces those numbers almost structurally.
Industry data supports this. Government and public administration leads every category at 34.19% open, 3.53% click-through and a 0.20% unsubscribe rate. Nonprofits follow at 27.03% and 3.10%. Insurance comes third at 25.48% and 2.36%. None of those sectors is known for marketing sophistication. What they share is that their email concerns something the recipient has a standing obligation or entitlement to care about: a benefits deadline, a cause they donated to, a policy they hold.
Commercial newsletters cannot manufacture obligation. They can get closer to it by being narrow enough that the subject matter itself does the work, which is why a newsletter about one specific thing routinely outperforms a company newsletter about everything.
There is a measurement trap in the top-decile figures too. Senders reach 44% open partly by mailing fewer people, and a program that prunes aggressively will show rising rates on falling absolute reach. Whether that is progress depends on what you are optimizing. If the newsletter exists to drive revenue, total clicks matter more than click rate, and a list half the size with double the rate has gained nothing. Keep both the rate and the raw count on the same screen so the tradeoff stays visible.
Unsubscribe and bounce rates predict your deliverability
The average unsubscribe rate is 0.46%. It is the least discussed number in the set and the one that most reliably precedes trouble.
Unsubscribes above roughly 1%, sustained across several sends, mean a mismatch between what people signed up for and what arrives. The revenue effect is slower than the deliverability effect. Mailbox providers read the pattern well before you notice the decline, and once filtering tightens, the campaigns that were working stop working for reasons that have nothing to do with the campaigns.
Treat the metric as a frequency and promise signal rather than a content one. A spike after a particular send usually points at a subject that broke the implicit contract of the signup, not at a bad email. The fix is the promise on the signup form more often than it is the writing.
Bounce rates belong in the same conversation and get even less attention. Hard bounces above a fraction of a percent on an established list mean addresses are decaying faster than you are removing them, usually because a form has no validation or an old import was never cleaned. The cost is not the wasted send. It is that mailbox providers read repeated delivery failures as a signal about the sender, and that signal depresses placement for the subscribers whose addresses are fine.
Run your own benchmark before you trust anyone else’s
Published email newsletter statistics are useful for sanity checks and useless for targets. Your list’s composition, geography, mail-client mix and signup promise determine more of your performance than any best practice.
The deeper problem with borrowed benchmarks is that they describe a different business. A sender in Brevo’s dataset with a 2.27% click rate might be mailing a purchased list weekly or a double opt-in list monthly, and those two programs share nothing except a metric name. Your own trailing twelve months is a better comparison set than any industry table, because it holds every variable constant except the ones you changed on purpose.
So do the four measurements that make the published numbers usable. Calculate your MPP Gap and write the correction on the dashboard. Compare against your region rather than the global figure. Split campaign performance from automated performance and look at the ratio. Track unsubscribes as a leading indicator rather than a vanity complaint.
Those four take an afternoon and they change which problem you work on next quarter. Which of them does your current reporting actually give you?