What is a good open rate for email marketing?

Somebody sends you a number and asks whether it is good. Twenty-two per cent. A good open rate for email marketing is the one that beats your own last number on the same kind of send to the same kind of list — which sounds like a dodge until you look at what the published averages are made of.

Two things are wrong with the comparison everybody makes. The number you are holding is not the same measurement as theirs. And a large part of it was never a person.

A share of your opens are machines

Apple shipped Mail Privacy Protection with iOS 15 in 2021. Apple Mail now fetches the images in a message through Apple's own proxy and caches them, whether or not anybody has looked at anything. A tracking pixel is an image. It fires on delivery.

Customer.io, to take the platform I work in most, documents this without euphemism: it counts a significant share of Apple Mail Protected opens as machine opens, warns that your overall open rate overstates real engagement when a lot of them come from that client, and tells you to look at human opens and clicks instead — human opens being the ones it attributes to real people rather than bots, scanners and prefetching.

So the honest reading of a raw open rate is three things added together. Some people. Every Apple Mail recipient the message reached. And whatever security scanner the recipient's employer points at inbound mail before a human sees it. Those three move independently of each other, and two of them move independently of anything you did.

The practical consequence is worse than the inaccuracy. A number that is inflated by a stable amount is still usable for comparison against itself. A number inflated by the share of your list that happens to use Apple Mail moves when your audience mix moves — a campaign that reaches more consumers and fewer work addresses will show a higher open rate for that reason alone.

The published average is an average of somebody else

Look up a benchmark and you get a figure with an industry label on it. What produced that figure is one vendor's own customers over some window: their list hygiene, their send types, their countries, their definition of a delivered message, their rules about what counts as an open.

Inside that one number sit transactional receipts that people genuinely want and promotional blasts they tolerate, lists built by double opt-in and lists bought in bulk, reactivation sends aimed at the dead and welcome emails aimed at somebody who signed up ninety seconds ago. Averaging those produces a number that describes no actual programme, including the ones it was computed from.

Then you compare your figure to it. Your figure comes from a different population, a different measurement and a different mix of send types. Whatever the gap turns out to be, it tells you nothing you can act on.

The same marketer, the same decade, 12% to 75%

I have run email across a job board, an esports product, an info-product business, a streaming service and a B2B software company. Over those years my own open rates covered almost the whole available range.

On a business software product whose customers use it to run their day, regular sends averaged around 75%. On webinar funnels in info-business, the funnel sends averaged about the same, with regular content closer to 51%. In esports, a base of roughly two million took content sends up to 50% and triggered messages to 65%. On a job board, a set of triggers I rebuilt went from 12% to 25% — and 25% was a serious win there. In a mass consumer product, 15% can be a perfectly healthy number.

Same person. Same methods, more or less. A five-fold spread.

The difference was never craft. It was who was on the list and why they were there. People who open a product's mail because the product is how they earn money behave nothing like people who signed up for a discount in 2019. Any benchmark that puts those two groups in one average has thrown away the only variable that mattered.

This is also why the figure is so easy to game without improving anything. Delete the people who never open, and every rate on the dashboard improves while the business underneath it does not. I have described how that one plays out — it is the quietest way to make a programme look healthy while it shrinks.

What to compare against instead

Your own previous number, on like against like. That means splitting the series before you look at it, not after:

By send type. Transactional, triggered and broadcast are three different things and belong in three different series. A password reset and a Tuesday newsletter have no business sharing an average.

By segment. The people who opened something in the last two or three months are a different population from the rest of your list, and the industry's own answer to list fatigue — send the things that matter most to the engaged segment first — only works if you are measuring the two separately.

By geography, where your audience spans several countries. Mail client mix differs, and so does the share of your opens that Apple is manufacturing for you.

Then watch movement rather than level. A series that went 31, 30, 32, 24 is telling you something. A single 24 compared to an industry's 21.5 is telling you nothing.

Where you can, demote the open rate to a supporting role and promote the things further down. Clicks are harder to fake than opens, and a click-to-open ratio at least divides two numbers that came from the same send. Better still is the thing the message was supposed to cause — a return visit, a renewal, a restarted subscription. That number has no proxy problem at all.

Clicks have a ceiling of their own. A send can sit at a click rate of 0.1% and still be followed by a day of conversions big enough to see in the revenue, because the product lives on a screen that is not the one the mail was read on. So the number worth arguing over is the target action itself, counted against the people who received the message as well as against the people who opened it, with post-click attribution kept beside it rather than in place of it. I will come back to that in a later post.

Where the open rate is still worth watching

Not as a measure of how good the campaign was. As an instrument on the deliverability panel.

A sudden collapse across the whole base, every segment at once, is not a content problem and nobody needs a benchmark to interpret it. It means mail has stopped reaching inboxes, and the clock on fixing it starts the moment you notice. I found exactly that once, watching a number drop from normal to 5% overnight, and the number was useful precisely because I was comparing it to the day before rather than to anybody's industry table.

That is the whole honest use of it. A tripwire, not a scoreboard.

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If you have to answer the question for somebody this week, the useful version is: show me the same send type to the same segment last quarter, and I will tell you whether this is good. Anyone who answers with a figure from a report has told you about their report, not about your programme.

RecurForge is being built to assemble that comparison for you, because doing it by hand is most of a week and the deciding is an hour. The form below is still the only working part of this site — leave an address if you want to hear when that changes.