Email Open Rate
Email open rate is the percentage of delivered emails that recipients open, calculated as unique opens divided by delivered emails.
Key takeaways
- An open is a pixel request, not evidence that anyone read anything.
- Privacy proxies pre-load images and inflate the count without any human action.
- Use open rate to compare subject lines within one list and day.
- Build engagement segments on clicks and replies rather than on opens.
- A fall across all campaigns points at placement, not at the subject line.
In depth
An open is not recorded by the email client telling you anything; it is inferred when a one-pixel image hosted on your sending platform's server is requested. The request carries a token identifying the recipient and the send, which is how the open is attributed. Plain-text messages therefore register no opens at all, and any client that blocks remote images records none either. The metric is a byproduct of image loading, not a report of human reading.
What moves the number is a mix of real and artificial factors. Sender name recognition, subject line relevance, send timing and inbox placement genuinely change how many people look. Privacy proxies that pre-fetch images inflate the count independently of any of that, and the size of the inflation depends on which clients your audience uses, so two lists with identical behaviour can report very different open rates. Segmenting by client is the only way to see the split.
Because the absolute level is unreliable, teams use open rate as a relative and diagnostic measure. It compares two subject lines sent to the same list on the same day, and it flags trouble when it falls across every campaign at once, which usually means placement rather than copy. In a lead funnel it is the first gate: someone who never opens the follow-up will never see the quiz invitation, so a falling open rate upstream explains a thin pipeline downstream.
Open rate cannot be used to set automation rules any more, because an inflated open from a privacy proxy is indistinguishable from a real one. Suppression lists built on opens will keep contacts who never looked and drop contacts who read in plain text. It is also useless for comparing across companies: list composition, client mix and how each platform counts a repeat view differ enough that any published benchmark says more about the audience than about performance.
Example in practice
How to measure it
The formula is unique opens divided by delivered messages, where delivered means sent minus bounces. Use unique rather than total opens, since one recipient scrolling back to a message repeatedly would otherwise count several times. Keep the denominator consistent: some platforms divide by sent instead of delivered, which lowers the figure, so check the definition before comparing any two reports.
Read it as a trend on your own list rather than against a published benchmark. Watch the direction over consecutive campaigns and, where your platform allows it, split by mailbox provider and by client so the proxy-inflated share is visible separately. Then pair it with click-to-open rate, which divides clicks by opens and shows whether the people who did look found anything worth acting on.
Common mistakes
The damaging mistake is running suppression on opens. A rule that removes anyone with no open in ninety days deletes contacts who read every message in plain text or with images off, while keeping contacts whose mail was merely pre-fetched by a privacy proxy. Base engagement segments on clicks, replies, site visits and purchases, and use opens only as a weak supporting signal when nothing else is available.
The second is optimising subject lines for curiosity alone. A vague or misleading line lifts opens because the reader cannot tell what is inside, then costs clicks, replies and eventually trust when the content does not match. Judge a subject line test by the clicks and conversions it produced, not by the opens; a variant that wins on opens and loses on clicks has made the campaign worse.
Frequently asked questions
Why is my open rate inaccurate?
Privacy features such as Apple Mail Privacy Protection pre-load tracking pixels, which counts opens that never happened. This means raw open rates skew high, so pair them with click and reply data for a truer read on engagement.
How can I improve email open rate?
Improve subject-line relevance, send from a recognizable sender name, and maintain a clean, warmed-up sending domain. Segmenting by quiz score or behavior so each message matches the lead's interest also lifts opens.
What is a good email open rate?
There is no reliable universal figure, because the number depends on list composition, how contacts were acquired and which mail clients they use. The useful comparison is your own list over time and between variants sent on the same day. A rate that is stable campaign to campaign matters more than a rate that looks high against someone else's published average.
Why did my open rate jump without anything changing?
Usually a shift in how your audience's mail clients handle images, or a change in your platform's counting rules, rather than anything you did. Check whether the increase is concentrated in one provider or client; if it is, treat the new level as a new baseline rather than a win. Real improvements normally show up in clicks as well.
Does open tracking work in plain-text emails?
No. Tracking depends on loading a remote image, and a genuine plain-text message contains none, so every open goes unrecorded. That is worth remembering before concluding that a plain-text sequence performed badly. If you need engagement data from such a send, measure replies and link clicks through tracked URLs instead of looking at opens.
Should you resend a campaign to non-openers?
Be careful, because the non-opener segment now contains people who did read. Resending to them looks like duplicate mail and generates complaints. If you resend at all, exclude anyone who clicked, wait several days, change the subject line rather than repeating it, and limit the practice to campaigns where the offer genuinely still applies.
What is click-to-open rate and why use it?
Click-to-open rate divides unique clicks by unique opens, so it isolates how persuasive the message body was once someone had it in front of them. It is useful for comparing content across campaigns, but it inherits the open metric's distortion in the denominator, so treat a change in it as a hint rather than proof.
Does the send time really affect open rate?
It affects when opens happen more than how many. Sending into a busy inbox at a common hour buries the message further down the list, so an unusual time can help, but the effect is small next to sender recognition and relevance. Test it on your own list rather than adopting a recommended hour, since audiences in different roles keep different schedules.