Your newsletter open rate is the share of delivered emails that were opened. It is counted with an invisible image inside the email, a tracking pixel: when the image loads, the email counts as opened. That is why the number is an estimate, not a reader count. Apple Mail's privacy protection loads images in the background ahead of time, even when nobody looks at the email, and classic Outlook blocks images by default. As a benchmark, Mailchimp reports an average open rate of 35.63% across all users, based on December 2023 data, and warns that Apple's Mail Privacy Protection can distort it. For decisions, clicks and replies tell you more. Opens are most useful when you compare yourself with yourself, issue by issue and by where your readers came from.
My German newsletter "Aus dem Maschinenraum" and its English edition "From the Engine Room" are sent with Listmonk; the first issue went out on October 27, 2026. As of November 3, 2026, 71% of subscribers who signed up from a blog post had opened at least one of the first two issues (12 of 17), against 54% of subscribers from Meta ads (7 of 13). Those are very small groups. This article shows how I read numbers like these, what they tell you and what they don't. How the newsletter itself is set up is covered in How to Start a Newsletter: My Own Setup.
How is the open rate calculated?
The formula: opened emails divided by delivered emails, times 100. That is how Mailchimp defines it. A made-up example: you send 210 emails, 10 bounce, 70 recipients open. That is 70 out of 200, so 35%, not 70 out of 210.
Three settings change the result without anyone reading more or less:
- Unique or total opens. Someone who opens an email three times counts once as a unique open and three times in the total. Only compare numbers counted the same way.
- Clicks counted as opens. Mailchimp counts a click as an open even if the tracking image did not load, because anyone who clicks must have had the email open. Not every tool does this.
- Per email or per person. A rate per issue is a different thing from "how many people opened anything at all". My numbers above are the second kind: per person, across two issues.
In Listmonk, which I run myself, tracking lives in the template. According to the docs, the {{ TrackView }} tag inserts a single tracking pixel, to be used once, ideally in the footer. Links are tracked with {{ TrackLink }}. The docs also say both can be tracked anonymously, without tying an open to a specific subscriber. Setting up Listmonk is covered in Self-Hosted Email Marketing with Listmonk.
What the open rate does not measure
A tracking pixel measures whether an image was loaded. It does not measure whether a person read anything. Two email apps show how far apart those can be.
Apple Mail counts too much. Apple describes Mail Privacy Protection ("Protect Mail Activity" in Settings) like this: instead of loading remote content only when you open an email, Mail downloads it in the background by default, regardless of whether you engage with the email. The requests go through two separate relays so the sender cannot see your IP address. For your stats this means an email can count as opened while it sits unread in the inbox. Mailchimp adds a note to its own benchmarks saying Apple's Mail Privacy Protection can affect the accuracy of open rates. In its help center, Mailchimp also recommends excluding Apple and other automated activity from reports.
Outlook counts too little. Microsoft says classic Outlook is configured by default to block automatic picture downloads from the internet, and lists avoiding tracking pixels as one of the benefits. People who read that way read without being counted. Plain-text emails are the same: with no image, there is nothing to load.
So the open rate is too high and too low at the same time, depending on which email apps your readers use. That is their choice, not yours.
What is a good newsletter open rate?
The most quoted benchmarks come from the email tools themselves, based on their customers' sends. Mailchimp publishes them by industry. An excerpt, data as of December 2023 according to Mailchimp, retrieved November 13, 2026:
Read this as a vendor's figure with three caveats: the data is almost three years old, Mailchimp counts per email by its own method, and Apple's background loading is baked in, as Mailchimp itself says.
My 71% and 54% cannot be compared with this table. They count people who opened at least once across two issues, not opens per email. A per-person rate across two issues will almost always sit higher than the rate of a single email. The yardstick that works is your own trend: how does the rate move from issue to issue, with the same tool and the same counting?
Why small lists swing so much
On a small list, every single person moves the rate visibly. The arithmetic: one person is worth 100 divided by the group size, in percentage points. With my 17 blog readers that is almost 6 points per person; with the 13 ad leads almost 8.
In practice: if two more ad leads had opened an issue, that group would be at 9 of 13, or 69%, and the gap to the blog readers would be nearly gone. Timing matters too: anyone who signed up after October 27 had received only one issue by the cutoff date, so only one chance to open. And the number does not show how many of the counted opens came from Apple's background loading.
As a rule of thumb, not a law of statistics: while a group has only a few dozen people, don't read single issues, read the direction across several. A gap that points the same way week after week says more than a large gap in one measurement.
Read opens by signup source
A list's overall rate mixes readers who are there for very different reasons. Someone who signs up after a blog post has just been reading you. Someone who comes in through an ad for a download mostly wanted the download. That the two groups read differently is a plausible guess, and my numbers point that way, but with 17 and 13 people it is a direction, nothing more.
To separate the two on your own list, you need the source at signup:
- Store the source when people sign up. According to my privacy policy, the page where the signup happened is stored. If someone arrives from an ad or a social post, the campaign parameter in the link tells you; see UTM Parameters: Examples and Common Mistakes.
- Define the groups before you analyze. Decide up front which sources you compare, for example blog post, ad, templates page. If you keep regrouping until a difference shows up, you will always find one.
- Same calculation for every group. Same period, same issues, same counting. And put the group size next to every rate.
Confirmation comes before opening: people who never confirm their signup never appear in any open rate. How large that share is on my list and how I count it is in Double Opt-In: Email Setup and Legal Basics. What signup source means for freebies and ad tests is covered in Lead Magnet Ideas and How to Test Them.
Metrics that tell you more than opens
The open is the weakest number your newsletter tool shows you. These say more:
Clicks are more reliable because, according to Apple, Mail Privacy Protection does not extend to links. They are not perfectly clean either: Mailchimp recommends excluding other automated activity as well as Apple's. For a click to show up on your website as a visit from the newsletter, the link needs a marker, as the UTM article explains.
Replies are the most honest signal a small list has. Ask one concrete question in an issue that can be answered in a sentence, and count the replies. A reply is always a person, and it tells you what is on their mind. A welcome email that ends with that kind of question is ready to copy in Email Newsletter Template and Welcome Email.
What to do with these numbers over a longer stretch, including when to follow up and when not to, is covered in Lead Nurturing Emails: Examples by Stage.
Am I allowed to track opens?
Opens and clicks per subscriber are personal data. Listmonk's own docs say tracking pixels and click tracking carry privacy implications and should be used in compliance with rules such as the GDPR. On my side, the tracking is described in my privacy policy (in German): issues are tracked, the confirmation and welcome emails are not, and opens and clicks older than twelve months are deleted once a month. How consent and proof for the newsletter itself work is covered in the double opt-in article.
Frequently asked questions
What is a good newsletter open rate?
According to Mailchimp, the average across all users is 35.63%, and the industries on its overview page range from 29.81% to 40.04%, based on December 2023 data. That is a vendor figure, inflated by Apple's background loading. Your own trend with the same tool beats any average.
Why did my open rate suddenly jump?
Often it is Apple Mail. With Mail Privacy Protection on, Mail loads images in the background even when nobody opens the email, and every email loaded that way counts as an open. In that case, look at clicks.
How do I calculate my email open rate?
Opened emails divided by delivered emails, times 100. Take bounces out first. Whether unique or total opens count, and whether clicks count as opens, is decided by your tool.
Can I measure opens without a tracking pixel?
Hardly. Without a tracking pixel there is no counted open; the most tools like Mailchimp do is also count a click as an open. If you don't want a tracking pixel, you give up the open rate and look at clicks, replies and unsubscribes instead.
Which matters more: open rate or click rate?
Click rate. A click is an action; an open is just a loaded image. The open rate is still useful for comparing subject lines and send times within your own list.
What to do next
After every issue, write three numbers into a sheet: delivered, clicks, replies, with opens next to them, split by signup source and always with the group size. After eight to ten issues you will see a direction; after one issue you only see chance. If you want to see what an issue with a fixed structure looks like, pick a free template on my templates page: my newsletter "From the Engine Room" comes with it once a week.
