How to Calculate Email Open Rate (With Real Examples)

Email open rate is unique opens divided by delivered emails, multiplied by 100. Delivered emails means the number you sent minus any bounces, not your total send count, and unique opens means each recipient counted once even if they opened the message several times.

That formula answers the query, but it hides three decisions that change the number you get: which denominator you use (sent, delivered, or accepted), whether you count total or unique opens, and whether you strip out the automated "opens" Apple Mail now generates without a human ever looking at the email. Get any of those three wrong and you'll compare your performance against a benchmark that isn't measuring the same thing.

Key takeaways

• Open rate = (unique opens / delivered emails) x 100. Delivered emails excludes hard bounces; dividing by sent instead of delivered artificially deflates your rate.
• Apple Mail Privacy Protection (MPP) pre-fetches tracking pixels for a large share of inboxes, registering an "open" whether or not a person read the email. Litmus has reported Apple Mail holding roughly half or more of global email client share in recent years, which is why raw open rate now needs a caveat.
• A "good" open rate lands around 20% to 35% for most B2B and SaaS senders, per multiple 2026 benchmark reports (sourced below), though it varies by list type and industry.
• Click-to-open rate (unique clicks / unique opens) is a cleaner engagement signal than open rate alone, since a click requires a real person to act, while a pixel load does not.
• Segment your denominator by send type. Transactional emails, lifecycle sequences, and one-time broadcasts have different natural open-rate ranges, so blending them into one number hides which part of your program needs work.
• A single low-open send is noise; a declining trend across several sends is a deliverability signal worth investigating.

The open rate formula, step by step

The standard formula, used consistently across email service providers and cited by Mailtrap and Campaign Monitor, is:

Open Rate (%) = (Unique Opens / Emails Delivered) x 100

Two parts matter more than they look:

Unique opens, not total opens. If one recipient opens your email three times, that's one unique open. Total opens counts every open event, inflating the number for anyone who forwards, previews, or reopens a message. Report the rate using unique opens.

Delivered emails, not sent emails. Delivered means your send total minus hard bounces (invalid or non-existent addresses) and anything your sending infrastructure rejected before attempting delivery. Divide by "sent" instead, and a list with a high bounce rate looks like it has a worse open rate than it really does, when the actual problem is list hygiene, not subject lines.

Worked example 1: basic calculation

You send a broadcast to 5,000 contacts. 120 emails hard bounce, so 4,880 are delivered. 1,024 unique recipients open it.

Open Rate = (1,024 / 4,880) x 100 = 20.98%

If you'd used "sent" instead of "delivered": 1,024 / 5,000 = 20.48%. The gap is small here since the bounce rate is low, but on a list with 8% to 10% bounces, the wrong denominator can swing your reported rate by two or three full points, enough to misread whether a campaign actually improved.

Worked example 2: comparing two sends

You want to know if a new subject line helped. Both sends went to comparable segments.

Send B has a higher open rate, so the subject line likely earned more attention. But its click-to-open rate is slightly lower: a smaller share of openers actually clicked through. That combination usually points to a subject line that over-promised relative to the body, worth checking before declaring Send B the outright winner.

Worked example 3: the bounce-rate trap

A list with poor hygiene sends to 10,000 contacts. 1,400 bounce (a 14% bounce rate, well above the healthy under-2% range). Only 8,600 are delivered, and 1,720 open.

Open Rate = (1,720 / 8,600) x 100 = 20.0%

A 20% open rate looks acceptable on its own. But a 14% bounce rate is a serious deliverability warning sign that a healthy-looking open rate can mask. Track open rate without also watching bounce rate, and you can miss a list-hygiene problem that's actively damaging your sender reputation with every send.

What counts as a good open rate for email

There's no single official number, and any source stating one without citing where it came from is guessing. Here's what several 2026 industry reports actually say:

• Qualtir: above 25% is good, above 35% is excellent, below 15% needs attention.
• WebFX: average open rate across industries is around 19.2%, with anything above 20% considered good.
• Omnisend: a good open rate for ecommerce and marketing sends generally falls between 28% and 35%.
• The Frank Agency: cross-industry average sits around 21.3%, with "good" defined as roughly 17% to 28%.
• Brevo's 2026 benchmark report: average marketing email open rate of 20.7% before adjusting for Apple MPP, rising to the mid-30s when MPP-inflated opens are included.

The spread exists because sources measure different things: some report the mean (dragged down by inactive segments), some report the median, and few separate genuine human opens from Apple's automated pixel fetches. A defensible working target for most SaaS and B2B senders in 2026 is 20% to 35%, with anything consistently below 15% worth investigating and anything above 35% a sign of a genuinely engaged list.

Industry also moves the number. Government, nonprofit, and education senders tend to run highest (30%+) because recipients have high trust and intent. Ecommerce and broad consumer lists often run lower. Transactional email (password resets, receipts, confirmations) runs well above marketing benchmarks because the recipient expects and often needs the message.

Why Apple Mail Privacy Protection complicates the math

Since September 2021, Apple's Mail Privacy Protection has routed Apple Mail traffic through proxy servers that pre-load message content, including tracking pixels, before a recipient ever opens the email. That pre-load registers as an "open" whether or not a human read the message. Because Apple Mail represents a large and growing share of inbox traffic, a meaningful chunk of the "opens" in your dashboard may be automated pre-fetches rather than genuine engagement.

This doesn't make the open-rate formula wrong. It's still unique opens divided by delivered emails. It means the resulting number is no longer a clean proxy for "a human looked at this email." Two practical adjustments help:

1. Track trend, not absolute value. If MPP inflates your opens by a roughly consistent amount send over send, a week-over-week or month-over-month change in open rate is still meaningful, even if the raw number is inflated.
2. Pair open rate with click-to-open rate. CTOR (unique clicks divided by unique opens) requires a deliberate action a pixel pre-fetch can't fake, so it's a better read on whether your content, not just your subject line, is landing.

Open rate vs. click rate vs. deliverability rate

These three metrics get conflated constantly, but they measure different stages of the same journey, and none substitutes for the others.

A true "deliverability rate," meaning what percentage of your email actually lands in the inbox versus the spam folder, isn't something you can calculate from your own send data. Bounce rate and complaint rate are proxies you do control: a rising bounce or complaint rate alongside a falling open rate is a stronger deliverability signal than a low open rate alone. For the fuller picture on inbox placement and the technical levers behind it, see this guide to email deliverability.

How to calculate open rate for a sequence or lifecycle email

Broadcasts are the easy case because you're measuring one send. Lifecycle and behavior-triggered emails, like onboarding or trial-to-paid sequences, need a slightly different lens: each step has its own natural open-rate range and its own audience size, which shrinks as people convert, unsubscribe, or exit the sequence.

Example: a 3-email trial-onboarding sequence

Each step's open rate uses the same formula, unique opens over that step's delivered count, but the steps aren't directly comparable to each other or to a broadcast, since the audience for step 2 is already filtered down to people who didn't convert after step 1. Report sequence open rates per step, not as one blended average, or you'll draw the wrong conclusion about which email needs work. For more on structuring these flows, see this breakdown of email sequences for SaaS.

Common mistakes that skew your open rate

• Dividing by sent instead of delivered. The single most common error, and it always understates your real rate on any list with meaningful bounces.
• Blending total opens and unique opens. If your dashboard doesn't clearly label which one it shows, check the definition before reporting the number externally.
• Comparing across list sizes without normalizing. A 40% open rate on a 200-person list and a 40% open rate on a 40,000-person list are not the same signal; smaller, highly engaged lists naturally run hotter.
• Ignoring bounce rate entirely. As Worked Example 3 shows, a healthy-looking open rate can sit on top of a bounce rate quietly damaging your sender reputation.
• Treating one bad send as a trend. A single low-open campaign can be a bad subject line or send time. A multi-send decline is the one worth investigating. For the technical fixes behind chronic low opens, SPF, DKIM, and DMARC misconfiguration and eroding sender reputation are the two most common root causes.

Getting open rate data you can actually trust

However you calculate it, the number is only as good as the tracking behind it. Most platforms report a blended open rate that mixes genuine human opens with Apple's automated proxy pre-fetches, so two sends with identical subject lines and audiences can show different open rates purely based on how many recipients use Apple Mail. Meisa's broadcast analytics separate true opens from scanner opens (Apple MPP plus pixel-scanning behavior from Mimecast, Proofpoint, and Microsoft Defender), so the rate you see is closer to actual human engagement, and its resend-to-non-openers feature lets you follow up with people who genuinely didn't see the first send rather than re-annoying people MPP already marked as "opened."

For the broader metrics stack this fits into, this SaaS email marketing playbook covers where open rate sits alongside activation and retention metrics that matter more to revenue. To see true-vs-scanner open reporting on your own broadcasts, Meisa is built around that distinction.

FAQ

What is the formula to calculate email open rate?

Open rate equals unique opens divided by delivered emails, multiplied by 100. Delivered emails means your total send minus hard bounces, and unique opens counts each recipient once regardless of how many times they opened the message.

Should I use sent emails or delivered emails as the denominator?

Use delivered emails. Dividing by sent emails includes addresses that bounced and were never actually delivered, which artificially lowers your reported open rate and hides your bounce rate as a separate problem worth tracking on its own.

What is a good open rate for email in 2026?

Most 2026 industry benchmarks place a good open rate for B2B and SaaS senders between 20% and 35%, with anything above 35% considered strong and anything consistently below 15% worth investigating. The exact number varies by industry, list quality, and send type (transactional email runs well above marketing benchmarks).

Why is my open rate higher than it used to be even though engagement feels flat?

Apple Mail Privacy Protection pre-loads tracking pixels for a large share of inboxes, registering an "open" before a human has necessarily seen the email. If a growing share of your list uses Apple Mail, your reported open rate can rise even when actual human engagement hasn't changed. Pairing open rate with click-to-open rate gives a more reliable read.

What counts as email deliverability rate, and is it the same as open rate?

There's no single calculable "deliverability rate" from your own data, since you can't see inside every recipient's spam folder. What you can calculate is delivery rate (delivered divided by sent, which only confirms the email wasn't rejected outright) and bounce and complaint rates, which are the closest proxies you have for deliverability health. Open rate is a downstream metric that partly reflects deliverability (a sudden drop can signal spam-folder placement) but it measures engagement, not delivery.

Is click-to-open rate better than open rate for measuring engagement?

Click-to-open rate (unique clicks divided by unique opens) is generally considered a more reliable engagement signal because clicking requires a deliberate action from a real person, something Apple's automated pixel pre-fetching can't replicate. Many teams now track CTOR alongside open rate rather than relying on open rate alone.