Email open rate
What is an email open rate?
An email open rate is a key email performance metric that measures the percentage of delivered emails that are opened by recipients. It represents how well your subject line, sender name, and preview text make the recipients engage with your message before they ever see the content inside.
The open rate is typically the first engagement signal in your email funnel. A strong open rate indicates that your recipient list is healthy, your sender reputation is good, and your subject lines are compelling enough to earn a click in a crowded inbox. It's one of the most widely tracked email KPIs alongside click-through rate (CTR), conversion rate, and unsubscribe rate.
In simple terms, it answers: “Out of all the emails successfully delivered, how many were actually opened?”
How to calculate the email open rate
The standard formula for the email open rate is:
Email Open Rate = (Unique Opens ÷ Emails Delivered) × 100
Note: Most platforms use unique opens, not total opens, to avoid counting the same person multiple times.
For example:
Emails sent = 5,000
Bounced = 120
Emails delivered = 4,880
Unique opens = 1,098
The open rate = 1,098 ÷ 4,880 × 100 = 22.5% open rate
Unique opens vs. total opens
Unique opens count each recipient only once, regardless of how many times they opened the email. Total opens count every single open event, including re-opens. For benchmarking purposes, unique open rate is the standard used across the industry. Total opens can be useful for understanding engagement depth—for example, a high total-to-unique ratio suggests that subscribers are returning to reference your content.
How are email opens tracked?
Open tracking works by embedding a tiny, invisible, 1×1 pixel image (called a tracking pixel) in the email HTML. When a recipient's email client loads images, it sends a request to the ESP's servers, which logs the open. This means opens are only recorded when images are loaded. If a recipient reads your email in plain text or with images blocked, that open goes uncounted, causing slight undercounting.
Why do email open rates matter?
The email open rate is often the first indicator of email performance. While it doesn’t measure conversions directly, it reflects how well your email captures initial interest.
Key reasons it matters:
Measures subject line effectiveness.
Indicates audience engagement.
Helps evaluate sender reputation.
Acts as an early performance signal.
Guides A/B testing decisions.
What is a good email open rate?
Open rates vary widely depending on industry, audience, and email type.
General benchmarks:
15% to 25% → Average
25% to 35% → Good
35%+ → Excellent
By email type:
Transactional emails: 40% to 60%+
Newsletters: 20% to 30%
Promotional campaigns: 15% to 25%
Factors that affect email open rate
There are various factors that could affect the open rate. Here are some of the most significant ones:
1. Subject line
Clear, concise, and curiosity-driven subject lines perform best.
Personalization increases open rates.
2. Sender name and reputation
Recognizable sender names build trust.
Poor sender reputation can push emails to spam.
3. Timing and frequency
Sending at optimal times improves visibility.
Over-emailing can reduce engagement.
4. Audience segmentation
Targeted emails outperform generic blasts.
Relevance is key to opens.
5. Preheader/preview text
Acts as a secondary subject line.
Can significantly impact open rates.
Limitations
The email open rate is imperfect as a signal of email performance. The key challenges are:
Apple Mail Privacy Protection inflates opens.
Image blocking reduces accuracy.
Bot activity can skew data.
Its reliability has been further complicated by Apple Mail Privacy Protection (MPP), introduced in iOS 15 in September 2021.
Apple MPP pre-fetches email content—including tracking pixels—on Apple's servers, regardless of whether the user actually opens the email. This causes ESPs to record phantom opens for all Apple Mail users, inflating open rates.
For many senders, Apple Mail accounts for 40% to 60% of their list. This means aggregate open rates reported by your ESP may be significantly overstated.
The best way to gather inference is:
Treating the open rate as a directional signal rather than an absolute metric.
Shifting focus to the click-through and conversion rates as more reliable indicators.
Filtering Apple MPP opens from engagement segmentation to avoid classifying unengaged subscribers as active.
Using click-based re-engagement criteria instead of open-based criteria for list hygiene.