What Open Rates Can and Cannot Tell You

Open rate is the first number most people check after sending an email, and it is one of the most misunderstood. A high open rate feels like success. A low one feels like failure. In reality, the number is fuzzy, easy to inflate, and increasingly unreliable. It can still tell you something, but only if you know what it does and does not measure.

The short version#

An open rate is the percentage of delivered emails that your tool recorded as "opened." It is measured with a tiny invisible tracking image, sometimes called a pixel, embedded in the email. When the recipient's email app loads that image, your tool counts an open. If the image never loads, the open never registers, even if the person read every word.

That method has always been imperfect, and it has gotten worse. Some email apps now load that tracking image automatically for privacy reasons, whether or not the person actually opened the email. That inflates opens. Other apps or settings block images entirely, which hides real opens. So the number you see is a mix of fake opens, missed opens, and real opens, and you usually cannot tell the proportions.

The takeaway: treat open rate as a rough, noisy signal, never as a precise measurement or a metric to optimize on its own.

Where this fits in making money#

Email makes money through the chain in how email marketing makes money: a subscriber opens, reads, clicks, and sometimes buys. Open rate only looks at the very first link in that chain, and it looks at it through a cracked lens.

Delivered
  ↓
Opened        ← open rate lives here (and it is unreliable)
  ↓
Read
  ↓
Clicked       ← clicks are a real action, harder to fake
  ↓
Purchased     ← revenue is the truth

Notice how far open rate sits from the thing that pays you. A great open rate with no clicks and no sales is a vanity number. Money lives further down.

What open rate cannot tell you#

• Whether people actually read the email. A loaded image is not a read. Auto loaded images count opens for emails nobody looked at.
• Whether the email worked. Opening is not clicking, and clicking is not buying. Plenty of "opened" emails accomplish nothing.
• A precise comparison over time. Because privacy features distort opens unevenly, comparing this month's open rate to last year's can be apples to oranges.
• The health of your list on its own. A high open rate padded by auto loads can hide a list that has quietly stopped engaging.

What open rate can still hint at#

It is not useless. Used carefully, it offers weak signals:

• Sudden drops. If your open rate falls off a cliff, something may be wrong: deliverability trouble, more of your mail hitting spam, or a list going cold. It is a smoke alarm, not a diagnosis.
• Rough subject line comparisons. In a controlled split test, where everything else is equal, a difference in opens can hint that one subject line pulled more attention. Even here, treat it as directional. Pair it with how to write subject lines without lying, because a subject line that boosts opens by overpromising will tank your clicks and trust.
• Relative engagement segments. Consistently low openers, tracked over many sends, are probably genuinely disengaged, which is useful for the engagement segments described in email segmentation explained.

A simple example with numbers (hypothetical)#

These numbers are invented to make the point, not to describe typical performance.

You send two emails to the same 1,000 subscribers.

Email A: curiosity gap subject line that oversells.

• Open rate: 40% (400 "opens")
• Clicks: 1% (10 clicks)
• Sales: 0

Email B: honest, specific subject line.

• Open rate: 28% (280 "opens")
• Clicks: 4% (40 clicks)
• Sales: 3

By open rate, Email A looks like the winner. By the numbers that matter, Email B did four times the clicks and made actual sales. If you optimized for opens, you would keep writing emails like A and slowly train your list to feel misled. This is exactly why open rate is a trap when treated as the goal. The click through rate told the real story here.

Why the number got less reliable#

It helps to understand why open tracking degraded, because it explains why old advice about "aim for a 25% open rate" no longer means much.

The tracking pixel was always a workaround. Email has no built in "read receipt," so marketers embedded a tiny transparent image and inferred an open from the image loading. That inference was shaky even in the best case, because an image can load without a human reading a word, and a human can read every word without the image loading.

Then privacy features changed the ground rules. Some mail providers now pre load that image for many messages before the recipient ever opens them, which manufactures opens that never happened. Other setups strip images by default, which hides opens that did happen. The result is a metric distorted in two opposite directions at once, and the distortion is not evenly spread across your list. Two subscribers who behave identically can show up completely differently in your open data purely because of the apps they use. That is why treating open rate as a precise, comparable number is a mistake, and why the metrics further down the chain deserve your attention.

What to watch instead#

• Click through rate. A click is a deliberate action that is much harder to fake than an open. It tells you the email actually moved someone.
• Conversions and revenue. The only metric that reflects money. Track sales back to the emails that drove them where you can.
• Revenue per subscriber over time. The clearest measure of list value, explained in revenue per subscriber explained. It cuts through every vanity metric.
• Unsubscribes and complaints. Rising numbers here warn you that you are sending the wrong thing, or too often.

For a fuller picture of what is worth tracking as a beginner, see which metrics should a beginner track.

What beginners usually get wrong#

• Chasing opens with clickbait. Subject lines that overpromise lift opens and destroy trust, clicks, and sales.
• Panicking over a single low number. One email's open rate is noisy. Trends across many sends mean more than any one figure.
• Ignoring clicks and revenue. These are harder to inflate and far more honest, yet they get less attention because they are less flattering.
• Comparing open rates across long time spans. Privacy changes have shifted the baseline, so old comparisons mislead.

How I would start#

I would glance at open rate as a rough pulse check, mainly to catch sudden drops, and then move straight to clicks and revenue to judge whether an email actually worked. When testing subject lines, I would use opens only as a tiebreaker and let clicks and sales make the real call.

What I would not do#

I would not set goals around open rate, and I would not write a single subject line whose only job is to boost it. The number is too easy to game and too easy to fool. Optimize for the actions that lead to money, clicks and purchases, and let the open rate be the loose, imperfect signal it actually is. </content>