If your cold email tool says your open rate is 60%, here is the uncomfortable question: opened by whom?

Where fake opens come from

An email “open” is measured with a tracking pixel — a tiny invisible image. When the image loads, the sender counts an open. The problem is that in 2026, most image loads aren’t people:

Apple Mail Privacy Protection pre-loads every image on Apple’s servers the moment the email arrives — whether the recipient ever looks at it or not. If your list has iPhone users, a chunk of your “opens” fire at delivery time, all by themselves.
Gmail’s image proxy fetches and caches images through Google’s servers. The first load tells you almost nothing about a human being present.
Corporate security scanners (Outlook ATP, Proofpoint, Mimecast) open every email and click every link in it before letting it through to the inbox. Yes — your click rates are inflated too.

Put together, it’s common for 40–60% of recorded opens to be machines. Your 60% open rate might be 30% of humans — or it might be 55%. The point is: you don’t know, and most tools won’t tell you.

Why this actually matters

Inflated numbers wouldn’t matter if everyone just ignored them. But nobody ignores them — teams make real decisions on fiction:

You pick the A/B winner by opens, and crown the variant that bots happened to like.
You judge a list or a copy angle as “working” because opens look healthy, while actual humans never saw the email.
You keep sending to dead segments because auto-opens make them look alive — and dead segments are exactly what poisons your sender reputation.

The cruel part: the metric that can’t be faked — replies — is the one that pays the bills, and it’s the one most dashboards bury.

How to find your real number

You don’t have to accept the fiction. A few practical steps:

Treat opens as a directional signal, never a KPI. If a decision matters (winner selection, list pruning, pausing a campaign), make it on replies, positive replies or meetings.
Look at open timing. Opens that fire within seconds of delivery, at 3am in the recipient’s timezone, or in perfectly uniform bursts are machines. Human opens cluster in working hours and trail off over days.
Segment by provider. If your “openers” are overwhelmingly on Apple domains, assume Mail Privacy Protection is doing the opening.
Use a platform that classifies before it counts. The detection signals (proxy fingerprints, fetch timing, user-agent patterns) are visible at the moment the open happens — a platform can check each one and label it human, machine, or uncertain instead of dumping everything into one number.

That last one is, transparently, what we built Norbelys to do: every open and click is verified as it happens, and the dashboard shows you real opens — with the filtered bots listed right next to them, so you can see exactly what was removed.

The honest trade

Filtering makes your numbers smaller. A 41% real open rate looks worse than 64% of fiction in a Monday report — until you remember that only one of them predicts replies.

Lower numbers you can act on beat higher numbers you can’t. That’s the whole trade, and it’s a good one.