Can you trust email open rates?

Can you trust email open rates? Use them as a tracking signal, not an exact count of readers. Privacy downloads and bots can create opens without reading; blocked images can hide real reads. Check the definition and filters before comparing campaigns, resending to non-openers or deciding who is inactive. Match your success measure to the action the email asks for.

A dashboard saying half your audience opened an email does not establish that half read it. Equally, a contact with no recorded open has not necessarily ignored the message. Before acting on either label, establish what your report actually observed.

What does email open rate measure?

Mailchimp’s calculation guide describes tracking a small transparent image downloaded from the email. Its open rate uses successfully delivered emails as the denominator. Image blocking can prevent a read from registering, while tracked click-throughs can also count as opens when the image did not load.

For an explicitly defined unique open rate, use:

Recipients with a recorded open ÷ successfully delivered messages × 100

Count each recipient once for that send, and state whether the numerator is filtered. This is a reporting definition you can make explicit; check your provider’s own metric rather than assuming every label follows it.

Keep unique recipients separate from total open events. A recipient associated with repeated events should not become several people in your calculation. Neither number tells you how carefully someone read or how they felt about the content.

Why can recorded opens differ from real reads?

Privacy downloads and security scanners can record opens without reading, while blocked tracking images can hide real reads. Recorded opens can therefore differ from reading in both directions.

Apple’s privacy explanation says that, with Protect Mail Activity enabled, remote content downloads in the background regardless of engagement when email is received in Mail or Mail on iCloud.com. A tracking-image download can therefore reflect privacy behaviour rather than a reader choosing to open.

This is about the app and setting, rather than just the address ending. Klaviyo’s MPP reporting guide gives a Gmail account connected to MPP-enabled Apple Mail as an affected example. It says an Apple Privacy Open cannot distinguish an actual human open from an automated one when MPP is enabled.

Security scanning adds a separate complication. Mailchimp’s bot-activity guide documents non-human opens and clicks from security services and link-preview generators. Moving from opens to unfiltered clicks does not remove that problem.

There is also a limit even when an event reflects reading. The University of Colorado’s practitioner guidance observes that someone pleased by the content and someone annoyed by it can both register an open. An open is not a satisfaction score.

What should you check in your report?

Check the delivered denominator, unique-versus-total numerator, tracking settings, privacy and bot filters, audience and observation period. Record any settings change before comparing campaigns. Use this open-rate audit card for the send you are investigating:

In Mailchimp, go to Analytics, then open Analytics settings (or click Bot filtering on) and use the toggles in the Bot activity section. The guide says filtering is enabled by default and applies across account reporting. Inspect the actual settings. Turning filtering on can reduce recorded opens and clicks, so a lower number after that change is not, by itself, evidence that the email became worse.

In Klaviyo, the documented custom-report path is Analytics → Custom Reports. For a Single Metric report, choose Opened Email, then group or filter by Apple Privacy Open, using True, False or both. Its guide says those properties are available for opens on or after November 20, 2021.

Use the classification to understand what is included. A non-MPP subset does not describe the whole audience, and filtering cannot recover reads that were never observed. Mailchimp’s bot-filter guide describes identifying most bot activity, rather than guaranteeing perfect detection.

Let the reporting tool calculate and deduplicate the filtered metric. Subtracting total privacy-open events from unique openers mixes events with people; it does not produce a defensible human-reader count.

How can filtering change the numbers without changing the email?

Filtering opens changes which recorded opens the report includes. Applied to the same send, it can change the open rate without changing the email or recorded clicks.

Consider one invented send with 10,000 delivered messages. Assume the same audience, observation time and delivered denominator in both views. A hypothetical reporting tool recomputes unique opens under its filter; the 300 bot-filtered unique clickers stay constant.

The lower open rate comes from a different reporting rule applied to the same send.

Now define an illustrative click-to-open ratio as unique clickers divided by unique reported openers, multiplied by 100. Before filtering, 300 ÷ 5,000 × 100 = 6%. After filtering, 300 ÷ 2,000 × 100 = 15%. That ratio rises because its denominator falls; there are no extra clickers.

These are invented inputs and an explicitly chosen ratio, not a provider benchmark or a claim that every tool calculates click-to-open rate this way. The practical check is to inspect the numerator and denominator before treating a rate change as improved performance.

Which decisions need more than an open rate?

Choose the measure that matches the job of the email. The University of Colorado’s practitioner guidance recommends considering further recipient actions, such as registrations and web traffic. Applied to retention work, that gives a useful starting point:

These are starting points, not guarantees that the action is completely observable. For orders, the email sales-tracking guide explains how to verify collection and interpret attribution before drawing conclusions from credited revenue.

Before a resend, inspect what non-opener means in that audience. Someone who read with images blocked may qualify; someone with a privacy-generated open may be excluded despite not reading. The label alone cannot decide who needs another message.

Apply the same care to suppression. Open-only engagement rules can misclassify contacts in either direction. The list-cleaning guide handles the broader decision about who to suppress; this article supplies no universal inactivity window.

If a rate changes unexpectedly, first establish whether tracking, filters or the audience definition changed. Then compare the available action signals under consistent rules. A content, audience or send-time hypothesis is something to investigate; the open rate alone does not identify the cause.

FAQ

Are email open rates useless?

No. They can supply a tracking signal when you understand the metric and compare consistent definitions. They do not give an exact human-reader count; use supporting evidence suited to the action the email asks for.

Does Mail Privacy Protection affect only iCloud addresses?

No. MPP-enabled Apple Mail can affect an account from another provider, including Gmail. The app and privacy setting matter; an address domain alone does not establish whether the recorded open reflects reading.

Does filtering out privacy opens reveal the real open rate?

No. It changes which recorded activity is included, but cannot recover unobserved reads or determine whether an MPP-flagged recipient actually read. Treat the result as a filtered tracking measure, rather than a complete human-reader count.

What is a good email open rate?

There is no universal percentage supplied here. Before using a benchmark, compare its audience, send type, period, denominator, unique-versus-total definition and privacy or bot treatment with your report. A mismatched percentage cannot establish how your campaign’s actual reading compares.