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Since September 2021, email open rates no longer measure what they were supposed to measure. With iOS 15, Apple launched its Mail Privacy Protection (MPP) feature, which automatically preloads tracking images from emails received on Apple devices, even if the user has never opened the message.
The result: if your prospect uses Apple Mail on an iPhone, iPad, or Mac, your lead generation tool records an "open" that may never have actually occurred. When it comes to cold email deliverability, this creates a dangerous illusion: artificially high open rates that mask a much lower level of engagement.
1. Understanding the Impact of Mail Privacy Protection
The mechanism is simple. Before MPP, the opening of an email was detected by the loading of an invisible tracking pixel embedded in the message. When the user opened the email, their email client loaded the pixel, signaling the opening to the sending tool.
With MPP, Apple uses a proxy that preloads all tracking pixels as soon as the email is received on the device, regardless of the user’s action. The email is therefore automatically marked as “opened” on Apple devices with MPP enabled.
According to data published by Litmus, Apple Mail accounts for a very significant share of business email opens, particularly on iPhones. This has a substantial impact on cold email deliverability statistics: for many teams, a significant portion of the "opens" recorded since 2021 are fictitious.
2. Why this is a problem for cold email deliverability
An inflated open rate creates several practical problems.
Optimization decisions based on inaccurate data. If you A/B test two email subject lines based on open rates, your results will be skewed for all Apple recipients. The subject line that "wins" may not be the one that actually generates the most opens.
Inaccurate lead scoring. If your scoring system awards points for every "open," contacts using Apple Mail will accumulate points without ever actually reading your emails. This lowers the quality of your pipeline.
An inaccurate assessment of cold email deliverability. A high open rate can mask the fact that your emails are ending up in the spam folder for non-Apple recipients, among whom the actual open rates are much lower.
3. Alternative metrics for measuring cold email deliverability in 2026
With open rates becoming increasingly unreliable, other metrics are stepping in as reliable indicators of cold email deliverability.
The response rate. This is the most reliable metric. A response is a deliberate human action that cannot be faked by any privacy protection mechanism. A response rate of 5% to 15% on a targeted sequence indicates both good cold email deliverability AND genuine engagement.
Click-through rate. Clicks on links in your emails require human action and are not affected by MPP. A high click-through rate is a sign of genuine interest.
The appointment conversion rate. The number of appointments generated relative to the number of emails sent is a key performance metric that cuts through all the intermediate measurement issues.
Google Postmaster Tools data. Domain reputation and the reported spam rate in Google Postmaster Tools are objective metrics that do not depend on sender-side tracking.
The bounce rate. A direct technical indicator of list quality and cold email deliverability, unaffected by MPP.
4. Segment your analyses by email client
Most modern email analytics platforms allow you to filter statistics by email client. By isolating data from Apple Mail users, you can get a more accurate picture of your actual engagement.
In practical terms: if your overall open rate is 45% but drops to 18% when you exclude Apple Mail opens, your actual engagement rate is closer to 18%. This is the figure you should use as a basis for making decisions about optimizing cold email deliverability.
5. Adapting Your Measurement Strategy in 2026
By 2026, a mature cold email deliverability strategy will no longer rely on open rates as its primary metric. Instead, it will build a multi-metric dashboard that includes:
The open rate as a primary engagement metric. The click-through rate as a secondary indicator of interest. Google Postmaster Tools data as a reputation metric. The appointment conversion rate as a final performance metric. The bounce rate and complaint rate as indicators of list health.
Conclusion
Apple MPP has changed the way we interpret cold email deliverability data. The open rate is no longer a reliable indicator of engagement, but it remains useful as a relative trend indicator (for comparing campaigns with one another rather than in absolute terms).
The right approach isn't to ignore the open rate, but to put it into context and supplement it with metrics that can't be skewed: responses, clicks, conversions, and reputation data.
