Why Open Rates Stopped Being Reliable

For two decades, open rate was the headline metric of email marketing. It was simple, it was immediate, and everybody had a benchmark for it. It is now unreliable enough that treating it as a primary decision metric will lead you to confidently wrong conclusions.

This is not a measurement bug that will be fixed; it is a structural consequence of how modern mailbox clients handle images.

How opens were ever counted

The mechanism was always indirect. An email contains a tiny invisible image hosted on the sender's server. When the client loads the image, the server records a hit and attributes it to that recipient.

It never measured reading. It measured image loading, which was usually a decent proxy for opening, back when clients loaded images on open.

What changed

Three separate developments broke that proxy.

Image proxying. Clients now fetch images through their own servers and cache them. The recipient's device may never contact your server at all, and a single cached fetch can generate one record for many recipients, or many records for one.

Prefetching. Some clients and security gateways load images before the recipient sees the message, sometimes automatically as part of scanning for malware. That registers an open for a message that may be deleted unread.

Privacy defaults. Mail Privacy Protection and similar features are opt-out rather than opt-in, and a large share of users never change the default. For those recipients, opens are effectively fabricated at delivery time.

The net effect: open rates are inflated, compressed toward a narrow band, and no longer comparable between audiences with different client mixes or over time as client defaults change.

What the inflation does to your decisions

Inflated opens cause specific, predictable errors.

• Subject line testing breaks. If a large share of opens are recorded at delivery, subject lines cannot differentially affect them. You will pick winners from noise.
• Segment comparisons mislead. A segment using clients that prefetch shows a higher open rate than one that does not, regardless of actual engagement.
• Deliverability signals get muddied. Engaged-recipient signals are what mailbox providers weight, and an inflated open count overstates how much genuine engagement you have.
• Trend lines become meaningless. A rise in open rate after a client update is not a content win.

What the inflation does to your decisions

Inflated opens cause specific, predictable errors.

• Subject line testing breaks. If a large share of opens are recorded at delivery, subject lines cannot differentially affect them. You will pick winners from noise.
• Segment comparisons mislead. A segment using clients that prefetch shows a higher open rate than one that does not, regardless of actual engagement.
• Deliverability signals get muddied. Engaged-recipient signals are what mailbox providers weight, and an inflated open count overstates how much genuine engagement you have.
• Trend lines become meaningless. A rise in open rate after a client update is not a content win.

Which clients inflate most

Inflation is not evenly distributed across your list, which is what makes comparisons unsafe rather than merely noisy. Apple Mail with Privacy Protection enabled records every message as opened at delivery. Corporate gateways that scan links and images register opens on messages that may never reach an inbox.

Webmail clients frequently prefetch for their own caching.

Meanwhile a recipient on a desktop client with images blocked may read a message fully and register nothing. So the segment with the most engaged users can plausibly show the lowest open rate, purely because of client configuration. Any conclusion drawn from comparing two such segments is measuring infrastructure, not interest.

Diagnosing whether your audience is affected

There is a quick sanity test. Segment by receiving domain and compare open rates across providers. If one provider shows open rates dramatically higher than the rest and clustered suspiciously close to delivery rate, that provider is prefetching for a large share of your list.

The size of the gap is a reasonable estimate of how much of your open data is fabricated.

A second test is to compare opens recorded in the first minute after send against opens recorded over the following day. Historically the pattern spread across hours. If a large spike appears within seconds of delivery, those are not readers.

What to measure instead

Click-through rate is the closest available substitute and is far harder to fabricate, since it requires a deliberate action. Use it as your primary engagement metric.

Then measure the thing you actually care about. For most teams that is a downstream outcome — trial starts, purchases, activated accounts — attributed to the campaign. This is the only metric that cannot be gamed by client behaviour, because it measures what the recipient did rather than what their mail client did.

Also watch complaint rate and unsubscribe rate per campaign. Rising complaints are the earliest reliable warning of a deliverability problem, and they are unaffected by image handling.

A practical hierarchy: conversion first, clicks second, complaints and unsubscribes as guardrails, opens as a rough directional signal you never make decisions on.

Comparing campaigns honestly

If you must use open rate for continuity with historical reporting, compare like with like. Segment by mailbox provider when comparing periods, because client behaviour drives the number. Compare against your own prior sends to the same segment rather than against industry averages, which are drawn from unknown mixes and are not actionable for an individual account.

Where possible, run holdout groups. A small percentage of a segment that receives no message tells you what would have happened anyway, which is the only way to attribute an outcome to email rather than to seasonality or a concurrent launch.

How Cresca reports this

Cresca records delivery, open, click, bounce and unsubscribe events from one stream, so the metrics are internally consistent and derive from the same source as automation triggers. Click and delivery figures are exact. Open figures are available for continuity but are not treated as an engagement signal in the product.

Because campaigns are queued and delivered through a managed pipeline, delivery events reflect actual handoff to the receiving server rather than an optimistic send, which makes delivery rate a meaningful reliability metric rather than a count of attempts.

Pricing

Analytics are included on every paid plan. The tiers differ by scale, not by reporting capability:

The short version

Open rate measured image loads, and image loading is no longer a proxy for reading. Treat opens as directional at best and never as the basis for a subject line test or a segment comparison. Decide on clicks and downstream conversion, guard with complaints and unsubscribes, and compare only against your own comparable sends.