Email Open Rate Calculator
Compute open rate against DELIVERED, then strip out the machine opens — because privacy pre-fetching inflates the reported figure by roughly twenty points.
Email Open Rate Calculator
Results recalculate instantly on every keystroke. Nothing you type is transmitted.
What this result does not account for
- The machine-open share is an estimate that varies by list.
- Not comparable across lists, platforms or years.
- Unique opens only — total opens count repeats.
In short: Roughly half of reported opens are machines. 19,183 opens on 44,689 delivered reads as 42.9256%, but at a 49% pre-fetch share the human rate is 21.8920% — an inflation of 21.0335 points.
Formula
open rate = unique opens ÷ DELIVERED
human estimate = opens × (1 − pre-fetch share) ÷ delivered
[('delivered', 'sent less bounced — the correct denominator'), ('pre-fetch', 'pixels loaded with no human involved'), ('inflation', 'commonly fifteen to twenty points'), ('downstream', 'the same distortion deflates CTOR')]
Worked Example
- Subtract bounces from sent to get delivered.
- Divide unique opens by delivered, never by sent.
- Estimate the machine share and remove it.
- Compare only against your own list's history.
- Use click rate and revenue for decisions.
45,000 sent with 311 bounces is 44,689 delivered — a 0.6911% bounce rate, comfortably inside the healthy target. 19,183 unique opens is a 42.9256% reported open rate; dividing by sent instead would read 42.6289%, understating it. At a 49% pre-fetch share only about 9,783 opens are plausibly human, giving 21.8920% — the reported figure is 1.9608 times the human one, inflated by 21.0335 points.
Strengths & Limits Of This Model
Where this engine is strong
- Uses delivered and shows the sent-based error
- Estimates the human rate and labels it as an estimate
- Warns that the same distortion deflates CTOR
Where it stops
- Estimate-dependent
- Not cross-comparable
Practical Use Cases
Reading a campaign report
Correcting the denominator and the machine share.
Setting expectations
Using the human estimate rather than the headline.
Monitoring deliverability
Watching the bounce rate against provider thresholds.
Explaining a year-on-year change
Separating client mix from content performance.
Feeding CTOR analysis
Supplying the opens that deflate the click-to-open rate.
Methodology & Editorial Standards
The open rate divides unique opens by DELIVERED rather than sent, and the incorrect sent-based figure is shown alongside so the difference is visible. The human estimate removes the entered pre-fetch share and is labelled explicitly as an estimate, since the true share is unknowable. Opens exceeding delivered are refused, because that condition indicates total rather than unique opens. The page states that the same inflation deflates click-to-open rate downstream.
Computation runs in IEEE-754 double precision at full internal precision; rounding to two decimal places occurs strictly at the display layer, so no cumulative drift enters the result. All monetary outputs use accounting presentation — grouped thousands, two decimals, negatives in parentheses — so figures can be transcribed directly into a model or working paper. Division-by-zero and out-of-domain inputs return an em-dash rather than a misleading number.
This engine was reconciled against an independent reference implementation and hand-verified for the worked example above before release. Our full five-stage review process is published on the About Us page.
Disclaimer. This calculator is provided for informational and modelling purposes only and does not constitute financial, tax, legal, medical, or engineering advice. Verify all figures with a qualified professional before acting on them.
Email Open Rate Calculator — 10 Expert FAQs
10 analyst-written answers to the questions practitioners actually ask — optimised for voice and answer-engine retrieval.
Should open rate use sent or delivered?
Delivered, always. A message that bounced could not be opened, so including it in the denominator understates your rate. The error is common and at least fails in the safe direction, but it makes period comparison unreliable if you switch between conventions.
Why are open rates unreliable now?
Because privacy features on major mail clients pre-load remote content, including the tracking pixel, whether or not anyone reads the message. Apple Mail alone represents around half of tracked opens, so a large share of reported opens involved no human at all.
How much does privacy pre-fetching inflate open rates?
Commonly fifteen to twenty percentage points, though it depends entirely on your subscribers' mail-client mix. A consumer list heavy in Apple Mail is far more contaminated than a corporate list on a different platform.
Can I still use open rate at all?
As a trend on your own list, yes — the contamination is reasonably stable month to month, so directional movement still means something. What you cannot do is compare across lists, across platforms, or against your own pre-2021 history.
Should I optimise subject lines for open rate?
Only cautiously. A metric half of which is automated is a poor optimisation target, and a subject line that wins on opens while losing on clicks has made things worse. Test subject lines against clicks and revenue instead.
What is a good open rate in 2026?
Published averages sit in the thirty to forty per cent range, but those figures include the same contamination your own do, so the comparison is at least like-for-like. The honest human engagement figure is far lower and no benchmark reports it.
Does this affect click-to-open rate?
Substantially, and in the opposite direction. CTOR uses opens as its denominator, so pre-fetch inflation makes CTOR look worse. The same distortion raises one metric and lowers another, which catches people out constantly.
What bounce rate should I worry about?
Major providers treat around two per cent as grounds for rejection, and the healthy target is below one. A rising bounce rate damages delivery of every future campaign, so it deserves attention long before it reaches the threshold.
Why did my open rate jump without any change?
Most likely a shift in your subscribers' mail clients, or a change in how your platform filters machine opens. Neither is a performance change, and treating either as one leads to confident conclusions about nothing.
What should I measure instead?
Click rate and revenue per recipient. Both require a human action that no pre-fetch can fake, both connect directly to money, and neither has been corrupted by privacy features.