Email Open Rates: Calculation, Benchmark Limits, and Apple MPP
Shusaku Yosa
Email open rate is commonly calculated as unique recorded opens divided by delivered messages, multiplied by 100. Providers can use different denominators and deduplication rules. An open record is not the same as a person reading the email. Establish your own measurement definition before adopting a benchmark.
There is no universally meaningful “20% average.” Audience, region, campaign type, date and automated-content handling all affect the figure. This guide focuses on comparable conditions rather than unsupported industry averages.
Check the denominator and duplicate handling
Suppose a fictional campaign targets 1,000 recipients, has 50 delivery failures, 950 delivered messages, 190 unique open records and 300 total open records.
Calculation | Result | Meaning |
|---|---|---|
190 ÷ 950 × 100 | 20% | Unique opens relative to delivered messages |
190 ÷ 1,000 × 100 | 19% | Unique opens relative to attempted sends |
300 ÷ 950 × 100 | About 31.6% | Total recorded activity, not unique open rate |
A unique metric generally deduplicates repeated activity for the provider’s recipient identifier. Verify its treatment of forwarding and automated retrieval. A delivered status also does not prove inbox placement or human readership.
Understand Apple MPP
Apple’s documentation explains that Mail Privacy Protection can download remote content in the background on receipt rather than when the message is viewed. Pixel-based open tracking therefore cannot be treated as direct evidence of reading.
The reverse problem also exists: a person may read without loading a tracking image. Avoid using an open alone to trigger a sales alert or a non-open alone to trigger repeated messages. Add other behavior or an explicit expression of interest.
Decide whether a benchmark is comparable
Condition | What to check |
|---|---|
Timing | Research year, sending period and product changes |
Audience | Country, language, B2B/B2C and customer relationship |
Purpose | Newsletter, requested resource, renewal notice or another message type |
Formula | Unique versus total; delivered versus attempted sends |
Automated activity | MPP treatment, inferred exclusions and bot filtering |
Aggregation | Pooled numerator/denominator or an average of campaign rates |
A stable internal comparison of similar campaigns is often more actionable than an unmatched external average. Record changes to list composition and measurement software as well as changes to the message.
Use an outcome-based diagnostic table
Observation | Possible explanation | Check next |
|---|---|---|
Delivery failures rise | Address quality, authentication or infrastructure | Failure reasons and recipient domains |
Only open rate suddenly rises | Automated retrieval or measurement change | Client mix and settings |
Opens but few relevant clicks | Message mismatch or unclear next step | Promise, content and destination |
Clicks but few successful actions | Automated checks, broken form or poor fit | Human journey and success condition |
Outcomes and complaints both rise | Excess frequency or unsuitable targeting | Audience selection and expectations |
Security software can inspect links automatically, so clicks are not always human intent either. Validate business outcomes through the successful action, deduplication, production environment and any cross-domain transition.
Test one hypothesis at a time
First check audience relevance, recognizable sender identity and an accurate subject line. A stronger promise does not help if the body fails to deliver it. Make the reason to read and the main next action clear.
For an A/B comparison, randomly split eligible recipients, change one major variable and define the observation window and primary measure before sending. Even a subject-line test should consider valid clicks or outcomes per delivered message and opt-outs alongside opens. A small difference from a small audience is not a reliable winner.
Keep a campaign review record
Record campaign ID, audience rule, time, delivered and failed messages, unique opens, relevant clicks, validated outcomes, unsubscribes, complaints, measurement changes and the next hypothesis. Include purchase, usage and support signals when assessing inactivity; do not rely exclusively on non-opens.
Open rate remains a useful diagnostic signal when its limits are understood. Evaluate whether recipients received useful information and could take the intended next step, rather than optimizing a tracking event in isolation.
Related practical guides
- Email Marketing: Campaign Setup, Deliverability, and Measurement
- 10 CRM Campaign Examples: Segments, Triggers, Stop Rules, and Metrics
- Digital Marketing KPIs: Formulas, Data Sources, and How to Choose
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