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Metrics & Analytics

Newsletter Open Rate Benchmarks

Newsletter open rate benchmarks are industry-standard reference points that help creators judge whether their email open rates are performing below, at, or above average for their niche or list size.

What Is Newsletter Open Rate Benchmarks?

A benchmark on its own means nothing without context. An open rate of 28% could be disappointing for a small, tightly curated personal newsletter and genuinely impressive for a high-volume B2C list. That's why benchmarks are broken down by industry, list size, send frequency, and audience type. In 2026, average open rates across the email marketing industry sit somewhere between 30% and 45% for newsletters, though figures vary considerably depending on which platform's data you're looking at and how they measure opens. One thing every newsletter creator needs to understand: open rate data has been unreliable since Apple introduced Mail Privacy Protection (MPP) in 2021. MPP pre-fetches email content, which triggers open pixels even when a subscriber hasn't actually read a word. That inflates open rates artificially, sometimes significantly. So when you compare your numbers to a published benchmark, you need to know whether that benchmark was calculated using MPP-affected data or not. Most modern benchmarks acknowledge this problem, but many older published figures don't. Treat any benchmark from before 2022 with real caution. The most useful benchmarks to track against are your own historical numbers. A consistent trend in your open rate tells you far more than a comparison against a generic industry average. That said, external benchmarks are valuable for a sense check, especially when you're launching a new newsletter and have no baseline of your own to work from.

Why It Matters for Newsletters

If you don't know what a healthy open rate looks like for a newsletter in your niche, you can't tell whether a number like 35% is something to celebrate or a warning sign. Benchmarks give you a frame of reference when you're optimising subject lines, testing send times, or making the case to a sponsor that your audience is genuinely engaged. Sponsors and advertisers often ask for open rate figures, and knowing where you sit relative to industry standards helps you price sponsorships confidently and negotiate from a position of knowledge. Benchmarks also help you spot problems earlier. If your open rate drops from 42% to 31% over three months, knowing that your niche typically sits around 40% tells you this is worth investigating rather than shrugging off. It could be a deliverability issue, list fatigue, or a content shift that's alienating your core readers. The benchmark gives your internal data meaningful weight.

Best Practices

  1. Compare your open rates against benchmarks specific to your industry or niche, not just overall email marketing averages, since B2B newsletters, consumer lifestyle titles, and media publications perform very differently.
  2. Account for Apple Mail Privacy Protection when interpreting your own data and any published benchmark figures. If a significant portion of your list uses Apple Mail, your open rate is likely inflated.
  3. Use click-through rate and click-to-open rate alongside open rate benchmarks to get a more accurate picture of genuine engagement, since clicks are far harder to fake than opens.
  4. Track your own trend line as your primary benchmark. Month-on-month and year-on-year comparisons against your own history are more actionable than comparisons against industry averages.
  5. Segment your benchmark analysis by list source or subscriber cohort. Readers who came in through a paid acquisition campaign often open less than those who discovered you organically, so blending them together can skew your headline number.

How Aldus Handles This

Aldus tracks your open rate trends over time and surfaces meaningful shifts in performance, so you're not left staring at a raw percentage and wondering what to make of it. Rather than pointing you at a generic industry chart, Aldus contextualises your numbers against your own historical baseline and flags when something looks genuinely off.

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