Newsletter Engagement by List Size: What 6 Lists on One Platform Actually Report
Across 6 newsletters we operate on the same platform, totalling 806,144 subscribers, click-to-open rate falls 4.8x as list size grows. AIpresso at 10,739 subscribers converts 16.9% of opens into clicks. Techpresso at 699,004 converts 3.5%.
Open rate does not follow that pattern at all. Techpresso, the largest list here, has the highest open rate of the set at 37.1%. Size does not appear to cost you opens. It costs you what happens after the open.
Most newsletter benchmarks aggregate survey responses across thousands of senders using different platforms, different definitions and different list hygiene. This is a smaller and narrower dataset, but every figure comes from one platform's API, measured identically, which makes the comparison between rows meaningful in a way cross-vendor averages are not.
The numbers
| Newsletter | Vertical | Subscribers | Open rate | Click rate | Click-to-open |
|---|---|---|---|---|---|
| Techpresso | AI and tech | 699,004 | 37.1% | 1.3% | 3.5% |
| MarketingShot | marketing | 25,858 | 31.2% | 2.1% | 6.7% |
| Cyberpresso | cybersecurity | 25,689 | 24.3% | 2% | 8.2% |
| Finpresso | finance and markets | 22,714 | 30.4% | 2.3% | 7.6% |
| Devshot | software development | 22,140 | 28.6% | 2.2% | 7.7% |
| AIpresso | artificial intelligence | 10,739 | 24.3% | 4.1% | 16.9% |
Read from the Beehiiv API on 2026-08-19. Open and click rates are the platform's own trailing averages. Click-to-open is derived: click rate divided by open rate.
Why click-to-open is the number to compare
Raw open rates have been unreliable since Apple's Mail Privacy Protection began pre-fetching images, and they vary with list age and acquisition source more than with content quality. Comparing a 10,739 list to a 699,004 one on open rate compares two different deliverability situations.
Click-to-open asks a narrower question that survives those problems: of the people who did open, how many went on to click. It isolates whether the content earned an action from an audience that was already looking at it.
On that measure the spread here is 4.8x, and the direction is consistent: smaller lists convert attention into clicks far better. Raw click rate tells the same story slightly less cleanly, at 3.2x between the smallest and largest list.
What actually causes the gap
Three mechanisms, and only the first is about the newsletter itself.
Audience specificity. A 10,739 subscriber list in one vertical is made of people who sought out that vertical. A 699,004 subscriber general-interest list contains many people who are interested in the category broadly and in any given story rarely. The larger list is not worse written; it is answering a wider question.
Accumulated inactives. Large lists are usually old lists. Subscribers who still open out of habit but stopped clicking years ago remain counted in the denominator, and they suppress click-to-open without any change in the product.
Link density per interest. A specialist newsletter can link to something every reader plausibly wants. A general one links to a spread, and each individual link addresses a fraction of the audience.
The practical reading for anyone buying newsletter placement: raw subscriber count and click volume are not proportional, and the ratio moves against you as lists grow. A list ten times the size does not deliver ten times the clicks.
Median figures across this set
The median open rate here is 30.4% and the median click rate 2.2%, across 6 B2B and tech newsletters. Those are useful as a sanity check on any benchmark you are quoted, with two caveats worth stating.
First, this is one platform. Deliverability and measurement differ enough between providers that cross-platform comparison of open rates is close to meaningless.
Second, 6 lists is a small sample and they share an operator, an editorial approach and largely one acquisition strategy. What this dataset supports is the relationship between size and engagement measured consistently. It does not support a claim about the industry.
How we measure
- Source. Beehiiv API v2, publication stats, fields
average_open_rateandaverage_click_rate, read 2026-08-19. - Subscribers. Active subscriptions on the same call. Figures are per publication, not deduplicated across lists.
- Click-to-open. Derived as click rate divided by open rate. Not reported by the platform.
- Refresh. Regenerated from the same weekly job that updates our audience figures, so the numbers and the date always move together.
- Correction, August 2026. Our previously published open rates were typed in by hand and never refreshed, overstating several lists by up to nine points. These come from the API.
Frequently asked questions
What is a good newsletter open rate in 2026?
Across these 6 lists the median is 30.4%, ranging from 24.3% to 37.1%. Treat any single benchmark figure carefully: open rate is heavily affected by privacy features that pre-fetch images and by how recently the list was built.
What is a good click-to-open rate?
The lists here run from 3.5% to 16.9%, and the smaller and more specialised the audience, the higher it goes. If you are comparing your own, compare against lists of a similar size rather than against an industry average.
Do bigger lists perform worse?
Not on opens. In this set the largest list has the highest open rate. They perform worse on converting those opens into clicks, by a factor of 4.8.
Related reading
- What software actually charges, by category, from the same approach applied to pricing.
- Advertise with Dupple for the current audience figures and formats.
