Newsletter churn rate benchmarks by niche
TL;DR
Newsletter churn sits between 0.2% and 2.5% monthly depending on your niche, with B2B SaaS lists running the lowest and creator lists the highest. The number matters less than what it sits next to: acquisition rate. A list with 0% churn has stopped acquiring, and that is worse than a list churning at 2% with healthy growth.
Most of what ranks for "newsletter churn rate" on Google is not about newsletters. It is SaaS revenue churn copied into an email-marketing wrapper, with a Mailchimp screenshot at the top. The math is different, the behaviour is different, and the fix is different. We track public newsletter sends across 12 ESPs at Newsletrix, and the churn numbers we see have almost no relationship to the 5% monthly figure quoted in subscription benchmark posts. This article lays out what newsletter churn rate benchmarks look like by niche, how to read your own number, and a position we will say out loud: a 0% churn list is broken.
What newsletter churn actually means (and what it doesn't)
Newsletter churn is the percentage of subscribers who leave your list in a given period, divided by the subscribers you started the period with. The period is almost always a month, because email list behaviour runs on a slower clock than SaaS subscription behaviour.
Gross churn counts everyone who left, whether they unsubscribed, hard-bounced, complained to spam, or got purged because the address turned into a role account or a domain catch-all. If you started January with 10,000 active subscribers and 90 disappeared by month-end, gross churn is 0.9%. Net churn subtracts new acquisitions. Add 250 subscribers in the same month and net churn is negative, the only state a healthy list wants to live in.
Voluntary churn is the unsubscribes. Involuntary churn is hard bounces, spam complaints, and addresses your ESP or sunset policy retires for you. The mix matters. A list with 0.8% voluntary and 0.1% involuntary has a content fit problem. A list with 0.1% voluntary and 1.2% involuntary has a list hygiene problem, and probably a deliverability problem already.
List decay is a third thing and it gets confused with churn constantly. Decay is the slow drop in engagement among subscribers who have not left. They stopped opening you in February but they are still on the list. Apple Mail Privacy Protection makes decay almost impossible to measure cleanly, which is why net engagement has stopped being a useful health metric on its own. We covered the open-rate inflation problem in the open-rate decline diagnostic.
Newsletter churn rate benchmarks by niche
These are the bands we see on lists we audit and from the public sender data we monitor. They are monthly voluntary churn rates, not gross, because voluntary is the figure operators can act on directly.
B2B SaaS: 0.3% to 0.6% per month. Long subscription life, niche topic, sober readers. Spikes happen after a CEO change or a topic pivot. If you are above 0.8% on a B2B list, the most likely cause is the second one we keep seeing: a marketing team has started using the editorial newsletter as a product-launch channel and readers signed up for analysis.
Creator and Substack: 1.2% to 2.5% per month. This looks alarming to a B2B operator but it is the cost of running an acquisition machine. Substack's discovery surface and creator co-ops drop high-volume, low-intent subscribers onto lists who then leave within their first three sends. The cohort matters. If you separate first-30-day subscribers from everyone else, the older cohort usually sits closer to 0.5%.
Ecommerce and retail: 0.5% to 1.0% baseline, with predictable spikes in early January (post-gifting cleanup) and mid-December (the second BFCM wave once readers realise the sale is over). Klaviyo lists in this category typically run hotter than Mailchimp lists because the average send cadence is higher.
Media and publisher: 0.4% to 0.8%. This band is sensitive to send frequency in a way the others are not. We have watched a Tuesday-Friday publisher add a Wednesday send and watch monthly churn climb 40% inside six weeks, even though the new send had a 38% open rate.
Finance and fintech: 0.2% to 0.5%. The lowest band. Regulated content, double opt-in still common, and the subscribers who get through that funnel rarely leave casually. Caveat: involuntary churn on these lists is usually higher than voluntary, because compliance teams sunset inactive addresses on a strict schedule.
If your monthly voluntary churn lands above the high end of your band, you have a problem. If it lands at the low end of someone else's band, that is not a win. The bands reflect what is normal for the audience type, not what is desirable in absolute terms.
Diagnose your cadence before you cut content
Churn often tracks send frequency, not subject lines or copy. Drop your niche and current cadence into our send-frequency recommender and see whether you are sending too often (or not often enough) before you touch the editorial side.
Try the send-frequency recommender →The signals we track from the outside
We have no back-end pipe into anyone else's ESP. What we do have is the visible surface of newsletter sending and the patterns inside it.
Public subscriber counts on Substack, beehiiv, and Kit leaderboards move in steps that map roughly onto monthly churn deltas. When a creator's counter stalls for four straight weeks but their send-receipt timing shows uninterrupted publishing, we read that as a churn-equals-acquisition state, the inflection point most operators do not catch until quarter-end.
Send-frequency drops are a clean churn-response signal. Operators slow down when complaints go up. We have watched a 3x weekly publisher cut to 2x within two weeks of a poorly-received content shift, and the public unsub data tracked it. If you saw your favourite newsletter quietly switch from Tuesday-Wednesday-Thursday to Tuesday-Friday this past quarter, the most likely cause was a churn spike.
Subject lines reveal more than they should. "Thanks for sticking with us" and "we made some changes" patterns appear in our corpus 6 to 9 weeks after a churn event. They are a tell that an operator is running a quiet win-back. You can pre-test those lines with our subject line tester before you ship one. We see this copy most often on retail lists in February and on creator lists in September.
ESP migrations are a fourth signal. A switch from Mailchimp to Klaviyo or from ConvertKit to beehiiv almost always coincides with a list scrub, which inflates short-term involuntary churn while improving deliverability later. How to find what ESP a company uses covers the detection method. The thing to watch is whether the operator scrubs aggressively (good, churn spikes for one month) or rolls the entire list over untouched (a deliverability problem hiding in the background).
How to diagnose your own churn rate
Stop looking at the headline number first. Look at acquisition.
If your monthly voluntary churn is 1.4% but acquisition is 3.0% of your starting list size, net growth is +1.6% per month and you are fine. If churn is 0.4% and acquisition is 0.2%, you are bleeding out slowly and the dashboard will not flag it, because the headline number looks healthy.
Segment churn by acquisition source. Lead magnets, content upgrades, partner co-ops, and referral programs produce different cohorts with different decay curves. Co-op subscribers churn 3-4x faster than organic search subscribers in our audit data. If you cannot tell which source a churned subscriber came from, that is the first thing to fix, not the churn rate itself.
Segment by tenure cohort. The 30-60 day window is the most informative. Most subscribers who will leave do so in this window. If your 30-day cohort churns at 8% but your 90+ day cohort sits at 0.3%, the list is doing its job. If 30-day is 2% and 90+ day is 1.5%, acquisition is fine but your content is not landing for the people who stay.
Segment by send frequency. If you send 3x a week but a quarter of your list is on a weekly digest preference, their churn should be lower, not higher. If it is higher, your digest send is too long, too sales-heavy, or hits at the wrong hour. The send-frequency benchmark has cadence bands by niche.
A content problem looks like rising voluntary churn in the first 24 hours after a send. A list hygiene problem looks like rising involuntary churn spread across the month with no send-time correlation. The fixes do not overlap.
Why ESP churn dashboards mislead
Your ESP only sees one list: yours. It cannot tell you whether 1.1% monthly is high or low for your niche, because it has no view across competitors. Mailchimp, Klaviyo, ConvertKit, and beehiiv dashboards default to comparing your number to your own past, not to peers.
The second problem is timing. Most ESPs report churn on a rolling 30-day window that resets every day, which smooths out the spikes that matter most. A Wednesday-after-Tuesday-send spike is the most diagnostic signal you have, and a rolling window hides it. Pull raw unsubscribe events out and bucket them by hours-since-send. The bucket shape tells you what the dashboard cannot. Cross-list comparison is why cross-newsletter benchmarking tools are now a category instead of a feature.
When higher churn is the right answer
A list with zero churn has stopped acquiring. There is no other reading. The 0% number you sometimes see operators brag about on social means the list has gone dormant or the acquisition machine has been turned off, neither of which is a good outcome.
Aggressive sunset policies are the second case. Cutting subscribers who have not opened in 90 days will spike your gross churn for one month and pull involuntary numbers up sharply. The benefit is downstream: complaint rates drop, deliverability improves at Gmail and Outlook, and the engaged cohort that remains lifts your open rate by 15-25% on the very next send. We recommend most lists run a sunset every six months for exactly this reason, even though the dashboard looks worse the month it happens.
The third case is a deliberate audience pivot. If you moved a list from one editorial direction to another, you want the people who do not want the new direction to leave. Holding onto them with milder content depresses open rate, sends complaint signals into the wrong place, and prolongs the deliverability cost. Let them go. The re-engagement playbook covers the win-back side, and also when not to bother.
The position we will not soften: gross churn is a vanity number on its own. Pair it with net growth, segment it by source and cohort, and stop comparing your monthly figure to a 5% SaaS-churn benchmark that has nothing to do with how an email list behaves. The closest sibling metric is per-send unsubscribe rate, which sits on the same shelf and answers a narrower question.
Frequently asked questions
What is a good newsletter churn rate?
A healthy monthly voluntary churn rate sits between 0.2% and 1.0% for most niches. B2B SaaS and finance lists run lowest at 0.2% to 0.6%. Creator and Substack lists run highest at 1.2% to 2.5% because they are paired with higher acquisition velocity. The number on its own does not tell you whether the list is healthy. Pair it with monthly acquisition rate and read net growth.
How do you calculate email list churn?
Divide subscribers lost during the period by subscribers at the start of the period, then multiply by 100. Gross churn counts everyone who left including hard bounces, spam complaints, and sunset-policy removals. Net churn subtracts new subscribers acquired during the same period. Always calculate against starting subscribers, not average subscribers, to keep the number comparable month over month.
Is 2% monthly churn bad for a newsletter?
Two percent monthly churn is bad for a B2B SaaS list and normal for a Substack creator list. Niche matters more than the absolute number. If your acquisition rate is above 2% per month, a 2% churn is sustainable. If acquisition is at 0.5%, the list is shrinking 1.5% per month and you need to fix acquisition before you touch content.
How is churn different from unsubscribe rate?
Unsubscribe rate is measured per send and counts only one type of loss: the recipient clicking unsubscribe. Churn is measured per month and includes unsubscribes plus hard bounces, spam complaints, and any subscribers removed by a sunset policy. A list can have a healthy 0.3% unsubscribe rate per send but a 1.5% monthly churn if involuntary removals are running hot in the background.
What causes newsletter churn?
The main causes in our audit work are send frequency mismatch, topic drift away from what subscribers signed up for, sender identity changes such as a new From name or domain, and sunset removals catching up after a period of weak list hygiene. The pattern of when subscribers leave (concentrated within 24 hours of a send versus spread evenly across the month) usually identifies which cause is responsible.