How to interpret your unsubscribe rate

When I send an SMS campaign, my unsubscribe rate ranges between one seemingly small number and a slightly less small number. It doesn’t have a clear trend. It doesn’t have huge peaks and valleys. It undulates. It dances. It salsas from 0.02% to 0.2% and back again. So... when should I do something about it?

Today I’m going to talk about the unsubscribe rate and how to interpret it. A fellow reader asked for more discussion about list health and I love the input! If you have thoughts on what I’ve written here, please leave a comment so we can have an interactive discussion.

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Most of the SMS platforms we’re using give us unsubscribe rate by campaign, a mostly useless way to look at it. Looking at unsubscribe rate by campaign naturally causes you to assume something about the campaign led to the unsubscribe. For example, my highest unsubscribe rate this month occurred when I re-sent a campaign from last year. Last year it performed well, but this year I saw a big uptick in my unsubscribes. Naturally I assumed it was the resend that turned people off.

But wait… I resent that campaign a few days after our biggest sale of the year. And because of the sale, we had just gotten a huge influx of new subscribers, and new subscribers are always more likely to jump ship than older ones. Maybe my unsubscribe rate had nothing to do with the fact that it was a resend at all, and everything to do with this giant group of new subscribers in my database.

So my first suggestion is that we start looking at unsubscribe rate not by campaign, but by cohort instead.

Typically this is what the unsubscribe rate looks like for cohorts of new subscribers.

You’re most likely to lose new subscribers in their first 30 days.

Now think about your last campaign and the unsubscribe rate you saw.

All of this is to say that looking at the unsubscribe rate at the campaign level will obscure the underlying dynamics at play.

A more actionable metric will be the 30-day unsubscribe rate.

Now to be clear I am not referring to the percent of unsubscribes that occurred in the last 30 days. I am referring to the percent of people who unsubscribed by the time they had been on your list for 30 days. This is a cohort analysis.

Here is an an example of what the data will look like if we create and compare monthly cohorts. (I added in a 60 day data point as an optional sense check as well.)

In this example the November cohort was bigger and by Day 30, 20% of the November cohort had unsubscribed.

Now to be honest, most SMS platforms do not make this analysis easy. So you may have to alter this plan slightly depending on what data you have easy access to.

Postscript users are going to be best positioned for this kind of analysis, through your Subscriber Analytics page.

In Attentive, you will need to create segments based on sign-up date and label them the “September 2025” cohort, the “October 2025” cohort etc., then take note of the unsubscribe rate for each cohort at the end of each month so you can see the trend.

In Klaviyo, you may have to compare 30 Day Conversion Rates instead. That’s a fine alternative to understand engagement too.

If you see the 30-Day engagement rate spiking or trending in the wrong direction, then you know you have a list health issue on your hands. Then you’re going to want to ask yourself:

That leads to another analysis worth doing - comparing the 30-Day Unsubscribe rate by Sign Up Unit and by Onboarding Series.

In the above example, I would want to take a dive into Onboard Series B.

The next step will be to change how we acquire and how we nurture our subscribers in the first 30 days. We can also develop plans to re-engage or sunset subscribers from cohorts that seem to churning quickly. These are topics we’ll cover in a future newsletter.

If you compile monthly SMS reporting for your executive team, add a 30-Day metric for your newest cohort to illustrate your list health.

Are there other list health indicators we could also add? Yes. You can also gain insights by looking at your older cohorts’ 3 month, 6 month, or 12 month behavior. You might also gain insights by looking at unsubscribe rate by campaign type.

But for now I focused on this 30 day metric because it is most likely to explain recent changes in unsubscribe behavior in your account. And as a manager I want to guide my team to the most fertile hunting ground first.

Agree? Disagree? Please challenge me (or build on what I wrote) here!

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