Some podcasts publish new episodes daily. Others release weekly. Some shows release a new episode every hour.

Podcast publishing frequency can be consistent or sporadic. Episodic or seasonal. "Always-on" or in limited-run batches. With this kind of variety, it's not surprising that one of the most common questions I hear from podcasters is a classic Goldilocks question...

Am I publishing too much? Or not enough?

It's easy to know how frequently a podcast releases episodes. But it can be much more difficult to understand whether a show's publishing frequency is "just right."

Often, episode publishing frequency is dictated by available time and resources. Or precedent set by another similar show. Or a sales team's need for available ad inventory.

I think there's a better way to think about the "right" number of episodes: listen to what your audience tells you when they vote with their play buttons. At Bumper, we use a derived metric to help us understand audience appetites: average episodes per verified listener.

For years, we've used this number to help our clients understand whether they're overproducing or underproducing, and to right-size the output of their ongoing shows to match audience appetites.

How to calculate average episodes per listener

Average episodes per verified listener is a derived metric, meaning it's calculated from other core metrics provided by platforms like Apple and Spotify. The key ingredients we use are:

Unique listeners per day/week/month at the podcast level
Unique listeners per day/week/month at the individual episode level

For example, if a Spotify user listens to three episodes from the same podcast in a single week, they count as one weekly listener at the podcast level. But they also count three times at the episode level (once for each of the episodes they've listened to).

We can use this multi-level deduplication to helps us understand the average number of episodes played per unique listener within a period of time:

This gives us an average we can track over time. Let's look at the average episodes per monthly listener for an example show:

In its first month, the average number of episodes per monthly listener was 1.4. Over the next few months, that average rose to around 2, where it's remained steadily ever since. This makes intuitive sense: early on in the show's life, there were plenty of "one and done" listeners who checked out a single episode of the show, pulling the average down.

Average episodes per listener vs. publishing volume

On its own, the average number of episodes per listener is an interesting bit of trivia. But this number quickly becomes actionable once compared to a show's publishing volume.

Let's look at our example show again. When we overlay average episodes per verified listener on top of the episode release volume, we can immediately see that in many months, on average, listeners consume fewer than half the number of episodes published that month:

Interestingly, for this show, the average number of episodes remains steady, regardless of the number of episodes published. It doesn't seem to matter whether the show published 4, 5, or 6 episodes per month... the average episodes per listener stays close to 2. This is the audience speaking, in aggregate, by voting with their play buttons.

I've looked at these ratios for many different types of shows. For most of the shows I've analyzed, publishing additional episodes tends to yield diminishing marginal returns. Because, of course, publishing more episodes doesn't mean your audience will play more episodes.

Of course, publishing additional episodes might get you additional downloads in the short term, because automatic downloads are still a thing in 2025. But of course, downloads don't measure consumption. And in some cases, increasing a show's output beyond audience appetites can backfire, and increase the chances of a triggering a pause in automatic downloads.

Daily shows vs. weekly shows

Data from Apple and Spotify allows us to calculate average episodes per listener at a daily, weekly, or monthly resolution. Given how these platforms deduplicate users across time, the choice of resolution matters a lot.

To illustrate this, I reached out to James Cridland, editor of Podnews, which publishes both Podnews Daily and Podnews Weekly Review. The release cadence of each show should be fairly obvious from their titles,

Podnews Daily publishes a new episode every weekday, like clockwork. That's 5 episodes every week. I'm sure some weekly listeners hit play every single day, pulling the average up. I'm also certain that some weekly listeners only hit play once a week, dragging the average down. But the notional "average" Podnews Daily listener in Apple Podcasts hits play on somewhere between 2-3 episodes a week.

Switching to a less-frequently published show, an analysis of Podnews Weekly is probably best suited to monthly resolution, given that it publishes multiple episodes per month. Like with the daily show, we see similar numbers over time, with the average Apple Podcasts listener playing roughly 2 episodes per month:

These averages are both strong and consistent, if you ask me.

What is "good?"

There's no single ideal average number of episodes per listener. But generally speaking:

100% is not a reasonable target. For most shows, it's unrealistic to expect your show's entire audience to spend time with every single episode you publish.
These averages are sensitive to the effectiveness of podcast marketing. When a marketing campaign sends a large number of listeners to a show, "one and done" listeners who sample a single episode can drag the average down. This is normal and expected.
It's usually not ideal to publish significantly more episodes than your average listener consumes. If your show publishes 14 episodes a week, and your average listener consumes 1.5, you're releasing a lot of stuff that goes unplayed by many. This may suggest a strong case for a "fewer, better" approach.
If your show's average episodes per listener is similar to your show's episodic output, it may be the result of a highly-engaged segment of the audience pulling the average up. Shows with high ratios may be good candidates for paid subscriptions options. This may also suggest an opportunity to increase your show's output.

A different way to grow

Our team at Bumper specializes in podcast growth and data. Our clients want to increase the reach and impact of their shows. And it's important to remember there are different ways to grow.

One way to grow is to find new listeners, and entice those new listeners to spend time with your show.

Another far less talked-about way to grow is to get your existing audience to come back more frequently, spend more time with your show, and deepen their engagement with you.

Successful shows do both.

Again, it's unrealistic to expect everyone in your audience to hit play on every single episode you publish. But there's almost always an opportunity to improve on your show's current average number of episodes per listener. If you publish 4 or 5 episodes per month, and your average listener hits play on 2 of them... here are the questions I'd be asking:

What can I do in my episodes to encourage existing listeners to spend more time with me?
How can I pitch ahead and get listeners excited about coming back for our next episode?
What non-podcast channels (e.g. social, email) can serve as a reminder to my existing audience that there's something great waiting for them in their podcast app?
What's in my back catalog that I can point people towards?
How can I best encourage repeat listening over time?

Know your number

I bet you already know how many episodes you publish, and at what frequency.

But do you know how many episodes your audience is actually playing?

If not, I recommend finding out. Otherwise, you may be missing out on a significant growth opportunity.

How often should I publish new podcast episodes? A data-informed approach