For advertisers, podcasting has been a mysterious medium.
They don’t really know how many people are actually listening to your podcast and they have no clue how many are listening until the very end.
This can make it seem like they’re throwing money down a long dark tunnel, without knowing what’s going to come out the other end.
It could be tremendous profits or it could be nothing.
Some podcasters have tried using things like affiliate codes to track the number of listeners that act on the “call-to-action” message.
Others have presented stats from services like Libsyn and other hosting providers to prove their traffic numbers to advertisers.
Now – everything has changed!
Apple recently released a “podcast analytics feature” which will show you tons of data regarding your show and individual episodes.
You’ll be able to see:
- The number of devices tuned into each episode
- Average listening time per device
- Listening trends
- and more…
Considering that podcast advertising revenues are shooting up, this was a good time to reveal the new functionality.
In fact, according to IAB, podcast ad revenues are expected to top $22 million in 2017, climbing 85% from the previous year!
It’s more important than ever that you show the ROI that advertisers are getting for sponsoring your show.
In this article, I’m going to cover how to get accurate iTunes stats for your podcast.
1. Log into the online “Podcasts Connect” application
You can log into Podcasts connect here.
This application is part of iTunes connect. When you log in, you’ll be able to access your podcasts and see the download numbers for each of them.
There are lots of other helpful resources, like how to sign up with iBooks and produce an ebook. Personally, I’ve found creating books on Audible to be a very profitable way to monetize my podcast.
I’m now getting started with iBooks to test the waters and see how it compares with the Amazon marketplace. I already have four books on Amazon (ebook + paperback).
2. Click on Your Show and Browse the Data
Once you have logged into Podcasts Connect, you’ll be able to select the show that you want and view the download data for this show.
You can sort the data according to a specific range, week, or month.
This can give you an idea how how responsive your community is and whether or not people are sticking around to consume multiple episodes of your show.
You’ll get even more data like:
- Countries your downloads are coming from
- Average time a show has been listened to per device
- How much of an episode has been played per device (on average)
- Listenings trends by episode, subscribed device, or country.
One of my favorite parts of this dashboard is the individual show data which explains what percentage of your listeners stick around until the end of a podcast episode.
As you can see, not every single one of your listeners is going to stick around until the end of the show. This data gives you an idea of the number of people that are “listening” or “primed” to respond to your call-to-action message.
It’s super powerful!
It also shows you a bit more information about the subscribed devices that are listening to the podcast.
3. Compare This Data with Your Hosting Provider
This data will give you a good idea of the number of devices or people that are listening to your show and how many are sticking around until the end.
If you want even more data, you can look into the analytics of your hosting provider. I use Libsyn for mine. I’ll show you my Libsyn stats below, which gives you an idea of the download trajectory of a podcast.
By comparing this data, you’ll get an idea of the overall consumption pattern of your show, both when it comes to downloads, unique devices, and the percentage of your show that people listen to.
This will give you a much fuller picture when it comes to understanding the average listener of your podcast. You can use this to improve your episodes, change up the structure, or persuade sponsors of the value of your podcast.
4. Compare this Data with YouTube
A lot of people don’t agree with me, but I think that YouTube is a great place to publish your podcast episodes.
Not only are big-name podcasters starting to do this like Joe Rogan and Lewis Howes, but I have also seen people watching my own show this way.
When I publish it on YouTube, I don’t get nearly as many listens, but I do get a lot more data than I do from my podcast hosting provider.
For example, let’s take this one episode which I published and got 95 views. You can see the download trajectory below, which is pretty similar to the other stats providers.
This gives you an idea of total views for this video or listens for the podcast on YouTube. In order to get a full picture, I gotta take a look at the percentage of viewers that are staying around until the end of the show.
Now… the next chart is a little confusing. Basically it shows average retention for your videos. My video’s stats indicate that about 15% of the audience stays around until the 30 minute mark (when the show is closing up) and probably only 5% until the very, very end.
In total, the average view duration is about 8 minutes, but that number is misleading. I don’t really care about the number of people who listen to my show.
I’m more interested in the number that are “raving fans” because they are more likely to become buyers or take action in some way.
5. How to Use this Data…
Now that you know how to get more data on the listeners of your podcast, the question is how do you use it?
Of course, you can use this data to persuade more people to sponsor your show. It will be easier for them to see a direct return on investment for their advertising spend.
I talk about more podcast marketing techniques in my book, Podcasting for Beginners.
I think another great way to use it is to change up the structure of your show so that you’re making sure your announcements or call-to-action messages get the maximum exposure.
Many podcasters will only have a call-to-action message at the end or in the very beginning. They forget that they can do this in the middle or allude to what they want their podcast listeners to do throughout the podcast.
I plan to use this data to be more aware of when my audience is tuned into my show and when they’re not.
What do you think?