🥲📊Let’s Be Normal About Data
Did we all forget correlation doesn’t equal causation?
Did you know that there is not an industry standard for what constitutes a “listen” in podcasting? Did you know that listeners writing a review on Apple Podcasts has never actually had a confirmed impact on Apple’s algorithm? And did you know that just because the data shows most people use YouTube to listen to podcasts right now, that doesn’t mean you need high-quality video?
Hi. Let’s talk about how to look at your data with level-headedness instead of . . . chicken-with-its-head-cut-off-edness.
✨If you come away with only one thing: Go subscribe to Sounds Profitable, and remember that one piece of data is just one piece of data, not any kind of analysis.
🫠Let’s be real – podcast data is, um, well . . .
When I tell people that first fact about listens, I usually get dropped jaws. I get it; it doesn’t seem to make any sense at all in an industry this solidified. But let me ask you this: what constitutes a “stream” on Netflix? It’s not the two minutes watch time it was back in 2020, and likely not even the 70% reported later that year (lol).
It’s worth remembering that podcasts were born not out of a desire to be podcasts, but because a friend of the guy who created RSS feeds thought it would be cool. It was never intended to have the nitty-gritty data needs impacted by countless listening platforms we see today.
(Anyone else listen back in those old days, btw? I got started in my podcast love back before iPods, when I had to download audio from RSS feeds directly to my computer desktop and listen with VLC.)
Why it’s so Like That™️
Don’t think of this data squishiness as malicious. Ultimately, platforms like Apple are trying to mitigate the possibility of its systems being manipulated by bots. It’s happened before, and it could happen again if algos aren’t treated like black boxes.
And let’s not forget that one time . . .
Do you remember that time Apple changed how its downloads worked and everyone lost their minds because their numbers were so much worse (or just kind of marginally worse, like going from 4 million to 3 million, lol)? Don’t fall in love with your numbers, ever. You never know when they can change for reasons you wouldn’t expect.
🔍 So where do you find the GOOD data?
You read Sounds Profitable. This isn’t sponcon. I’m just right.
Are you a data guy? I’m not a data guy. My degree is in English Education. I know words. You put a number in front of my face and I go, “Oh, no thanks, I’m still full from lunch.”
Sounds Profitable is a publication done by people who are actual data people. So, first and foremost, they’re probably going to be better at analyzing this data than most of us – and they’re also the ones finding the data in the first place.
We don’t need to reinvent the wheel here. Sounds Profitable releases data on the podcast landscape annually, and they have more targeted survey data they release throughout the year. Want to know when those are released? Guess you better read Sounds Profitable.
🤓 Let’s get big brain about how to think about that data
Okay. So YouTube, according to Sounds Profitable, is currently the #1 way people are listening to podcasts.
Are you thinking ahead a few steps? Are you thinking something like, “Well, I don’t want to be a YouTuber!” or “I guess I need to hire a video editor . . .”
STOP THAT!
One piece of data is not the same as data, analysis, prediction, or interpretation! Here’s another piece of data that, for some reason, gets completely left out of the YouTube conversation: most people do not watch video podcasts. They press play and then do something in a different tab! They’re using YouTube like any other podcatcher. They’re listening while doing something else.
Before extrapolating meaning from data, ask yourself:
Does this data carry any actual further-reaching implication, or would that be conjecture?
Is there any other data that would be useful to know before making an assessment?
Am I listening to people who are good at data talk about this subject, or am I making my own interpretations as a guy whose job isn’t looking at podcast data?
I think we could all sit back and stop giving ourselves heart attacks. Let’s listen to the smartypants dataguys. Let’s let ourselves be agile and unbeholden to some numbers. Let’s all remember that data is just data. Clear eyes, full hearts, can’t lose.
✨ More Magic
Speaking of YouTube – please check out this mandatory reading from Erik Nuzum.
And speaking of demystifying data! The Podcast Marketing Academy is running its trends survey for 2025! Please please please take this very important survey and help us understand the industry better. Submissions close March 21!
Missing the days of auto posts to Twitter before the API, uhhhhhh, debacle? Amazing news: Transistor can now auto-publish your episodes to Bluesky!
🎧 From the Desk of Tink
I’ve been absolutely loving Before the Chorus, a podcast that’s the perfect next listen for fans of Song Exploder, fans of really great music, and fans of frank conversations about neurodiversity and mental health. From Nilüfer Yanya to Jamila Woods to Vagabon and so many more of my absolute favorite musicians, host Sofia Loporcaro dives into the lives of musicians and their music leading up to that moment the first listener gets to press play. Loporcaro gets real with guests as the guests get real with their tracks.
Go be normal! Next week, Shreya returns with some more podcast marketing magic. ✨
– Wil 🦇
We. Need. Better. Podcast. Analytics!!