A podcast listener's decision to click play boils down to a small number of touchpoints.
Most prominent are your show title, artwork, and episode titles. But while these (and other) touchpoints have always had a significant impact on listener acquisition, we've never had a way to track it.
Until now.
In 2024, Spotify rolled out a new discovery dashboard, giving us as creators a new set of metrics to understand how listeners flow through our listener acquisition funnel.
The best part is that if you know how to interpret the data this dashboard provides, it gives you a map of exactly what you need to do you improve your listener acquisition and grow your show.
Here's what you need to know.
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Resources
Spotify Conversion Rate Benchmarks Report
How a Podcast Cover Art Redesign Helped Growth Mindset Psychology Grow 45% in One Month (+2,000 Downloads/Episode)
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More on Podcast Analytics
While podcast analytics might appear to be stuck in the stone age compared to platforms like YouTube, Google Analytics, and many social media platforms, there's a lot creators can learn about how to improve their shows and their marketing... if you know where to look.
Beyond podcast downloads, metrics like listen time, consumption rate, Spotify's podcast discovery dashboard, and more give us clues about the issues facing your show—and often tell you exactly how to navigate them. Which mean understanding your podcast analytics & metrics, and how they compare to industry benchmarks is an essential skill for marketing and growing your podcast.
About Podcast Marketing Trends Explained
Podcast Marketing Trends Explained is a podcast about data-driven podcast marketing, growth, and monetization. In every episode, we take a peek under the hood of one data point from the Podcast M
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jeremy (00:01):
Spotify wants people to engage with content, and so they
are trying to match users withpodcasts. If they show it to a
thousand people and none of themclick into that, that's now a
signal to Spotify saying eitherwe're showing this to the wrong
people or something about theshow is not worth promoting.
Justin (00:15):
This is such a paradigm shift from the way episode
consumption used to work. It'sgonna be way more like YouTube.
How do we grab people on thehomepage versus how do we bring
back our regular listeners?
Jeremy (00:28):
You can optimize all the internal things. You can create
an incredible show. But ifyou're not getting people to
click play into the show in thefirst place, all of that work
doesn't really matter that much.So, Justin, in the back half of
2024, Spotify rolled out thisnew feature in the back end of
the Spotify for Podcasters kindadashboard. And most people
(00:50):
didn't really pay much attentionto it.
They kinda missed it, but it wassomething that actually got me
super excited, and it'ssomething that I've been
literally waiting years for.It's been on my podcast wish
list for as long as I've been inpodcasting. And I'm wondering if
you have any guesses what thatnew feature might be.
Justin (01:05):
A vocal fry intensifier.
Jeremy (01:10):
It's not that. Any other, thoughts?
Justin (01:13):
Podcast auto tune. If you wanna hear all your podcasts
sung to you.
Jeremy (01:17):
Definitely on my wish list, but that was not the
feature that was released. I'llgive you one more chance. You
got, one more guess at what thisfeature was.
Justin (01:24):
Okay. If you really love podcast tangents, there's, like,
an AI podcast tangent, expanderso you can get more tangent. You
could have a whole episodethat's just one big tangent.
Jeremy (01:37):
% tangent. No no content whatsoever? Okay. That would
that was also not the feature.I'm gonna save you the the pain
of any more guesses.
It was their new discoverydashboard that they rolled out.
So pretty boring compared to allthe feature recommendations that
you made. Spotify, if you'relistening, take note of these.
Roll these out, please.
Justin (01:55):
That's right.
Jeremy (01:56):
And so, essentially, the discovery dashboard is a tab
that's nested within theiroverall analytics platform. And
what it does is it allows us,for the first time, to actually
see conversion rates of peoplewho saw the podcast to people
who showed interest in thepodcast to people who ultimately
went on to listen to thepodcast. And for me, this is
just a huge game changer for howpodcasters are able to approach
(02:19):
the craft of packaging theirshows and creating their shows.
And so this is something that, Iwould love to spend some time
digging into and tell people alittle bit about why this is
such a big deal.
Justin (02:29):
Yeah. I think it's worth looking at for sure. When I
first saw it, especially if youhave a marketing background, all
we think about is funnels. Andso seeing this actual funnel
view and going, oh, wow. Like,now we can actually visualize
how many people saw the showsomewhere and then how many
showed interest and then howmany actually listened.
(02:51):
Whereas before, maybe you couldget this, like, you know,
sometimes people would runovercast ads and they'd get some
of this information, but this isvery interesting.
Jeremy (03:00):
So, obviously, you and I are marketing nerds. But for
the, you know, 99.9 of peoplewho are not, we should probably
start with just talking through,like, when we're talking about
conversion rates, what are weactually talking about? Like,
how would you define aconversion rate?
Justin (03:13):
Conversion rate is, you know, how many people saw the ad
and then how many peopleactually clicked the ad. So in
that case, the conversion ratewould be you know, you've got a
hundred people who saw the ad,10 people clicked. That's a 10%
conversion rate from saw the adto actually clicked. And then we
would even go one step furtherand say, okay. Of the 10 people
(03:34):
who clicked, maybe one personbought.
Well, that's again another 10%conversion rate on that side. So
you're just calculating how manypeople actually took action
based on the bigger group.
Jeremy (03:48):
And so you kind of, with your language there, you
mentioned, you know, how manypeople saw the ad. And so,
really, conversion rates are theone of the ultimate metrics in
advertising where especially ifyou're doing any kind of digital
advertising where you're puttingan ad, let's say, on Facebook or
Meta or Google or YouTube orwherever else, and you see,
okay. This ad got a hundredthousand impressions. This many
people, you know, watched it. OnYouTube, you can see how many
people watch this percentage ofthe video or click the link and
(04:10):
then went on to buy.
And so that's a world where, youknow, conversion rates are the
bread and butter of any kind ofadvertisers, analytics. But they
also show up a bunch of otherplaces. You can go if you use
Google Analytics or Fathom orany of these other analytics
platforms. You can see how manypeople went to your home page,
and then you can kind of eventrace them around how they went
throughout the other pages ofyour website. And so depending
(04:32):
on what your goal is withpeople, you can kind of, use
conversion rates to track theeffectiveness of your overall
system that you've created.
So you can get hyper hyper nerdyin in the marketing, analytics
world here. And, basically, ifthere is anything that you can
measure, on route to gettingpeople to take results, there is
somebody tracking the conversionrate of how people are are
(04:52):
moving through that system. Andso you can kind of think of it
as this funnel where peoplestart a large number of people
start at the top and a smallernumber of people trickle down to
the bottom, which is essentiallywhat Spotify has now given us.
Justin (05:02):
Yeah. And I would say that these conversion rates on
their own are generally not thathelpful. I think the big gift
you've given the podcastcommunity is that you've
collected all this informationand allowed people to compare
their rates to the rates they'reseeing in their Spotify
dashboard. So if you just go inand look and go, okay. Well,
(05:24):
that's my conversion rate.
Is that good? Is gonna be yournext question. You've basically
answered that for them in thisreport that you put out.
Jeremy (05:33):
Yeah. And, we're gonna mention this page that I put
together, which you can find atpodcastmarketingacademy.com/spotify-benchmarks.
And so, you can find that there.That'll be linked in the show
notes as well. We're not gonnago into all the data because
there's actually quite a bit onthat page and there's a bunch of
helpful resources on how todiagnose what your conversion
rates are and what to do basedon on how you're kind of scoring
there.
(05:53):
But, yeah, with most conversionrates, it's, you know, you look
at it and you're like, is thisgood? I don't know. You can
always measure against yourself.And so I think that's a good
starting point to say, okay, myconversion rate right now is
10%. Let's get that to 15%.
And and how do I do that? Andwe'll we'll look at some of the
ways that you can in thisepisode. But I think to start
out, it's probably helpful justto talk through a little bit of
the data that Spotify actuallygives us. And so there's really
(06:17):
kind of a handful of corecategories or stats that they
give us, and then we can kind ofinfer or do some calculations to
get, a little bit more beyondthat. So what are some of those
those analytics that Spotify isgiving us now in the discovery
dashboard?
Justin (06:31):
Alright. So at the top of the funnel is people you
reached, and this is the numberof distinct people who have seen
this episode on Spotify. So I'mguessing this includes, you
know, you land on the home pageand there's a bunch of cover art
there. So that would get countedas people you reached. You're
scrolling through searchresults.
(06:52):
If you zip by a, you know, somecover art there, that gets
included. So this is a fairlybig bucket. This is actually
kind of funny. Can I if I just atangent here is people you
reached is a little it's morelike your cover art appeared on
a page that people were lookingat? It doesn't mean that you
reached them.
Doesn't mean they saw it.Doesn't mean they paid attention
(07:14):
to it. It just means you werethere for some period of time.
And this is actually the onemetric where they don't give us
any sort of rubric for whatdistinguishes a reach. Like, do
they have to have pause on thescreen for five seconds or
twenty seconds?
So this is the big bucket at thetop. It's important to know.
(07:36):
It's gonna be helpful once weknow the conversion rate. But
Jeremy (07:38):
My assumption is that this is the unique number of
users Spotify delivered yourshow to on their screen. Who
knows if they if they didn'tscroll down to see it, but it
was on their home screen. Ifthey had scrolled, does that
count? I I think the otherinteresting thing here is a lot
of times when we're talkingabout conversion rates, we're
talking about impressions, andSpotify does give us that stat,
but this is actually uniqueindividuals. And so the calculus
(08:02):
maybe changes a little bit ifyou are used to thinking of
conversion rates in terms ofabsolute impressions.
This is a little bit different.This is unique individuals who
were shown your podcast who thenwent on to take some further
action.
Justin (08:11):
So That's right.
Jeremy (08:13):
People you reached with number one. The second one that
we've got here is people whoshowed interest. So what does
Spotify give us a definition forinterest here?
Justin (08:23):
Now this is where it gets kinda saucy because this is
the number of distinct peoplewho have gone to the episode
page, added this episode to aplaylist, or played this episode
on Spotify for zero seconds ormore. So in terms of, like, the
value chain here, we've gonedramatically from people who may
(08:43):
have seen your cover art tothey've actually clicked through
and said, oh, that episode looksinteresting or, oh, I should add
that to a playlist or, oh, Ishould actually play this
episode. So going from peopleyou reached to people who showed
interest, that is a veryinteresting number.
Jeremy (09:03):
Yeah. And then the, final step in the chain here is
people who streamed. And what isthis is, you know, one of the
maddening things. Apple's gottheir definition of what counts
as a a streamer play. Spotifyhas their own.
What is Spotify's definitionthat they give us here for a
stream?
Justin (09:17):
So that's anyone who played the episode on Spotify
for at least sixty seconds. Youknow, I I used to have a podcast
called six seconds where everyepisode was only six seconds. So
I wonder if I would not get minecounted here. It's like sorry.
My stats would be nothing,because it
Jeremy (09:38):
You need that, that tangent expander tool. When
Spotify gets that in, then herewe go. We're cooking.
Justin (09:44):
So, yeah, to get anybody to listen for any period of time
is a meaningful so this is stillvery top of funnel, I would say,
though. They could add anothersection here that is, like, how
many people made it at least 70%of the way through the episode.
Jeremy (10:01):
Yeah.
Justin (10:01):
But it's still interesting to see the
conversion rates along each stephere.
Jeremy (10:07):
Yeah. And they do so Spotify does give us a chart
that charts your follower count.And so you could potentially try
and correlate some of this alittle bit, but they don't
attach it to this actual funnelhere. Probably because a lot of
people listen to a show and thenthey end up following it some
amount of time later in thefuture. And so it becomes a bit
murky onto, you know, the chainof events here.
(10:28):
Mhmm. But that is, you know,something that you can look
into, in your own Spotifydashboard as well. So those are
the, essentially, the threeanalytics, the data points that
are included in the funnel. Sopeople you reached, people who
showed interest, and then peoplewho streamed. And so those give
us these kind of two differentconversion rates, which is the
what I'm calling the awarenessto interest conversion rate.
(10:49):
So people who were aware of theshow or at least it was
delivered to them who showedsome kind of interest in it and
then the interest to streamconversion rate. And so people
who took some initial click intothe show and then listened, for
sixty seconds. But they alsogive us, a couple other other
analytics here, which aredisconnected from the funnel,
but also play an interestingkind of role in our
understanding of how our show isdoing. And the main one here is
(11:11):
the impressions. And so wementioned before that in the
funnel, we're talking aboutindividual users.
Here, when they're talking aboutimpressions, they're saying,
okay. How many times was eitherthe show or any one of your
episodes displayed to somebody?And so what we can calculate
with that then is we canactually see how many times on
average on a per user basisSpotify showed your show to
(11:32):
them. And so you you can see insome of the shows that I have
the data for, some shows wereshown something like 15 times
per person on average, whereasother shows were only two times
per person. And so we don'tnecessarily know why that
happens, but you can do some ofthese calculations and and see
how Spotify is promoting yourshow to people.
And so that's the the one thingwe can kinda calculate and infer
from the data. The other thingis we can calculate the overall
(11:55):
conversion rate. So people tostreams, and so they give us
these two conversion rates, theawareness to interest and the
interest to stream. And then wecan take the overall of those,
how many people who, were shownmy podcast ultimately went on to
stream sixty seconds. And sothat's the the data they give us
here.
And the the one other thing,that we didn't mention is under
the impressions, they actuallybreak that out into four
(12:15):
different, locations within theapp. And so the first is Spotify
home. So that's in the homescreen. If you click over to the
podcast tab, then there is thesearch. And so, obviously, if
you're searching for a phraseand shows get recommended and
come up, that's gonna be there.
Then you have the library, whichis if you use Spotify, it's in
your left hand sidebar. That'syour library there. And then the
final one is other. I assumethat that might be some episodes
(12:39):
had a recommended episodes atthe bottom of them. And so I'm
guessing that is probably inthat category, and maybe there
are other things where Spotifyshows, podcast to people, that
are not in any of those priorthree categories.
So
Justin (12:52):
Yeah. I'm wondering if some of that is clips and other
of their beta features as well.
Jeremy (12:57):
Right. So you mentioned before that, you know, this data
isn't really all that useful ifyou don't have any benchmarks.
But if you do, I'm curious,like, what does knowing your
conversion rates in relation toa established benchmark allow
you to do as a creator?
Justin (13:15):
Well, I mean, having the average the way that you have it
in that report, I think, isactually really helpful. Because
if your conversion rates arejust way lower than the average,
then something is wrong. Thatcould even be the show itself.
Like, you've got a fundamentalproblem. So I like these things
because sometimes people areputting a lot of effort into a
(13:39):
show, and there might be afundamental problem they're not
seeing.
So I think if you could compareit to, you know, an average
benchmark that's alreadyinteresting, and then for
yourself, it gives you somethingto improve on. So now let's try
some experiments and see if wecan increase the number of
(14:00):
impressions and then increasethe impression to interest
conversion rate. And over time,you can try to get lift. And it
gives us a little bit of insightalso into the black box that can
be these recommendation enginesand, you know, how are they
promoting my podcast or notpromoting my podcast? It gives
you something to look at.
(14:22):
All of these things that wesometimes say, you know, like,
people will say, well, adding avideo to Spotify will give you
lift. Well, you can test thatout. Like, add video and see if
video episodes get promoted morethan audio only episodes. So you
can have a hypothesis and thentest it out.
Jeremy (14:42):
I think for me, the thing that I was most excited
about was the diagnosis that itallows us to do. Mhmm. And so I
think in podcasting, there'sthis kind of, like, plausible
deniability that you could kindaput your head in the sand and
say, no. No. No.
My cover art's fine. My showtitle's fine. My episode titles
are fine. I don't need to worryabout those. And, like, that's
not the reason I'm not gettingmore clicks into it.
(15:02):
And you could do some reallymanual testing with this. And
so, you know, one of your tricksthat you've mentioned a lot of
times is going to a conferenceand just, like, pulling up, you
know, your cover up, maybe alist of competitors or doing a
search and asking people, youknow, which would you click on
and and seeing, you know, whichone they pick. Mhmm. You can do
that in a bunch of differentways with a bunch of different
variables, but that's not reallythat easy to do all the time for
all of us. And the thing about adashboard like this is that it
(15:24):
is unbiased by how we ask thequestion or things like that.
And so it's like people thatSpotify thinks should be
interested in our show. I thinkthat's the important thing to
note here too is that Spotifywants people to engage with
content, and so they are tryingto match users with podcasts.
And so if they're showing ourshow to someone, based on the
data they have, they suspectthis person might be interested
(15:44):
in a show like ours. And so ifthey show it to a thousand
people and none of them clickinto that, that's now a signal
to Spotify saying, like, eitherwe're showing this to the wrong
people or something about theshow is not interesting and not
worth promoting. And so I thinkthat this analytics dashboard
now allows us to actuallypinpoint some of the potential
issues.
And we're gonna talk a littlebit more about how to diagnose
(16:06):
specifically, based on, youknow, high and low conversion
rates on either the, awarenessto interest or the interest to
stream. But maybe to start off,let's dig into some of the the
benchmarks here. And so I gotabout a hundred people to submit
their Spotify dashboard data.This was a range of podcasters.
Some have millions ofimpressions in Spotify in a
thirty day period.
Some have basically, like, ahundred or 200 very low numbers.
(16:28):
So we've got the whole range ofvastly different types of
podcasts, different purposes. Soit really covers the spectrum
here. And, what are those kindof benchmarks that, this group
kind of surfaced here?
Justin (16:39):
So in terms of awareness to interest, 8.6% was the
average conversion rate. So Isaw something and then I clicked
on the episode to check it out.And then from interest to
stream, 63% was the averageconversion rate. So I saw it. I
(17:00):
went to the page, and then Iactually clicked play and
listened for sixty seconds.
Jeremy (17:05):
Any initial reactions to those numbers?
Justin (17:08):
I actually have a few other questions. My my guess is
that folks with less trafficoverall and just, like, less
streams overall, my guess isthat their data will be
different and could and couldactually be wildly skewed in
either direction. So if you havea hundred fans that listen to
(17:30):
your show and they're verycommitted, you might get, oh, I
I had a 20 people have awarenessabout an episode, and then 95%
of them click through on theepisode, and then another 90%,
you know, actually listened. Itit it's gonna really depend on
how big your audience isalready. As I looked at some of
(17:53):
the bigger shows, I definitelysaw higher awareness to interest
conversion rates up into the 30%range.
So that's interesting too. So Ithink it's a nice place to
start. Like, if you're gettingway below 8.6% for awareness to
interest, Something might bewrong. And I think if you're
(18:16):
getting way less interest tostream, the interest to stream
conversion rate actually seemsquite high to me. Meaning Mhmm.
Spotify has probably tuned thisfairly well that if you actually
show interest, you're highlylikely to click play. That was a
lot higher than I thought itwould be.
Jeremy (18:34):
What stands out to me is how much of the battle for
winning over a new listener iskinda won and lost at that
awareness to interest stage. Andso you can kind of optimize all
the internal things. You cancreate an incredible show. You
can really hone your showdescription that's in your
listing and your episode titles.But if you're not getting people
(18:55):
to click play into the show inthe first place, all of that
work kind of doesn't reallymatter that much.
And it's not to say it doesn'tmatter to create a great show.
People can still talk about itword-of-mouth and there's all
these other things. But from adiscoverability standpoint of
getting people to see the showto go to click play, I think
that this really puts theemphasis on the show title and
the, cover art of the show. Andso that's this this number that
(19:16):
is way lower. And it's like,okay.
If you can get somebody to clickin, here, the average 63% of
people, you know, the odds arethat they're going to click play
and stream an episode for sixtyseconds, but less than one in 10
people are actually clickinginto a show when they see it.
And the other thing, again, thisis people. And so when we look
at some of the shows, individualpeople are shown a podcast on
(19:38):
average multiple times. And soif people are seeing that show
multiple times and not clickingplay, you know, that's something
to to take into account. There'sa whole bunch of stuff that we
Amit Kapoor (19:47):
just don't know about how this is calculated on
Spotify's back end. And so itcould
Jeremy (19:47):
be that that impressions per back end. And so it could be
that that impressions perSpotify user are inflated by
people who subscribe to the showor follow the show, and then
they see, you know, dozens ofepisodes. And Spotify only shows
the show one time to people whoit thinks it might be
interested, and they don't clickplay. So we don't really have a
window into that. But I think tome, that's the really the big
(20:09):
thing of, like, the thing tofocus on, the highest leverage,
that you have here in thissystem is focusing on title and
cover art.
Because if you can get thatnumber up, then you're kind of
rolling downhill at that point.
Justin (20:22):
I do think there's still a there's a fundamental like, I
always say that product ismarketing, and I can see a
certain type of podcast doingbetter in these scenarios than
others. And so I for a certaintype of show, these stats will
be very interesting and helpful.And I can also see a certain
(20:44):
type of show where this justdoesn't really apply as much.
And you could try, you know,tweaking all these things, which
I think are important to coverart, titles, descriptions. But
there's a fundamental kind ofmismatch between this kind of
discoverability and the way yourshow is structured or the topic
or whatever.
(21:05):
For example, like, a aninterview show that is highly
dependent on the guests and the,name recognition of a guest. You
can imagine that if you'rereally into, Billy Idol and a
Billy Idol podcast episode showsup on your home page, you're
gonna be much more likely toclick through and listen to
(21:25):
that. So, yeah, there's someother factors there that I think
are worth keeping in mind as wemove forward.
Jeremy (21:31):
The other thing that's kind of interesting is, you
know, I looked at of the highimpression shows. And so there
was about 10 or 12, somethinglike that, that that were
getting over a hundred thousand,impressions in the previous
thirty days. And so Spotify intheir dashboard, they only
measure the kind of from todayback thirty days. You can't
filter by past dates or anythinglike that, which is a little bit
frustrating. But, we'll takewhat they've given us for now.
(21:52):
And so these shows, a hundredthousand impressions over the
past thirty days, there weresome interesting data here. And,
again, this is not to say thatevery show should be comparing
themselves to this because notevery show has the potential.
Like, our show is never gonnaget a hundred thousand
impressions in Spotify becauseit's so incredibly niche. Like,
there is not the audience outthere to be delivered those
impressions. And so one of thethings that
Justin (22:13):
put, interview with Billy Idol in one of our episode
titles.
Jeremy (22:16):
Maybe. And we had that, tangent expander too and the
auto too. We just crankeverything up, the vocal fry,
all the effects up to 11. Therewe go.
Justin (22:24):
Vocal fry.
Jeremy (22:25):
So, like, one of the things with high impression
shows was that, 70% of the showswere kind of broad audience,
which actually makes sense.Like, if you have a mass appeal
show, there's a larger pool ofpeople to tap into. And so this
is not to say that every showshould try to appeal to the
biggest audience possible. Like,that's actually the hardest show
to market. But if you are asolid show or a great show in
(22:47):
that category, your ceiling isway higher than a show like ours
or any other huge show.
The other thing that was alittle bit interesting here was
the show purpose. Now I definethese based on what I could see
from the descriptions and thetitles and things like that, the
episode titles. And so Icategorized 60% of the high,
impression shows as pleasuregivers. And so there are things
(23:09):
that they're, like, not usefulin a way of, like, a practical
problem solver type approach.And so that is up almost 30% or
27% higher than other shows inthe the kind of main category.
And so, 60% of shows wereactually just things that people
are listening to to unplug orunwind their entertainment.
They're just like, you wannajust, you know, immerse yourself
(23:30):
in something, which I think is,again, more indicative of mass
appeal shows. And so, the thesecond most popular was problem
solving type shows at 35%, but,really, 60%. They were more
entertainment kind of pleasuregiver type shows. And then the
last thing that was interestingabout the high impression shows,
was that 45% of them were soloshows, which I thought was quite
(23:52):
interesting.
And this was, 14% higher, degreeof solo shows than the general
audience or the pool of all theshows here. And so interview was
second with 30%, and then therest were split between, 10% co
hosted, ten % narrative, andthere was one daily news show
that was a a tiny percentage aswell. So some some interesting
(24:12):
trends there on the highimpression shows, but I actually
don't think that's what's worthpaying attention to. I think
it's much more the conversionrates, which are where you can
kind of improve you know, giventhe potential audience that you
have, you can kind of attractmore of them back to your show.
Justin (24:26):
I would also say, like, I'm looking at my stats for an
inactive show or a show where wedon't publish very often. And
the these stats are definitelymore interesting to people who
are publishing regularly. If youhave, like, a a serial show
that's been out for three years,maybe it'll be interesting. I I
(24:48):
you know, you should still lookat it, but it it seems like a
lot of this even in thedecisions they've made to kinda
highlight impressions in thelast thirty days, this is very
focused on people who areactively publishing Yes. Within
a thirty day window.
Jeremy (25:04):
So we've kind of talked a little bit around you know, we
have these benchmarks, and wecan kind of diagnose some of the
issues. And then the next stepis to actually run some
experiments. And so I know we'vetalked about this in season one
of the show, some potentialexperiments that you could run.
But how would you kind ofapproach, using these conversion
rates to test differenthypotheses and and kind of pull
(25:25):
different levers that, you'reable to with your show?
Justin (25:28):
Yeah. I mean, when we do podcast marketing tear downs, we
almost always focus on podcasttitle, podcast cover art, and
then description, episodetitles, whether or not there's a
teaser episode. So my guess isthose are really the levers you
can pull outside of what topicyou've chosen, outside of, you
(25:52):
know, some other things. But Ithink if you're just looking for
a nice benchmark, like, oh,well, we we've had this cover
art for three years. Whathappens when we change the cover
art?
Does anything happen? And thisis a nice way to test against
that. You already have a,benchmark to test against
because most of us haven'tchanged our cover art in a long
(26:14):
time. Most of us have notchanged our title in a long
time, description, etcetera. Sothose are some of the levers I
think you can pull here and seeif you can get some lift.
Jeremy (26:23):
Yeah. And I would just like if you are thinking about
doing an experiment, I wouldjust put yourself in your
listener shoes and open upSpotify and just go to the home
tab and think about, okay, hereare the shows that show up here.
Amit Kapoor (26:34):
Mhmm. What
Jeremy (26:35):
is there that is present to me that is influencing my
decision? Anything that you cansee on that screen, if you can
change that, that is somethingthat is going to influence
listener behavior or potentiallistener behavior. And then you
click into a show and you lookat the screen, you say, okay,
what are all the differentelements on the screen that I
have control over? And you say,okay, well, once they click in,
then I can edit my description,I can change my episode titles
(26:57):
and I can, you know, upload ateaser episode and, you know,
the teaser episode probably,like, I think if there is a
lower barrier to entry togetting somebody to listen to
anything on your page, and so ateaser episode that's two
minutes is a much lower barrierto entry than a sixty minute
episode. Or if all the episodesare sixty minutes, that's
probably also going to get morepeople to stream an episode,
(27:19):
probably that episode.
And so opening up Spotify andjust seeing what you have there
and then going into the back endand playing with those things,
is is basically kind of the theapproach here.
Justin (27:29):
I wanna note that this session is interesting paradigm
shift from the way episodeconsumption used to work, which
is you used to just get achronological feed basically of
what's new. And my guess is alot of people were scrolling
through episode titles bait on,you know, the what they were
already subscribed to, and thenthey're like, oh, what seems
(27:49):
interesting? Or and thisresulted in, you know, a lot of
kind of esoteric, cute titlesthat were kind of, like Yeah.
Inside jokes and things likethat. And I can see your
approach in an algorithmicallydriven world.
And I I don't know if we'vementioned this, but, like, the
Spotify homepage is by far thebiggest driver of impressions.
(28:14):
And so if that's true, way morethan search or library, if
that's true, then, whereas theold world was the library first.
Right? It's like, what's in yourlibrary? You're scrolling your
library.
Now it's what am I being shownon my home page, and what is
kind of, like, grabbing me inthat moment. So it's it's gonna
(28:34):
be way more, like, YouTube stylethinking here. How do we grab
people on the home page versus,you know, how do we bring back
our regular listeners or thosekind of
Jeremy (28:46):
questions. It is, worth noting here that some of the
numbers, in regards to where theimpressions happen. And so, what
we see based on the, data thatpeople submitted to me, and I
compiled this, the Spotifyhomepage accounts for basically
65% of all impressions. And sowell over half of all the
impressions that people have ofa podcast in Spotify come on the
(29:07):
homepage. And then the fairlydistant second is search at 26%,
which I thought this would justbe way higher.
I thought the majority of itwould be through search, but
actually, this is Spotify. Onthe homepage, it shows both
shows that you have alreadysubscribed to or engaged with in
the past, but also recommendsnew shows. And so some of those
are going to be from yourlibrary even though it's not in
(29:27):
what Spotify designates as thelibrary, the sidebar. Mhmm. And
some of them are recommended.
And then, the library is at, 9%essentially of impressions
happen in that library tab. Andso I I'm curious. Again, we just
don't know what user behavior ison Spotify. Like, it could be
that many people treat theirhome tab as their library.
Amit Kapoor (29:49):
Mhmm.
Jeremy (29:49):
And so they go there and they just click on their
episodes from there. Like, Imean, I I don't really use I use
the library for music andSpotify. I don't listen to
podcasts and Spotify. I use mylibrary all day every day to
find albums and artists and, youknow, anything else. And so it
could be that people treat itthat way for podcasting as well,
but their whole at least ondesktop, their whole UI is very
geared towards that home tab.
(30:11):
Like, the the sidebar is kind ofhard to navigate. And so it it
wouldn't surprise me if, youknow, partly home is a little
bit like the traditional feedjust with more recommendations
built in.
Justin (30:22):
Yeah. I I mean, this is what we're seeing on YouTube as
well. It used to be that youwould go to your subscribed tab
first and then just see, oh,what new videos are there from
the people I subscribe to. Onething I will note is that the
long tail so if an episode hasbeen out for a while or a show,
(30:42):
hasn't published an episode in awhile, search is a much higher
percentage of discovery. I'veseen up to almost 50% can be,
search based.
So people are looking for pastguests. People are looking for a
topic. You're gonna see in thelong tail way more search, but
home is still king by far.
Jeremy (31:03):
Yeah. And the one other thing that I wanna mention on
that note regarding episodes isthat the overall analytics that
show up in your main Spotifyanalytics dashboard, you go over
to the discovery tab, that isfor your show as a whole and all
the episodes within it. And soit could be an impression of the
show being recommended or anindividual episode, but you can
also look at the exact samedashboard within any individual
(31:26):
episode. And so you could lookat those and they'll show the
same funnel and you'll be ableto see, hey. Some episodes have
a much higher conversion ratefrom awareness to interest or
interest to stream, and youmight be able to pull some
additional insights out of thatof which episodes are maybe
outperforming others.
And maybe that gives you someinsight into, oh, maybe I
should, you know, do moreepisodes on this topic or maybe
my title was really good thereand maybe I've stumbled onto
(31:48):
something that I wannareplicate. And so there are two
ways to kind of use this data oror view it. Yep. So let's maybe
close this out with some bestpractices when it comes to
experimentation because I thinkit's one thing to say, okay.
Like, we know what some of thevariables are and now let's,
like, make a bunch of changesand see what happens.
What would your approach be toactually running a more
controlled experiment thatactually gives you some useful
(32:10):
data?
Justin (32:11):
Yeah. You only wanna change one variable at a time.
If you change too many things,you don't know what change
actually created a meaningfuleffect. So what we're trying to
minimize is the number ofvariables at play. There's
always gonna be other variableslike, you know, you might have a
big guest one week.
(32:31):
You can control that, but here'sthe variables you can change.
So, you know, if you're gonnaadd or change a title tagline,
that would be one thing. Doesthat help? Mhmm. Does that not
help?
I think you had a case studywhere someone went from, like,
mindfulness podcast tomindfulness psychology, and
(32:53):
there was a big lift orsomething like that?
Jeremy (32:55):
Yeah. So this was one of my clients, Sam Webster Harris.
He actually has the name SamHarris is his first and last
name, but he's not that SamHarris, which we have a
discussion on this that I'mgonna share one day. But he
actually thinks that hurt hisconversion rate because people
clicked in thinking this was theother Sam Harris and then were
disappointed. Oh, this isn'tthat Sam Harris, so they left.
And so he thinks that that wasactually a bit of a a headwind
that he's had to work against.But he yeah. His old show, was
(33:18):
called the Growth MindsetPodcast, and he changed it to
Growth Mindset Psychology. Andhe also so he did a he committed
a couple of, flaws with hisexperiment here, which he
acknowledges that he wasactually working on a bunch of
stuff. And so he had beentightening up his episode titles
for one already, but then hechanged the name of the show to
growth mindset psychology.
(33:39):
Andy changed the cover art,which was a subtle modernization
of the cover art. Like, itwasn't a complete overall. He
actually did a big overall firstand it tanked his his numbers.
And then he Interesting. Coursecorrected and was said, let's
actually just make the existingcover art a little bit cleaner
and more modern and less amateurlooking.
And he ended up adding over2,000 downloads an episode,
within about a month, and thenit kept growing from there. And
(34:02):
so, like, that is, you know, acouple small changes to that
packaging that had a huge impacton his show. And so, you know,
that's not gonna say that that'sgonna every show is gonna see
those results. He was alreadygetting some traffic and some
impressions because he has along running show that is was
already doing pretty well. Butit is interesting to see how the
cover art can both hurt or,drastically increase the
(34:22):
downloads.
And so that's this kind ofprinciple in action here of of
doing experiments and seeing,you know, what what works and
what doesn't.
Justin (34:28):
Mhmm. So let's say people wanna set this up. My
guess is the first thing they'lldo is take a a literal
screenshot of their existingstats. Because there's no way
of, like, bookmarking yourexisting stats or running an AB
test inside of the Spotifydashboard. So you're gonna have
to manually screenshot yourexisting stats.
(34:48):
Anything else in terms of thesetup that people should know?
Jeremy (34:51):
Yeah. I would screenshot that or put it in spreadsheet,
and I would just write yourselfa hypothesis on, like, what is
the variable I'm testing, whatdo I think is going to happen,
and then check back in thirtydays. And so I would probably
run this on a thirty day windowbecause that's what Spotify
gives us. And so we say from,you know, whatever, January 1 to
February 1, I got, this manypeople were showing my show.
This was the conversion rate, ateach of the conversion rates,
(35:14):
and maybe I look at impressionsas well.
And then I say, okay. From I'mgonna make the change at
February 1, and then I measurefrom February 1 to March 1. And
I see, okay. Was that anydifferent from January? And the
other thing to keep in mind iswhen we're talking about
limiting variables, it's saying,like, okay.
Let's change the the title orthe tagline of the show, but
then also let's keep everythingelse the same. And so I'm gonna
publish the same amount ofepisodes. I wanna try and
(35:36):
control that to make sure that,you know, I'm actually comparing
apples to apples.
Justin (35:40):
Yeah. You're right. Like, this is the time to just
keep consistently doing what youwere doing before. So if you're
publishing every week, just keeppublishing every week. Don't
drop a, like, a bonus episode inthere because that'll that could
throw things off.
Just, like, if you're once aweek, just keep publishing once
a week and then try to comparethat over a a thirty day window.
Jeremy (36:01):
Yeah. I think the last thing that I would just add on
the experimentation front isassuming that you don't actually
know anything. And I think thatthis is a mindset that I never
really understood. I I thought,like, Facebook ads experts were
just wizards that they just knewhow to write the copy and do all
the targeting and set up allthis technical stuff on the back
end to be able to just make adsrun profitably or get people to
(36:23):
your podcast or email list orwhatever. And what I know now
after having been in marketing alot more is that basically every
Facebook ads expert goes in withsome assumptions that they are
almost certain are gonna beproven wrong.
And so you'll often hear peoplein this world talk about, you
know, this kind of two to threemonth window of optimizing the
ads. And, again, this issomething that I thought was
about training the algorithm,but what I now know is that it's
(36:44):
about you as the person runningthe ads, testing different
things and just slowly pruninguntil you get to, oh, this is
the thing that works best. Andso you run, you know, six
different headlines and you havesix different graphics and you
slowly whittle it away untilyou're like, okay. Out of all
the permutations that arepossible, this is the thing that
is performing best, but I'mgonna keep testing that as well.
(37:04):
And so you just see this sooften if you do AB testing with
headlines or you come up witheverybody's, I think, had that
episode that for some reason,that one got a bunch of
downloads.
So you got all this feedback onit and you're like, why that
episode? I don't I just don'tunderstand that. And so this is
true for all of these kindadifferent variables that you're
playing with. And so going inand just thinking, like, the way
that I'm going to get better andimprove my conversion rates is
(37:26):
just testing things constantly.Small tweaks here and there.
And over the years, the monthsand the years, it's actually
going to improve slowly butsurely to the point where now
you're hopefully outranking allthese benchmarks by quite a bit.
Mhmm.
Justin (37:38):
I would also recommend that people add other
qualitative sources of data tothis. So one thing I've just
started doing, I I just starteda new podcast on the trailer
episode and the bonus episodethat we've put out. I encourage
people to go sign up for theemail list, and I say, I'm gonna
send you an email right awaywith some questions. And
(38:01):
eventually, I'm going to usethat automatic email to ask
people, hey, how did you findthe show? What drew you to the
show?
What brought you here? And Ithink adding in those
qualitative answers to whatyou're seeing on the
quantitative side here will behelpful. It's gonna help you
check your assumptions. Maybeyou're used to just getting
answers like, oh, I had a friendtell me about it and I went and
(38:23):
checked it out. Maybe you startgetting answers like, oh, like I
saw it on the Spotify homepageand the cover art looked
interesting.
Well, if you've recently updatedyour cover art and then that
qualitative response just startsshowing up more and more often,
I think you can start to say,okay. These activities are
really correlated. Like, Ichanged the cover art and we saw
(38:44):
Lyft, but now, anecdotally,people are actually telling me
that that's what made adifference.
Jeremy (38:50):
So for anybody listening, if you do start
experimenting with any of thesevariables, looking at things
like the title, the cover art,your show description, episode
titles, teaser episodes, all ofthese kind of external and
internal packaging elements thatwill influence, these conversion
rates, both the awareness tointerest and the interest to
stream, we would love to hearabout it. So send us an email.
You can find the email in theshow notes for this episode And,
(39:13):
personally, I would befascinated to see what people
are playing with and how that isimpacting your discoverability
in Spotify.
Justin (39:20):
And there's a whole other part of this discovery
screen that we didn't talkabout, which was sharing links
where you can actually create alink to an episode or a show and
then track how many people visitthat link. So if you're
promoting it on social media,we'll talk about that in the
future. But just so you know,there is this other link
tracking piece of this that'salso interesting.
Jeremy (39:40):
So there is a lot more information on this Spotify
discovery dashboard if you wantto look at even more benchmarks
and data. Again, you can findthat at
And there's also a bunch ofrecommendations on how to
diagnose, you know, thedifferent kind of conversion
rates or combinations of, youknow, a high awareness to
interest come combined with alow interest to stream or any of
(40:03):
the other variables and how tothink about, like, what might be
the problem and what do you doabout it. So again,
dash benchmarks. And other thanthat, I think it's time to crank
up the, tangent extender andmaybe the, the auto tune and
play ourselves out.
Spotify wants people to engage with content, and so they
are trying to match users withpodcasts. If they show it to a
thousand people and none of themclick into that, that's now a
signal to Spotify saying eitherwe're showing this to the wrong
people or something about theshow is not worth promoting.
Justin (00:15):
This is such a paradigm shift from the way episode
consumption used to work. It'sgonna be way more like YouTube.
How do we grab people on thehomepage versus how do we bring
back our regular listeners?
Jeremy (00:28):
You can optimize all the internal things. You can create
an incredible show. But ifyou're not getting people to
click play into the show in thefirst place, all of that work
doesn't really matter that much.So, Justin, in the back half of
2024, Spotify rolled out thisnew feature in the back end of
the Spotify for Podcasters kindadashboard. And most people
(00:50):
didn't really pay much attentionto it.
They kinda missed it, but it wassomething that actually got me
super excited, and it'ssomething that I've been
literally waiting years for.It's been on my podcast wish
list for as long as I've been inpodcasting. And I'm wondering if
you have any guesses what thatnew feature might be.
Justin (01:05):
A vocal fry intensifier.
Jeremy (01:10):
It's not that. Any other, thoughts?
Justin (01:13):
Podcast auto tune. If you wanna hear all your podcasts
sung to you.
Jeremy (01:17):
Definitely on my wish list, but that was not the
feature that was released. I'llgive you one more chance. You
got, one more guess at what thisfeature was.
Justin (01:24):
Okay. If you really love podcast tangents, there's, like,
an AI podcast tangent, expanderso you can get more tangent. You
could have a whole episodethat's just one big tangent.
Jeremy (01:37):
% tangent. No no content whatsoever? Okay. That would
that was also not the feature.I'm gonna save you the the pain
of any more guesses.
It was their new discoverydashboard that they rolled out.
So pretty boring compared to allthe feature recommendations that
you made. Spotify, if you'relistening, take note of these.
Roll these out, please.
Justin (01:55):
That's right.
Jeremy (01:56):
And so, essentially, the discovery dashboard is a tab
that's nested within theiroverall analytics platform. And
what it does is it allows us,for the first time, to actually
see conversion rates of peoplewho saw the podcast to people
who showed interest in thepodcast to people who ultimately
went on to listen to thepodcast. And for me, this is
just a huge game changer for howpodcasters are able to approach
(02:19):
the craft of packaging theirshows and creating their shows.
And so this is something that, Iwould love to spend some time
digging into and tell people alittle bit about why this is
such a big deal.
Justin (02:29):
Yeah. I think it's worth looking at for sure. When I
first saw it, especially if youhave a marketing background, all
we think about is funnels. Andso seeing this actual funnel
view and going, oh, wow. Like,now we can actually visualize
how many people saw the showsomewhere and then how many
showed interest and then howmany actually listened.
(02:51):
Whereas before, maybe you couldget this, like, you know,
sometimes people would runovercast ads and they'd get some
of this information, but this isvery interesting.
Jeremy (03:00):
So, obviously, you and I are marketing nerds. But for
the, you know, 99.9 of peoplewho are not, we should probably
start with just talking through,like, when we're talking about
conversion rates, what are weactually talking about? Like,
how would you define aconversion rate?
Justin (03:13):
Conversion rate is, you know, how many people saw the ad
and then how many peopleactually clicked the ad. So in
that case, the conversion ratewould be you know, you've got a
hundred people who saw the ad,10 people clicked. That's a 10%
conversion rate from saw the adto actually clicked. And then we
would even go one step furtherand say, okay. Of the 10 people
(03:34):
who clicked, maybe one personbought.
Well, that's again another 10%conversion rate on that side. So
you're just calculating how manypeople actually took action
based on the bigger group.
Jeremy (03:48):
And so you kind of, with your language there, you
mentioned, you know, how manypeople saw the ad. And so,
really, conversion rates are theone of the ultimate metrics in
advertising where especially ifyou're doing any kind of digital
advertising where you're puttingan ad, let's say, on Facebook or
Meta or Google or YouTube orwherever else, and you see,
okay. This ad got a hundredthousand impressions. This many
people, you know, watched it. OnYouTube, you can see how many
people watch this percentage ofthe video or click the link and
(04:10):
then went on to buy.
And so that's a world where, youknow, conversion rates are the
bread and butter of any kind ofadvertisers, analytics. But they
also show up a bunch of otherplaces. You can go if you use
Google Analytics or Fathom orany of these other analytics
platforms. You can see how manypeople went to your home page,
and then you can kind of eventrace them around how they went
throughout the other pages ofyour website. And so depending
(04:32):
on what your goal is withpeople, you can kind of, use
conversion rates to track theeffectiveness of your overall
system that you've created.
So you can get hyper hyper nerdyin in the marketing, analytics
world here. And, basically, ifthere is anything that you can
measure, on route to gettingpeople to take results, there is
somebody tracking the conversionrate of how people are are
(04:52):
moving through that system. Andso you can kind of think of it
as this funnel where peoplestart a large number of people
start at the top and a smallernumber of people trickle down to
the bottom, which is essentiallywhat Spotify has now given us.
Justin (05:02):
Yeah. And I would say that these conversion rates on
their own are generally not thathelpful. I think the big gift
you've given the podcastcommunity is that you've
collected all this informationand allowed people to compare
their rates to the rates they'reseeing in their Spotify
dashboard. So if you just go inand look and go, okay. Well,
(05:24):
that's my conversion rate.
Is that good? Is gonna be yournext question. You've basically
answered that for them in thisreport that you put out.
Jeremy (05:33):
Yeah. And, we're gonna mention this page that I put
together, which you can find atpodcastmarketingacademy.com/spotify-benchmarks.
And so, you can find that there.That'll be linked in the show
notes as well. We're not gonnago into all the data because
there's actually quite a bit onthat page and there's a bunch of
helpful resources on how todiagnose what your conversion
rates are and what to do basedon on how you're kind of scoring
there.
(05:53):
But, yeah, with most conversionrates, it's, you know, you look
at it and you're like, is thisgood? I don't know. You can
always measure against yourself.And so I think that's a good
starting point to say, okay, myconversion rate right now is
10%. Let's get that to 15%.
And and how do I do that? Andwe'll we'll look at some of the
ways that you can in thisepisode. But I think to start
out, it's probably helpful justto talk through a little bit of
the data that Spotify actuallygives us. And so there's really
(06:17):
kind of a handful of corecategories or stats that they
give us, and then we can kind ofinfer or do some calculations to
get, a little bit more beyondthat. So what are some of those
those analytics that Spotify isgiving us now in the discovery
dashboard?
Justin (06:31):
Alright. So at the top of the funnel is people you
reached, and this is the numberof distinct people who have seen
this episode on Spotify. So I'mguessing this includes, you
know, you land on the home pageand there's a bunch of cover art
there. So that would get countedas people you reached. You're
scrolling through searchresults.
(06:52):
If you zip by a, you know, somecover art there, that gets
included. So this is a fairlybig bucket. This is actually
kind of funny. Can I if I just atangent here is people you
reached is a little it's morelike your cover art appeared on
a page that people were lookingat? It doesn't mean that you
reached them.
Doesn't mean they saw it.Doesn't mean they paid attention
(07:14):
to it. It just means you werethere for some period of time.
And this is actually the onemetric where they don't give us
any sort of rubric for whatdistinguishes a reach. Like, do
they have to have pause on thescreen for five seconds or
twenty seconds?
So this is the big bucket at thetop. It's important to know.
(07:36):
It's gonna be helpful once weknow the conversion rate. But
Jeremy (07:38):
My assumption is that this is the unique number of
users Spotify delivered yourshow to on their screen. Who
knows if they if they didn'tscroll down to see it, but it
was on their home screen. Ifthey had scrolled, does that
count? I I think the otherinteresting thing here is a lot
of times when we're talkingabout conversion rates, we're
talking about impressions, andSpotify does give us that stat,
but this is actually uniqueindividuals. And so the calculus
(08:02):
maybe changes a little bit ifyou are used to thinking of
conversion rates in terms ofabsolute impressions.
This is a little bit different.This is unique individuals who
were shown your podcast who thenwent on to take some further
action.
Justin (08:11):
So That's right.
Jeremy (08:13):
People you reached with number one. The second one that
we've got here is people whoshowed interest. So what does
Spotify give us a definition forinterest here?
Justin (08:23):
Now this is where it gets kinda saucy because this is
the number of distinct peoplewho have gone to the episode
page, added this episode to aplaylist, or played this episode
on Spotify for zero seconds ormore. So in terms of, like, the
value chain here, we've gonedramatically from people who may
(08:43):
have seen your cover art tothey've actually clicked through
and said, oh, that episode looksinteresting or, oh, I should add
that to a playlist or, oh, Ishould actually play this
episode. So going from peopleyou reached to people who showed
interest, that is a veryinteresting number.
Jeremy (09:03):
Yeah. And then the, final step in the chain here is
people who streamed. And what isthis is, you know, one of the
maddening things. Apple's gottheir definition of what counts
as a a streamer play. Spotifyhas their own.
What is Spotify's definitionthat they give us here for a
stream?
Justin (09:17):
So that's anyone who played the episode on Spotify
for at least sixty seconds. Youknow, I I used to have a podcast
called six seconds where everyepisode was only six seconds. So
I wonder if I would not get minecounted here. It's like sorry.
My stats would be nothing,because it
Jeremy (09:38):
You need that, that tangent expander tool. When
Spotify gets that in, then herewe go. We're cooking.
Justin (09:44):
So, yeah, to get anybody to listen for any period of time
is a meaningful so this is stillvery top of funnel, I would say,
though. They could add anothersection here that is, like, how
many people made it at least 70%of the way through the episode.
Jeremy (10:01):
Yeah.
Justin (10:01):
But it's still interesting to see the
conversion rates along each stephere.
Jeremy (10:07):
Yeah. And they do so Spotify does give us a chart
that charts your follower count.And so you could potentially try
and correlate some of this alittle bit, but they don't
attach it to this actual funnelhere. Probably because a lot of
people listen to a show and thenthey end up following it some
amount of time later in thefuture. And so it becomes a bit
murky onto, you know, the chainof events here.
(10:28):
Mhmm. But that is, you know,something that you can look
into, in your own Spotifydashboard as well. So those are
the, essentially, the threeanalytics, the data points that
are included in the funnel. Sopeople you reached, people who
showed interest, and then peoplewho streamed. And so those give
us these kind of two differentconversion rates, which is the
what I'm calling the awarenessto interest conversion rate.
(10:49):
So people who were aware of theshow or at least it was
delivered to them who showedsome kind of interest in it and
then the interest to streamconversion rate. And so people
who took some initial click intothe show and then listened, for
sixty seconds. But they alsogive us, a couple other other
analytics here, which aredisconnected from the funnel,
but also play an interestingkind of role in our
understanding of how our show isdoing. And the main one here is
(11:11):
the impressions. And so wementioned before that in the
funnel, we're talking aboutindividual users.
Here, when they're talking aboutimpressions, they're saying,
okay. How many times was eitherthe show or any one of your
episodes displayed to somebody?And so what we can calculate
with that then is we canactually see how many times on
average on a per user basisSpotify showed your show to
(11:32):
them. And so you you can see insome of the shows that I have
the data for, some shows wereshown something like 15 times
per person on average, whereasother shows were only two times
per person. And so we don'tnecessarily know why that
happens, but you can do some ofthese calculations and and see
how Spotify is promoting yourshow to people.
And so that's the the one thingwe can kinda calculate and infer
from the data. The other thingis we can calculate the overall
(11:55):
conversion rate. So people tostreams, and so they give us
these two conversion rates, theawareness to interest and the
interest to stream. And then wecan take the overall of those,
how many people who, were shownmy podcast ultimately went on to
stream sixty seconds. And sothat's the the data they give us
here.
And the the one other thing,that we didn't mention is under
the impressions, they actuallybreak that out into four
(12:15):
different, locations within theapp. And so the first is Spotify
home. So that's in the homescreen. If you click over to the
podcast tab, then there is thesearch. And so, obviously, if
you're searching for a phraseand shows get recommended and
come up, that's gonna be there.
Then you have the library, whichis if you use Spotify, it's in
your left hand sidebar. That'syour library there. And then the
final one is other. I assumethat that might be some episodes
(12:39):
had a recommended episodes atthe bottom of them. And so I'm
guessing that is probably inthat category, and maybe there
are other things where Spotifyshows, podcast to people, that
are not in any of those priorthree categories.
So
Justin (12:52):
Yeah. I'm wondering if some of that is clips and other
of their beta features as well.
Jeremy (12:57):
Right. So you mentioned before that, you know, this data
isn't really all that useful ifyou don't have any benchmarks.
But if you do, I'm curious,like, what does knowing your
conversion rates in relation toa established benchmark allow
you to do as a creator?
Justin (13:15):
Well, I mean, having the average the way that you have it
in that report, I think, isactually really helpful. Because
if your conversion rates arejust way lower than the average,
then something is wrong. Thatcould even be the show itself.
Like, you've got a fundamentalproblem. So I like these things
because sometimes people areputting a lot of effort into a
(13:39):
show, and there might be afundamental problem they're not
seeing.
So I think if you could compareit to, you know, an average
benchmark that's alreadyinteresting, and then for
yourself, it gives you somethingto improve on. So now let's try
some experiments and see if wecan increase the number of
(14:00):
impressions and then increasethe impression to interest
conversion rate. And over time,you can try to get lift. And it
gives us a little bit of insightalso into the black box that can
be these recommendation enginesand, you know, how are they
promoting my podcast or notpromoting my podcast? It gives
you something to look at.
(14:22):
All of these things that wesometimes say, you know, like,
people will say, well, adding avideo to Spotify will give you
lift. Well, you can test thatout. Like, add video and see if
video episodes get promoted morethan audio only episodes. So you
can have a hypothesis and thentest it out.
Jeremy (14:42):
I think for me, the thing that I was most excited
about was the diagnosis that itallows us to do. Mhmm. And so I
think in podcasting, there'sthis kind of, like, plausible
deniability that you could kindaput your head in the sand and
say, no. No. No.
My cover art's fine. My showtitle's fine. My episode titles
are fine. I don't need to worryabout those. And, like, that's
not the reason I'm not gettingmore clicks into it.
(15:02):
And you could do some reallymanual testing with this. And
so, you know, one of your tricksthat you've mentioned a lot of
times is going to a conferenceand just, like, pulling up, you
know, your cover up, maybe alist of competitors or doing a
search and asking people, youknow, which would you click on
and and seeing, you know, whichone they pick. Mhmm. You can do
that in a bunch of differentways with a bunch of different
variables, but that's not reallythat easy to do all the time for
all of us. And the thing about adashboard like this is that it
(15:24):
is unbiased by how we ask thequestion or things like that.
And so it's like people thatSpotify thinks should be
interested in our show. I thinkthat's the important thing to
note here too is that Spotifywants people to engage with
content, and so they are tryingto match users with podcasts.
And so if they're showing ourshow to someone, based on the
data they have, they suspectthis person might be interested
(15:44):
in a show like ours. And so ifthey show it to a thousand
people and none of them clickinto that, that's now a signal
to Spotify saying, like, eitherwe're showing this to the wrong
people or something about theshow is not interesting and not
worth promoting. And so I thinkthat this analytics dashboard
now allows us to actuallypinpoint some of the potential
issues.
And we're gonna talk a littlebit more about how to diagnose
(16:06):
specifically, based on, youknow, high and low conversion
rates on either the, awarenessto interest or the interest to
stream. But maybe to start off,let's dig into some of the the
benchmarks here. And so I gotabout a hundred people to submit
their Spotify dashboard data.This was a range of podcasters.
Some have millions ofimpressions in Spotify in a
thirty day period.
Some have basically, like, ahundred or 200 very low numbers.
(16:28):
So we've got the whole range ofvastly different types of
podcasts, different purposes. Soit really covers the spectrum
here. And, what are those kindof benchmarks that, this group
kind of surfaced here?
Justin (16:39):
So in terms of awareness to interest, 8.6% was the
average conversion rate. So Isaw something and then I clicked
on the episode to check it out.And then from interest to
stream, 63% was the averageconversion rate. So I saw it. I
(17:00):
went to the page, and then Iactually clicked play and
listened for sixty seconds.
Jeremy (17:05):
Any initial reactions to those numbers?
Justin (17:08):
I actually have a few other questions. My my guess is
that folks with less trafficoverall and just, like, less
streams overall, my guess isthat their data will be
different and could and couldactually be wildly skewed in
either direction. So if you havea hundred fans that listen to
(17:30):
your show and they're verycommitted, you might get, oh, I
I had a 20 people have awarenessabout an episode, and then 95%
of them click through on theepisode, and then another 90%,
you know, actually listened. Itit it's gonna really depend on
how big your audience isalready. As I looked at some of
(17:53):
the bigger shows, I definitelysaw higher awareness to interest
conversion rates up into the 30%range.
So that's interesting too. So Ithink it's a nice place to
start. Like, if you're gettingway below 8.6% for awareness to
interest, Something might bewrong. And I think if you're
(18:16):
getting way less interest tostream, the interest to stream
conversion rate actually seemsquite high to me. Meaning Mhmm.
Spotify has probably tuned thisfairly well that if you actually
show interest, you're highlylikely to click play. That was a
lot higher than I thought itwould be.
Jeremy (18:34):
What stands out to me is how much of the battle for
winning over a new listener iskinda won and lost at that
awareness to interest stage. Andso you can kind of optimize all
the internal things. You cancreate an incredible show. You
can really hone your showdescription that's in your
listing and your episode titles.But if you're not getting people
(18:55):
to click play into the show inthe first place, all of that
work kind of doesn't reallymatter that much.
And it's not to say it doesn'tmatter to create a great show.
People can still talk about itword-of-mouth and there's all
these other things. But from adiscoverability standpoint of
getting people to see the showto go to click play, I think
that this really puts theemphasis on the show title and
the, cover art of the show. Andso that's this this number that
(19:16):
is way lower. And it's like,okay.
If you can get somebody to clickin, here, the average 63% of
people, you know, the odds arethat they're going to click play
and stream an episode for sixtyseconds, but less than one in 10
people are actually clickinginto a show when they see it.
And the other thing, again, thisis people. And so when we look
at some of the shows, individualpeople are shown a podcast on
(19:38):
average multiple times. And soif people are seeing that show
multiple times and not clickingplay, you know, that's something
to to take into account. There'sa whole bunch of stuff that we
Amit Kapoor (19:47):
just don't know about how this is calculated on
Spotify's back end. And so itcould
Jeremy (19:47):
be that that impressions per back end. And so it could be
that that impressions perSpotify user are inflated by
people who subscribe to the showor follow the show, and then
they see, you know, dozens ofepisodes. And Spotify only shows
the show one time to people whoit thinks it might be
interested, and they don't clickplay. So we don't really have a
window into that. But I think tome, that's the really the big
(20:09):
thing of, like, the thing tofocus on, the highest leverage,
that you have here in thissystem is focusing on title and
cover art.
Because if you can get thatnumber up, then you're kind of
rolling downhill at that point.
Justin (20:22):
I do think there's still a there's a fundamental like, I
always say that product ismarketing, and I can see a
certain type of podcast doingbetter in these scenarios than
others. And so I for a certaintype of show, these stats will
be very interesting and helpful.And I can also see a certain
(20:44):
type of show where this justdoesn't really apply as much.
And you could try, you know,tweaking all these things, which
I think are important to coverart, titles, descriptions. But
there's a fundamental kind ofmismatch between this kind of
discoverability and the way yourshow is structured or the topic
or whatever.
(21:05):
For example, like, a aninterview show that is highly
dependent on the guests and the,name recognition of a guest. You
can imagine that if you'rereally into, Billy Idol and a
Billy Idol podcast episode showsup on your home page, you're
gonna be much more likely toclick through and listen to
(21:25):
that. So, yeah, there's someother factors there that I think
are worth keeping in mind as wemove forward.
Jeremy (21:31):
The other thing that's kind of interesting is, you
know, I looked at of the highimpression shows. And so there
was about 10 or 12, somethinglike that, that that were
getting over a hundred thousand,impressions in the previous
thirty days. And so Spotify intheir dashboard, they only
measure the kind of from todayback thirty days. You can't
filter by past dates or anythinglike that, which is a little bit
frustrating. But, we'll takewhat they've given us for now.
(21:52):
And so these shows, a hundredthousand impressions over the
past thirty days, there weresome interesting data here. And,
again, this is not to say thatevery show should be comparing
themselves to this because notevery show has the potential.
Like, our show is never gonnaget a hundred thousand
impressions in Spotify becauseit's so incredibly niche. Like,
there is not the audience outthere to be delivered those
impressions. And so one of thethings that
Justin (22:13):
put, interview with Billy Idol in one of our episode
titles.
Jeremy (22:16):
Maybe. And we had that, tangent expander too and the
auto too. We just crankeverything up, the vocal fry,
all the effects up to 11. Therewe go.
Justin (22:24):
Vocal fry.
Jeremy (22:25):
So, like, one of the things with high impression
shows was that, 70% of the showswere kind of broad audience,
which actually makes sense.Like, if you have a mass appeal
show, there's a larger pool ofpeople to tap into. And so this
is not to say that every showshould try to appeal to the
biggest audience possible. Like,that's actually the hardest show
to market. But if you are asolid show or a great show in
(22:47):
that category, your ceiling isway higher than a show like ours
or any other huge show.
The other thing that was alittle bit interesting here was
the show purpose. Now I definethese based on what I could see
from the descriptions and thetitles and things like that, the
episode titles. And so Icategorized 60% of the high,
impression shows as pleasuregivers. And so there are things
(23:09):
that they're, like, not usefulin a way of, like, a practical
problem solver type approach.And so that is up almost 30% or
27% higher than other shows inthe the kind of main category.
And so, 60% of shows wereactually just things that people
are listening to to unplug orunwind their entertainment.
They're just like, you wannajust, you know, immerse yourself
(23:30):
in something, which I think is,again, more indicative of mass
appeal shows. And so, the thesecond most popular was problem
solving type shows at 35%, but,really, 60%. They were more
entertainment kind of pleasuregiver type shows. And then the
last thing that was interestingabout the high impression shows,
was that 45% of them were soloshows, which I thought was quite
(23:52):
interesting.
And this was, 14% higher, degreeof solo shows than the general
audience or the pool of all theshows here. And so interview was
second with 30%, and then therest were split between, 10% co
hosted, ten % narrative, andthere was one daily news show
that was a a tiny percentage aswell. So some some interesting
(24:12):
trends there on the highimpression shows, but I actually
don't think that's what's worthpaying attention to. I think
it's much more the conversionrates, which are where you can
kind of improve you know, giventhe potential audience that you
have, you can kind of attractmore of them back to your show.
Justin (24:26):
I would also say, like, I'm looking at my stats for an
inactive show or a show where wedon't publish very often. And
the these stats are definitelymore interesting to people who
are publishing regularly. If youhave, like, a a serial show
that's been out for three years,maybe it'll be interesting. I I
(24:48):
you know, you should still lookat it, but it it seems like a
lot of this even in thedecisions they've made to kinda
highlight impressions in thelast thirty days, this is very
focused on people who areactively publishing Yes. Within
a thirty day window.
Jeremy (25:04):
So we've kind of talked a little bit around you know, we
have these benchmarks, and wecan kind of diagnose some of the
issues. And then the next stepis to actually run some
experiments. And so I know we'vetalked about this in season one
of the show, some potentialexperiments that you could run.
But how would you kind ofapproach, using these conversion
rates to test differenthypotheses and and kind of pull
(25:25):
different levers that, you'reable to with your show?
Justin (25:28):
Yeah. I mean, when we do podcast marketing tear downs, we
almost always focus on podcasttitle, podcast cover art, and
then description, episodetitles, whether or not there's a
teaser episode. So my guess isthose are really the levers you
can pull outside of what topicyou've chosen, outside of, you
(25:52):
know, some other things. But Ithink if you're just looking for
a nice benchmark, like, oh,well, we we've had this cover
art for three years. Whathappens when we change the cover
art?
Does anything happen? And thisis a nice way to test against
that. You already have a,benchmark to test against
because most of us haven'tchanged our cover art in a long
(26:14):
time. Most of us have notchanged our title in a long
time, description, etcetera. Sothose are some of the levers I
think you can pull here and seeif you can get some lift.
Jeremy (26:23):
Yeah. And I would just like if you are thinking about
doing an experiment, I wouldjust put yourself in your
listener shoes and open upSpotify and just go to the home
tab and think about, okay, hereare the shows that show up here.
Amit Kapoor (26:34):
Mhmm. What
Jeremy (26:35):
is there that is present to me that is influencing my
decision? Anything that you cansee on that screen, if you can
change that, that is somethingthat is going to influence
listener behavior or potentiallistener behavior. And then you
click into a show and you lookat the screen, you say, okay,
what are all the differentelements on the screen that I
have control over? And you say,okay, well, once they click in,
then I can edit my description,I can change my episode titles
(26:57):
and I can, you know, upload ateaser episode and, you know,
the teaser episode probably,like, I think if there is a
lower barrier to entry togetting somebody to listen to
anything on your page, and so ateaser episode that's two
minutes is a much lower barrierto entry than a sixty minute
episode. Or if all the episodesare sixty minutes, that's
probably also going to get morepeople to stream an episode,
(27:19):
probably that episode.
And so opening up Spotify andjust seeing what you have there
and then going into the back endand playing with those things,
is is basically kind of the theapproach here.
Justin (27:29):
I wanna note that this session is interesting paradigm
shift from the way episodeconsumption used to work, which
is you used to just get achronological feed basically of
what's new. And my guess is alot of people were scrolling
through episode titles bait on,you know, the what they were
already subscribed to, and thenthey're like, oh, what seems
(27:49):
interesting? Or and thisresulted in, you know, a lot of
kind of esoteric, cute titlesthat were kind of, like Yeah.
Inside jokes and things likethat. And I can see your
approach in an algorithmicallydriven world.
And I I don't know if we'vementioned this, but, like, the
Spotify homepage is by far thebiggest driver of impressions.
(28:14):
And so if that's true, way morethan search or library, if
that's true, then, whereas theold world was the library first.
Right? It's like, what's in yourlibrary? You're scrolling your
library.
Now it's what am I being shownon my home page, and what is
kind of, like, grabbing me inthat moment. So it's it's gonna
(28:34):
be way more, like, YouTube stylethinking here. How do we grab
people on the home page versus,you know, how do we bring back
our regular listeners or thosekind of
Jeremy (28:46):
questions. It is, worth noting here that some of the
numbers, in regards to where theimpressions happen. And so, what
we see based on the, data thatpeople submitted to me, and I
compiled this, the Spotifyhomepage accounts for basically
65% of all impressions. And sowell over half of all the
impressions that people have ofa podcast in Spotify come on the
(29:07):
homepage. And then the fairlydistant second is search at 26%,
which I thought this would justbe way higher.
I thought the majority of itwould be through search, but
actually, this is Spotify. Onthe homepage, it shows both
shows that you have alreadysubscribed to or engaged with in
the past, but also recommendsnew shows. And so some of those
are going to be from yourlibrary even though it's not in
(29:27):
what Spotify designates as thelibrary, the sidebar. Mhmm. And
some of them are recommended.
And then, the library is at, 9%essentially of impressions
happen in that library tab. Andso I I'm curious. Again, we just
don't know what user behavior ison Spotify. Like, it could be
that many people treat theirhome tab as their library.
Amit Kapoor (29:49):
Mhmm.
Jeremy (29:49):
And so they go there and they just click on their
episodes from there. Like, Imean, I I don't really use I use
the library for music andSpotify. I don't listen to
podcasts and Spotify. I use mylibrary all day every day to
find albums and artists and, youknow, anything else. And so it
could be that people treat itthat way for podcasting as well,
but their whole at least ondesktop, their whole UI is very
geared towards that home tab.
(30:11):
Like, the the sidebar is kind ofhard to navigate. And so it it
wouldn't surprise me if, youknow, partly home is a little
bit like the traditional feedjust with more recommendations
built in.
Justin (30:22):
Yeah. I I mean, this is what we're seeing on YouTube as
well. It used to be that youwould go to your subscribed tab
first and then just see, oh,what new videos are there from
the people I subscribe to. Onething I will note is that the
long tail so if an episode hasbeen out for a while or a show,
(30:42):
hasn't published an episode in awhile, search is a much higher
percentage of discovery. I'veseen up to almost 50% can be,
search based.
So people are looking for pastguests. People are looking for a
topic. You're gonna see in thelong tail way more search, but
home is still king by far.
Jeremy (31:03):
Yeah. And the one other thing that I wanna mention on
that note regarding episodes isthat the overall analytics that
show up in your main Spotifyanalytics dashboard, you go over
to the discovery tab, that isfor your show as a whole and all
the episodes within it. And soit could be an impression of the
show being recommended or anindividual episode, but you can
also look at the exact samedashboard within any individual
(31:26):
episode. And so you could lookat those and they'll show the
same funnel and you'll be ableto see, hey. Some episodes have
a much higher conversion ratefrom awareness to interest or
interest to stream, and youmight be able to pull some
additional insights out of thatof which episodes are maybe
outperforming others.
And maybe that gives you someinsight into, oh, maybe I
should, you know, do moreepisodes on this topic or maybe
my title was really good thereand maybe I've stumbled onto
(31:48):
something that I wannareplicate. And so there are two
ways to kind of use this data oror view it. Yep. So let's maybe
close this out with some bestpractices when it comes to
experimentation because I thinkit's one thing to say, okay.
Like, we know what some of thevariables are and now let's,
like, make a bunch of changesand see what happens.
What would your approach be toactually running a more
controlled experiment thatactually gives you some useful
(32:10):
data?
Justin (32:11):
Yeah. You only wanna change one variable at a time.
If you change too many things,you don't know what change
actually created a meaningfuleffect. So what we're trying to
minimize is the number ofvariables at play. There's
always gonna be other variableslike, you know, you might have a
big guest one week.
(32:31):
You can control that, but here'sthe variables you can change.
So, you know, if you're gonnaadd or change a title tagline,
that would be one thing. Doesthat help? Mhmm. Does that not
help?
I think you had a case studywhere someone went from, like,
mindfulness podcast tomindfulness psychology, and
(32:53):
there was a big lift orsomething like that?
Jeremy (32:55):
Yeah. So this was one of my clients, Sam Webster Harris.
He actually has the name SamHarris is his first and last
name, but he's not that SamHarris, which we have a
discussion on this that I'mgonna share one day. But he
actually thinks that hurt hisconversion rate because people
clicked in thinking this was theother Sam Harris and then were
disappointed. Oh, this isn'tthat Sam Harris, so they left.
And so he thinks that that wasactually a bit of a a headwind
that he's had to work against.But he yeah. His old show, was
(33:18):
called the Growth MindsetPodcast, and he changed it to
Growth Mindset Psychology. Andhe also so he did a he committed
a couple of, flaws with hisexperiment here, which he
acknowledges that he wasactually working on a bunch of
stuff. And so he had beentightening up his episode titles
for one already, but then hechanged the name of the show to
growth mindset psychology.
(33:39):
Andy changed the cover art,which was a subtle modernization
of the cover art. Like, itwasn't a complete overall. He
actually did a big overall firstand it tanked his his numbers.
And then he Interesting. Coursecorrected and was said, let's
actually just make the existingcover art a little bit cleaner
and more modern and less amateurlooking.
And he ended up adding over2,000 downloads an episode,
within about a month, and thenit kept growing from there. And
(34:02):
so, like, that is, you know, acouple small changes to that
packaging that had a huge impacton his show. And so, you know,
that's not gonna say that that'sgonna every show is gonna see
those results. He was alreadygetting some traffic and some
impressions because he has along running show that is was
already doing pretty well. Butit is interesting to see how the
cover art can both hurt or,drastically increase the
(34:22):
downloads.
And so that's this kind ofprinciple in action here of of
doing experiments and seeing,you know, what what works and
what doesn't.
Justin (34:28):
Mhmm. So let's say people wanna set this up. My
guess is the first thing they'lldo is take a a literal
screenshot of their existingstats. Because there's no way
of, like, bookmarking yourexisting stats or running an AB
test inside of the Spotifydashboard. So you're gonna have
to manually screenshot yourexisting stats.
(34:48):
Anything else in terms of thesetup that people should know?
Jeremy (34:51):
Yeah. I would screenshot that or put it in spreadsheet,
and I would just write yourselfa hypothesis on, like, what is
the variable I'm testing, whatdo I think is going to happen,
and then check back in thirtydays. And so I would probably
run this on a thirty day windowbecause that's what Spotify
gives us. And so we say from,you know, whatever, January 1 to
February 1, I got, this manypeople were showing my show.
This was the conversion rate, ateach of the conversion rates,
(35:14):
and maybe I look at impressionsas well.
And then I say, okay. From I'mgonna make the change at
February 1, and then I measurefrom February 1 to March 1. And
I see, okay. Was that anydifferent from January? And the
other thing to keep in mind iswhen we're talking about
limiting variables, it's saying,like, okay.
Let's change the the title orthe tagline of the show, but
then also let's keep everythingelse the same. And so I'm gonna
publish the same amount ofepisodes. I wanna try and
(35:36):
control that to make sure that,you know, I'm actually comparing
apples to apples.
Justin (35:40):
Yeah. You're right. Like, this is the time to just
keep consistently doing what youwere doing before. So if you're
publishing every week, just keeppublishing every week. Don't
drop a, like, a bonus episode inthere because that'll that could
throw things off.
Just, like, if you're once aweek, just keep publishing once
a week and then try to comparethat over a a thirty day window.
Jeremy (36:01):
Yeah. I think the last thing that I would just add on
the experimentation front isassuming that you don't actually
know anything. And I think thatthis is a mindset that I never
really understood. I I thought,like, Facebook ads experts were
just wizards that they just knewhow to write the copy and do all
the targeting and set up allthis technical stuff on the back
end to be able to just make adsrun profitably or get people to
(36:23):
your podcast or email list orwhatever. And what I know now
after having been in marketing alot more is that basically every
Facebook ads expert goes in withsome assumptions that they are
almost certain are gonna beproven wrong.
And so you'll often hear peoplein this world talk about, you
know, this kind of two to threemonth window of optimizing the
ads. And, again, this issomething that I thought was
about training the algorithm,but what I now know is that it's
(36:44):
about you as the person runningthe ads, testing different
things and just slowly pruninguntil you get to, oh, this is
the thing that works best. Andso you run, you know, six
different headlines and you havesix different graphics and you
slowly whittle it away untilyou're like, okay. Out of all
the permutations that arepossible, this is the thing that
is performing best, but I'mgonna keep testing that as well.
(37:04):
And so you just see this sooften if you do AB testing with
headlines or you come up witheverybody's, I think, had that
episode that for some reason,that one got a bunch of
downloads.
So you got all this feedback onit and you're like, why that
episode? I don't I just don'tunderstand that. And so this is
true for all of these kindadifferent variables that you're
playing with. And so going inand just thinking, like, the way
that I'm going to get better andimprove my conversion rates is
(37:26):
just testing things constantly.Small tweaks here and there.
And over the years, the monthsand the years, it's actually
going to improve slowly butsurely to the point where now
you're hopefully outranking allthese benchmarks by quite a bit.
Mhmm.
Justin (37:38):
I would also recommend that people add other
qualitative sources of data tothis. So one thing I've just
started doing, I I just starteda new podcast on the trailer
episode and the bonus episodethat we've put out. I encourage
people to go sign up for theemail list, and I say, I'm gonna
send you an email right awaywith some questions. And
(38:01):
eventually, I'm going to usethat automatic email to ask
people, hey, how did you findthe show? What drew you to the
show?
What brought you here? And Ithink adding in those
qualitative answers to whatyou're seeing on the
quantitative side here will behelpful. It's gonna help you
check your assumptions. Maybeyou're used to just getting
answers like, oh, I had a friendtell me about it and I went and
(38:23):
checked it out. Maybe you startgetting answers like, oh, like I
saw it on the Spotify homepageand the cover art looked
interesting.
Well, if you've recently updatedyour cover art and then that
qualitative response just startsshowing up more and more often,
I think you can start to say,okay. These activities are
really correlated. Like, Ichanged the cover art and we saw
(38:44):
Lyft, but now, anecdotally,people are actually telling me
that that's what made adifference.
Jeremy (38:50):
So for anybody listening, if you do start
experimenting with any of thesevariables, looking at things
like the title, the cover art,your show description, episode
titles, teaser episodes, all ofthese kind of external and
internal packaging elements thatwill influence, these conversion
rates, both the awareness tointerest and the interest to
stream, we would love to hearabout it. So send us an email.
You can find the email in theshow notes for this episode And,
(39:13):
personally, I would befascinated to see what people
are playing with and how that isimpacting your discoverability
in Spotify.
Justin (39:20):
And there's a whole other part of this discovery
screen that we didn't talkabout, which was sharing links
where you can actually create alink to an episode or a show and
then track how many people visitthat link. So if you're
promoting it on social media,we'll talk about that in the
future. But just so you know,there is this other link
tracking piece of this that'salso interesting.
Jeremy (39:40):
So there is a lot more information on this Spotify
discovery dashboard if you wantto look at even more benchmarks
and data. Again, you can findthat at
And there's also a bunch ofrecommendations on how to
diagnose, you know, thedifferent kind of conversion
rates or combinations of, youknow, a high awareness to
interest come combined with alow interest to stream or any of
(40:03):
the other variables and how tothink about, like, what might be
the problem and what do you doabout it. So again,
dash benchmarks. And other thanthat, I think it's time to crank
up the, tangent extender andmaybe the, the auto tune and
play ourselves out.
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