This post will be the last in a series of three conversations on the ways in which artist popularity, listener demand, and time spent listening impact any redistribution of wealth that could occur when comparing Total Pool and Per User (aka, User Centric) methods for royalty payouts from streaming music services. These are the models that folks like Laguna, Lowery, Mulligan, and others are now debating if not taking a position.
In the end, any resolution of this debate over the streaming models will likely have more to do with opinions and the influence of those opinions than it has to do with the numbers. Ultimately, I believe that this streaming payout concern boils down to two simple questions:
- Should the dollars I spend on a music service go into a big pool of money, from which the artists we all listen to are paid—based upon everyone’s listening behavior? or
- Should the dollars I spend on a music service go only into my own pool of money from which the artists I listen to are paid—based upon only my listening behavior?
The answer to the “Should” has nothing, ultimately, to do with who gets how much compared to whom. Instead, these are questions of process not payout. The nature of the consumer connection between dollars spent and royalties paid matters, regardless of whether artist payouts ultimately differ between these two models.
Frankly speaking, #1 looks like the way in which royalties from from venues such as Radio and TV. #2 looks like the way in which royalties flows from a sale—unique to each transaction.
Beyond these questions, in this post I am going to introduce a few additional scenarios that will hopefully surface the underlying lever(s) that ultimately determine whether any shift in payout model—regardless of opinions—will have any substantive (i.e., meaningful) impact upon royalty payouts.
What we are going to find is that these these two models—Total Pool versus Per User—result in substantively different artist payouts only when a systematic pattern exists. That systematic pattern is simple:
IF the most active listeners on the service listen to a different portfolio of artists from that portfolio to which the least active listeners on the service listen. The more unique the portfolio of artists listened to by these two user populations—heavy listeners versus lite listeners—the more dramatic the difference in royalty payouts as we shift models.
The above conclusion means that so-called independent or newbie or middle-class artists will only benefit from a shift in these two models if these artists happen to also be the primary interest of those listeners who spend the least amount of time listening to music. If, however, listener behavior is rather well-distributed across any and all “classes” of artists, a shift between these two model will have little impact upon artist payouts.
Furthermore, this conclusion operates in the context that free users on services such as Spotify stream a far lesser number of tracks each month than paid users—at least according to various new stories. And, these free streams mostly likely carry their own, per-stream payout (at least for artists signed to major rightsholders).
And so, if the least active listeners are actually just streaming the hits, while the most active listeners are streaming across the wide range of “classes,” some artists may find the redistribution of wealth under the Per User model goes counter to their expectations.
Let’s dig in…
In this section, I am going to introduce only four scenarios, simply because it seems these four scenarios are all it really takes to “tease out” this interaction between time spent listening and popularity.
In scenario one, all subscribers will listen to the same amount of music (900 listens), while there will be variety in terms of the portfolio of artists to which users listen—some users will listen to a wider range of artists than others. In scenario two, all artists will enjoy that same amount of demand (900 plays), while there will be variety in terms of the portfolio of artists to which users listen—some users will listen to a wider range of artists than others.
In scenario three, we are going to return to the basics—every subscriber will listen to the same number of tracks of each artists and the same number of tracks over all. And scenario four, we are simply going to twist scenario three to introduce the scenario that ultimately drives the difference in payouts between the two models: some artists less popular (i.e., in smaller number of playlists) and some listeners less active (i.e., listening to few tracks).
Note that the average daily listening hours (111), as well as the average payout per stream ($0.0078) in this example line up with the average released by Spotify through various new sources.
Varied Popularity, Equal User Demand, Identical Listening Hours
In this scenario, all subscribers spend the same amount of time listening to music, and in turn, stream the same number of tracks. Each artist, however, experiences very different levels of popularity (i.e., number of people listening) and demand (i.e., number of tracks streamed). In fact, Artist A experiences 29-times the number of streams as compared to Artist K.
As we might expect, Artist A enjoys the greatest proportion of the royalty payouts: 29.28%.
What we might not expect, however is that the payouts across all of the artists would not change if we shifted from Total Pool to a Per User method.
Essentially, even though some artists are far more popular than others (i.e., in a greater number of subscriber playlists) and far more in demand (i.e., streamed a greater number of times), because of the similarity in the time users spent listening to music, there is no different between the Total Pool and Per User approaches to payouts.
Varied Popularity, Equal Overall Demand, Varied Listening Hours
In this second scenario each Artists, A through K, experience that same level of demand across all Users, Q through Z. In other words, once all listens across all Users have been tallied, all Artists enjoyed the same number of listens: 900. While some artists are more popular than others (i.e. found in the greater number of listener playlists), they are all equally demanded, in terms of the total number of streams.
That said, User Z was the most active user, streaming the greatest number of tracks across the widest range of music, while User Q was the least active user, listening to the smallest number of tracks across only a single artist.
In other words, we took the variety in demand from the prior scenario and moved it to time spent listening in this scenario—to test whether Demand for Artists or Time Spent Listening ultimately drive any difference in payouts.
Under a Total Pool approach to the royalty payouts, each Artist receives the same payout—$7.00—as each artist was equally in demand as the next. Under a Per User approach to royalty payouts, however, each Artist recevies a very different payout—ranging from $2.39 to $17.51—given total listener hours varied across all of the Users.
Equally worth noting, under a Per User approach some streams become worth more than others. Streams for Subscriber Q are worth 7.8 cents per stream, while Subscriber Z is paying out 0.27 cents per stream. Meaning a stream for Subscriber Q is, effectively, 28 times greater in value than a stream for Subscriber Z.
Is the music of Artist A worth more simple because it is preferred by the subscribers who stream less music? Are those moments with music truly more valuable to Subscriber Q? Or was he/she simply too busy to listen to music?
Artists E through K, popular among only those Users who listen to over an hour of music a day, all receive less money under the Per User approach. In particular, Artist K, uniquely listened to by the most active User, takes the most significant hit in a shift from the Total Pool to the Per user method.
Artist A, broadly popular among all Users, albeit not-so-in-demand by any User, experiences the greatest benefit in a shift from the Total Pool to a Per User approach. In the case of the Per User approach, it pays more to be more popular among those Users least passionate about listening to music on the serivce.
Note as well that Artist K experiences an effective royalty per play of $0.0027, about a quarter of a penny, when average royalty per play is actually $0.0078.
Essentially, Artist K receives a significant lower payout for being in very high demand from the most active set of users. Fair?
Equal Popularity, Equal Demand, Equal Listening Hours
Its time to go back to the basics and, in fact, back to where we started. In scenario three, every subscriber listens to the same number of tracks of each artists and the same number of tracks overall. Essentially, all variables are equal in this situation—popularity, demand, and time.
As we might expect, when all is held equal, there is no difference in payouts between the two methods:
Varied Popularity, Equal Demand, Varied Listening Hours
In our last scenario we are varying popularity and demand, while leaving equal the number of tracks any subscriber streams from any artist. In fact, not only are some artists more popular than others (i.e., found within a greater number of subscriber playlists), but also popularity is exclusive — some artists are listened to by some subscribers (thus the “zero”s in some artist/subscriber cells .
Perhaps most important for our purposes here, in this scenario the least popular artists happen to be found in the playlists of those subscribers who also happen to listen to the least amount of music.
What do we find?
Perhaps as no surprise by now, those artists who were not just more popular, but also in the playlists of the more actives subscribers, receive smaller payouts under the Per User method. However, those less popular artists, who also happened to be found in the playlists of the less (if not least) active subscribers, benefit most from the shift to a Per User approach.
For anyone who has actually bothered to read this far, I will just say the following.
Basing this decision over the payout method upon who wins or loses may be far less appropriate, and less loaded, then simply basing this decision upon which payout process seems the more direct for fans.
In either model, artists are going to see per stream payouts measured in the fraction of a penny. In fact, they are going to see variety in payouts—some streams become more more than others. A situation which will lead to a debate over whether and why the listens of less active users are worth more than the streams of more active users.
All the best, in the fairness debate.