@Rockonomic: $0.00238 — the estimated royalty payout for a VEVO play, per stream per viewer

I found a napkin next to my coffee cup this morning and decided to estimate the value of a video stream, per viewer, for VEVO.

The maths scribbled on this napkin didn’t have to be too complicated since the company has been rather forthcoming in public regarding (a) the total amount paid in royalties to music owners and (b) the number of video views, on a global basis, during various months.

Using a collection of the inputs (a) and (b), a back-of-the-napkin estimate of the effective rate per stream paid by VEVO would be:

$0.00238 per viewer stream (pvs), with a range from $0.00222 to $0.0025 pvs.

I will leave it up to the reader to compare that pvs rate for VEVO to the various per listener stream (pls) for the range of alternative venues for music experiences (e.g., music services like Spotify, Rhapsody; YouTube; Webcasters; Broadcast Radio). For example, most reports would now place Spotify pls rates at about 2 to 3 times larger than this estimated VEVO pvs rate.

For more details, head to Rockonomic.com

@Rockonomic: Is 3 to 1, or 10 to 1, the better Tracks to Album Equivalent ratio?

In this post, I am going to seriously question the “10 Tracks sold equal 1 Album sold” that has historically been the default setting for the “Album Equivalent” ratio for the music industry. I am going to recalculate the total Equivalent Albums sold over the last decade assuming this ratio were actually around 3, in order to provide an example of just what a difference a new ratio makes.

Most importantly, I am going to suggest that the Album Equivalent ratio should be based upon actual consumer behavior — meaning, based upon observations of the actual ratio at which consumers are willing to substitute track purchases/streams for albums purchases/streams — rather than based upon how the recorded music industry puts tracks into buckets and calls those buckets albums.

Read more at Rockonomic



@Rockonomic: Maximum G, measuring and comparing attention inequality in media markets.

If the media industry were an economy — which it is — the distribution of wealth and attention in this economy suggests a circumstance far more derelict than that of the worst national economies on the planet. A small few collect a great proportion of the wealth in this attention nation, and contrary to the hopes and dreams of tech utopians, this concentrated distribution may not be changing.

A significant measure of the vitality of an economy is being ignored by most discussions in the media industries these days. That measure of vitality is simply the relative distribution of stuff throughout the system. For the music industry, that “stuff” might be track/album sales, or online video streams, or music service streams.

We could call this measure “G,” a shortened form of Gini, a coefficient widely employed to characterize the distribution of things throughout a society. We could calculate the G-index for a wide range of media venues — radio, television, webcasters, storefronts — as well as media models and interfaces — subscriptions, free, machine recommendations, curators.

For example, my estimate of G for one online music video destination that shall go unnamed is 99.27, if all available videos on the service were considered to be “in the economy.” Were only those videos that were in fact streamed during a week considered to be “in the economy,” the G falls to 83.08. In either case, this venue for attention has a greater concentration of wealth than any national economy on the planet, by a landslide.

To read on, head to Rockonomic.com

Rockonomic: Pinhead, Pot Belly, or Long Tail? A quick glance at behavior in a free market for music videos

On this Mathy Monday I reckoned it might be fun (depending upon your definition of the term) to take a peek as some data related to music consumption patterns online. The purpose of this post is pretty simple: to return to the question of the general shape of consumer behavior as that behavior relates to cultural goods, in this case music.

Reeling the clock back a few years, remember that the underlying premise of so-called Long Tail Theory was that broader availability of and access to products online would lead “the Tail” of this inventory accounting for the majority of consumer interest. The days of super-hits — what I like to call Pinhead theory — would likely be over.  Furthermore, there might also be a sort of Pot Belly Theory, one that suggests consumption of media will expand in “the Belly” of these sorts of distributions.

Will Page and Eric Garland engaged in a similar inquiry a few years back to the one taking place in this post (my name showed up in there as well, in the acknowledgments). In that work, data from peer-to-peer network behavior was under the microscope.

This time around, I reckoned it might be fun to return to these prior questions of Pinheads, Pot Bellies, and Long Tails with some music video data as the raw ingredients. Comments on the underlying data can be found at the end of this post. These data come from a moment in time after the work of Will, Eric and others. And I hope to follow this post with observations of subsequent data, from different slices in time, to see if any changes are afoot (or, a’tail).

Ultimately, at least in my mind, (a) culture is what we all share, so these data in some way reflect culture, and (b) its quite easy to glimpse at these patterns of consumption in simple percentage terms, no logarithms or bio-rhythms required. Per protocol, I am not taking a side in the debate over whether any general pattern of consumption is good or bad or otherwise for the industry or society. Instead, I reckon we might as well be permitted some glimpse at these patterns so we can have some sense of what “is” at some moment, at least in some corner of the internet.

And so, below you will find a couple graphs to visualize what I observe in these data:

In the first graph, the left-side Y-axis is the Total Number of Views for the viewed videos. These data are graphed as a continuous bar, from left to right in the X-axis queue, from the most viewed to the least viewed video.  The right-side Y-axis is the Cumulative Percentage of all Video Views. For example, the cumulative percentage of video views for the tenth video in the queue would be the sum of all views for videos 1 through 10 divided by the total number of views for all videos.

In short, 1.05% of the available videos account for 90% of all video views during the week in question. 2.67% of the available videos account for 99% all views.

Pinheads and Long Tails - a glimpse at music videos

In the second graph I simply try to provide some sense of scale were the first graph to include on the X-axis the entire queue of available videos — those viewed during the period and those never viewed.

Pinheads and Long Tail - a glimpse of music videos

Frankly, only 4.2% of the available videos were viewed during the week in question, so the graph would have to be 20x wider if I were to include in the analysis all of these videos that collected digital dust during the week.

That’s it.


The underlying data for this inquiry shall not be disclosed, neither in terms of the source nor the metrics of size (e.g., number of videos, number of views). Therefore, I simply ask the reader to accept that this blob (i.e., sample) of data is sufficiently large — in terms of available videos and videos viewed — to be at least representative of general patterns online during a single week of music video enjoyment. Furthermore, the reader might guess that the nifty accoutrements assumed to lead to broader consumption of media are present in the underlying video venue.