This sort of “price discrimination of demand” — to use a dismal term from Economics — has been applied before on music services, but in the US. Via this method, a product or service is priced based upon characteristics of the buyer not the product/service itself.  Here, students get a discount — the price is a function of the buyer.

Importantly, the wider context within which these discounts are being offered has changed significant.  And so, this time around, its likely a more positive outcome might result from this appeal to university students.

Back in 2003 and 2004, Napster began offering discounts of various kinds to University students in the US.  These discounts came in the form of (a) bundling Napster within university activity fees (e.g., Penn State), or (b) significantly discounting the streaming service down to $2 (e.g., USC, Middlebury, Vanderbilt, and others). Some of these methods were considered controversial at the time, with Napster requesting that schools not share partnership details and students, at times, complaining about the use of their fees.

Fast Forward eight years, and the dynamics in the marketplace have changed.  First, the service on offer at a discount is mobile this time around, not simply streaming only.  That means, students might actually be interested.

Second, yet related to the first dynamic, millions upon millions of mobile devices exist in the marketplace upon which this mobile service can reasonably and reliably operate.  Mobile services in 2004 could only run on a certain set of portable media devices, and the iPod was not in that set.

Third, I think attitudes have changed around paying for music on a subscription basis, such that the model “makes sense” to many people.  When I first attempted a subscription-based service, the first response from potential customers and partners: Who rents music?  Steve Jobs famously asked this same question.  Second response, “Is this a music club?”  The positive or negative opinion for licensing such a service was contingent upon how record club financials, as prescribed in record deals, were quite different for labels than for artists.

Finally, price.  I have presented research in a few venues the results of which suggest the market for music services could expand dramatically if prices were lowered.  And importantly, for every $1 in price drop the industry would gain additional customers such that the total pool of money on the table is larger overall — a 25% price drop could lead to greater than a 25% increase total dollars.

For example, Muve Music, offered by Cricket Wireless, has signed up between 600,000 and 700,000 subscribers to its service at an effective price of less than $9.99/month (the extra $10 for music includes a bump up to 3G speeds, a music service, as well as unlimited ringtones and ringbacks).  Cricket wireless has around 6 million total customers, so the service has signed up between 10% to 12% of its customer base.  If an equivalent base existed across all wireless customers in the US, the mobile music subscriber base would be between 25 million and 35 million accounts.

Only time will tell whether these sorts of discounts will expand the market, however.

Two tendencies that I have encountered as people respond to data (in excess or in shortage) would be that of (1) ascribing rules to the exceptions (i.e., inferring general rules from single case studies), or (2) asserting exceptions to the rules (i.e., highlighting slim exceptions when presented with big data patterns).

I think of these tendencies as biases, because I believe they may represent some underlying processing errors our brains fall into when dealing with data, particularly given the context within which the analyses take place.

Ideally, we should be responding quite differently, and oppositely, than these two tendencies when analyzing data.  Case studies should engage our understanding of exceptions, or the exceptional aspects of a specific situation.  Big data should trigger our pursuit of general patterns, or the unexceptional things that a wide range of cases might have in common.

Bias #1: Rules to the exceptions

How often have you heard, “What we learned from this example is that the best way to accomplish X is to do Y!”

Yet drawing broad conclusions from a single example is (most likely) not the way in which we should be responding to these sorts of data. In reality, we just learned about a single case of X and Y, and have no idea if across of range of attempts at X we would get a Y more often than not (or at an appropriate level of risk).

Case studies are compelling, however, because they are rich stories, capturing our interest in the specific and at times sordid details of some unique experience.  Oftentimes, the case studies we really love are those that recount truly exceptional experiences—breakaway successes or full on failures.

Bias #2: Exceptions to the rules

When presented with a large pile of data and the patterns therein, however, it seems that we can’t help but use each additional data point as a means to question, or counter, the general trends.  After (or while) presenting some large-scale analysis, how often do we hear, “Ok, but what about Z?” “Did you take Q into account?”

It is as if our brains just have to find something exceptional when presented with a pile of data through which we confront the unexceptional patterns therein. Or, problematically, we find it hard to believe patterns unless they apply to just about every instance we can imagine.

Yet general patterns are not supposed to be natural laws.  Patterns are simply links that exist “in general.”

The decision-maker’s paradox

So why do we respond to data scarcity and excess in these ways?  Are we fools?

I actually don’t think so.  And am trying to understand more.

First Sale leads to the following scenario:  You go to the a store and buy a textbook or DVD.  After a few reads or views, you decide to sell that textbook or DVD, or rent it to someone, or lend it to someone.  Your opportunity to resell, rent, or lend the work is covered by first sale, and therefore OK under the law.  The “first sale” of the copyrighted work effectively brings to a close the copyright owners exclusive control over that copy of the work—only that copy.

In the absence of First Sale, owners of copyrighted works essentially retain ongoing control over the distribution, sale, rental, etc of  their works, as that control is defined under the law.

The first sale plot thickened in the past decade, however.

In 2008, the Ninth Circuit Court of Appeals in the US  ruled in favor of Omega (watch) in a case involving Costco (aka, Costco versus Omega).  Costco was purchasing Omega watches in cheaper markets overseas and then selling those watches in the US.  The Circuit Court determined that the first sale doctrine would not apply to copyrighted items manufactured overseas.  The Supreme Court heard the case on appeal, and split 4-4 in late 2010.

The result of of the Supreme Court’s lack of a decision was (a) a lack of national clarity on this first sale question, but (b) some clarity on when first sale might still apply.  In this case, if the product was manufactured overseas, but US sale was authorized by the copyright holder, then first sale could still kick in.

Meaning: Copyright owners could not simply manufacturer a product overseas to create a workaround for the First Sale doctrine. This doctrine would still apply to products manufactured overseas but legally sold in the US.  You could resell these CDs, DVDs, and Books after your first purchase.

Fast forward to 2012 and a case that is both similar and yet different in a important way now heads to the Supreme Court.  The pathway for the case flowed through the 2nd Circuit Court.  In the case Kirtsaeng v. Wiley, the court will come to an opinion on whether works first copyrighted in the US, then produced overseas, would or would not be covered by the first sale doctrine in the US.

The important difference in this case would be that the 2nd Circuit Court’s ruling applied even to those products manufactured overseas and then sold with the permission of the Copyright holder in the US.

Meaning: Copyright owners could simply manufacturer a product overseas and retain exclusive control over the sale, resale, rental of their work — control that could not be trumped by the First Sale doctrine.  Buyers of a CD, DVD, Book, or other copyrighted thing in the US could not legally resell the item.

This case has significant implications.  So please, pay attention.

In the same way that Google’s “Onebox” play button that was momentarily embedded in search pages (remember 2009, anyone?) was a cruddy hack. although somewhat less of an unfortunate hack than Spotify’s.

So, what’s the problem?  Well, everything comes in three’s so there are three parts to this problem.

(1) Music is so much a part of human culture these days, of web culture no less, that the necessity of playing the file through the Spotify app is ultimately of function of a copyright problem not a technical problem.  We should not have to jerry-rig technology just so we can fit within the eye of the copyright needle. Copyright law should adapt.

(2) and somewhat related to (1), The play button requires that we experience a technical inconvenience in order to work—confirm you have Spotify or you must download the app to hear the track.  That’s annoying. If the play button is on the page, all we should have to do is hit play.

(3) It could lead to the same sort of Highlander effect—”there can be only one”—that the music industry now finds itself whinging about, in regards to iTunes.  Tactically, the play button is ideal for Spotify.  First, the service has a way to virally spread beyond Facebook, triggering downloads of the application.  Second, the method for embedding provides a way to minimize competitive creep, since partner sites will probably not want to have 2 or 3 or 4 embedded “Play” buttons, accommodating the 2 or 3 or 4 (or more) services that are out there.

Let’s check the math, using some of the inputs from Glenn Peoples over at Billboard. For the moment, we will limit this little discussion to the US only. And we will hold iTunes growth at nil, essentially seeing how big Spotify would need to get to equal iTunes music revenue/royalties in 2011.  To account for growth towards 2013, just add your own growth factor to the answers I get.

I will also make two key assumptions:

(1) That 70% of the Spotify subscription price is what goes into the pool for royalties, just as 70% of the iTunes sale (in general) goes into the pool for royalties).  For example, at the $9.99 premium price, roughly $6.99 becomes the royalty pool.  This assumption is not only being made in order to make life simple, but also because this 70% payout appears to be the case (at least according to Glenn@Billboard).  Your mileage may vary.

NOTE: I know I am being a little sloppy here given it would be better to include a more appropriate accounting of mechanical royalties.  However, given that in the US these royalties can flow through the sound recording owners (my emphasis and potential sarcasm added), I am just bundling the whole problem within the 70% for simplicity.

(2) That the “effective” obligation per active FREE user is $1.99 per month, 70% of which is paid as royalties.  Just to be interesting, I will also work with $2.99 and $0.99 as the FREE user value.  In essence, I am treating FREE users as a cost to Spotify on a per subscriber basis (regardless of whether enough ad revenue is earned) and a resulting royalty payment for rightsholders.  Not a perfect treatment, but this technique will permit us to account for the fact that these users do lead to royalties—while free to the user, free users are not free for Spotify.

Glenn stated, over at Business Matters in Billboard:

Billboard estimates there was roughly $2.4 billion of consumer spending on music downloads in the U.S. in 2011, based on Nielsen SoundScan sales figures and rough estimates of per-unit prices. iTunes was assigned a 70% share of the download market, giving it estimated sales of $1.7 billion.

And so we are looking for a kitty about equal to $1.7 billion in the United State for comparison.

Now, Spotify presently claims roughly 10 million active users, 3 million of which pay for the service in some way.  Therefore, at least for the moment, let’s work with 30% of the “active user base” converting to paid users.

The firm has previously stated that about 85% of paying subscribers are at the $9.99 tier, while 15% are at the $4.99 tier. Lets move forward as if this ratio between high/low tier subscribers remains the same.

I could write out some fancy equation right now, but I won’t. Suffice it to say we are looking to find out how many Active users it will take to result in $1.7 billion in pre-royalty revenue (or ~$1.2 billion in post-royalty), given that (a) of these Active Users, 30% will pay for a subscription and (b) 85% of those who pay will pay at the High tier ($9.99) while 15% will pay at the Low tier ($4.99).

Whew.

Alternate Answer #1: 30,000,000 Active Users if Free users are effectively worth/cost $2.99 each month.

A top-line number that would lead to:

30,000,000 Free users -> $753,480,000 in yearly “revenue” pre-royalty
9,000,000   Paid users -> $997,920,000 in yearly revenue pre-royalty

if Free users are effectively worth/cost $2.99 each month.

Alternate answer #2: 35,000,000 Active Users if Free users are effectively worth/cost $1.99 each month.

A top-line number that would lead to:

24,500,000 Free users -> $558,600,000 in yearly “revenue” pre-royalty
10,500,000 Paid users -> $1,164,240,000 in yearly revenue pre-royalty

Alternate answer #3: 42,000,000 Active Users if Free users are effectively worth/cost $0.99 each month.

A top-line number that would lead to:
(number below are corrected from earlier post)

29,400,000 Free users -> $349,272,000 in yearly “revenue” pre-royalty
12,600,000 Paid users -> $1,397,088,000 in yearly revenue pre-royalty

Is the number of tracks to which any user listens during any month actually falling for services such as Spotify?  In other words, is average “user engagement” (if measured as tracks streamed per month) falling?

Or, are free users simply falling away?

Since privately shared data is, well, private, I can only discuss recent payout data reported publicly by bands such as Uniform Motion.  This group recently reported that their payouts from both Spotify and Deezer increased (in the EU, no doubt).

From Spotify, the payouts increased between 2010 and 2011 from an effective €0.0033 per stream to an effective €0.0047 per stream.  From Deezer, the payouts increased as well, from somewhere in the ballpark of  €0.006 per stream to a somewhat astounding  €0.0127.  Converted to dollars that means the band is receiving greater than $0.60 per stream from Spotify and and astounding $0.0165 from Deezer.

Now, since most of these deals are likely structured on a percentage of per user revenue basis and few users are causing any per stream minima to trigger payments greater than the user subscription fee, I have to wonder how the effective payment per stream is rising.

As Spotify and other services like Deezer incorporated restrictions upon the length of term over which users were provided free access to the services, the number of active monthly users seems to have stalled a bit.  These services are still growing, but at a slower rate.

Furthermore, if you peek at Daily Active Users, as compared to Monthly Active Users of the Spotify Facebook app (thanks to AppData), it seems that daily active users have been rather stable (i.e., no growth) as monthly active users increase.

As the number of free users peeled away, we would expect the effective per stream payout to increase.  Furthermore, since the total number of users is not increasing drastically (any growth being culled by extant free users falling out) it would seem that ad revenues are not on a major upswing.

And so, is this increase in effective payouts a simply function of free user fallout?  Or, are paid users losing interest in the services such that their monthly payments are being divided among a smaller number of monthly plays?

(1) Creative expression

‘Nuff said.

(2) Community funding

The project was funded via Kickstarter, and the funders are listed as Senior Producers, Producers, Associate Producers, or Additional Supporters according to the amount of funding they provided.

(3) Copyright conundrum

The Girl Walk // All Day film is synced to large portions of Girl Talk’s latest album, All Day.  In other words, the walking sampling dilemma that is Girl Talk is now synced with a film, introducing a new layer in the licensing paradox.

There is significant debate over just how and at what cost GirlTalk would license all of the umpteen samples that are involved in his work.

Now there would be another stage of the debate, in terms of whether and how his collection of samples and the samples therein might be licensed for “sync” with the film.

Frankly speaking, this preference for one of four P’s makes the industry no different from societies-at-large. In just about any society, particularly as the underlying reasons for events become obscure, these sorts of analytical heroes surface.

Prophets—people who can see the future and are here to prepare us.

The insight on offer for many industry luminaries is their professed capacity to see or anticipate that which will happen next. Of particular interest to a prophet is truly the next big thing. These sorts of insights usually begin with phrases like, “The future of your industry looks like this….,” or “The DVD market will….,” or “Three dimensions of consumer demand in 2020….” Many people have built their careers on being prophetic once, or  maybe a few times, and being apparently correct.

The use of data by prophets is, well, very prophetic. A few crumbs of data that can be organized in such a way as to provide anecdotal evidence of a trend are all a prophet requires. Take a chart with a line sloping up or down and just extend the line into the future. Hang out with some “generation [blank]-ers” and describe their behavior as not only eventually widespread but also inevitable.

Let’s be clear and honest about data and analytics—neither can predict the future. The combination of the two can only hope to provide you with some better understanding of the past and present. That understanding might be applied towards the future, but only to the extent that the future is an awful lot like the past.

Pundits—people who can see the truth in the past requiring little more than a bar or pie chart.

Pundits are a powerful class of interpreters as they claim to have the ability to impute general trends from wonky data. Wonky data comes in many flavors, such as: rather bland (like a bar or pie chart) or skimpy (like a sample of ten) or truly sparse (like a single case study).

The favorite sorts of phrases from pundits sound something like: “This industry is failing because of X….,” or “This industry has a preference for three….”

Punditry is powerful because it usually taps into some hunch or belief we have in the underlying cause(s) for experience for which we might lack sufficient evidence (aka, confirmatory bias). The pundit provides a sort of social confirmation upon which we can rely to overcome the uncertainty in the data.  Explanations gets more highly weighted alongside support from a authoritative pundit.

The unfortunate truth is that no analytical method, at least none that is firmly stationed within the class of tools know as scientific or by falsification, will ever prove something beyond doubt. In fact, except in the case of certain methods that treat the counterfactual in a particular way, most tools—like the ever-popular regression—are actually setup to simply question the counterfactual (or null hypothesis). As a result, we simply are reasonably certain that the opposite of that premise may not be the case, or that the data seem to conform to some explanation/expectation within some margin of error.

In simple-but-convoluted english: Data and analytics never prove any premise. Get over it.

Polemics—people who only reveal analyses with the “right” conclusion.

Polemics are a powerful class of interpreters and their impact can be seen in the political debates of governments, industries, and organizations. To the polemic, data and analysis may be tools for understanding but are most importantly tools for influence.

So important is this influential dimension of interpretation that data/analysis that is not sufficient to “move the needle” will be discarded or, in some circumstances, massaged.

Rationally, when there are two sides to an issue and both appear to have support built upon decent data and analysis, its probably time to understand why and how the truth may involve a little bit of both sides from the debate.

Pedantics—people who only believe the sort of analyses that cannot exist

Pedantics operate on what is probably the opposite extreme from prophets and pundits as far as their analytical threshold. For the pedantic, pretty much any and every conclusion is lacking in sufficient rigor for a conclusion to be drawn.

Since no analysis that can be performed will every be a perfect analysis, and those trained in scientific (or highbrow, in the minds of some people) methods are highly sensitive to this inadequacy, the pedantic can shut down any analysis with simple comments like: “But what about….,” or “Now how big is this sample and is it sufficiently diverse to have included….,” or “I think we need a little more….”

And so, if we are going to make decent use of data and analytics, we have to accept that any and all methods come with a grain of error and maybe some salt as well. Even with a little salt, however, and because we can take error into account, these insights have value.

The only problem with this presentation of iCloud would be that the scenarios in which the photos are being taken—on the beach, in a a snowy park, etc.— would questionably fit the scenarios in which Photo Stream via iCloud will actually work so automagically.  Why, do you ask?

Because Photo Stream is not setup to work over cellular data networks, the sorts of networks to which you are most likely connected when you are at places like the beach, public parks, etc.  While this sort of instant sync would be excellent, it is not exactly possible (at this point) with an iPhone—even apparently if you have an unlimited data account.

According to Apple’s own description inside the iOS setting, Photo Stream “automatically uploads new photos to iCloud and downloads them to all of your devices, when connected to Wi-Fi.”

Alternatively, from Apple’s own support site: “

On an iOS device, new photos you take will be automatically uploaded to your Photo Stream when you leave the Camera app and are connected to Wi-Fi. Note: Photo Stream does not push photos over cellular connections.

I added the bold highlights because these highlights matter.

So while iCloud does enable calendar items, contacts, and similar sorts of data do update on iClouded machines almost instantly, even over cellular data connections, Photo Stream is different.

Unless the iOS is doing something other than what it says it does, you will not see the same sort of instant gratification presented in the commercial when taking pictures while connected to cellular data networks.  You have to be hanging out on a beach that offers public WiFi. Oh, and wait until after you close the camera app.

As Spotify continues to grow, both in terms of the number of countries within which it operates and the number of users signing up for the service—(paid or free), I reckon its time to try and make some sense of the “mixed methods” that are being applied to reckon user base and conversion rate estimates and reports.

My final takeaway away will be that the conversion rates that matter should be between the registered user base and the active free user base, as well as the paid user base.  Tossing out registered users from the calculation—as Spotify is doing by reporting metrics based upon active users— is leading to an over-estimate of the allure of these services in the context of the wide range of service options that might exist.

In other words, by reporting conversion rates based upon Active Free users, the music services market appears to be more attractive as presently priced and structured than that market might actually be.

Now, to be clear, Spotify is/are dealing with not only the expectations of license and investor agreements, but also the cliff of free users being, well, un-freed after their six months of free access to the service.  These dynamics might lead to both the reported ratio of paid to free users and the reported number of registered and active users to move around.  Regardless, the numbers that are hitting the news feeds are moving all over and we might as well be honest about how crazy this experience is.

NOTE: a robots.txt file within the certain folders of the Spotify site prevent a more directed inquiry into the “official” user numbers as reported, but comments to the press make a little backtracking possible.

First: Spotify now reveal(s) it’s free-paid conversion rate based upon a report of active monthly users and not registered users. This method is a bit wonky.

As a result of the method through which Spotify communicates conversion, as free users fall off the active user cliff—by being “un-freed” from the service—Spotify’s conversion rate will increase even though their actual conversion rate for users who have signed up for the service has not changed and may even be falling.

Example:

(A) I have 20 users of a service, two of which pay for the service while 18 use a free version.  10% of my total user base is paying for the service.

(B) I deny access to the service to 6 of the prior free users, leaving 12 free users.  One of these free dropouts chooses to pay.  I now have 3 paid users and 12 free users, for a total of 15 users.  20% of my total user base is paying for the service.

The conversion rate in this example increased from scenario (A) to (B) even though no newly registered users emerged and the total user base, in fact, fell in number.

Second, as a result of the switchbacks in metrics reporting, explicit or implied conversion rates for Spotify are all over the place.

Most recently, Spotify execs claims there were 3,000,000 paying users (at either of the two paying tiers) and that this user base was 20% of the total “active monthly” user base.  Or, at least this is how Tim Bradshaw at the Financial Times interpreted the numbers as either presented or hinted to him.

If 3,000,000 were 20% of some larger number, that number would most likely be 15,000,000.  Please feel free to check my math.

And so, we have reason to believe that Spotify now claims approximately 15,000,000 monthly active users.  Where active users implies individual accounts that have used the service during the past 30 days.  And their conversion of active users resides at 10%

Back in November of 2011, Spotify claimed 2,500,000 paid subscribers and an active user base of 10,000,000 users.  This stat is repeated in many places, including an interview for Grammy Week. The quote from Ek the CEO:

You’re talking 10 million active users, 2.5 million subscribers — most of them paying $120 a year, which is double the amount of your average iTunes user.

And so, in November of 2011 it looked like 25% of the active user base was paying for the service, a seemingly higher conversion rate.

While back in March of 2011, the company reported 1 million paid subscribers and a mathematically wonderful figure for users as 6.67 million (or 6,666,666), suggesting a 15% conversion rate (or 14.9925….%).

Finally, the conversion rates that really ought matter—if we are to understand the attractiveness of these sorts of music services— would involve a consideration of the registered user base (i.e., everyone who has signed up and tried the service) as compared to the Free and Paid user bases.

The music industry needs to come to terms with the true appeal of BOTH the Free and the Paid versions of these music services.  Focusing nearly exclusively on the Paid conversion of active Free users leads to a misunderstanding of both the purpose and the draw of these services in the market.

Additionally, when conversion to Paid users is measured as a function of active Free users, the limit applied  to the time period for free usage will result in overstating the appeal (i.e., the increase in the conversion rate) of the Paid services as these Free subscribers drop out.  And as these Free subscribers drop out, we lose them to other non-paying options.

As a result, it appears as if services such as Spotify are increasing in their appeal to the general public when, in fact, over time the appeal may have remained the same.

In September of 2010 the firm reported 10 million  ”users,” which back then referred to registered users.  At this point in time, there were a reported 500,000 paid subscribers and the country base for official users was limited to the UK, France, Spain, Finland, Netherlands, Norway, and home country Sweden.  Let’s be honest, these numbers implied that 5% of the registered user based paid for the service.

Since 2010, the firm enjoyed not only expansion to additional countries, but also prime promotion through Facebook thanks to a investor family connection.

The number of paid subscribers has grown by a factor of six since the fall of 2010, from 500,000 to a reported 3,000,000.  If the registered user base grew by a similar ratio, a total of 60 million users would have signed up.  Given the tweaks in the free service limitation since initial launch, combined with certain growth trends and metrics along the way, it seems reasonable that the total registered user base grow by 4x over the period.

As far as I can tell, upwards of 40 million (+/- 5 million) people have signed up for a Spotify account since their launch. If I were to guesstimate their actual conversion rate for registered users to paid users that rate would be 7.5% (+/- 1.5%).

And so, when that conversion rate is measured as a function of registered users who have converted to paid users, this uptick in addressable market and promotion may have resulted in only a slight increase in the true conversion rate for the service. Something about these services are leading them to appeal to only a subsection of a subsection of the market.