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Canaccord Genuity analyst Mike Walkley writes in a note to clients today. “In fact, we believe iPhones are outselling all other smartphones combined at Sprint and AT&T and selling at roughly equal volume to all Android smartphones at Verizon.”
via iPhone Tops Sales Charts at Each of Its U.S. Carriers – John Paczkowski – Mobile – AllThingsD.
That’s useful data. Mainly because we can use it in combination with comScore data that tracks a different market measure. comScore’s MobiLens service tracks US mobile installed base. By measuring the difference between their stats one month to the next, one can measure the gain in a particular platform.
Comparing that gain with the sell-through rate in the same period can yield a figure for the number of units sold as upgrades vs. those sold to new users.
Horace talks again with Dan Abrams about film budgeting, Kickstarter, pre-production, location, technology for production, and a surprise announcement. We also discuss the project-oriented nature of movie production vis-a-vis “pipelined” product development, the history of studios and how they evolved, Pixar and much. much more.
via 5by5 | The Critical Path #32: Mockumentary.
Show Notes and Links:
- Roger Corman blog post
- The False Profit Kickstarter page
- Discussion of the film by Bill Torgerson, another CP guest
- Steven Bach and the history of United Artists “Final Cut”
- Steven Bach on Wikipedia
- Technology drivers for the Impressionist era
This was quite a fun show.
Philip Elmer-Dewitt published a table from Piper Jaffray’s Gene Munster which has some interesting details. Munster has taken a four year “tech sector” view of value creation (and destruction) and tried to see if there is a bound on the value Apple can continue to capture. This “share of value” is one of many approaches to bounding an opportunity. You could consider “share of wallet” by measuring disposable income, or “share of eyeballs” by measuring screen time available or even “share of GDP”.
The attractive part of the share of value of industry is that we have an implicit way to see winners and losers, or the transfer of wealth from one group to another. I charted the data published as follows:
Seen in this context, Apple generated nearly as much value as RIM, Nokia, Sony, Dell, HP and Microsoft destroyed. The rest of
At the end of 2011 Apple’s net property, plant and equipment (PP&E) was $7.8 billion. This reflects $12.34 billion gross PP&E net accumulated depreciation and amortization of $4.5 billion. The depreciation and amortization increased by a total of $533 million and the gross PP&E increased by only $572 million. I say “only” because in the previous quarter PP&E increased by $1.42 billion. If we assume that this growth is equivalent to capital expenditures for the quarter, it’s also a very small amount given the company’s stated intentions to spend $7.1 billion during the fiscal year.
The gap is illustrated in the following chart:
Charles Arthur, writing for The Guardian, has noted that court filings seem to be revealing Google’s Android revenues. If this is the case, we have a significant breakthrough in understanding the economics of Android and the overall mobile platform strategy of Google.
The new data is a reference to a settlement offer Google made to Oracle of $2.8 million and 0.515% of Android revenues on an ongoing basis. The key assumption to make this data useful is that the $2.8 million offer represents 0.515% of revenues to date.
In other words, that revenues from 2008 to end of 2011 multiplied by 0.00515 results in $2.8 million. That implies that revenues from 2008 to 2011 were $544 million.
I think that’s a fair assumption. I don’t see why Google would offer a higher or lower royalty rate for years 2008 through 2011 than for years after 2011. The offer would seem to be 0.0515% in the future as well as retroactively in the past.
If we work with this assumption then the next question is how to distribute this $544 million over the four years 2008 through 2011? The installed base of Android has grown exponentially and it would seem logical to assume that revenues have followed in a similar pattern. Here is a chart of installed base given activation data supplied by Google.
If we assume revenues were distributed the same way we would get something like this: