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The allure of iTunes

My estimate of last quarter’s iTunes gross revenues suggested a spending rate of $40 per iTunes account. It would make sense to consider how that figure changed over time. The following graph shows the pattern:

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You can read each bar in the graph as the total “ARPU” or average revenue per iTunes user[1].

I overlaid a graph showing the total number of accounts as reported by Apple to the (retroactively) estimated revenue structure. Account totals are measured with the right axis and ARPU with the left.  Note that I also broke down each component of iTunes as currently defined (Music, Video, Apps, Books, Software and Services.)[2]

The time frame covered is from Q2 2007, or the quarter prior to the iPhone launch. A few patterns emerge:

iTunes users spending at the rate of $40/yr.

In the latest quarter the iTunes top line grew by 32%. Additional newly reported items:

  • Quarterly revenues topped $4 billion (a new high) and the company suggests that this rate is maintainable by stating it has a “$16 billion annual run rate”. The pattern of revenues is shown below.

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  • The content portion of iTunes revenues was $2.4 billion, up from $2.1 billion sequentially. Growth into Q1 is not unusual as many holiday iTunes gift cards are redeemed during January.
  • Revenue growth has been surprisingly steady, averaging 29%/quarter for more than six years.

Measuring Platform Churn

The latest comScore data shows consistent growth in US smartphone penetration. The rate is now 58.4% of adult consumers who own phones. This is up from 20% only three years ago. The rate of growth remains a remarkable 1.2% per month. That’s 700,000 new-to-smartphone users every week. The historic average over 3 years has been 1.07%/month This after having crossed over 50% on schedule in August 2012. There appears to be no slowing.

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The next milestone I have pencilled in is the 80% mark which I extrapolate to be achieved by October 2014. 80% could be considered “saturation” which would signify a rapid slowing of new user addition. However, that might still not happen until 100%, depending on the availability (or lack thereof) of non-smartphones to buy.

Happy Birthday iTunes Store

iTunes (including software and services) revenues in Q1 topped $4 billion and were 30% higher than (re-stated) 2012 Q1 revenues. Accompanying this revenue figure were additional data points from the company:

  • Cumulative app downloads have surpassed 45 billion
  • Payments to developers reached a cumulative total of $9 billion
  • Payments to developers were $4.5 billion in most recent four quarters
  • Now paying $1 billion to developers every quarter
  • 800 apps are downloaded every second
  • iOS app revenues doubled since year-ago quarter
  • App Store accounted for 74% of all app sales in the quarter (citing Canalys)
  • App stores reach customers in 155 countries (850k Apps, 350k iPad apps)
  • iTunes music downloads are available in 119 countries (35 million songs)
  • Movies are sold in 109 countries (60k titles)
  • iBookstore is available in 155 countries (1.75 million titles)

This data allows for a few inferences:

The job the iPhone is hired to do, part II

I repeat what I’ve mentioned before: The iPhone is primarily hired as a premium network service salesman. It receives a “commission” for selling a premium service in the form of a premium price. Because it’s so good at it, the premium is quite high.

The job the iPhone is hired to do 

The original post on the hiring of the iPhone by operators was anchored in data about the revenue per unit (or price) that the product was able to obtain. The remarkable resilience in the exceptionally high average price showed that the iPhone was still getting a premium for moving users to higher levels of spending on network services.

The evidence was circumstantial however: By knowing the price and knowing it was far higher than competing products and knowing that much of it was paid by the operator and not the consumer (at least not up-front) implied that it was the iPhone, and only the iPhone, that was hired as a network service sales tool.

Now we have more evidence thanks to Ben Thompson (@monkbent). I illustrate the data here as an x-y scatterplot.

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