Theory.
iOS unit sales crossed over 700 million units last month. That is a significant milestone but the total number of units in use is likely to be lower. My estimate based on device replacement assumptions is that about 500 million are still in use.
The estimated break-down of units sold and in use by device type is shown below:
Continue reading “When will there be one billion iOS devices in use?”
Benedict Evans explains well the problem with measuring Android tablets. There are no reliable data collected because many of the devices are invisible through the regular, measurable channels:
The only measured statistic happens to be component shipments. Items such as screens, CPUs or perhaps memory might be visible to market analysts. It’s therefore tempting to add up tires manufactured to determine what’s getting sold in auto dealerships.
But it’s also hugely problematic.
In the post Seeing What’s Next, I showed how the rate of change of adoption of technology varies with time and asked what might be experienced by present and future generations.
It turns out that knowing how what innovations become universal and the speed at which these technologies are replaced can give us an idea of what individuals might experience in their lifetimes.
Here’s how to think about it:
The adoption of smartphones in the US is on track for reaching 90% of the available audience by August 2016. This is a mere eight years after smartphones reached 10% penetration. As far as technologies go, that’s pretty fast. To get an idea of how rapid, I plotted a few other technologies and the time they took to grow within the US market.
A few things to note: Continue reading “Seeing What’s Next”
Using logistic curves to measure diffusion of innovations is a powerful method of analysis. However there are limits to what can be learned. The methodology helps in understanding how quickly a pervasive technology is adopted. It assumes that the technology “fills all available space” within a market. It therefore also assumes that whatever problems the technology solves are universal problems.
Put another way, if a technology is not universally useful, it tends to peak before a market saturates. This “universality” condition is in evidence when observing that pervasive technologies are adopted not only by all members of one national market but also all nations and through all means of government and regulation. In other words that the jobs that the technologies are hired to do are so important that they bulldoze any and all obstacles placed in the path of adoption.
The only difference is one of timing. Some regions are quicker than others. Institutionalized obstacles essentially defer rather than deter adoption. They impede rather than block.
And I am pretty sure that smartphones solve universal needs and their adoption will be nearly 100%. They also have fairly low impedance given the speed of adoption (50% penetration in most large markets seems to come in less than 5 years.)
That’s the story for the technology, but how value is captured is another story.
Who captures and how it’s captured are questions of commerce not economics. They are informed by competitive advantage and business models. The puzzle seems to be that individual companies don’t capture value in the patterns of Logistic curves. Or at least I don’t think they do.
Consider the graph below.
Continue reading “How many mobile platforms can a market sustain?”
Cinematic is to film as Literary is to prose. It’s not a measure of quality or of beauty. It’s mainly the application of technique to impart an additional layer of meaning to the writing, performance and direction of the medium. Cinematic technique lifts the viewer to another, more profound point beyond the literal.
Whether it’s film, literature, verse, stagecraft or other visual arts, techniques vary but they have the same purpose. They make the audience feel more than what is said.
The quality of a good work of art is that it tells a truth without saying it.
And this, I believe, is what the analyst should also strive for. It is at least what I try to do and, having practiced for a while, I think I can convey some of the techniques I learned.
We don’t have a better word for describing these techniques than “cinematic”. They allow the presenter of complex and rich data to convey meaning with an economy of rhetoric. They draw the audience into completing the picture thus making it their own. They persuade without pleading. They teach without lecturing. They create, in a way, a poetic language for data that combines techniques from several arts, especially cinema.
The means for teaching this is a workshop called Airshow.
Together with IBM, we are proud to bring Airshow to Boston, New York and Helsinki this year.
Register here.
The most interesting juxtaposition in market data happened this week.
Apple announced 9 million units of the iPhone 5s/c sold in their opening weekend while BlackBerry recognized 3.7 million smartphones sold in the three months ended August 31.
I will state these data points with a different emphasis: while Apple explicitly reported, both in a press release and in an SEC filing, Sales of 9 million units, BlackBerry reported recognition of revenues on 3.7 million units. At the same time BlackBerry also reported sales to end users of 5.9 million units.
So, did Apple sell 9 million iPhones in three days? What about units ordered and not delivered? Which of these units will show up in the company’s income statement? Conversely, did BlackBerry sell 3.7 million or did it sell 5.9 million smartphones in three months?
The answer is dependent on what constitutes a sale. I suggest re-reading the Sold and Shipped: A Brief Introduction post from last year. Understanding is complicated by many factors, not least of which could be intentional signaling by management. We may never come to a perfectly matched comparison of the two companies’ situations but our job as analysts is to see through the signals and obfuscating language and interpret a pattern. A pattern that extends over a time and helps us learn.
My observation is one of contrast. The juxtaposition this time is that Apple emphasized sold and not shipped while BlackBerry sold more units to end users than it recognized revenue. These signals reflect precisely the inverted fortunes of the two companies.
For BlackBerry the higher sold than shipped recognition was due to a product launch failure. Units which were shipped (and recognized as revenue) last quarter did not sell and the company is not only writing off the inventory but has drastically reduced its deliveries of new units in order to drain inventory. The company explains:
During the second quarter the company recognized hardware revenue on approximately 3.7 million BlackBerry smartphones. Most of the units recognized are BlackBerry 7 devices, in part because certain BlackBerry 10 devices that were shipped in the second quarter of fiscal 2014 will not be recognized until those devices are sold through to end customers. During the quarter, approximately 5.9 million BlackBerry smartphones were sold through to end customers, which included shipments made prior to the second quarter and which reduced the Company’s inventory in the channel.
The company is essentially saying that due to the unusual circumstances of a product launch failure, they will change how they account for their business. They don’t have the confidence that units shipped will actually sell and will not recognize them since they fear they will have to write some off. They are signaling: They are being far more conservative, not reporting shipments alone because those shipments could essentially be value free.
When seen as a pattern, the new figure on recognized revenue units needs to be shown relative to the history of recognized revenue units. Continue reading “The figurative sales of iPhones and BlackBerries”
My thanks to Eric Jackson for his thoughtful questions and post on Forbes.com. You can read full interview here but I repeat a few non-biographical questions and answers here for discussion:
An Interview With Horace Dediu: On Blogging, Apple And What’s Next
Q: Turning to Apple, where is it at right now as a company in this post-Steve Jobs period?
A: Still too early to tell. They seem to be cooking a lot of things and the great experiment of whether a company can be Jobsian without Jobs is still going on. I have been trying to put together a picture of how it operates. It’s hard because that’s their biggest secret. It’s also a picture that few people have ever seen, even those who worked there a long time. The glimpses so far are tantalizing but there is so much we don’t know and thus can’t assess how robust it is. One thing that is clear to me is that there is no absorption by mainstream observers of what makes Apple tick. It’s hiding in plain sight because what it is isn’t anything anyone can recognize. Case in point is the functional and integrated dimensions. It’s the largest functional organization outside the US Army and more integrated than Henry Ford’s production system. Just describing it sounds medieval and it’s so far outside convention that it’s not something reasonable people are willing to believe actually exists.
Q: Is Tim Cook the right CEO for the company at this time?
A: I hold the belief that he’s been CEO for much longer than it seems. Jobs was not a CEO in any traditional sense. He was head of product and culture and all-around micromanager. He left the operational side of the company to Cook who actually built it into a colossus. Think along the lines of the pairing of Howard Hughes and Frank William Gay. What people look for in Cook is the qualities that Jobs had but those qualities and duties are now dispersed among a large team. The question isn’t whether Cook can be the “Chief Magical Officer” but rather whether the functional team that’s around Cook can do the things Jobs used to do.
Look at it another way: I subscribe to the idea that any sufficiently large company is a system and needs to be analyzed using a lost art called “Systems Analysis”. This is a complete review of all parts and the way they inter-relate. However, since for most of its life Apple was personified as an individual, what came to pass for Apple analysis was actually the psychoanalysis of that individual. It makes for great journalism and best selling books. It’s also banal and almost certainly wrong. The proof is in the vastness of complexity and number of people involved. Engineers tend to think about constraints and the constraints on companies are innumerable.
Q: You’ve written extensively on the post-PC period, when will we come to the post-phone period – if ever?
Continue reading “An Interview by Eric Jackson: On Blogging, Apple And What's Next”
I recently tweeted that any discussion related to wearable technology needs to begin with a description of the job it would be hired to do. Without a reason for building a product, you are building it simply because you can.
The reason a product deserves to exist is that it can do a job that needs doing and that few, if any others can also do it. This happens when the job is unstated and difficult to perceive. Put another way, the difficulty behind jobs-to-be-done based design is that jobs are never plainly evident. In contradiction to invention, where the problem being solved must be as clearly stated as its solution, value-creating innovation meets new and unarticulated needs. Even when created, the value is more subtly perceived, often only after prolonged use.
Which brings me to the M7. Apple chose to highlight a component (curious in itself) which it bills as a “motion coprocessor”. It claims to be measuring motion data via accelerometer, compass and gyroscope and processing the information in some way. By bundling the sensors and their management into one integrated chip battery consumption is reduced and these motion monitoring functions are performed more efficiently.
But what for? The examples given during the presentation for an iPhone with M7 don’t add up to a lot of benefit. An iPhone could be used as a fitness companion but it would not a very good one. Compared with, for example, a Nike FuelBand, an iPhone could not track your activity well. It’s often not moving, sitting on a desk or in a purse or pocket while you are doing exercise. It’s too big to take into a basketball game. It can’t “observe” your activity because it’s not worn during many activities and if it is worn it is in a position which does not inform much about what you’re doing. Phones are too big to be used as physical activity monitors.
Hence the question of what is the M7’s job to be done. As part of an iPhone, it does not seem to have one. Saving a bit of battery life is not a job, and certainly not one that needs to have billing in a media special event. The answer must be that the M7 was developed for some other, as yet unstated reason.
When the A series chips were created Apple leveraged the in-house design and cost reduction to make a wide range of products with more than 700 million examples built. Designing a chip needs a broad application domain.
Perhaps this is why Apple chose to describe the iPhone 5s as “forward-thinking”. The M7 and the Touch ID are like research projects whose actual value will be realized at some future time, in probably different contexts. The M series of chips may become Apple’s “low end” microprocessor as the A series climbs the trajectory into core computing tasks (read: phone, tablets, TV, laptops).
M might be the chip for the wearable segment, woven into a whole new fabric of uses and jobs to be done.
When looking at the Race to a Billion data I noticed that some platform adoption ramps look quite different from others. It’s not just a matter of “slope” or rate of growth but a distinctly different shape.
If you look at the graph below you might see the difference yourself.
First, note that the graph is logarithmic. A straight line on a log chart implies exponential growth (the y values are a constant to the power of the x value). However, none of the graphs show straight lines. None of the platforms follow exponential growth1.
Second, we can eliminate linear or logarithmic functions. They simply do not fit.
That leaves: polynomial or power. It’s easy to test these alternatives for all the functions and it becomes clear that most of the platforms split into one of these two classes.
Continue reading “A new platform classification”
