What if we could find patterns in data about the industry to confirm or deny these oral histories? Fortunately some data actually exists. There are collections of data about movies released and some of the attributes of these releases such as box office receipts, theaters opening, budgets (sparsely), and distributors. This data is typically used for marketing purposes to celebrate the “hits” and thus entice more people to see the movies.

However, other information can also be obtained through analysis of the data. In fact, a rather compelling story can be told. That’s the purpose of this post. Our story will be expanded over time but this first draft should be enough to allow a discussion to begin. The story is built in four sub-stories:

1. Drama / Comedy vs. Adventure / Fantasy: Why the Ancient Greeks had it all wrong.
2. The magic of summertime: Market access and school schedules
3. The perpetual incumbency: What happens to Hollywood startups
4. The Popcorn economy: The peculiar power of theaters in an age of television

These stories were the result of observations in data alone. We have no first-hand knowledge of the industry and have only used basic software tools to seek out patterns in public data[1].

The data set includes approximately 12,000 titles released between 1975 and present with various degree of completeness. Some of these years are not completely populated and some data is undoubtedly in error. However the large sample should offer enough substance for patterns to emerge in spite of this.

Drama / Comedy vs. Adventure / Fantasy: Why the Ancient Greeks had it all wrong

Storytelling has not changed throughout history. The same types of stories affect audiences the same way since stories were first told. The earliest known “genres” were tragedy and comedy and they are still seen as the bedrock of theater today. The same is true for movies. The following bar chart shows the distribution of genres as cited by movies over our data set. Drama and comedy are about 50% of all movies made. If we add romance they are 60% of cited genres. This is understandable given the history of theater. However the profitability (or revenue potential) of those genres is not as strong as Action, Adventure and Fantasy. The following chart shows the same count of genre citation but only for movies grossing over $200 million (which we chose to call “blockbusters”). Action, adventure and fantasy handily beat Comedy and even SciFi beats drama. Romance, musicals and and mystery genres typically associated with female audiences are very rarely successful as blockbusters. The overall data is shown in the following table. The rows represent gross revenues and the columns genres cited ranked in order of frequency. One can easily observe the density of low earning drama and comedy (grey colored) vs. the more lucrative instances of fantasy and adventure. These male-dominated genres have consistent success into middle, high and very high revenue tiers.

The reason for this blockbuster attention to action and adventure is probably the prevailing theater-going audience demographic: adolescent males. The industry still produces the classic genres but less profitably than what the current targeted markets.

Indeed, these genres were very uncommon in eras predating the late 1970s. More “adult-oriented” movies like romance (Gone with the Wind), musicals (The Sound of Music) and epic movies (Ben Hur, The Ten Comandments) were common in the “golden age” of Hollywood. As we’ll see in the next section, the age of the audience is a now driving more than just genre selection.

The magic of summertime: Market access and school schedules

Using the word “Summer” before “Blockbuster” has become almost redundant. What is less understood is that watching movies in the summer is rather a new idea. It started effectively with Jaws in 1975  and ever since major film studios have planned their annual marketing around a summer schedule. There is clear evidence for this in the data. Here is a table showing the blockbuster release months since 1975. (The number of titles grossing over $200 million and the months in which they were released).

The same pattern repeats for the next tier of highly successful movies–those grossing between $100 and $200 million.

But what about the less successful movies? Those earning less than $50 million actually show a counter-success pattern.

To contrast this further, here is a stacked bar chart comparison between frequency of release by month for the most successful tier vs. the least successful tier (colors represent years with oldest at the bottom).

It would seem that summer and success are in a causal relationship. There is a clear pattern with the only non-summer months having blockbuster releases being November and December.

But there is more to this pattern.

The blockbuster schedule happens to coincide with the school holiday schedule in the US. This again points to the “profitable” audience being school-age children, and mostly male.

The perpetual incumbency: Where are the Hollywood startups?

The third observation has to do with the  major film studios. We took the box office revenues and allocated them to the studios over time. The following chart shows the share of those revenues for the top 30 studios. The top five were earning 64% of revenues in 1975 and the top five were earning 60% in 2011. One of the top five from 1975 is no longer in the running this year (MGM) and one new major was added (Buena Vista, owned by Disney).

There has been one other notable change: Columbia was acquired by Sony but stayed out of the top 5. Beside Disney there is one new significant entrant in Dreamworks gaining share in the last decade.

But the prevailing impression from the data is that the incumbents remained as such during the last four decades. There are many small studios but they have not “disrupted” the market by shifting significant revenues out of the hands of the majors. Indeed the typical strategy seems to be to start a studio with the hope of it being acquired by a major.

Even the entrants are industry insiders: Disney and Dreamworks are not asymmetric in their business models in any way. In fact, when viewing the data by blockbuster creation, the majors feature prominently. There are only a handful of blockbusters from the smaller companies.

When looking at overall production rates, again the majors (and their subsidiaries) dominate. Here are the top 75 distributors of movies showing nearly 9000 movies (click image for full-size):

The conclusion from this data might be that Hollywood does not change all that much in terms of who makes the money. But that itself is a symptom of a deeper reality: that there is little that changes at all.

We can even perhaps hypothesize that there is no business model innovation taking place. Any such innovation is usually manifested in a reversal of fortunes for incumbents.

But technological change is happening. The following section describes how this appears in the data.

The Popcorn economy: The peculiar power of theaters in an age of television

The data we are sampling (box office gross revenues) is actually a small and shrinking subset of what movies generate. In addition, there are international revenues, media sales (DVD, video tape), TV rights, and merchandise to name a few. But theaters are still an important part of the picture.

Although they make up less than 20% of gross, their importance is disproportionate due to the signal they send other markets about the popularity of a movie. In other words, a movie must first be proven in a US theater before its value is priced by the other channels.

However, this essential theatrical release has changed in one fundamental way. Releases are now geared for larger initial scopes with shorter runs and less dependency on “word of mouth” to increase sales.

Consider the following view of release breadth (how many theaters are in an opening) by openings over time. The columns represent the number of theaters used in the opening of a movie in increments of 100. The rows are years. The cells contain the number of movies opening with that size footprint.

The pattern shows the number of theaters used in openings increasing steadily. the mean number of releases is shifting to the right. Part of the reason is that theaters shrank in size over time, but there is also the factor of needing to create a large audience at the outset of a release. The increase in “footprint” at launch (a factor of 10) is in excess of the slicing of theater sizes (a factor of two)

This broadening of launch is coincident with a decrease in viewers. As a result the notion of how movies are marketed has changed. The idea that a movie could start small and gain a following gradually through word of mouth is obsolete. That low cost approach to marketing (called “earned media exposure” in marketing) is unreliable and completely discounted.

Instead, studios manufacture an audience through paid advertising and promotion. This initial burst of viewers is essential not just for generating revenues. It’s essential to driving long-term revenues.

The reason is that technology now allows the movie to be “monetized” through multiple channels. Recorded media, broadcast rights and even in-flight entertainment are all there to milk a franchise. But there is a need to get that initial vote of confidence from one crucial audience. That happens to come only from theatrical release.

So with the scheduling pressures, the narrow window of purchased release buzz, the onus is on a broad release as quickly as possible. This holds especially true if we filter out only the blockbusters. Note how the release has increased steadily as well and that since 2000 all blockbusters released to over 3000 theaters. Compare that to Star Wars in 1977 releasing to only 30 theaters.   Notes:

1. See Jay Epstien’s The Hollywood Economist.
2. Tools used include Excel, Numbers and our own App
3. Data sources: Coolector, Imdb, BoxOfficeMojo, The-numbers.com

I’ve also been scoring my performance and discussing the causes of errors. Here is the analysis for the fourth quarter 2011.

My estimate for Mac growth was very precise. It was based on a 27% growth estimate. It turns out that growth was well within historic range and thus quite predictable.

iPhone and iPad error was very modest at less than 5%. This led to a very low error on Revenues (as I also did not expect ASP to change).

I was too pessimistic on the iPod, expecting 30% decline and 13.6 million units. In reality the business declined by only 21% and Apple still sold 15.3 million units at an average selling price of $164.

Due to an underestimate on margins which are highly sensitive to iPhone margins my gross margin estimate was off by 6%. This led to a relatively poor EPS error of 11%.

Nevertheless, the “grade point average” for the quarter was 3.6 which is nearly an A-. This is an improvement from the B- from the last quarter.

Because the accuracy was good, there is little to be learned from the quarter. Perhaps the most important observation is that the iPhone recovered from two outlying quarters. The second quarter where it overshot expectations and the third quarter where it undershot. These were hard to predict because the product went through a delayed transition which whip-sawed both production and sell-in.

What we saw in the fourth quarter is a  return to the predictable historic growth of nearly 100%.

To give a perspective on the performance of other analysts, a motion chart showing their errors is here.

See also the video PED published of the same data here. (This video was produced from the same motion chart shown above, selecting bars instead of bubble as the chart type).

Notes:

1.  The scoring of the error is according to the following table:
2. 
3. The results can be seen for all the other analysts as a table here (with data since mid 2008).

via 5by5 | The Critical Path #24: Spidey Sense.

The relative shares of revenues and profits are shown below:

Individually, Apple’s share of units, revenues and profits is chronicled below:

Apple reached 75% of profit share, nearly 40% of revenue share and 9% of units share.

Apple and Samsung combined for about 91% of profits with RIM third at 3.7%, HTC fourth at 3.0% and Nokia last at 1.8% of a $15 billion total for the quarter.

In terms of revenues, Apple had 39% to Samsung’s 25%. Third was Nokia with 12.6% and fourth RIM at 8%. HTC only managed 5.5%, Motorola 4%, LG 3.3% and Sony Ericsson 2.7%.

Apple achieved this with a total market share of about 9%. Second place Samsung has 22.6% so there is significantly more room to go before Apple gets to be second. Nevertheless, the climb to third was achieved in less than five years. At the time when Apple entered the market third place was held by Motorola with shipments of 35.5 million units (Q2 2007). Motorola only managed 10.5 million in the last quarter.

To see growth I prepared the following chart showing the fourth quarter’s growth by vendor according to four years’ compounded, sequential and y/y growth.[1]

On a four year compounded basis, the pattern is that of Apple, HTC and RIM showing significant growth with Samsung showing moderate growth. The other vendors all had negative real volume growth. The list does not include “Others” which had 13% compounded growth.

Projecting Apple’s 60% rate of growth and a market growth of 10% leads to Apple obtaining 27% share by the end of 2014. My guess would be that is when we’ll be able to write a post about Apple being second.

—

Notes:

1. I don’t have HTC data for Q4 2007 so the compounded rate is measured for three years.
2. Additional volume data

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Note that the scale is logarithmic so that the growth patterns can be seen clearly.

It shows Apple’s cash/share vs its price per share. First, it should be noted that the company’s cash has been growing reliably on a log scale, increasing by a factor of 10 in five years. As a proxy for retained earnings, the cash trajectory shows exceptionally predictable earnings.

Second, although the share price has grown it has not grown either as reliably or as quickly. It’s as if the reward for reliable earnings is a significant dissent.

Third, if we subtract the cash/share from the price/share we obtain the enterprise value (EV) per share. The EV reflects the future value built into the price. It reflects the expectation of more earnings. In the chart above one can see the shrinking of the gap as a multiple of the cash value. To bring that gap into focus, I prepared this chart showing the percent of the share price allocated to past value (cash) and future value (EV).

One can see the gradual erosion of emphasis of future value. With the exception of the credit crisis induced equities crash, future value as a percent of total value is at a new low. In late 2006 the future was was about 86% of the share price. At the end of last week it had dropped to 79%.

Another way to interpret this is that in 2007 when the iPhone was born nearly 90% of the business value was thought to be in its future. Today only 80% is future value.

To clarify further, if a company reaches a price where 100% of its value is cash then it’s seen as having no future value. Having any significant portion of the price attached to the past is extremely rare for a growing company and I would say unheard of in a company growing as rapidly as Apple is.

This preoccupation with the past and discomfort with the future is, I believe, the key to understanding the valuation paradox for Apple. I have gone over the conventional metrics of P/E and P/E/G(trailing) that illustrate absurd pricing (updated below). But they don’t help explain the mis-pricing.

A disruptive company is inherently unpredictable. Success and growth are unrewarded because there no understanding of the underlying causes of these phenomena and therefore there is no expectation of repetition.

In other words, a five year dead-straight line of logarithmic growth is not a sign of anything resembling reliability if the underlying causes are not understood. Today Apple is still a mystery wrapped in an enigma and without a causal understanding of its suceess its pricing will remain paradoxical.

Another form of investing (spending now, reaping benefits later) is the spending on research and development (R&D). Let’s have a look at Apple’s R&D expenses by quarter since Q1/2006.

Apples R&D expenses as percentage of sales have been declining from almost 4% to close to 2% in the last six years as shown in the following chart:

While Apple’s absolute R&D expenses have grown at an annual rate of 33% they have not kept up with the far higher sales growth rate. Apple’s current rate of R&D expenditure is compared to a peer group [1] below.

Apple’s R&D/sales ratio is between Dell and HP, companies which do not develop their own software or hardware.

Beside the absolute, relative to sales and relative to peers, another perspective is to measure relative growth. The following chart represents shows the 5 year average growth of R&D expenses and on the y-axis the size of last 12 months R&D expenses as a multiple of Apple’s R&D costs [2].

Acer’s R&D expenses for example are almost non-existent compared to Apple, but have grown almost 100% per year while Microsoft’s last 12 months R&D expenses are almost 4x that of Apple’s and have only grown slightly over the past five years. Google’s R&D costs have grown at a similar rate compared to Apple, but remained double those of Apple’s throughout the last five years.

Finally, we should look at the payoff from R&D spending over the last five years.  R&D growth should result in margin improvement, but does it? The following chart shows the relationship between spending (size of bubble), margin improvement (x-axis) and sales growth (y-axis).

Of our sample, only three companies have achieved sales growth AND simultaneous margin improvement over the last five years. But while Microsoft and Dell have achieved sales growth and margin improvement slightly above zero, Apple has improved its bottom line by 13 percentage points and its top line by an average rate of 45%.

In spite of Microsoft’s, Google and Nokia’s massive R&D growth, their returns from that investment have been meagre to negative in terms of margin and only Google showed appreciable sales growth.

Discussion

R&D expenditures on a stand-alone basis, in absolute or relative terms, do not correlate with disruptive growth. Essentially, you cannot buy innovation.

In fact, sometimes it’s nearly free. Most startups spend less on R&D than large companies do on stationery and yet the bulk of wealth creation has come from these tiny companies. The challenge is, in fact, that if you have resources you find ways of spending them (the quaint American cliche of ‘cash burning a hole in your pocket’). The temptation is always there to fund a new initiative which bright people can always envision.

Leadership in innovation is not the courage to spend but the courage to focus.

“Focus is about saying ‘No’. And the result of that focus is going to be some really great products where the total is much greater than the sum of the parts.”, Steve Jobs (1997)

[1] Our set of peer companies as shown in previous posts but excluding LG, Lenovo and Motorola Mobility due to lack of data, but including Sony Ericsson and Nokia’s mobile phone division Devices & Services.

[2] In this post “year” and “last 12 months” refers to the time frame from Q4/(year-1) year to Q3/year due to lack of published data regarding Q4/2011. Five year data runs from Q3/2006-Q3/2011.

[3] A note on capitalized R&D: Most of the companies analyzed do not have capitalized R&D costs. Some have small capitalization, but so small that the inclusion would not change the decimal points of percentages. The only significant amount of capitalized R&D appeared in Amazon’s earnings releases: capitalized R&D cost were USD 100 million in Q3/2011. The amortization of theses costs in included in Amazon’s EBIT (Operating imcome).

Q3 turned out to be a transitional quarter with 52% growth but Q4 proved to be  ”exceptional” with growth above 100%.  This is the third highest rate of growth in the five year span shown. Sales growth was also very near the top of the range, coming in fourth historically.

When looking at the components making up sales, we can see each product line’s revenue growth in the following table: 

The growth leaders were also the largest product lines in terms of revenue. iPhone and iPad had triple digit growth rates and that translated into the bottom line growth.

Music related products and services which Includes revenue from sales from the iTunes Store, App Store, and iBookstore in addition to sales of iPod services and Apple-branded and third-party iPod accessories grew at 42%. This is the highest rate during the time frame shown.

Peripherals which Includes revenue from sales of displays, networking products, and Apple TV was next with 29% revenue growth.

The Mac line followed with 22% growth and Software which includes sales of Apple-branded and third-party Mac software, and services through the Mac App Store grew at 7%.

The iPod line continued to shrink with -26% decline.

I used color coding in the table to show the patterns of change and what I note is that the iPhone and iPad lines continue to be exceptionally fast growing businesses. The duration and amplitude of these streaks of growth should be a clear signal about the dynamics of these markets and Apple’s role within. Too often, I find, these signals are ignored.

5by5 | The Critical Path #23: Auteur Theory.

If you make software you really should pay attention to how art is made. And vice versa.

Show notes:

• Interview with Horace about his writing process
• The Rise of Motion Pictures
• Auteur Theory
• Love on the Big Screen

The evidence is still hard to pin down directly but the fact that PC sales (excluding the Mac) are down gives credence to the claim. Mac growth, in contrast, is largely unaffected. The following chart shows the the iPad volumes vis-a-vis Mac volumes and the Mac growth through recent history.

The iPad has out-sold the Mac since inception and is now about three times the volume. However, the growth rate in the Mac has not changed much.

The impact on the PC market has been discussed recently and the data in the following chart is an update given more precision on the units from Apple:

The new data shows that the iPad alone would be the largest PC vendor and Apple with iPad and Mac combined is selling 5 million more units (or 30%) than the top PC vendor.

The impact of the iPad is not specific to any single vendor (Apple included). It competes for time and purchase decisions across all computing alternatives and though many times it’s additive, it is also substitutive and will become increasingly so.

Tim Cook mentioned on the earnings conference call that Apple sees the iPad as addressing a market larger that the PC market:

I truly believe, and many others in the company believe, that there will come a day that the tablet market in units is larger than the PC market. In fact, it’s interesting to note that in the U.S., it’s clear from the IDC’s recent data on desktop PCs in the U.S., that tablets exceeded desktop PC sales last quarter in the U.S.

MindNode is easy mind mapping for your Mac, iPad, and iPhone. Try out Mindnode Pro and MindNode touch today!

This expectation was evident when AT&T had an exclusive on the iPhone. The assumption among many (expressed in comments here as well as elsewhere) was that a switch to multi-carrier distribution would result in a price reduction and consequently a reduction in margins.

It was evident in the iPod and the Mac businesses over the years as prices for competing products collapsed.

It is also evident with respect to the iPad where expectations are that the Kindle will cause a reduction in the iPad price (and presumably in the margin).

However, the data we have from Apple on their pricing shows no concessions to competitive price pressure. The following chart shows the historic prices that Apple was able to obtain for its main product lines:

Over the time span shown (six years) the Mac and iPod saw modest price erosion as would be expected from declines in component costs but there is little evidence that Apple altered fundamental price points (note that these values represent pricing to the channel not to consumers who pay for retail mark-up or discounting/subsidy/taxation).

Similarly, both the iPhone and iPad have held prices very consistently. There is some seasonality as products are launched and there were a few quarters where the original iPhone was priced differently, with operator service revenue sharing lowering the up-front price. But there is a consistency to these products that speaks of a strategy that avoids price-based competition.

In the latest quarter Apple launched a few new price points for the iPhone. With the 3GS and the iPhone 4 and 4S available, Apple had the closest it ever came to a phone portfolio. There was some anticipation that the 3GS price point ($375) would impact the average selling price. However Tim Cook stated that the 4S was the most popular product. Furthermore, the 4S introduced a higher capacity (64GB) which offered a new, higher price point.

As a result, the ASP for the iPhone actually increased to about $660 in Q4. During the launch quarter the iPhone ASP does tend to be higher but history shows that the variance is small. Gifting seasons also affect pricing where the mix shifts to lower priced variants as seen with the iPad and iPod, but again, these are not great changes and entirely due to mix shift. There is no discounting.

The evidence I see is that Apple does not change pricing but rather stakes out a specific price point as resonating with consumers given their positioning. They then doggedly stick to it. Competitors tend to exploit open price points or try to position with specs on the same spots Apple occupies. That’s not a game I observe being played by Apple.

The following chart shows the composition and scale of Apple’s cash holdings.

At the closing price, Apple’s market value is equivalent to 4.3 times the value of their cash.

This network of rail was itself very efficient and soon overtook the ability of restaurants and small shops to absorb the produce. In order to keep the pipeline of food filled, the farmers (now with their own railways) bought most restaurants and grocery stores. They transformed them into more efficient food retailers and the result was even more consumption and demand for the food.

To keep the population happy, the farmers constantly devised new food hybrids and new recipes for the restaurants. The food design process became a profession, even an art form. The farmers employed the best and most creative minds  when it came to food. They created unique recipes that became instant hits and were widely consumed. They invented “star foods” and “blockbuster dishes”. The process of churning out these hits was so highly honed that farms became factories of ideas.

At the same time, the workers on the farms became unionized and they reached an understanding with the farmers so that every new “food production” allocated healthy wages to the workers. Due to their newfound fame, some of the food designers even got a portion of the profits from their creativity. Food making reached unprecedented levels of quality with very wide distribution to almost every restaurant in the world.

After a while the government became nervous about the level of control the farms (and their railways etc.) had over the nation’s food supply. The government worried that this level of concentration would lead to price gouging. They sought concessions and eventually the Supreme Court decreed that farmers could not own restaurants. The restaurants became independent. However, since the main food supply came from the same farmers and they owned the railways, the independent restaurants could do nothing but serve the same food that was on offer when they were part of the syndicate. The consumer did not see much change in either the availability or the quality of food though food prices did remain steady and the government was satisfied.

This system worked so well that it remained in place for over 100 years. The food production system became the stuff of myth and legend. It had its own name based on the fertile valley where it started: Fernforest.

It was not a system without any innovation. In the interest of efficiency small changes were always made. These “innovations” included outsourcing the farming so independent farmers who concentrated on efficiency had the largest share of production. Fernforest innovated in their financing practices and in their accounting for new food products. Fernforest accepted new ways of packaging the food. For example, freezing dishes and selling them in supermarkets ready to eat. They gave more creative freedom to the food designers and the farm technicians, many of who became rich.

But they all depended on the robust rail network. Over time, the rail network became the most critical component in the Fernforest system. So much so that 30 to 40 percent of all the money collected from food sales went to the railway consortium. With that vast cash flow the magnates of Fernforest decided which food hybrid would be grown, how much money would be spent in its production and essentially determined the flavors and tastes of society. They claimed that only they knew how to gauge what meal would become a hit and only they could be trusted to bet heavily on producing the source ingredients. Each production ended up costing hundreds of millions of dollars and since only they could ship the stuff, only they would finance the production. It was a self-fulfilling prophecy: Fernforest became taste makers and their executives used that advantage to maintain the enormous distribution fees which financed the next hit.

It looked like the system would remain in place, essentially unchanged, forever. Food was a necessity, people would always need to eat, and this was the most efficient way of making and selling food. And it almost always tasted good.

Then a strange new invention came into being. It was completely unrelated to the food business. It was, in fact, a new engine–the internal combustion engine. This spawned a new invention called the automobile. The automobile was so useful for many transportation tasks that it developed its own network architecture: the so-called “road network”. A new industry developed to harness the value of this engine, vehicle and transportation network. It became based in a different part of the country, coincidentally also a valley. It was called Petro Dale after the fuel the new engines used.

Fernforest took no notice of this new industry. They did not find use for the new technology. The Petro engines were too small and inefficient for their food train locomotives. Automobiles were designed for moving around small loads. Fernforest wanted to increase the size of its trains, not to decrease them. Autos were taking stuff in small portions to small consumers. It all looked inefficient and useless.

But Petro Dale was booming anyway. They found that their engines and cars were hugely useful for individuals transporting all manner of goods beside food. They thrived even without the support of Fernforest. The car owners built small businesses and grew them as the power of the engines grew. Eventually some entrepreneurs with cars decided that they could build gardens, greenhouses and farms and get their produce to local markets quite efficiently. It was not a big opportunity and some of these creative food innovators were acquired by the big farms and their recipes were scaled up for the Fernforest system. But small operators kept popping up and many made a modest living growing food.

However, some unscrupulous car owners copied the recipes of the big hits from Fernforest and duplicated the food locally and sold it locally in violation of the laws protecting recipe rights. Fernforest saw this as a clear and present danger and reacted very angrily. So much so that they sought to ban all cars. They saw cars and roads as built primarily to steal food ideas. Quickly, this became a toxic political conflict. After an outcry from car owners, the efforts of the rail and farm protection society (Nourishment Association of America or NAA) failed. They had reached the limit of their political power.

However, the attempt to ban cars caused Petro Dale to wake up to the threat of railways. They realized that even though much more commerce took place with cars and roads, and even though railways were becoming increasingly irrelevant, they would remain a potential regressive threat. In the past Petro Dale had sought to do business with the incumbent food network distributors, suggesting ways of taking the mass market food produce and distributing it to new franchises like mobile food trucks and fast food restaurants. They were spurned. Now they realized that trying to work with Fernforest was not just futile but  harmful.

Instead, Petro Dale changed strategy and sought to encourage local food production and guaranteed that any local recipe designer could get their dish distributed via a far more pervasive road and take-out network. Thousands of food designers took up the challenge. Even some of the designers that worked on the big farms saw the future was in independent distribution. Even though they had grown rich working for the big farms, they had been chafing under the “green lighting” process of railway magnates.They wanted to connect to take-outs and consumers directly, bypassing both rail and restaurants. Cars seemed suddenly good enough.

Farmers themselves began to be disenchanted with the railway system and Fernforest as a way to produce food. Their role had been reduced to being outsourced factories. The decisions on what to plant came from the railway executives. They felt that their technical knowledge, which created the industry in the first place, was not valued. What’s worse, from their point of view, the service railways provided was becoming inefficient. Freighting by train meant that they could not package food in novel ways. Being disconnected from the consumer, they could not create some of the hybrids they knew were in demand (as small growers had already proven). The creative people that had been working for them were fleeing while the cost structures (and union wages) remained. Their idea factories were falling apart.

Even the public woke up to the blandness of Fernforest products. They were becoming increasingly attracted to meal alternatives that came direct to their house (home delivery and take-out) and were tiring of chain restaurants. The local stuff was fresher. Besides, they reasoned, I own a car myself. Why should so much of my money be paid to railroads?

I did not get a response.

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5by5 | The Critical Path #22: Asymconf.

I had a huge headache while doing this.

Attendees will be treated to an engaging, participatory experience. Using the case method, you will be guided through four probing questions pertinent in modern market analysis.

1. What is disruption and how can it be harnessed? The case of mobile computing.
2. What are the jobs that the entertainment industry is hired to do? The case of Hollywood.
3. Is Wall Street more than good enough? The case of financial over-service.
4. What are the limits of education? The case of learning and the theater.

If you would like to take part, please register your interest here.

—Horace Dediu & Indrek Vainu

Underlying these lives (and deaths) of platforms were the growth (and decline) of fortunes of companies and people. I hoped that by observing these patterns insight could be gained into the conditions for success or failure.

The following graph shows the companies which were predominant (in the top five ranking by shipments[1]) during each year of the industry’s history and the volumes they were able to obtain during that period. I also added Nokia and RIM as examples of the challengers coming from mobile devices.

It’s a difficult graph to interpret. The pattern I observed was that most companies were not able to sustain presence in the top five ranking for long. Indeed, regardless of having one’s own platform or being a licensee, companies seemed to fade reliably. Some were acquired, some sold their operations but there is a distinct impermanence to their prosperity.

To sharpen this perspective of longevity I indexed each company to the point when they entered the “prosperity zone” and measured their staying power within it. The following graph shows these indexed lives.

Apart from Apple, IBM remained in the business the longest (24 years). Compaq was second with 20 years before being bought by HP. HP and Dell are still operating after 15 years, having tied Atari and Commodore. After these ancients, the upstarts Nokia and RIM are struggling to remain relevant after 10 to 12 years.

Then there are the transients like Packard-Bell/NEC, Toshiba, Radio Shack and Fujitsu which lasted less than 10 years. The newest PC companies are all from China or Taiwan: Acer, Asus and Lenovo. Their longevity cannot be called yet though Acer seems to be in decline. Lenovo, which took over IBM’s brands continues to grow. However, as yesterday’s post showed, without diversification into devices, pure PC makers are likely to fade.

But besides this interesting history lesson, there is a deeper, unsettling observation. The scale of these periods of prosperity is far shorter than the average person’s career. Indeed, the entire time span of the study is 35 years, about the time an average person will be employed. All the companies failed to maintain prosperity over this time frame.

These are a small sample, flawed by showing only hardware units, but I consider it a proxy for the overall technology market. Software-only or services companies have had no better records.

The implications for company managers and workers is profound. Chances are that the business you work for will not prosper for more than a decade and will reward you for an even shorter period. Similarly, as witnessed by the P/E ratios, investors have  given up on this industry as one with any quality of earnings.

There is no “rule” that this has to be so. The high mortality rate and short longevity of technology companies is a consequence of a failure to manage innovation and disruption. Apple may be an exception–one I think worth studying. Though not always prosperous, it has managed to survive, being the only company from the early era of personal computing to still be operating in the industry. Indeed, it’s now the most prosperous. Maybe it’s a fluke, but I rather think it’s due to an ability to re-define itself as the market shifts. Its essential quality being an ability to disrupt itself.

And if we think about it this way, ultimately, longevity is more important than prosperity. Without patience there is nothing but chance determining prosperity. The fate of more than company finances rests in the balance.

Notes:

1. Apple did not maintain a position in the top 5 throughout this period but is shown as a benchmark as it remained in the same business throughout this period.

I  added data from the smartphone industry, Apple and updated the PC industry figures with those from Gartner. Note the log scale.

The same information is available as an animation in the following video (Music by Nora Tagle):

www.youtube.com/watch?v=8h-C6u4yLj4

This data combines several “categories” of products and is not complete in that not all mobile phone platforms are represented. However, the zooming out  offers several possible observations into the state of the “personal computing” world as of today.

1. We cannot consider the iPad as a “niche”. The absolute volume of units sold after less than two years is enough to place it within an order of magnitude of all PCs sold. We can also observe that it has a higher trajectory than the iPhone which became a disruptive force in itself. Compare these challengers to NeXT in 1991.
2. The “entrants” into personal computing, the iPad, iPhone and Android, have a combined volume that is higher than the PCs sold in the same period (358 million estimated iOS+Android vs. 336 million PCs excluding Macs in 2011.) The growth rate and the scale itself combine to make the entrants impossible to ignore.
3. There is a distinct grouping of platform options into three phases or eras. The first lasting from 1975 to 1991 was an era of rapid growth but also of multiple standards and experiments. It was typical of an industry in emergence. The personalization of computing brought about a new set of entrants. The second phase lasted between 1991 and 2007 and was characterized by a near monopoly of Microsoft, but, crucially one alternative platform did survive. The third phase can be seen as starting five years ago with the emergence of the iPhone and its derivatives. It has similarities to the first phase.

We can also look at the data through a slightly different view: market share. Share is a bit more subjective because we need to combine products in ways that are considered comparable (or competing).

First, this is a “traditionalist” view of the PC market as defined by Gartner and IDC, and excluding tablets and smartphones.

This view would imply that the PC market is not changing in any substantial way. Although the Mac platform is gaining share, there is no significant erosion in the power of the incumbent.

Second, is a view where the iPad is added to the traditionalist view.

This view is more alarming. Given the first chart, in order for the iPad to be significant, it would need to be “visible” for a market that already ships over 350 million units. And there it is. If counted, the iPad begins to show the first disruption in the status quo since 1991.

The third view is with the addition of iPhone and Android.

This last view corresponds to the data in the first graph (line chart). If iOS and Android are added as potential substitutions for personal computing, the share of PCs suddenly collapses to less than 50%. It also suggests much more collapse to come.

I will concede that this last view is extremist. It does not reflect a competition that exists in real life. However, I put this data together to show a historic pattern. Sometimes extremism is a better point of view than conservatism. Ignoring this view is very harmful as these not-good-enough computers will surely get better. A competitor that has no strategy to deal with this shift is likely to suffer the fate of those companies in the left side of the chart. Treating the first share chart as reality is surely much more dangerous than contemplating the third.

I’ve used anecdotes in the past to tell the story of the disruptive shift in the fortunes of computing incumbents and entrants. I’ve also shown how the entry of smart devices has disrupted the telecom world and caused a transfer of wealth away from the old guard.

The data shown here frames these anecdotes. The data is not the whole story but it solidifies what should be an intuition.

[Update]

Additional platforms added to the first chart.

“Yes. I think it’s possible if you integrate tablets”

- Meg Whitman, HP CEO in response to the question of whether Apple could overtake HP in computer sales in 2012.

via HP CEO Meg Whitman Admits Apple Could Surpass HP in 2012 | MacTrast.

However, according to Gartner, HP shipped 14.7 million PCs in the last quarter. While we don’t have the total global figure for Apple, my estimates are 5.2 million Macs and 14.7 million iPads, for a total of 19.9 million computers in the same period.

There might be an error in both Gartner’s and my numbers, but the gap of over five million in Apple’s favor is unlikely to disappear.

The history of shipments from these vendors  is shown below.

The market shares are also shown here:

I included the market shares thee years ago, and the rankings with the iPad excluded.[1] Apple went from 3.42% share at the end of 2008 to 5.6% share today excluding the iPad or 17.6% including the iPad. HP went from 19.3% in 2008 to 16% today or 13% if iPad is included.

Apple is ahead of schedule on their taking[2] the top spot in terms of units.

Notes:

1. Although vendors other than Apple also sell tablets, their totals are unpublished and I am not aware of any estimates so. If anyone has seen estimates I would be happy to amend the data.
2. Throughout the history of the industry, Apple never held the top spot.   The Apple II peaked at 15.8% in 1984 and the Mac at 12% in 1992.

I also take into consideration the iPad as a potential competitor for computing purchases. Apple will report fourth quarter results including Mac and iPad shipments in less than two weeks but I am using my own estimates until then.

Here is how the platforms grew over the last few quarters (all figures are global):

The Gartner data implies a 3% decline in the size of the Windows PC market during the last quarter. This is the fourth quarter of disappointing Windows performance. If you observe the pattern for the Mac vis-a-vis  Windows there is a similarity in the overall cycle but a divergence in terms of growth. The “spread” between Windows and Mac OS X is increasing and has reached an all-time high of 28 points.

But the more interesting story is that the decline in Windows seems to be coincident with the growth in the iPad. The green (Mac + iPad) shows the effect of the new category while the red line (iPad only) shows that growth in isolation (year-on-year).

Coincidence?

Ever since the iPad launched the hypothesis that it has an impact on PC has been hanging in the air. Most analysts (including Gartner) argued that there was no effect from iPad and that the problems with Windows were all due to one-time circumstances. We hear the same about the last quarter with floods in Thailand being blamed.

However, in the latest quarter Gartner began suggesting that “consumers’ attention was diverted toward other product categories, especially smartphones and media tablets.”

One wonders if these “media tablets” are not PCs and yet they negatively affect the purchase of PCs whether they are indeed competing with PCs.

Like rebar’s however, open source is typically the least glamorous part of software and often powers the lower levels of a software stack making it easy to ignore. The other non-glamorous but essential part of the software industry is support and the “Help Desk”. Without this service, consumers would simply stop consuming.

So with this in mind, I’m grateful to HelpSpot for sponsoring Asymco this week.   Six years ago they created Open Source Help Desk List to assist companies looking for an open source help desk software solution. It’s been very successful, serving as an invaluable tool for thousands of companies.

They hope it can help you as well.

If you’d prefer a professionally developed and supported help desk application, then give them a look: HelpSpot: Help Desk Software

P.S. Check out the newest project they’re working on, the PHP framework Laravel.

5by5 | The Critical Path #21: Negative Costs.

Compensation based on “Net income” depends on there being a surplus rather than deficit. The problem is that the Fees and claims on income increase as they are defined as a percent of total income so the goal of break-even is a moving target.

The Negative Costs are approximated by the production costs as discussed and visualized in the previous podcast here.

Things did not improve much in the years following. The following chart shows the split between Windows and Mac OS X for the value of enterprise computers sold in 2007 through 2009.

Apple’s share of value went from 2.8% to 3.7%, an increase of 1 point of share, but one which in real terms was not very valuable because the overall market declined due to recession. Revenues for Apple were basically flat at around $2 billion each year as shown in the second chart.

However, the situation changed very rapidly in the last two years.

Apple’s share of enterprise revenues doubled from 3.7% to 7.8% in 2010 and nearly doubled again to 14.6% last year. A large part of that acceleration was due to the iPad which generated an estimated 6 billion in sales in 2011, triple what Macs were grossing in 2007.

Forrester goes further and estimates the same data for the next two years.

The estimate seems to be an extrapolation of the last two years which may or may not happen and we should treat it with caution.

However, if the pattern emerges as an extension of the near past, Apple’s position in the market at nearly 30% share next year would be a sea change. It may challenge for the top spot among all vendors to the sector.

It’s been said many times that “if only Apple put more emphasis” on Enterprise sales, imagine how much more growth they would obtain. However, I believe that this reversal of fortune did not come about from paying more attention but rather from paying less attention to Enterprise.

Enterprise buyers are demanding customers. They have highly specific requirements and ask vendors to conform to multiple layers of dependencies. As a result, enterprise vendors (led by Microsoft) have done all they could to make their software and hardware conformable to these needs. For decades this was the modus operandi and, due to the early adopter nature of businesses in personal computing, the resulting product was passed on to consumers as the “standard”. Consumers accepted it as it was better than having no computers at all.

The problem is that customers, especially demanding ones, don’t realize that a new basis of performance might be more beneficial. Apple and mobile devices in general presented this “low-end” alternative not in response to requirements from business but in response to requirements from consumers who had been much more eager in the last decade to adopt new technologies with fewer strings attached. All the security, conformability and legacy issues of the early business adopters turned them into late adopters as technology shifted.

So a company focused on an asymmetric market led with new solutions to problems that enterprises did not think they had. Mobility was something few IT managers asked for and was granted begrudgingly to only those “most valuable” who needed it due to cost and security considerations. As Apple willfully ignored these Enterprise needs it, paradoxically, created exactly what they needed.

It pried entry into the most difficult of markets by not even wanting to enter.

In thinking about how this market will evolve, I hear about “consumerization” and cost reduction as benefits and justification of this shift. But fundamentally, what is happening is a classic low end disruption. Enterprises increased their demands beyond what the users in those enterprises could absorb. Instead of ultra-secure, locked-down and immobile computing, users were looking for flexibility, agility and mobility. That’s what Apple was listening to.

Apple’s best strategy today would be to persist on this trajectory and listen to the under-served consumer rather than the over-served and over-demanding IT manager.

Many have forecast Apple’s performance but there are many other companies which are not easily estimated (Sony Ericsson, LG, Nokia, ZTE, Others.) But rather than dwelling on the specific quarter, we can make some long-range estimates of performance based on the historic data.

One method I use is to look at patterns of growth. Growth is the first derivative of market performance and it sometimes shows patterns which the performance itself does not. Consider the following chart showing the growth pattern for RIM.

The blue line shows the absolute year-on-year quarterly growth based on reported numbers of smartphones shipped. I’ve also shown the growth “relative to market” which is unit growth with market growth subtracted. This orange line represents how much faster (or slower) the company is growing relative to the market. If the orange line is above zero, the company gained share. If it’s below zero, it lost share.

I also overlaid the performance of the share price (right scale).

Now compare that with another long-term incumbent in the smartphone market, Nokia. 

Bearing in mind that the scales are identical, Nokia’s relative performance has been negative since early 2008. Persistent negative smartphone performance relative to market growth causes persistent loss in share price.

What about other, more recent competitors?

HTC is not a recent entrant in smartphones, and has gone through a phase of growth with Windows Mobile in the 2000′s. This is its pattern of growth (and its share price).

Keeping the scale for growth consistent with the other companies leads to a bit of clipping but the pattern of rise and fall also seems correlated to share price performance.

Finally, the following is the same analysis applied Apple.

Again, keeping the growth scale consistent leads to some clipping, but the pattern remains observable.

What seems to be rewarded to various degrees is consistency in growth of smartphones for companies which are primarily engaged in the market. However, failure to grow is punished with severe share price declines.

Being able to forecast smartphone growth seems to be useful, at least in the time frame of one or two years.

It may sound strange to hear me say that I don’t make predictions even though I often talk about how things will change and even provide some forecasts. The difference is one of degrees. A prediction to me is a very specific, time-sensitive and materially valuable recommendation. An observation about the future is an imprecise, intuitive hunch based on pattern recognition. It’s mushy. It’s theoretical. It’s the difference between saying a company is great and recommending to buy its stock with a price target in a time frame.

But it gets even weirder.

One of the recurring themes in Apple analysis has been that independent analysts, as a group, have been, on average, more accurate in quarterly forecasting than highly paid Wall Street Analysts. Many have asked why.  I believe that the answer is because independent analysts are theorists who make observations while professionals are data gatherers who make predictions.

First, we should understand the reasons why each group does what it does.  Wall Street sell side analysts are rewarded for making Buy/Sell recommendations that cause clients to retain the services of the firm they work for. They are not paid to be accurate with specific forecasts in fundamentals. They are paid to predict how stocks move. The objective is not to be accurate in the quarterly but to support the “call” or prediction. The disconnect from accuracy comes in trying to sustain the call with a “conservative” forecast.

For example,  an analyst with a “Buy” recommendation (and a prediction in the form of a price target) thinks Apple could ship 30 million iPhones this quarter and generate EPS of $10. But he also thinks the stock looks cheap even if they only ship 28 million iPhones and report EPS of $8.50. In this scenario he’s incentivized to publish the lower (less accurate) number. That way he supports his prediction. By dampening expectations, the stock rises on the “surprise”.

Because an analyst sells predictions, he self-censors what he believes.

Contrast that with the motivation of an independent analyst (aka blogger). His motivation is to (presumably) gain a reputation of accuracy and insight. He sells his reputation as a keen observer. Precise predictions or recommendations can turn out wrong quite often. Getting caught with these errors exposes the independent to instant negative feedback. The risk is far greater than the  opportunity.

So here is the difference: Analysts have an incentive to put forth a version of the future that supports their call on the stock. Bloggers have an incentive to put forth the most accurate version of the future. By taking the prediction out of the picture, accuracy in describing the future improves.

So I don’t have any predictions or recommendations. I just hope to help you see patterns that lead you to making your own predictions.

The total for AT&T during the first three quarters has been 9.9 million. If we assume the same 2x sequential increase in Q4 relative to Q3 as observed by Verizon we obtain an estimate of 5.4 million iPhone for AT&T.[1]

The quarter also saw the addition of Sprint to the US iPhone distribution network. Sprint is rumored to have committed to buy 30.5 million iPhones over the next four years. My estimate for the first year was 7 million, placing first quarter sell-in at about 2 million.

An approximate total for Q4 US iPhone activations results in 11.6 million. The following chart illustrates these estimates:

Notes:

1. AT&T reported that smartphone sales during October and November totaled over 6 million units. Assuming only 2 million for December and a 70% iPhone mix yields 5.6 million units.

I am referring to the kernel behind OS X and iOS: Mach.[1]

When I was a researcher at GTE Laboratories, I remember following the progress of this alternative kernel. As a research project it was one of the earliest microkernels and, along with virtual memory management, inter-process communication and control innovations, pioneered what became the basis of highly modular operating systems. Those innovations enabled an architecture which allowed complex systems to scale down to micro computers and eventually to devices.

There is a huge amount of lore around Unix and CMU’s efforts are deeply interweaved into it (as are Berkeley and AT&T). I strongly recommend a stroll down that memory lane. But I’ll keep it short here and say that original developers of Mach at CMU went on to be key executives at both Apple and Microsoft. It was really a spectacular success as far as academic research projects in computer science. A real inspiration.

So with that history, I want to thank Carnegie Mellon University for their sponsorship and I’m glad to see continuing innovation in their degree programs.

Today they are offering a Master of Information Systems Management  degree with a Business Intelligence and Data Analytics concentration (MISM-BIDA). This particular degree program is essentially cross-training in business process analysis and predictive modeling, two methodologies which deeply benefit from one another. Much of what I do for this blog is exactly this:  mapping, analytical reporting, segmentation analysis, and data visualization. I’m glad to see that his has been codified into a degree program.

Students in the MISM-BIDA program learn to integrate information filters and mining tools with applied business methods yielding insights that you see celebrated in the media every day. They do this with world-renowned faculty teaching a cohesive blend of data analytics, management, strategy, and IT courses.

I can only assume that this unique mix makes graduates highly valued by  financial service firms, consulting companies, technology agencies and start-ups.

If you like the results of this web site and would like to learn how it’s done “by the book”, consider the degree programs at Carnegie Mellon Heinz College.

Highly recommended.

—-

Notes:

1. Carnegie Mellon also had a role in the development of Siri.

via 5by5 | The Critical Path #20: Below the (belt)line.

During the show I refer to the diagram below which represents the allocation of costs of production for a sample of movies:

To be more precise, analysts value the wave of growth of every new product and heavily discount the post-growth phase assuming commoditization. There is no value assigned to Apple for extending market reach to the mass market.

Consider: Analysts currently forecast an operating income (or EBIT) of $43.3bn for 2012 and $49.7bn for 2013. That implies growth of 28% in 2012 and 15% in 2013. These growth rates are modest in light of Apple’s recent historic growth and especially 84% in 2011 on EBIT level. Much of this growth has been due to iPhone which quickly captured 4% market share in four years. To suggest 15% growth in 2013 is to suggest that Apple will not increase its phone market share by an appreciable amount. The implicit assumption in that growth figure alone is that Apple will remain a niche player.

Bear in mind that 15% growth is so low as to be half the the growth in the Mac business and below the likely growth in the overall phone market. Considering the risk premium on equities, this growth forecast assumes essentially a no growth scenario. But even with no growth, Apple’s cash flows are huge and thus they should add up to some significant present value. Since we know the value as reflected in the current price, we can work backwards to determine the discount rate being applied to those future flows. In other words, we can calculate how risky Apple’s existing product cash flows are seen to be.

Based on  assumptions originating from analysts’ consensus, we can construct a simple model that will approximate the discount the stock market puts on Apple’s future cash flows. Let’s have a look at an adjusted Gordon or dividend discount valuation model on EBIT basis:

Shares are valued based on the projected future cash flows of a company. Discounting EBIT with Weighted Average Cost of Capital and subtracting growth will yield enterprise value (= market capitalization – cash).

Applying analysts’ assumption of close to zero growth to Apple’s post 2012 EBIT means EBIT stays constant for 2013 and beyond. Let’s solve for the implied capital costs. Right away we can eliminate growth (g) as it equals zero. EBIT reflects operating cash flow since in a zero growth scenario CapEx will equal deprecation and there are no changes in net working capital. As Apple has no debt but a cash surplus, WACC will be equal to cost of equity[1].

Solving for capital cost (WACC) with available analysts’ EBIT estimates reveals a discount rate of 15%.

But let’s remember that analysts have consistently and by a large margin missed Apple’s financial performance. So let’s dial in a more aggressive estimate of around $56bn of EBIT for the fiscal year 2012. The discount rate goes up to 19% and implies an EBIT multiple of 5.3x.

In other words, Apple’s cash flows for a zero growth scenario are being discounted at a rate of 15% to 19%. This places the company in an extremely risky category of investment. It is certainly not something that can be categorized as a value stock.

The discussions on this blog regarding Apple’s valuation have been insightful, but only danced around the triangle of price, earnings and growth. When the growth forecasts are considered vis-a-vis current pricing, the assumptions built into ‘consensus’ forecasts for Apple are condemnations of the company.

They present a view of a company that is extremely vulnerable and can neither preserve its current cash flows nor find new sources of growth. It is, quite plainly, a company doomed to commoditization.

—

Notes:

1. Or one can even assume that cash has a positive effect lowering costs of equity (less indirect bankruptcy costs).

Apple Analyst Data | asymco. (Requires Flash)

As with previous interactive data sets, this is based on Motion Charts “gadget” in Google Docs. Try selecting the Motion Charts tab at the bottom of the page and hit the play button.

The way this is set up now is that the origin (0,0) represents actual performance (labeled as “Oppenheimer”). Every bubble is an analyst’s performance measured as a distance from actual. The further the location of a bubble from origin, the worse the error. Placement above zero (or to the right) indicates over-estimation for Revenues (and EPS). Placement below zero (or to the left) indicates under-estimation. Color of bubble represents affiliation (professional or amateur). Size of bubble is iPhone unit error.

You can change the axes, chart type (tabs in upper right), size, colors, and track individual analysts’ performance (by checking a name checkbox). Or just download the data and analyze on your own by selecting from the Data tab.

The growth is equivalent to 1.4 million new smartphone users (i.e. users who switched from a non-smart device for the first time.) The problem is that this is half the growth of the previous period. The following chart shows the growth as the weekly add rate.

As you can see, the growth has fallen to a level not seen since 2010. The cause may be seasonal as last November was also a slow month. I added a three month moving average which shows that although there seems to be seasonality, the last period did not show the peak of previous periods.

To better understand what happened, I looked at the performance by platform. The following chart shows the net user gains by platform.

What appears immediately is that Android growth was the primary cause of a change in the pattern. The iPhone grew slightly more slowly sequentially but the difference between November and previous periods is not great (941k adds vs. an average of 904k over the previous 12 months.) BlackBerry moderated its losses and Windows is quite a small influence on the market.

Android gain was 1.2 million vs a trailing twelve months’ average of 2.24 million. Android’s slowing in the US is significant.

We also have access to additional data from Google themselves. We had an update that showed that by late November there were 200 million Android devices activated. We also had an update from mid October of 190 million activations. That makes the increase in activations of at least 10 million during the same period that comScore was measuring the US.

comScore’s increase in Android users of 1.2 million then becomes even more interesting. It implies that the US consumers were responsible for no more than 12% of Android activations in the November time frame.

Furthermore we can derive from comScore’s data the total devices in use in the US by platform: the figure for Android is 42.9 million (46.9% of 91.4 million smartphones in use). By the end of November, the total activations from Android adds up to about 204 million. Excluding some devices which may be out of use, we can estimate that about 20% of all active Android phones are in use in the US. Conversely 80% of Android devices are in use outside the US.

This means that the US share of Android activations is decreasing. From a cumulative average of 20% to a recent 12%.

So the mystery of what caused the slowing in Android (and hence in all smartphones) in the US remains. Seasonal effects would affect all platforms equally and that is not the case. I’m not comfortable ascribing a cause until we hear more evidence and the pattern is more established. But something does seem to be happening.

[Update: The source data is available here. Note:  multiple tabs.]

Today we provide the same quick start for creating in OmniGraffle: a five-step introduction attempt in less than 140 words.

Let’s say you wanted to design a new Website. Here’s how you create a mockup:

1. Start it up. Download OmniGraffle here. Choose “Blank” from the template window.
2. Frame it. Stencils→Software→Konigi Wireframes. Designing for an iPhone? Drag out the iPhone browser. Lock object in place with ⌘+L.
3. Build it. Check out what else the Konigi stencil offers: position placeholders, buttons, and forms on your canvas. Turn on Snap to Grid (Arrange→Grid→Snap to Grid) for quick alignment.
4. Fine-tune it. Replace Konigi elements with real copy or graphics if ready. Add labels for the benefit of others.
5. Share it. Email, show off to colleagues via AirPlay, and more.

And to top it all off, it can all be done on the iPad.

You can also explore a bit more about this process. Visit OmniTools for more info about OmniGraffle and and all the other products that they offer.

In a discussion rich with examples from multiple industries Bob illustrates how marketing, design and engineering are all dancing around the question of how product should be developed.

Could the universally accepted compartmentalization of corporate functions be a root cause of product failure?

via 5by5 | The Critical Path #19: The hiring and firing of milkshakes and candy bars.

You can follow up with Bob here: The Re-wired group.

The analysis that I hoped to perform on the data was to see if Apple was pricing specific products differently in international markets. It was prompted by some apparently anomalous pricing of the iPhone 4S in Brazil.

To summarize, the idea is to calculate the “expected” price of an Apple product by taking the untaxed US price and adding duties and tariffs and taxes to determine what that product “should” cost in another country. Then taking the difference between this “expected” price and the actual price to determine if Apple is using pricing to signal in a particular market.

The analysis basically eliminates the effect of government on price and leaves currency and actual pricing signals from Apple as variables.

The analysis is not simple because there are many obscure tax rules. Some products are taxed differently in the same country. I have not completed the country-level analysis but have been able to see some averages over the countries reported (total of 45 reports.)

The following chart shows the average deviation from “Expected” as a percent:

Here are some potential interpretations of this data:

• The more a product deviates from expectation (e.g. the Apple TV) the more it’s likely to be targeted to the US or potentially has advantages of scale in the US
• The less of a deviation (nearer to zero, e.g. iPads) the more the international market is interesting to Apple. They are essentially signaling that those products are targeted at wide distribution.

If we take this train of thought it takes us to these conclusions:

1. The Apple TV is being “subsidized” in the US. The price is so low there probably because it is benefiting from content sales. This is a compelling argument and it might suggest a strategy for Apple TV closer to that of Amazon’s Kindle than to any of the telecom or computing products Apple sells. This may have significant implications in the pricing of any future “TV” product from Apple.
2. The iPhone 3GS seems to be a US-targeted product. Anecdotal  evidence shows that the product is not particularly strong selling in international markets. Apple is seemingly not trying to stoke demand with discounting.
3. The iPhone as a brand is not tuned to compete on price globally. Indeed, the iPhone 4S is the most “overpriced” product and seems to be premium priced vs. iPhone 4. The iPod touch is also something of a prima donna when it comes to pricing.
4. In contrast to the iPhone, the iPad is being positioned through pricing for broad global distribution. This is undoubtedly due to their unlocked nature but there might be some other penetration tactics at play here.
5. Macs in general are not priced aggressively internationally. However the Air does seem to be positioned for volume growth (while the mini is not.)

Admittedly, these are quite aggressive conclusions given the limited data. What gives me some confidence in these conclusions is additional circumstantial data. The demand for some products is clearly higher outside the US than others. I believe and this data seems to support the notion that the overall strategy for the iPhone, iPad, Mac and iPod is being tuned for the international markets with some precision.

It represents 123 countries. It also shows where the iPhone is currently available.

The following map shows the countries where iTunes music can be purchased.

The countries in white are countries where apps are available but music isn’t. There are 51 countries where you can buy music and 72 where you can buy apps but not music.

The following map shows the countries where iTunes TV shows can be purchased.

As in the previous chart, the countries in white are those where apps are available but TV shows aren’t. There are six countries where you can buy TV shows and 117 countries where you can buy apps but not music.

The data is here (along with a widget which lets you generate the maps–if there are errors, please correct them and let me know).

Digital music (as iTunes defines it) started expanding outside the United States in 2004 and managed to reach these 50 other countries in seven years. TV shows took a much slower route. Not only has there been a limited selection of countries in six years of expansion but even in the US, the programs available are a tiny subset of what is being produced.

Is the TV business going to expand digital distribution at the glacial rate of music or not at all? In either case, the distribution of Apps as a digital medium is positively viral.

via 5by5 | The Critical Path #18: Who’s Paying for My Lunch?.

Notes:

• Helsinki
• GigaOM
• About Om
• There ain’t no such thing as a free lunch [Wikipedia]
• Letters of Note: The internet Tidal Wave

The estimates look like characteristic “tell-tales” of a company running strong into the wind.

This conservatism in the face of rapid growth sounds “reasonable” but is it always practiced? And what about the ability of this conservative strategy to predict dramatic changes in growth? To test, we started to look at the predictions for RIM. RIM has also enjoyed strong growth over a similar time frame as Apple. How did analysts predict its performance? The following chart was prepared using the same technique as the one for Apple[1].

Surprisingly, the “tell-tales” look very different.[2] For every year we looked at the first year estimates were over-optimistic with a decline expected after one or two years. This pattern is remarkably consistent within the RIM data and remarkably inconsistent when compared to Apple.

It’s as if there was nothing in common with the methods used. This is perhaps appropriate, but the “quality” of predictions are not much better. With RIM they were too optimistic in the short term and too pessimistic in the medium term. With Apple they were far too pessimistic consistently.

More worrisome, the 2010 forecast did not offer any hint of the dramatic decline which followed only a few months later. And after three quarters of really bad news, the consensus is still higher than what will likely happen (ensuring an uninterrupted string “misses”).

The study of these two companies’ forecasts is a study in contrasts. We can say that the forecasting is disturbingly consistently inaccurate but we can also say that there seems to have been a bias of pessimism for Apple and optimism for RIM. We can only guess at the cause, but the consistency of bias points to something institutional.

—

Notes:

1. The three year forecast consensus (sourced from Bloomberg) was sampled one month following the end of each fiscal year. This should offer the analyst enough time digest the year passed and deeply contemplate the future.
2. If we may stretch the analogy, these are indicative of an imminent stall.

Today we’re highlighting the OmniFocus task management software again. We are hoping you are going to take advantage of the free trial available.

To that end, here’s a quick 5-step jumpstart.

1. Capture everything. Take 15 minutes to move things out of your head and in to OmniFocus. Anything from long-term goals (earn pilots license) to quick errands (card for mother).
2. Define next actions. “Earn pilots license” deserves its own project. Move it to your library and decide what to do next.
3. Organize actions with contexts. “Research area flight schools” might be assigned to a Mac context for googling, “card for mother” to Walgreen’s.
4. Now do stuff. If you’re at the office, focus on work projects to get stuff done! At home, take care of errands.
5. Review mode. Take time to consider each active project. Does it need more work?

Find out more about OmniFocus here, and don’t hesitate to ask questions.

via 5by5 | The Critical Path #17: Working with Clay.

If we take these estimates and then compile a cumulative total of activations we get the green line in the chart below.

The wrinkle in the picture is that Google also occasionally reports cumulative estimates of total Android shipped. They are shown as the blue circles in the chart above. There is some room for error as the cumulative totals may not be reported the same day they happen and the assumptions in the activation rates may not be reflecting occasional slowing.

However that leads to a problem. By adjusting for the reported totals we get the orange line. The trouble with it is that it has these improbable “kinks” where the total is adjusted down, something that is not happening in reality. It’s a kludge we need to make estimates fit reality. Normally, this is something we can sweep under the carpet, but with the size of the market, the errors creep up to tens of millions of unitis.

The first downward adjustment would have been 19 million in May. Today, the difference between the green line and the orange is about 25 million.

So the best we can say right now is that there have been between 224 and 253 million Android devices activated to date. Why Google does not report this data regularly and consistently remains a mystery.

Update: The data used in the charts above is available as a Google Docs spreadsheet here.

However, laws do not permit the import and friction-free trading of electronic products. In addition to regulations there are duties levied and there are sales or consumption taxes levied on the purchase.

However, knowing the retail price of an Apple product in a particular location, and knowing the tax and duties levied, can we work out if the price is consistent?

Here is an example:

I illustrated the price of a basket of Apple products in the US online Apple store in blue and the same products in the Finnish online Apple store where the prices were converted to US dollars in yellow. I also calculated what the price in Finland “should” be if the products were sold with the known VAT of 23% added. I called this “Finland (expected)” price and it’s shown in green.

I then took the difference between the “expected” and actual prices as a fraction of the expected and called that the “surcharge”. The surcharge can be interpreted as “how much more does a buyer in a different country pay over the price in the US (excluding US sales tax) plus VAT and duties.”

What the chart shows is that not all Apple products are treated equally by Apple. In this particular case Apple TV and the iPad are “overpriced” significantly. Interestingly, the iPhones are nearly what would be “expected” price.

It gets curiouser when comparing with other countries. In this case I compare the same basket in Brazil.

Here the story is reversed. The iPads are cheaper than one would expect while the iPhones, especially the 4S, are more expensive, though not by as much as the iPads are in Finland.

Why is this?

I don’t know. These prices are set by Apple though they may be influenced by operator deals they have locally or other terms. It would require local knowledge of the market to solve this.

What would also be instructive, I believe, is to collect data from many more countries. If you are living in another country, you can help by providing the following data: For each of the products in this list,

iPhone 3GS
iPhone 4
iPhone 4S 16
iPhone 4S 32
iPhone 4S 64
Apple TV
iPod Touch 8
iPad 2 16 Wifi
iPad 2 32 Wifi
iPad 2 64 Wifi
MB Air 11:
MB Pro 13
Mac mini
iMac 21

provide the retail prices (preferably from an online store) and the local VAT or duty that is expected for electronic products (enter data here.) You can provide the prices in local currency. We can then try to put a global picture together and understand what affects price differences across borders[2][3].

Update: You can view and enter data in the spreadsheet here.

Notes:

1. There are minor differences like power adapters but they are not materially more expensive to produce.
2. See also The Big Mac index.
3. Thanks to Paulo Silva for supplying the prices from Brazil and asking the pertinent questions.

• 14.1 million BlackBerry smartphones shipped, 13 million sold through
• 150k PlayBook shipped with sell-through slightly higher. 800k PlayBooks shipped so far.
• BlackBerry subscriber base up to 75 million
• High growth cited for U.K., France, South Africa, Mexico and Argentina, Indonesia, Saudi Arabia and South Africa. RIM is the #1 smartphone vendor in the Latin America and Caribbean region. Sales outside the US, UK and Canada were 61% of revenues. US is now 20% of sales, UK 11%.
• Hardware growth outside the US was 56%
• There are 630 carriers
• 50k apps in App World with 5 million downloads per day
• Forecasting 11 to 12 million smartphones next quarter

Given the channel fill with a new product, the device business was marginal at best. The company obtained -1% growth y/y in units but 31% sequential growth from a transitional quarter. The average selling price (inclusive of service revenues) is $354 and about $280 excluding service revenues. I estimate that operating margins have dropped to about 11%. Not a good story, but one we have been warned to expect.

But a crucial new twist to the story is that RIM announced that they don’t expect new BlackBerry 10 devices until late next year. That came as a surprise and the stock sold off significantly, valuing the company at well below book value.

Stepping back, the biggest surprise is that the company seems to have had no plan for sustaining itself.

Let me explain.

If you go back to 2005 or so the world of smartphones was a mix of modular OSs and integrated platforms, much as it is today.  Not unlike Android today, Windows Mobile was successful in licensing to dozens of vendors who in turn released hundreds of products. Symbian also was licensed but most volumes came from Nokia and they held the lion’s share. Then there was the BlackBerry integrated solution which worked smoothly and had devoted fans who upgraded every chance they had and operators who were joyous at the new ARPU. There was also PalmOS as offered by PalmSource trying to be a “third horse” and unseat stodgy old Microsoft.

It was into that fray that both Apple and Google jumped. Google acquired Android in 2005 and Apple was busy polishing a version of OS X that would became iOS. In three and two years respectively these platforms would be in the market.

Regardless of the actions of rivals, all competitors must have faced the same questions in 2005: As technology was changing rapidly and as they all had access to component roadmaps, what is the best technology approach to this market? Should they embrace certain innovations to sustain their business trajectory? RIM had a tightly integrated software, hardware and service offering. What could make it better? Nokia was also basing its products on a more loosely integrated offering (nominally it was modular, but only just). It saw the writing on the wall and began developing a Linux variant (Maemo) as a potential candidate to replace Symbian. Microsoft was basing its solution of a modular approach. Windows CE was ok as a kernel but they had layers on top which were optimized around stylus-based user experiences (analogous to mouse actions). Could it take on a rich computing experience?

There were many experience candidates. Stylus, keypad, keyboard all had adherents. There was a lot of hesitation. After the iPhone launched with capacitive (finger) touch, resetting user expectations, it still took a year for many of the vendors to re-consider their technology plans. The weakest changed first.

Microsoft felt the pain quickest. They also had the least to lose since revenues from Windows Mobile were paltry. By 2008 internally they turned 90 degrees. They embarked on re-building their user experience and orphaning the Windows Mobile ecosystem and many of the vendor relationships they had built. Palm dropped PalmOS and built WebOS internally quite quickly. They licensed Windows Mobile as a stop-gap. Around the same time Nokia doubled down on Maemo and joined with Intel to form MeeGo. They did not orphan Symbian though and kept trying to adapt it to the new experiences and input methods.

But RIM did nothing. Almost nothing. They were the healthiest competitor. They felt no pain from iPhone’s entry. The platform they had built was still growing and they were tweaking it constantly. There were always improvements to point to but fundamentally the code was limited. It was very difficult to adapt it to touch input and the first attempts at a touch UI were embarrassing. But there were no signs of a new platform that reflected the Unix-like competition.

As I pointed out in an earlier post, Unix-like operating systems were having their revenge. To a computer scientist the future of mobile computing would mirror that of personal computing in terms of architecture. The foundations of the early smartphones were built on constraints that were disappearing while being unable to accommodate new input methods. In other words, the early efforts were optimized around those constraints and sub-optimal for rich user experiences. They had to be. There were limited processors, memories and screens and battery life.

It turned out that all the early mobile operating systems were unsuitable for the advances in technology that came after 2007. Companies which were able to adapt or embrace Unix-like operating systems in a mobile context gained the ability to grow with the new input methods. The sooner they “pulled the trigger” on the shift, the better off they were. This is in fact what enabled entrants to gain footholds. Entrants entered with Unix-like OSs straight away while incumbents had to justify changing and the more successful they were the harder it was to justify changing.

RIM had trouble justifying this change. It waited until 2010 to finally acquire the QNX asset that would offer them a stronger software foundation.  It still had to adapt this acquisition and it looks like it won’t be done until at least 2012, seven years after they should have started. And this should be seen as a sustaining technological change. A new OS is not a disruptive technology to what is already a mobile computer.

However, delays like these can be fatal. We see the impact on Microsoft who was earliest to push the reset button in 2008. It still took two years to rebuild Windows Mobile into Windows Phone and is now still struggling to regain lost share (less than 2% today). We see the impact on Nokia which was unable to affect a transition to MeeGo due to a litany of issues and had to throw in the towel on self-determination. We see the impact on Palm which lost its independence as WebOS was a bit too little too late.

These companies had the right technical strategies but suffered from execution and business architecture issues. RIM is years late in embracing what appears to be a sustaining improvement.

But whenever you see what appears to be a coordinated effort by companies to fail simultaneously you have to ask if the change they struggle with is really sustaining or disruptive. The pattern makes me suspicious that what RIM, Microsoft, Palm and Nokia faced was more than a failure to embrace a sustaining improvement. I suspect that the failure is evidence of tectonic shifting of business models.

Perhaps they don’t project growth based on historic growth, but project earnings given historic earnings. In other words they don’t look at the first derivative (change in earnings) but the  shape of the actual data.

The following chart shows that data, i.e. forecasts as an extension of a sales trajectory. The blue area are actuals and the grey branches show projections at a given end of fiscal year.

Seen this way, we can imagine how the projections can be considered “reasonable”. Some appear to be linear extrapolations while others show up as the end of “S-curves”.[2]

What none of them imply is  exponential growth. But would forecasting exponential growth be considered reasonable? Clearly not since it’s never been consensus. But disruptive companies do follow non-linear growth. In fact, every company that has gone from being small to being big (which is to say all large companies) went through non-linear growth phases. The “natural” shape of growth is exponential.

The failure is therefore not of reason but of failing to use a model that assumes acceleration of sales. I believe that institutional financial advisors are conditioned (or coerced) into assuming that nothing unreasonable ever happens. That seems like a completely flawed foundation to stand on.

—

Notes:

1. This post is inspired by the New York Times chart showing budget forecasts.
2. The same data shown on a log scale: 

There is some comfort in knowing that there will be many others who will offer such a forecast and your contribution is thus not the only way investors can calibrate the price. However, you should think hard about what you are predicting as it also will reflect your skill in predicting such a visible company.

Apple just had a tremendous two years. 2004 and 2005 saw EPS grow at 274% and 337%. This is largely due to the runaway hit iPod. Given all that is known about the company, what will you put forward? While you’re at it can you also forecast two years forward, namely provide a growth forecast for 2006, 2007 and 2008?

Here is what you and your cohorts publish as a consensus:

You go with a 13% growth for 2006,  15% for 2007 and 5% for 2008. The chances are, you reason, that the iPod will not carry the company’s growth much longer. The competition is sprouting all over and Microsoft is rumored to be launching its own music player.

It makes sense to be conservative and offer a modest growth of 13%. At the same time you can rate the stock a buy as it is still growing. The stock just doubled in the last 12 months and the law of large numbers says that growth cannot last at the same rate as we’ve just seen.

It’s now late 2006. Apple just closed out another big year. Contrary to your forecast last year, the company grew at a rate of 46%, more than three times faster than you expected.

It turns out the iPod still has some legs and the company’s Mac business seems to be growing. Looking forward you take your 15% growth for 2007 and increase it to 20% and suggest 16% for 2008 and 32% for 2009.

There are rumors of Apple getting into the phone business.

It’s now late 2007. Apple just launched the iPhone and even though it was available in limited numbers on one carrier for one full quarter, the company still turned in an amazing 73%. Again more than three times faster growth than you forecast. What do you do about 2008?

It might make sense to increase the previous 16% estimate to 17% but since there is a real estate bubble brewing, the 32% growth for 2009 should come down to 22% and defer that growth out to 2010, dialing in 27%.

It’s now late 2008. Apple closed out another huge year. 73% growth was once again more than triple your forecast of 17% growth. It’s clear that the iPhone is a huge hit.

However, there is a credit crunch and talk of a huge recession, even depression looming. Apple is a luxury/premium brand and it is unlikely that those expensive products will sell well into these “macro headwinds”. You predict a decline in the earnings of 13% in 2009, inline with many other tech stocks. You are optimistic however that 2010 will be a year of recovery and predict 18% increase from recession lows followed by a more reasonable 11% growth in 2011.

It’s now late 2009. To everyone’s surprise, Apple grew through the credit crisis. It turned in 34% growth (vs. your predicted 13% decline). The success is hard to believe but the iPhone had a lot to do with that. There are new competitors coming and the recession has proven resilient. With unemployment still high you predict 2010 will still be a tough year. You go with -14% growth for 2010 and postpone recovery out to 2011 with 24% “bounce”. Longer term you see 2012 as a flat year, with Apple earnings shrinking slightly by 1%.

2010 ends with Apple achieving 67% growth. Shocking relative to a -14% growth forecast. But then again, Apple launched the iPad and who could have thought that was a hit? Apple achieved growth throughout the recession and it looks like it might keep going. Your 2011 forecast of 24% looks weak so you increase it to 25%. The 2012 decline thesis is abandoned in favor of 16% growth and, given the likely competition in smartphones, you tone down growth in 2013 to 6%.

It’s November 2011. The company turned in 83% growth. It’s  more than three times faster than your forecast. Some people are suggesting that Apple may have a lot of headroom with the iPhone and iPad but the law of large numbers is what it is. The company became the largest by market cap in the world and Android is gaining huge market share. However, in deference to their strong recent performance you increase the 2012 forecast from 16% growth to 24% growth. 2013 goes from 6% to more than double: 13% and 2014 is when growth begins to slacken, though still a healthy 8%.

As the following chart shows, look ahead one year accuracy has not been all that great, but you are at least consistent. In any case, everything you based your decisions on showed remarkable consistency with consensus. You’re just reflecting common sense.

Note:

• Though the numbers are all real, the narrative is fictitious. I suspect however that the truth is actually stranger than this fiction.

I used an iPhone OS design stencil to do app mock-ups and was impressed at how easy it was. It has smart shapes, a large stencil library and contextual styling elements to guide you from rough outline to pixel perfection.

OmniGraffle is still the easiest and most elegant way to create website wireframes, process flows, organization hierarchies, and, even infographics (if you’re into that sort of thing).

There’s now an iPad version that lets you share the design process with anyone. Even through an Apple TV.

For ages 5 to 105, OmniGraffle is available here.

—

Note:

1. The very first version of Asymco.com was posted in February 2010 and ran as an iWeb site hosted by Apple’s .Mac service. I even used iWeb as the blog authoring tool.

via 5by5 | The Critical Path #16: The Existence Proof.

Turning the tables in more ways than one.

I maintain visualizations of ITU data which shows overall mobile consumption and broadband consumption and penetration. In order to maintain a consistent basis of comparison, I prefer to use ITU’s measure of consumption which is “subscriptions” rather than “population”. The ITU derives this measure because mobile operators think of points of connection (subs) rather than people as the measure of consumption. This makes some sense because connections are what are monetized–not people.

So the first piece of analysis is to show this measure of penetration (smartphones as a percent of subscriptions) for the 42 sampled countries.

This view shows which countries “lead” adoption in terms of penetration. It shows that the US is quite high in the ranking and the most penetrated “large” country.

This is highlighted by the following chart which shows penetration of smartphones vs. total subscriptions with bubble size representing population size.

The US stands out from the inverse relationship between size of mobile population and smartphone penetration.

Part of this is due to the US having relatively low mobile penetration in general. It’s below the median because multiple mobile phone use is uncommon.

But these bits of data are not the most interesting. In my opinion the following is:

This last chart shows the total size of non-smart subscriptions vs. the total size of smartphones in use for the 42 countries listed in the legend.

There are 3.7 billion non-smart connections and 0.6 billion smartphones in use. Although not all of these 3.7 billion connections need to be enabled with a smartphone, the fact remains that there are six times as many non-consuming network connections that can still be addressed.

How and when this ocean of non-consumption will be converted to computing devices are the most obvious questions the data raises. Of the selected countries only 13.5% of the connections are “smart”.

ITU data shows that at the end of 2010 there were 5.4 billion global subs. This survey was conducted more recently so the total is likely much higher (perhaps around 6 billion). The survey thus covers about 72% of the world. That implies that at least 90% of the market remains unserved.