All theoretical and empirical diffusion studies agree that an innovation diffuses along a S-shaped trajectory. Indeed, the S-shaped pattern of diffusion appears to be a basic anthropologic phenomenon.
This observation dates as far back as 1895 when the French sociologist Gabriel Tarde first described the process of social change by an imitative “group-think” mechanism and a S-shaped pattern. In 1983 Everett Rogers, developed a more complete four stage model of the innovation decision process consisting of: (1) knowledge, (2) persuasion, (3) decision and implementation, and (4) confirmation.
Consequently, Rogers divided the population of potential adopters according to their adoption date and categorized them in terms of their standard deviation from the mean adoption date. He presented extensive empirical evidence to suggest a symmetric bell shaped curve for the distribution of adopters over time. This curve matches in shape the first derivative of the logistic growth and substitution curve as shown below.
In the graph above I applied the Rogers adopter characterization to the data we have on the adoption of smartphones in the US. The latest data covering September is included.
Microsoft spent $2.6 billion for Advertising in the fiscal year ended June. Apple spent $1.1 billion in its fiscal year ended October.
Other companies will report their full year ad spending later but their previous years’ spending is shown below.
I added a second graph showing the percent of sales that each ad budget represents. Note that Coca Cola retains the crown as the most prolific advertiser when it comes to budgeting.
In fiscal 2013 there were 395 million visits to Apple retail stores. In 2012 there were 372 million.
The difference is approximately the population of Australia. This was in addition to the population of the US and Canada already passing through. Although this is a fun way to think about total traffic, it does not reflect performance of the stores themselves since new stores are always being opened. 21 new stores in 2013, to be precise.
The better benchmark should be the number of visitors per store.
This shows that, except for seasonal peaks, the visitors per store per quarter has been a fairly steady 240k since mid-2010. What’s more, this rate was also remarkably steady at around 160k/store/quarter from 2007 to 2010.
So what caused this quantum jump in traffic?
Apple’s latest product launch (new OSX, iPads, Macs and iWork/iLife) came with a change in pricing for software. OS X and iWork and iLife and updates are now made available free on new Macs and, in the case of the suites, on iOS devices as well.
Recall also that iOS updates are now free as well and that OS X had been reduced in price from about $129 to $29 with Snow Leopard in August 2009 and to $19 with Mountain Lion in July 2012. The iSuites have also dropped in price over time so the pattern of evaporating software prices is long-running.
But how fast and what is the impact? The historic performance Apple’s Software business is not easily determined since it was always blended with additional businesses. Until September of last year, Software was reported as part of “Software and Services” and since then as part of “iTunes, Software and Services.” Some assumptions allow the following picture to be drawn:
One additional wrinkle to the Apple software story is that OS X and iWork/iLife are not all the software titles available. Apple’s software includes Pro apps as well as the non-free OS X server. The non-free software US prices are:
Using logistic curves to measure diffusion of innovations is a powerful method of analysis. However there are limits to what can be learned. The methodology helps in understanding how quickly a pervasive technology is adopted. It assumes that the technology “fills all available space” within a market. It therefore also assumes that whatever problems the technology solves are universal problems.
Put another way, if a technology is not universally useful, it tends to peak before a market saturates. This “universality” condition is in evidence when observing that pervasive technologies are adopted not only by all members of one national market but also all nations and through all means of government and regulation. In other words that the jobs that the technologies are hired to do are so important that they bulldoze any and all obstacles placed in the path of adoption.
The only difference is one of timing. Some regions are quicker than others. Institutionalized obstacles essentially defer rather than deter adoption. They impede rather than block.
And I am pretty sure that smartphones solve universal needs and their adoption will be nearly 100%. They also have fairly low impedance given the speed of adoption (50% penetration in most large markets seems to come in less than 5 years.)
That’s the story for the technology, but how value is captured is another story.
Who captures and how it’s captured are questions of commerce not economics. They are informed by competitive advantage and business models. The puzzle seems to be that individual companies don’t capture value in the patterns of Logistic curves. Or at least I don’t think they do.
Consider the graph below.
The analyses of adoption of smartphones in the US and EU5 are remarkably consistent with each other. They also turn out to be consistent with the valuation of Apple.
I show the stages of adoption overlaid with the derivative of the Logistic Function and Apple’s enterprise value. The derivative of the Logistic Function shows the speed of adoption, peaking at the inflection point when adoption ceases to accelerate and begins to decelerate.
Thanks to Symbian, the EU5 countries (France, Germany, Italy, Spain and the UK) had an earlier start in the conversion of phone usage from non-smart to smart devices. According to published comScore data, in July 2010 the EU5 were at 26.6% penetration of smartphones and the US was at 22.8%.
However, with the aid of mobile operator subsidies, by the beginning of this year, the US caught up. According to comScore EU5 reached 57% penetration in March 2013 while the equivalent figure for the US was 58%.
Using the logistic curve model introduced last week, it’s possible to get an approximate categorization of the adopters of the technology:
As with the previous analysis, the graph identifies the following dates:
Gartner reported that PC shipments totaled 80.3 million units in Q3. Subtracting an estimated 4.4 million Macs yields an estimated 75.9 million Windows PCs.
This total is lower than the total shipped in the same period of 2008.
The graphs above show the
In May 2012 I wrote:
The pattern may be that companies either have short post-trauma lives of about two to three years or relatively long post-trauma lives lasting 4 to 5 years. What determines this life expectancy and how long do RIM, Nokia and LG have?
via Post-traumatic life expectancy of phone vendors | asymco.
These comments came right after BlackBerry (then RIM) announced a loss and thus entered what I called the “post-traumatic” phase of its existence. The observation I have been making is that once a company begins to generate negative operating margins from phone sales, that phone business never recovers.
The question then becomes one of gauging how long they have before the business is sold, dissolved or merged. Since that update, both Nokia and RIM have tentatively agreed to be sold. If the sales go through then we can update the graphs as follows:
[Graph note: solid bars in the second graph indicate companies which exited and thus the duration of life post-trauma.
Publicly, Mr. Lazaridis and Mr. Balsillie belittled the iPhone and its shortcomings, including its short battery life, weaker security and initial lack of e-mail. […]
Internally, he had a very different message. “If that thing catches on, we’re competing with a Mac, not a Nokia,” he recalled telling his staff.
From How BlackBerry blew it: The inside story – The Globe and Mail
The whole article is worth reading, detailing as it does the decision process inside BlackBerry during the painful disruption of its core business.
What struck me most however was how similar their decisions were to those of Nokia at about the same time. Consider:
- The engineering priorities placed on optimization around constrained hardware. Although engineers knew how to build the right products, the business priorities caused them to be deployed in the wrong direction.
- The delays these misdirected efforts caused. Mobile phones have narrow windows of opportunity but long lead times. A strategic mistake is very costly and most probably impossible to remedy. In the case of BlackBerry, buying QNX came too late while for Nokia the deprecation of Symbian was catastrophically managed.
- The feedback loop from network operators which shut down any initiatives for improved user experiences. Your best customers provide all the wrong information when the market is being disrupted. Ignoring them is impossible while complying is a strategic mistake.
- The demand from network operators to develop “killers” to competing platform-based products and the subsequent “jumping at the opportunity”.
- Listening to large buyers at the expense of users. While BlackBerry was guided to omit consumer features from its enterprise buyers, Nokia never secured enterprise buyers of any significance. Nevertheless it created the “E series” business-friendly phones which suppressed features like cameras and music.
- The celebrity sponsorships and wasted promotional efforts in the face of structural failures. This is manifested today by HTC as well.
The parallelism of this synchronized failure can be seen in the following graph showing smartphone volumes.