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Category Theory

The Innovator’s Stopwatch. Part 2

The adoption curve has been used to categorize adopters into groups by their behavior: innovators, early adopters, early majority, late majority and laggards. This categorization asserts that adoption is function of psychology, or the likelihood of people to act or react within social systems. [Rogers first edition 1962]

It’s a compelling model and has been proposed as a tool for firms to help with their marketing strategy. As diffusion proceeds through each adopter category, the product is re-positioned to address each group’s presumed behavior. Innovators (first 2.5% of the population) are offered novelty, a chance to experiment and uniqueness of experience; early adopters are offered a chance to create or enhance their position of social leadership; the early majority build imitate the leadership of the early adopters and justify it with productivity gains; the late majority are skeptics but, given a set of specific benefits, join the earlier adopters. Finally the laggards reluctantly agree to adopt as their preferred alternative of not adopting disappears.

The theory suggests that a firm can be successful if they modify their marketing and perhaps product mix to accommodate these adopter categories in a timely manner.

If this is the case however, why is it that those who have access to these data (i.e. who is buying and when) not to do the right thing? Why is it that during a technology adoption curve, there is a high degree of turnover in the firms which capture profits from the products that deliver this technology?

If you don’t believe this to be the case, consider the smartphone market. The data about buyers is easily obtained (even without paying a fee). Shown below is the US smartphone penetration data as obtained by comScore (including teenage survey data from Piper Jaffray).

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Following the penetration data there is a second graph showing the smartphone shipments for the largest vendors as well as the sum of the “others” which make up the difference with the total market. I used vertical registration lines to align the different data sets to the same time scale.

The Innovator’s Stopwatch. Part 1

At the end of October 2014 about 73% of US mobile users owned a smartphone. In March of 2005 2% of US mobile users owned a smartphone  (comScore). In absolute terms the number of users increased from about 4 million to 176 million and these 172 million new users were added in less than one decade.

Remarkable as that may be, what is even more exciting is that the pattern of adoption is predictable. The following graph charts the adoption of this product category with a monthly resolution between January 2010 and October 2014.

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Knowing the datum for March 2005 allows us to fill-in the graph with a logistic function approximation for the period to date.

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The logistic function is a wonderful model for how technologies are adopted. It’s been evident in samples take for dozens of diffusions, from 18th century canal construction, 19th century railroads, 20th century consumer products as well as industrial and agricultural innovations and the internet itself.

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The reason the logistic curve is so commonly observed is because of its reflection of sociological behavior. When a technology serves a manifest need (or can be hired for a distinct, unmet job to be done) its universal adoption is a certainty. The only unknown is the rate at which this happens. As the graphs above show, some technologies are rapid (examples) and some are slow. Some could be constrained by the communication of its benefits or by the presence of regulation or by the unavailability of infrastructure or resources or financing. Conversely, some could be accelerated by conformability with existing infrastructure or by network effects resulting from communication between adopters. The balance between accelerants to adopting and the constraints on adoption yields the “slope” in the logistic curve.

So given a high degree of confidence in the model, we can forecast how smartphones will be adopted. This model yields further details such as when the various classes of adopter (Early/late/laggard) will join and perhaps that itself will allow managers to plan their marketing and product development.

It would seem therefore that this tool is the answer to building a successful and sustainable enterprise. It would seem that the early movers would have an advantage as their users create the virtuous cycle of learning which the firm will use to capture later adopters. It would seem that firms can effect strategy changes to adopt to each wave of users added to the user base.

It would seem but it has not proven to be the case. For most technology categories, the predictability of adoption has not aided or informed success for firms competing to supply the burgeoning market. If we look at the firms which supply the smartphone market in the US (with platforms as proxies) we see how much turnover has occurred. All the early movers are highly disadvantaged and even some of the later entrants are not assured of viability into the later stages of the market.

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It’s a something of a paradox that as a technology takes root we might be able to predict how it gets bought but not who will sell it.

This paradox is at the root of the volatility in asset pricing around technology firms. The question investors typically sweat is not whether a company is in the right market, but whether it’s in the right time.

 

Measuring the Apple Watch opportunity

When the Apple Watch was launched, all eyes turned to the Swiss watch industry. Analysts measured it and asked if it’s big enough to be interesting. Industry observers questioned the competitiveness of an entrant vis-à-vis the ancien régime. Marketers weighed in with segmentation hypotheses and how Apple’s queer new device might best fit.

These are all mistakes in analysis.

The market for Apple Watch is not the Swiss (or Chinese) watch market. The market for Apple Watch is the number of wrists in the world. To the extent that those wrists will be covered with Apple hardware will determine whether it is successful or not.

Measuring the existing market is a mistake because the existing products are hired for different jobs. Those measurements will yield only an answer to how big that job is.

Assessing competitiveness vs. incumbents is a mistake because incumbents have perfected solving the problems of wrist-worn timekeeping devices over a century. Apple’s watch is not a wrist-worn timekeeping device any more than the iPhone is a phone or the iPad is a pad.

Segmenting the market by whatever means are convenient today is irrelevant because the segments are currently positioned on the current jobs to be done. It’s no more relevant than classifying the iPhone along the segments defined for phones in 2007.[1]

Some have tried to wedge the Apple Watch among the “fitness tracker” market. This is no more plausible given that fitness tracking is no more interesting than timekeeping is to Watch.

The best way to measure the opportunity is to quantify the “wrist-space-time” continuum and deciding what is and what isn’t addressable. The wrist is an interesting place to put a computer and Apple makes computers. The rest is left as an exercise to the reader.

Notes:
  1. e.g. keyboard phones, flip phones, and feature phones []

The new iPad. Is it better?

The problem with getting better is that if you’re more than good enough you’re actually getting worse. Improving beyond the point where your improvements can be absorbed is not only wasteful but it’s also dangerous. It opens the door to competitors who compete asymmetrically.

This is the perverse and pervasive threat hanging over all system vendors. The temptation to “get better” is not coming from incentives and human nature. It’s  always there as Moore’s Law offers an exponential increase in power. People don’t naturally have exponentially increasing needs. For them to absorb this new power, it has to be couched in new uses.

What has permitted the absorption of improvements in semiconductor performance (and production) have been other aspects of the system: the software, communications and services innovations have been positioned on more demanding jobs to be done which, once hired for those jobs, saturate the available processing and storage.

This is most easily evident in how digital photography has advanced. The constraints on sensors meant that quality was initially poor and as cameras were unconnected, they were relatively under-utilized. But once software and communications were added (by inclusion in smartphones) digital photo creation exploded. This, in turn, led to more storage needs both on the device and the servers. In a virtuous cycle, more processing power meant video was possible, then high definition video, then slow motion high definition video. The previous storage limits on mobile devices were quickly overwhelmed. Megabytes of storage became gigabytes and then hundreds of gigabytes. Video editing meant processing power was suddenly in demand again. Cores multiplied.

Third party media (music and videos) storage and playback used to be the main job that storage was hired to do[1] but as cameras got better, user-generated content suddenly bellied up to the bar.

That is now the story for phones, which are gobbling up all the storage and bandwidth we can throw at them. But what about the larger form factors? Are iPads (and laptops) growing in their demands? Paradoxically, it would seem that the smaller devices are hungrier than their larger cousins.

The answer lies with the jobs to be done. If highly portable devices are more usable, they will be used more. Large devices are left behind, literally, because their jobs are not as pervasive in place and time. For a large screen like the iPad to increase its attractiveness, it has to be the stage for a set of jobs that only it can perform.

The new iPad has the horsepower. It has more portability (thinner, lighter) and it has the touch ID convenience. It even has a better camera. But for it to succeed it needs to be hired for a set of jobs as expansive in usage as the user-generated photo/video jobs that the iPhone has been called to do.

I hire my iPad for one such job: to persuade audiences small and large. I use it across a dining table and across an auditorium to appeal with a visual language. I use Perspective to create stories that have to be seen to be believed and once seen, create belief.

The tool is demanding however. As the stories are fed by data and the visualizations are rendered algorithmically and not as stored images it is hungry for processing power. I also need it to record performances which taxes storage. I need to transmit those performances both in real-time and as recordings, tasking the WiFi and cellular bandwidth. I need as much screen as I can get to be able to interact with the elements on screen, of which there may be hundreds. I need to export video versions of performances and thus I need a video studio with all its extravagance. I need it to run for all-day workshops connected to a projector, sometimes through AirPlay, under stage lights, which pushes the battery.

Consider my last padcast. It was recorded in one take lasting 18 minutes. I then exported the results to a video that was uploaded to Vimeo and viewed by thousands. However it took over one hour to render it and due to that constraint I did not have the luxury of editing it. I could not easily add, subtract or annotate the video production. This was done on an iPad Air and I was happy to get it done at all.

But if I had an iPad Air 2, not only would production time be shrunk[2] the things I could attempt to do with a presentation suddenly expand. It’s not just about more efficiency but an expansion of scope. More power means more work I choose to do.

So is the new iPad better? As far as the jobs I hire it do do, the new iPad is better. It is in fact not good enough. Which is the best thing to be.

Notes:
  1. Which was far more demanding than office-like documents []
  2. To about half the time []

The Process of Theory Building

I started working at The Clayton Christensen Institute and my job is to help develop the theory of disruptive innovation.

In order to do this I need to understand at least two concepts:

  • The process of theory building
  • Disruptive innovation theory

I’m more comfortable with the latter–having been a student (and victim) of it for more than a decade–but the the process of theory building is a new concept. At least to me but also, I believe, to many. The belief that a theory is fully cooked when first conceived is not the way science developed and the idea that business management theories are singular ideas rather than processes is symptomatic of an immaturity in the field.

So here are the basics of theory building as put forward by Clay Christensen and David Sundahl:

Definition: A theory is a statement of what causes what, and why, and under what circumstances. A theory can be a contingent statement or a proven statement. That is all.

Many managers shy away from using the word “theory” because it is associated with the term theoretical which suggests impractical. But managers use theory every day. They make decisions on some basis of cause and effect, often without being specific about their reasoning.

Process: First comes observation. Second, description. Third categorization. Fourth comes analysis and a statement of what causes what and why. This analysis can be simply an observation of a pattern or a more rigorous correlation analysis.

But that’s not the end of the process. The causal statement needs to be tested by predictions whose validity is tested with further observations and confirmation or denial of the statement. If the statement is denied we need to decide if it’s an anomaly that expands the theory or whether it contradicts the theory making it less useful.

The anomaly allows a new categorization to take shape. Getting the categories right is the key to the usefulness of the theory. The discovery of anomalies can thus make a theory stronger. The discovery of anomalous phenomena is the pivotal element in the process of building an improved theory.

This iteration between prediction/confirmation/anomaly handling can go for quite some time. As anomalies are accounted for on a regular basis then they can either be exhausted or depleted enough that a robust enough categorization emerges and the predictive power is nearly complete.

Example: In my reading of Apple’s financial statements I observed that Capital Expenditures were rising dramatically after the company began to sell iPhones. The observations were made over a few years. The pattern observed showed some correlation between spending and shipments of units.

The company’s spending was then compared with a group of other technology companies. These observations suggested that spending varied according to business model and strategy and that Apple seemed to be transitioning from one type of spending (on infrastructure) to another (on manufacturing equipment.)

Then a statement was made that Apple was using capital expenditures to not only ensure supply of components but also of component manufacturing equipment. This was borne of necessity but had the side effect of creating competitive advantage as its unibody devices and Macs were unique and differentiated.

As the more data came in, by the prediction was made that capital expenditures– which are incurred before production starts and which are pre-announced on a fiscal year basis — indicate new product ramps or new product introductions.

A few anomalies were experienced when spending increased but production didn’t and vice versa. These were studied and explained by shifts in technology (mainly screens) which required “out-of-phase” investment. Additionally, the companies in the cohort also varied their spending on the basis of opportunities in the short term.

As it stands, the theory that Apple uses capital investment in tooling to manage its quality and quantity of production and that in doing so it integrates deeply into its supply chain creating competitive lock-outs is holding up. It is not sufficiently precise to forecast actual production volumes for individual product lines but the growth in the business is broadly foretold by the growth in capital expenditures.

Indeed the share price generally reflects this:

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Proposition: At a basic (micro) level, the process of theory building is something we do instinctively. We observe patterns, make statements that A causes B and carry on with the theory as an assumption. The challenge is more on a macro level. Few theories are built rigorously about the causes of success or failure of business systems. This includes understanding why large, powerful firms fail when confronted with small, weak competitors. Why, how and when industries disappear. How resources are allocated and how priorities are set. It’s as if individuals behave with far more intuitive insight than firms.

That is what must change.

Because firms are increasingly determining the prosperity and sustainability of nations and the world. We can’t afford mismanagement.

The counter-point to this quest is that large systems are intractable and business is inherently chaotic, unpredictable. It may be, but much of what we know as science today was once thought of as impossibly mysterious and unknowable. I have faith that as the physical universe, the affairs of men have laws which govern them.

Revolutionary User Interfaces, Part 2

In 2011 I wrote:

My hypothesis is that The Primary Cause for the shift of profits from Incumbents to Entrants has been the disruptive impact of a new input method.

It was a description of what I considered to be the “disruptive technology” which caused incumbents which had a “front-row seat” to the future of their industry to be completely displaced and marginalized by an entrant[1] with no discernible right to do what they did.

I illustrated what underpinned the sea change in the phone business via the slide that Steve Jobs used in the iPhone launch event:

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I added the years when each input method was introduced and the  platform/ecosystems created as a result. These new ecosystems were the primary cause for dramatic industry-sized shifts in profits.

Not coincidentally, during the 2014 Apple Watch launch, the presentation began[2] with a re-telling of the “mouse, click wheel and Multi-Touch” story.

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Seven years later, the difference is that there is a new object added to the story. It answers the question that has been on my mind since that first post on revolutionary user interfaces was written: what will come next.

Now that we have an answer, the next step is to understand the new platform, its ecosystem; which industry will be affected and which incumbents will be displaced and to what degree will value be created beyond that which will be displaced.

Piece of cake.

Notes:
  1. later more than one []
  2. Begins one hour into the 2 hour downloadable video []

Going where the money is

The bank robber Willie Sutton did not say, when asked why he robbed banks, “because that’s where the money is.” He did agree with the idea however saying “Go where the money is…and go there often”.

Regardless of it being apocryphal, this idea came to be called Sutton’s Law and is often taught to medical students. It’s similar to the notion of Occam’s Razor: when an obvious or simple answer competes with an obscure or complicated answer, pick the obvious one first.

These are sound analytical rules of thumb. When thinking about what products and services could arise in the immediate future, those most obvious and with fewest assumptions should be put forward first. The what part is relatively easy. The tough question is more about when will they emerge?

We now know that Apple will announce new products on September 9th[1]. This gives us an idea of when something will happen, answering the tougher question. It leaves the simpler question of what will emerge.

I put forward my predictions as follows:

  • Regarding iPhone, a tweet on product mix and pricing.
  • Regarding an “iWatch”, an answer to a question from Eric Jackson.
  • Regarding the potential for wearables, a post on the subject.

One more item has surfaced on the potential of payments processing which I want to address now.

Handling payments, to me, is a perfectly plausible activity for Apple mostly because the company has made quite a few comments on the value of their “customers with credit cards” and the effort that went into Touch ID (which seems to be extravagant relative to the value of rapid unlocking).

But one word of caution: if Apple does enable payments it’s important to realize that being a (payment) bit pipe is not a particularly profitable business. It will undoubtedly bind value to the iOS devices which make it possible, but I don’t think there will be a direct capture of profit from the transactions themselves.

Notes:
  1. I’ll be there and will report via Twitter and a special session of The Critical Path podcast []

Apparel is next

If software can be injected into an industry’s product it will bend to the will of the software writers.

This theory expands on Marc Andreessen’s observation that “software is eating the world”. The evidence is that software, coupled with microprocessors, sensors, batteries and networking becomes applicable to an increasingly larger set of problems to be solved[1]. Software has “eaten” large portions of entertainment (e.g. Pixar, iTunes, video games), telecommunications (iPhone, Android, Messaging), various professions including journalism, management and law, and is entering transportation, energy and health care and poised over banking, finance and government.

As entry happens, asymmetries are enabled and disruption follows. This is the bending to the will of the writers–who tend not to be incumbents. The incumbents can’t embrace the changes in business models enabled by software without destroying their core businesses and thus, invariably, they disappear.

The pattern is easily observed but the speed and timing of it is difficult to predict and hence investment success is not certain.[2] There are many entrants who try and few succeed and there are many incumbents who will survive longer than a prophet can stay hungry.

Nevertheless, this process of software-induced turnover in wealth–and, incidentally, vast, additional wealth creation–is inevitable.

But can we predict anything other than timing? For example, can we predict the next industry to succumb to this force?

Notes:
  1. Or, put another way, is eligible to be hired to perform an increasingly large set of jobs []
  2. Which, ironically, means that the jobs of venture capitalists are still safe. At least until the theory develops to the point where it can predict with more accuracy winners and losers. []

Beleaguered

Amazon’s recent disputes with publishers (Hachette and Disney) shows a degree of market power that is closer to monopsony than to monopoly but this power is nevertheless real. It may not not be something that requires intervention, regulation or even scrutiny but market power is evident in both how companies operate and in how they are valued.

If you look at the following graph, it’s easy to spot those with “monopoly” power. The graph shows a short history of revenues/operating income and P/E ratios. Modest or no growth in earnings coupled with extraordinary high P/E ratios indicate that the market understands the business is not threatened by competition.

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On Capital Allocation

One of the paradoxes of the “post-industrial” era is the aversion to application of capital to growth opportunities. Generally speaking, capital has become trapped in bank accounts as opposed to equipment which could be used to produce value. This aversion is rooted in many dysfunctions, chief among them being the misunderstanding of the purpose of the firm.

But there are exceptions. Illustrated below are the patterns of spending in property plant and equipment (capital expenditures) by companies that still recognize that there are opportunities to be obtained by investment in the means of production.

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