Category: Theory

When the Apple Watch will begin sales, there will have been dozens of “smart watches” released. At CES this week at least 56 “wearables” were on display. One could be forgiven for thinking that Apple’s Watch will compete with at least that many alternatives. Those alternatives don’t even include the entire existing mechanical and electronic watch market, which, surely, is also filled with competitors.

When analyzing competition, it’s easy to get caught up in one-on-one competitive comparisons, each posited as a decisive life-or-death battle. Consider the list of competitors that Apple has been declared as being in a death-match with:

• Real Networks. Yes, there was a time when Apple’s survival depended on success vs. alternative media encoding technologies.
• Adobe. Remember Flash? No Flash support meant that Apple’s fledgling phone would fail.
• “The Music Industry”. Unhappy partners could surely shut down the iTunes music store, and their insistence on DRM would surely cripple the experience.
• IBM. In nearly every aspect, their business/strategy/inclination and glimmer of intent was an existential threat the Apple.
• Microsoft. In nearly every aspect, their business/strategy/inclination and glimmer of intent was an existential threat the Apple.
• Google. In nearly every aspect, their business/strategy/inclination and glimmer of intent is an existential threat the Apple.
• Samsung. Obviously. But not just phones or tablets. They have control over key components that Apple used in many of its products, before the iPhone even.
• Palm/BlackBerry/Nokia/HTC/Huawei/Xiaomi et.al. Every phone maker (and every phone) was/is an existential threat to Apple.
• Dell/HP/Asus/Lenovo et. al. Every PC maker was a threat to Apple. Some of them made MP3 players. Some of them make tablets.
• Amazon. Obviously. Not only as an iTunes killer but as a device disruptor. They are working on drones, after all.
• Sony. Remember them? No longer a PC maker but they moved in many circles Apple moved in. While we’re at it, add all the consumer electronics companies in Japan.
• Dropbox. “If Apple can’t do iCloud right, they’re doomed”.

This is a very short list (feel free to suggest more) and it becomes clear that the total count of competitors that Apple has to counter “or else” seems to number in the thousands. Practically every hardware, software and service company is positioned as an “Apple Killer”. in fact, the more interesting question might be which companies are not competing with Apple.

Another interesting question relates to why there is no transitive property of competition. I.e. if company A competes with Apple and Apple competes with company B then it does not follow that company A competes with company B.

To wit, whereas HTC competes with Apple and so does Dropbox, it does not follow that HTC competes with Dropbox. So it’s entirely possible that it’s axiomatic that

“Most companies compete with Apple but few of them compete with each other”.

Recognizing a pattern, one could build a model of the technology world where Apple is the focus of all competitive efforts. But this starts to sound absurd.

Indeed, the flaw in the logic is that these competitive pairings are based on the overlap of features of products/services being offered. The features become the attributes of a product which supposedly defines their competitive power. But this is false for the same reason that the attributes of a buyer do not determine their buying behavior. Buyer attributes1  are easy to measure and they may correlate to purchasing behavior but they don’t cause it.

Similarly, product or company attributes are easy to measure and they may correlate to competitive behavior but they don’t cause the substitution of a purchase.

Therefore, appealing to Apple to change its strategy, operations or even its core beliefs in response to a competitor’s behavior is deeply misguided. The cause of success and failure in the marketplace is based on being hired by the customer to get a job done. Once hired, the chances are that the trust is secured and the relationship continues even if alternatives are available. There is comfort in the knowledge of whom you’re working with.

This aspect of trusted relationship between the buyer and the product and the interweaving of ‘brand’ (aka intentions) of the hired is the root of loyalty. Of course, loyalties can be betrayed and trust can be lost. But that implies that the primary responsibility of the manager is the creation and preservation of trust. When seen in this light, an alternative axiom becomes clear:

“Great companies don’t have any competition.”

Great companies are “monopolists of customer trust” and are unaffected by alternatives. They are positioned on and nailing the job their products and services are hired for. The alternatives must not only duplicate the exact job (which they almost never do), but they must also overcome the switching costs.

Remember this when analyzing the impact of yet another competitor and considering the “Apple must fix/do X or else” assertions.

 

1. E.g. demographic, sociographic [↩]

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).

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.

Continue reading “The Innovator’s Stopwatch. Part 2”

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.

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

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.

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.

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.

 

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 do1 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 shrunk2 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.

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

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:

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.