A company is nothing more (and nothing less) than three things: people, processes and purposes. In the language of the software engineer these would be inputs, algorithms and specifications. In the language of classical business analysis they are assets (or resources), organization structures and business models. In military theory, these are logistics, tactics and strategy.
This is the trinity which allows for an understanding of a complex system: the physical, the operational and the guiding principle. The what, the how and the why.
When approaching any analysis problem, these questions form the foundation of causal inference. What is it, how does it work and why does it exist?
When analyzing nature the sciences often help with the what and the how but rarely address the why. In contrast, man-made systems (e.g. systems of law, religion and commerce) require an answer to the why as there is a presumption of a will in their creation and preservation. The why allows ultimate judgement on the merit of an enterprise. The why may escape us but it’s assumed to always be there. For instance, in criminal law the motive is often a crucial piece of evidence but it’s not always found. In business, the motive for action or for organization is a crucial piece of the puzzle which often explains the what, who and how, but here the ultimate why is usually profit. This the characteristic of a for-profit business, the purpose is explicit.
When describing the process of disruptive innovation, Clay Christensen set about to also describe the process by which a technology is developed by visionaries in a commercially unsuccessful way. He called it cramming.
Cramming is a process of trying to make a not-yet-good-enough technology great without allowing it to be bad. In other words, it’s taking an ambitious goal and aiming at it with vast resources of time and money without allowing the mundane trial and error experimentation in business models.
To illustrate cramming I borrowed his story of how the transistor was embraced by incumbents in the US vs. entrants in Japan and how that led to the downfall of the US consumer electronics industry.
Small upstarts were able to take the invention, wrap a new business model around it that motivated the current players to ignore or flee their entry. They thus successfully displaced the entrenched incumbents even though the incumbents were investing heavily in the technology and the entrants weren’t.
In the image below, the blue “path taken by established vacuum tube manufacturers” is the cramming approach vs. the green entry by outsiders who worked on minor new products which could make use of the rough state of transistors at their early stages of development.
The history of investment in transistor-based electronics shows how following the money (i.e. R&D) did not lead to value creation, quite the opposite. There are many such examples: The billions spent on R&D by Microsoft did not help them build a mobile future and the billions spent on R&D by Nokia did not help them build a computing future.
There are other white elephant stories such as IBM’s investment in speech recognition to replace word processing, the Japanese government spending on “Fifth Generation Computing” and almost all research into machine translation and learning from the 1960s to the present.
But today we hear about initiatives such as package delivery drones and driverless cars and robots and Hyperloops and are hopeful. Perhaps under the guiding vision of the wisest, most benevolent business wizards, breakthrough technologies and new infrastructures can finally be realized and we can gain the growth and wealth that we deserve but are so sorely lacking.
This was initially posted on LinkedIn December 16, 2013.
Innovation comes in many forms. Many times innovation is thought of as technological improvement or as invention. We can all cite examples of inventions which turned into industries which re-defined civilization. The steam engine comes to mind but there were many others before and after. Inventing something is certainly a way to create value but it’s not as common or as reliable a method as it might seem. Creating Intellectual Property is one thing, finding a defensible market and business model is quite another.
More often companies innovate in terms of processes or the “algorithms” which are used to deploy existing resources. Wal*Mart was immensely innovative in the way it organized itself and laid out a low-cost business model. More recently Amazon has innovated in distribution and fulfillment based on the ability to dispense with showrooms for products and sell directly online. There is little in terms of technology which Amazon “invented”. Rather, it deployed off-the-shelf technology in a novel way.
But what I want to address is a more mundane sort of innovation: marketing innovation, specifically pricing. Few would consider a price model to be an innovation but in fact it’s a core lynchpin to many breakthrough innovations. It was pricing which permitted Henry Ford to build an industrial empire. He could have built cars for those who could afford them as cars were defined in 1907 but he chose to build a car around a price point which was around the median of the population. A car “so low in price that no man making a good salary will be unable to own one.” His business logic began with a price and the product and process followed.
As previously noted, the US smartphone market has followed an almost perfectly logistic growth. The measured data (via comScore, in green below) follows a predictive logistic function (thin blue whose formula is discussed here).
The other notable market observation is how closely the iPhone follows the same pattern as the market. The red line representing the iPhone above is almost perfectly parallel to the green and blue lines which represent the overall market. The reason for this seems to be that consumers are absorbing the product in similar way to how they are absorbing the technology. The “learning model” which underpins logistic models could offer clues as to the cause. It suggests that there is a direct communication that happens between the product and the consumer.
iOS unit sales crossed over 700 million units last month. That is a significant milestone but the total number of units in use is likely to be lower. My estimate based on device replacement assumptions is that about 500 million are still in use.
The estimated break-down of units sold and in use by device type is shown below:
Benedict Evans explains well the problem with measuring Android tablets. There are no reliable data collected because many of the devices are invisible through the regular, measurable channels:
- There are no firms which report their shipments
- They are not sold through retail chains which normally are sampled in the US and Europe (NPD and GfK respectively.)
- They don’t show up in browsing or ad transaction data
- Google Play statistics are missing most of the activations since they are not sold as bona fide Google-sanctioned Android.
The only measured statistic happens to be component shipments. Items such as screens, CPUs or perhaps memory might be visible to market analysts. It’s therefore tempting to add up tires manufactured to determine what’s getting sold in auto dealerships.
But it’s also hugely problematic.
In the post Seeing What’s Next, I showed how the rate of change of adoption of technology varies with time and asked what might be experienced by present and future generations.
It turns out that knowing how what innovations become universal and the speed at which these technologies are replaced can give us an idea of what individuals might experience in their lifetimes.
Here’s how to think about it:
The adoption of smartphones in the US is on track for reaching 90% of the available audience by August 2016. This is a mere eight years after smartphones reached 10% penetration. As far as technologies go, that’s pretty fast. To get an idea of how rapid, I plotted a few other technologies and the time they took to grow within the US market.
A few things to note:
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.
Cinematic is to film as Literary is to prose. It’s not a measure of quality or of beauty. It’s mainly the application of technique to impart an additional layer of meaning to the writing, performance and direction of the medium. Cinematic technique lifts the viewer to another, more profound point beyond the literal.
Whether it’s film, literature, verse, stagecraft or other visual arts, techniques vary but they have the same purpose. They make the audience feel more than what is said.
The quality of a good work of art is that it tells a truth without saying it.
And this, I believe, is what the analyst should also strive for. It is at least what I try to do and, having practiced for a while, I think I can convey some of the techniques I learned.
We don’t have a better word for describing these techniques than “cinematic”. They allow the presenter of complex and rich data to convey meaning with an economy of rhetoric. They draw the audience into completing the picture thus making it their own. They persuade without pleading. They teach without lecturing. They create, in a way, a poetic language for data that combines techniques from several arts, especially cinema.
The means for teaching this is a workshop called Airshow.
Together with IBM, we are proud to bring Airshow to Boston, New York and Helsinki this year.