1. Cyanogen. This company should develop a credible path for AOSP (non-Google Android) especially in India. I expect a lot of traction as OEMs who embrace Android reject Google.
2. iPad. Not as a consumer product but for the Enterprise. The iPad grows up into a solid product for business while being replaced by phones in consumer “jobs to be done”.
As corporate romances go, IBM and Apple's must rank among the most unexpected. As I wrote on the date they changed their Facebook status, the two companies were antagonists for the better part of twenty years and their rapprochement was met with a shrug mostly because yet more decades passed since.
Nostalgia aside, this new union is profoundly important. It indicates and evidences change on a vast scale. The companies' antagonism was due to being once aimed at the same business: computing. Since the early 1980s, "computing" came to be modularized into hundreds, perhaps thousands of business models. It is no longer as simple as selling beige boxes. IBM was forced out of building computers and into services and consulting while Apple moved to make devices and the software and services which make its hardware valuable.
The convergence of interests which was consummated into a deal this year stems from the migration of computing around what has come to be called "mobile". Apple intends to accelerate the adoption of its mobile platforms among the remaining non-adopters: enterprises–a group which, by now, qualifies as laggards. ((There was a time–when Apple was young–when enterprises were the innovators, early adopters. That role ended approximately in the year 2000)) Simultaneously IBM intends to connect data warehouses at those same enterprises to their employed users.
Both can achieve their goals better if they join forces: Apple offers assistance and expertise while obtaining access and lubrication for orifice entry. IBM offers modularized access to databases through its own APIs and obtains credible client experiences. The combination increases adoption of mobile (i.e. intuitive) solutions and an increase in productivity, benefiting the buyer.
The recent apps release showed just how transformative this relationship could be. We were witnesses to apps which appeared to be designed for users[!] They were not designed for committees that prepare checklists of requirements.
We must applaud IBM for having the courage to resist the featuritis which plagues enterprise software design. This resistance requires saying No to those who specify and are thus authorized to purchase software and hardware. IBM has had to essentially say no to those who buy and yes to those who are paid to use. The quality of the experience is evident at first sight. The number of user actions, the number of screens to wade through have been ruthlessly culled. These are concepts and ideas which now permeate app design best practices. Yet they are practices which still elude the spec-driven enterprise software wastelands.
Simultaneously we must applaud Apple for accepting that there are throngs of unwashed masses laboring in cubicles which deserve to be served. They may be employed by laggards but they are people too. Apple management has made clear that they are being more inclusive and open minded. Extending that open hand to the downtrodden salaried masses must surely bring a tear to one's eye.
Being unexpected and yet explanatory of the state of technology today, the partnership between IBM and Apple is, in my opinion, the most significant technology news of 2014.
As a romance, it is at least as newsworthy as the divorce of Wintel was in 2012.
I recall details of a recent Tesla Model S test drive while evaluating their innovation on jobs to be done, form factor, design, production methods and their business model.
(Also, other goings on with Uber, BMW and Porsche).
Horace outlines his work at The Clayton Christensen Institute and sets out a number of topics for upcoming shows. We also revisit YouTube and the art of self promotion.
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.
Having observed the market for some time, it's possible to see a set of "epochs" which show the rise and fall of various platforms/vendors/geographic production areas. I applied these "epochs" as a rough sketch over the production data.
I define these epochs as:
The Windows Mobile epoch from 2003 to about 2010 peaking in 2007. Competitors from Taiwan using Windows Mobile fielded a large number of devices and led that platform to win over proprietary Palm and Symbian.
The Nokia/RIM epoch from 2005 to about 2012 peaking in 2010. Nokia's distribution power takes its platform to global dominance. At the same time BlackBerry surges in popularity peaking at about the same time.
The Samsung epoch from 2010 to (probably) 2016 peaking in 2014. Samsung surges from almost no smartphone volumes to double the nearest competitor in a matter of three years.
The Chinese epoch from 2012 and ongoing. With a vast number of new competitors doubling or tripling sales over a few short years, "Other" becomes once again the bulk of volumes with additional brands like Lenovo, Huawei, ZTE, Xiaomi, Coolpad adding to the total.
The (probable) Indian epoch from 2014. This epoch is not so much of or about India as the "local brands" which begin to have large, even majority, market share in markets such as India, Indonesia, Philippines, Vietnam, etc. This epoch is the triumph of the local over the global and represents the latter stages of adoption.
Now I deliberately left out Apple because, as can be seen in the shipments data, Apple never held a pre-eminent volume position. It is however a competitor with a steady rate of growth. So steady in fact, that it seems to mirror the overall market. Essentially, Apple shows that there is a potential for growth without competitive flux. A type of growth that is "systemic" rather than a function of some rivalry and hence turnover.
But Apple is an exception. As so often it tends to be.
The real puzzle is why are there so many turnovers in leadership in such a short span of time. The idea that five distinct eras can exist in a mere decade seems preposterous. These turnovers are not without economic drama. They are booms as well as busts. They happen in a market where there are literally billions of consumers spending the equivalent of $300 billion/yr. so the stakes are pretty high.
But it's even more strange. If you super-impose the epochs on the (early) diffusion curve you can observe how a predictable adoption curve ((I.e. one whose shape can be determined well in advance and updated with very high precision)) results in unpredictable success in the market.
There must be something about the way companies behave *while* they are watching the evolution of the market. They seem to be running at a cycle time far faster than the market's overall absorption rate. Is this because competition is forcing them to mis-spend their energies?
Could it be that the behavior of firms is also a function of the psychology and reward systems of managers? That they seem compelled to optimize for a time frame shorter than the economic lifespan of the technology? That this mismatch of phase between lifespans of a technology market and their profit model leads to premature demise of their market position?
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.
Turning our focus back to "Jobs to be Done" theory, we look at how producers can exceed the expectations of consumers and the role of the focus group from a "Jobs to be Done" perspective.
We examine "Jobs to be Done" as an essential core of the product development process. Where does this kind of thinking belong in an organization?
Should we be redefining what being "the best" means? We close with a segmentation of social media services based on the seven deadly sins.
This year's Thanksgiving and Black Friday data from IBM shows a continuing pattern of growth for mobile devices. As the graph below shows, in the five years since 2010 mobile devices grew from 5% of the online shopping traffic to 50%. Traditional computing (desktop and laptop) made up the difference.
The graph also shows that sales value via mobile devices crossed over 25% of online spending. The fact that mobile shopping is not equal to mobile spending is due to the convenience factor of mobile. It's more likely that users will spend idle time scanning for bargains or tracking down ideas from friends but wait until they are at home to make the final purchase decisions in front of a computer.
The transition to spending directly from a device is a slower process, but that process was also one that online had to undertake as buyers became comfortable with online commerce. When it comes to payment, buyers are understandably more cautious.
This does not change the prediction made last year that "the transition to post-PC consumption will also be practically completed by 2020". That leaves six years for mobile saturation and a total transition time of one decade.
At that point I expect 90% of browsing and perhaps 75% of spending to be happening on devices. Some of this will undoubtedly be enabled by biometric authentication as shown by Apple Pay. Trust and ease of use in this technology will undoubtedly accelerate the transition making mobile payments more comfortable and secure than on the legacy computer.
What is less predictable is how much those devices will also be used to transact payments for the physical retail stores. In some scenarios it's possible that by 2020 a majority of all shopping will be enabled by devices. ((There is also the matter of in-store discovery and advertising via NFC and bluetooth i.e. iBeacon)) That would subjugate the retail segment to the power politics of mobile platforms.
It is interesting therefore to note the mix between the platforms in the graphs above.
I tried to assess the opportunity of Apple Pay but found it to be mostly dependent on how quickly card payments will overtake cash. It seems that as payments move to a digital format they will move to a mobile device. The hurdle isn't going from a card to a phone but from cash to card.
Data published in The Growth and Diffusion of Credit Cards in Society shows that between 1970 and 2001 households with at least one credit card in the US grew from 17% to 70%. More recent data shows 82% of US consumers have at least one credit card and 77% have a debit card.
The Total Addressable Market for Apple Pay then is dependent on how quickly this pattern repeats over the markets where iOS devices are in widespread use. Once cards are in use they are used with higher frequency and quickly overtake cash for the user.
The only assumption that needs to be made is that the device then replaces the plastic card. This seems a safe assumption as the benefits of the device as payment authenticator are high and the costs are negligible given a penetrated market.
The following graph shows an extrapolation of transaction volumes where Visa and MasterCard and Amex are showing moderating growth with UnionPay showing 20% constant growth through 2019. ((Supporting these assumptions is a forecast from Nilsen showing total number of cards in circulation by issuer and a forecast of total transactions
))
Two more assumptions are needed: the share of transaction value captured by Apple Pay and the Apple Pay fee. I used 15 basis points ($15/$10000) as the assumed Apple fee and a share schedule as follows:
2015: 0.5%
2016: 1%
2017: 1.5%
2018: 2%
2019: 3%
These assumptions result in a cumulative revenue of over $1 billion by 2018 and a yearly run rate of $1 billion in revenue by 2020.
Although this is not a huge amount relative to Apple's overall revenues ((No more than half of one percent of 2015 revenues)), it's important to note that these revenues are likely to go directly to earnings ((About 2% of 2015 earnings)) as there are negligible costs associated with the transactions that Apple needs to bear.
These assumptions make the statement that Apple Pay can become "A Billion Dollar Business" plausible by 2020. Perhaps sooner.
Having reached $700 billion we ask whether $1 trillion is an achievable valuation for Apple. We also discuss why this is not at all interesting. Also, the future of banking.
