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Theory.

The policymaker’s dilemma

Nov 6, ’12 11:43 AM

Here is an exchange with Robert van Apeldoorn, Journalist with Trends Tendances Magazine in Belgium. (www.trends.be/fr). The exchange took place in early September via e-mail.

Robert: -Information and Communications Technology (ICT) is considered in Europe as a way to push growth, and is a target of national and EU policies (digital growth,etc), but the result seems to be a failure: the European computer industry (hardware) is almost dead (ICL, Siemens computers bought by Fujitsu, Olivetti almost out of computer business, Nixdorf dead) since the 90’, and the telco industry seems to be in crisis. All European companies are out of the handset business (big and fading exception is Nokia, but with  American software), and Alcatel is suffering with telco equipement manufacturing. It seems that at best, Europe can be a good niche player, with companies like ARM (chips). Technology seems to be reduced to localized services (computer services), some software businesses. What do you thing about that point of view? Is it correct or exaggerated ?
What will remain to the European companies ?

The main problem is perphaps the creation of European platform/ecosystems. Almost all are American today: Apple IOS-iTunes, Android, Amazon,…

Why Symbian didn’t succeed as a competitive platform ?

Is it possible to create European platforms? After all, IOS succeed after a short period of time.

What are the European tech companies that could play an important role in the near future ?

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The App Revolution (in Filmmaking)

Nov 1, ’12 3:20 PM

The following article is published in Filmmaker Magazine. Fall 2012, Vol. 21 #1.

There is a saying I once heard: “Once you change the method of distribution, the product has to change.”

This itself is a take on the idea that distribution defines the product. You see this around you every day in the products you buy. Cars are influenced by the dealership networks that sell them. Phones by the mobile network operators and the choice of computer you use at work by whatever the IT department or value added reseller prefers to work with. Mass market restaurants offer what can be sold by wholesalers–typically frozen, long shelf-life staples. Almost every product category is shaped more by what can be distributed than what can be produced. That’s simply because in mature economies distribution is harder than production. In consumer products it requires access to wholesalers who then require access to shops who themselves have access to prime real estate which attracts foot traffic. Production only requires capital. Distribution requires relationships, often exclusive ones.

This pattern is even more pronounced when looking at media products. Production is arguably easier since it’s constrained by talent, which is fungible. But distribution is even harder as it is addressing bigger audiences in shorter time frames. You see this lopsided balance of power in the abundance of books being written relative to those being published. There are thousands of films produced and hundreds get distributed.

But the saying suggests that if distribution were to change then the product itself would change. Indeed, if you can sell ebooks direct, then they tend to evolve into new genres (e.g. Fifty Shades of Grey). If you can sell cheap adult video online it tends to evolve into new genres as well (I’ll leave examples to the imagination.) YouTube videos quickly cluster around “Fail” or “Win” compilations which evolved from America’s Funniest Home Videos. They get millions of views. Even before the Internet, the availability of cable created the genre of music video, which created the first music broadcast alternative to radio. And of course, cinema itself redefined theater once it could get shown to millions rather than thousands. The new methods of distribution of media affected what gets produced rather than the other way around. Consider the converse: innovative filmmakers who try new storytelling methods are stymied by a lack of acceptance by existing distributors and find their material languishing in festivals or perpetual cult status.

So we can re-state the saying to a new “Law of new media”: Once you change the method of distribution, the medium itself has to change.

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The omnivorous app

Oct 16, ’12 5:43 PM

Marc Andreessen famously coined the phrase “software is eating the world.” It’s an apt observation. If you look back on the history of computing you’re likely to measure computers sold or devices sold or users harvested or productivity gained. These things are measured because they can be measured. But the greatest cause of value created and captured has been the development of software. An ephemeral product whose value is often ignored in analytical discourse.

Software is not easily measured and it’s not easily valued due to its intractable nature. Firstly, because businesses that make software tend to have weird cost structures–absurdly high fixed costs and operating margins: They operate without income for years and then suddenly are massively profitable with a minimal set of resources. They have a non-linear, “big bang” trajectory.

Secondly, software companies tend to capture revenues from something other than the direct sale of the good. Software is rarely sold. Services sometimes are sold on the basis of software but more likely audiences for services are sold to a set of bidders, or revenue is obtained in even more circuitous ways.

Thirdly, because there are curious multi-sided markets for software platforms. Charlie Kindel hints strongly at how difficult it is to understand the dynamics of software platforms. There is the prospect of lock-in of users and data. There are relationships to nurture with developers and there’s the principle of an ecosystem that creates network effects. The virtuous/vicious cycles are non-linear and unpredictable even for the experts who have been at it for decades (e.g. Microsoft).

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Reverting to the mean

Oct 1, ’12 3:47 PM

Quarterly financial data is often a lagging indicator of strategic success. RIM’s vital signs were exceptionally strong up until early 2011. Consider the following graph showing RIM’s device growth.

Using language commonly heard among analysts, one would say that the company was “reverting to the mean” and growing nearly in-line with the market. In other words, exceptional growth was over but continuing growth was likely. The company was returning to something “normal.”

However, keen observers of the market would have been hard pressed to find any reason for justifying that performance. Seen through a disruptive lens, it was evident as early as 2008 that RIM’s strategy was not sustainable. The company had a very weak smartphone product relative to emergent iOS and Android ecosystems. And yet, the company continued to prosper for nearly three years, through 2008, 2009 and 2010. Those shorting the stock during this period would have been unrewarded.

But then in early 2011 it fell off a cliff.

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Is the iPhone good enough?

Sep 18, ’12 11:44 AM

We don’t want to just make a new phone. We want to make a much better phone.

- Jony Ive, video at iPhone 5 launch event

Disruption theory has taught us that the greatest danger facing a company is making a product better than it needs to be. There are numerous incentives for making products better but few incentives to re-directing improvements away from the prevailing basis of competition.

This danger is more acute for technology companies. Coupling incentives with the speed of improvement in various technologies (aka Moore’s law) means that over-service can come suddenly and more quickly than warnings from the marketplace. A product can tip from under- to over-shooting the market within one product cycle. One year the product is under-performing and trying to catch up to the competition and the next it’s superfluous and commoditized. The dilemma is compounded by the cycle time of development which can span multiple product cycles.

Therefore, how to tell whether a product is over-serving a market is one of the most important and frequently asked questions I get asked. It’s easy to see over-service in the rear view mirror when looking at a multi-year pattern. The trouble is that by the time you see the data, it’s too late. How do you tell you’re on the cusp of good enough, subject to imminent disruption before you get there?

I consider measuring a product’s absorbability to be a marketing problem. The marketer’s job is to read the signals from the market[1]. Determining absorbability comes down to reading two market signals, both of which must be met before green-lighting an improvement:  (a) a product’s improvements must be used and (b) a product’s improvements must be valued.

If a product’s improvements are not used and the buyer will not pay more for them then they are not being absorbed and the effort to develop the improvements should be redirected.

Now the problem becomes one of measurement. Of the two, utilization is easier. Data can be gathered on whether a feature is being used. Research methods exist to tell if a feature would be used even if it’s not available[2]

The more difficult assessment is that of the value of a feature. You can usually only tell value by trying to price it and watching what happens. For example, you add more speed/memory/capacity and try charging more (or the same) for the product. The acceptance will be measured by sales growth and will give you an indication of whether these improvements are valuable.

If you have to add features and drop prices at the same time then it’s likely that the market does not value the improvement.

But this is extremely risky. You need to wait through a sales cycle and iterate through a development cycle before you have an answer. In a space where competitors are placing opposite bets, the experiment fails even if you get the data.

How can you structure a value measurement experiment without wasting an opportunity?

Rather than dealing with hypotheticals, let’s use the iPhone as a test case. As Jony Ive states, the focus for the latest iPhone was to make it better. Is this improvement absorbable? What happens if Apple’s bet on being better is wrong?

First, we can confirm that the iPhone has been on a trajectory of getting better and that those improvements have been absorbed so far. We can measure the history of performance of the product (roughly doubling every year) and we can also measure proxies for performance as I have in the following charts:

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