Sometimes Market Share Conceals More than it Reveals

As useful as market share analysis might be, it fails to capture the underlying market dynamics when a disruption is underway. Consider that, after nearly two decades, online commerce claims only about seven percent of total retail commerce volume.

Most of us, asked to evaluate the potential impact of a substitute technology platform that has gotten only seven percent share after nearly two decades would likely say that technology is not a major disruptor of the legacy platform.

But we would be quite wrong. If history is a useful guide, we are about three share points away from a decisive change in the adoption rate--and market share--of the new platform.

The reason for that assertion is that, in the past, transformative technologies and successful consumer electronics innovations hit an inflection point at 10 percent adoption, no matter how incremental the prior moves had seemed.

This chart by Asymco shows adoption rates of popular consumer products after 10 percent adoption was reached, no matter how long the gestation.


As you probably would expect, particular products introduced into developed ecosystems tend to be adopted faster, while products that require further development of an ecosystem can take longer to reach 10-percent adoption rates. Automobiles required a huge infrastructure of roads and gas stations.

The telephone required network economics, as the value of having a phone line was fairly low, for most users, when few other people had them. Likewise, supplying electricity required power plants and transmission lines, plus local power distribution networks.


And though it is a lesser point, as in some other markets, online commerce represents virtually 100 percent of the net growth.

Now consider a situation where multiple disruptive technologies are developing simultaneously, and where the net value which can be produced is an interaction between and among those technologies.

Big data now requires cloud computing. Big data will get bigger as internet of things sensors are widely deployed. So only artificial intelligence can sift through all the data to discern useful patterns.

And some of that data will have to be analyzed fast enough that edge computing is necessary. But 5G and other connectivity solutions will be needed to acquire all the data.

It is nearly impossible for a human to model all the possible interactions with enough detail to make the output useful. From a mobile operator’s point of view, it might be logical to put 5G at the center.

Other industries are going to put AI, or cloud, or IoT or big data at the center. No matter. The point is that the cluster of technologies is what really matters, not any single one of the technology trends.

In the past, it has been easier to model the impact of a single innovation (personal computer, mobile phone, internet). It will be much harder in the coming era, since so many fundamentally disruptive technologies are emerging at the same time.



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