In Mobile, Sometimes the First Winds Up Last
Mobile networks never remain constant, in terms of capacity, end user demand or coverage. Within any 24-hour period, demand will shift even on any single operator’s network as people move about.
Operators add new capacity, while the number of users of data bandwidth also changes virtually daily, so demand and supply are dynamic.
That means any snapshot of how fast a mobile network might be, or how extensive its coverage, will change over time. The latest study by Open Signal of Long Term Evolution networks globally shows how fluid speeds are, over time.
Some networks “got faster,” while others “got slower,” a function of supply and demand. Networks that got faster likely added supply, but did not see a commensurate increase in usage.
Networks that got slower probably “suffered” because users were added to existing networks faster than supply investments, increasing contention for available bandwidth.
So it is that U.S LTE networks, overall, showed slower average speeds in the second half of 2013, compared to the first half of 2013, with “average speeds” decreasing 32 percent.
Among the possible reasons, one might argue, are saturation of some cell sites operated by Verizon, growing demand on all the networks and relatively slow performance on Sprint’s network, for example.
MetroPCS averaged just 2.4 Mbps; Sprint 4.2 Mbps, for example. Verizon averaged 7.6 Mbps while AT&T averaged 8.9 Mbps.
Likewise, U.S. consumers are rapidly approaching smartphone ownership levels of the United Kingdom, an early leader in smartphone adoption.
According to eMarketer, 65 percent of U.S. mobile phone users have smartphones, compared to 66 percent of U.K. users.
The point is that communication markets are dynamic. As much as European policymakers might worry about “Europe falling behind,” it wasn’t so long ago that the U.S. market was considered “behind,” on several measures of mobile adoption.
Things change. A perception of “being behind” will spur investment. Also, there is a tendency to be crudely deterministic about correlations between attributes such as broadband speed and other economic outputs.
How people use high speed access is more important than “how fast” they can use it, at many levels.
In other words, it is “how” technology is used productively that counts, not the amount of raw computing power or connectivity.