Do Mobile Data Plans Reflect or Shape Usage?

What is the relationship between the structure of a data plan and usage? Do people adapt behavior to the plans, or does the choice of a data plan reflect existing behavior? It’s a harder question than might first appear, as several processes likely are at work.

Over time, people tend to consume more data. Use of video-based applications is growing. But virtually all studies show that, even in instances where bandwidth usage actually is “unlimited,” or so generous that no typical user ever approaches a limit, only a small percentage of users actually push the limits.

It is an unquestioned fact that a small percentage of broadband users, on virtually any network, use vastly more data than typical users do. The top one percent of data consumers account for 20 percent of the overall consumption, for example, a fact the study by Benoît Felten, Yankee Group analyst,  confirms.

But users also seem to be able to adjust their behavior and expectations. When bandwidth usage carries direct financial implications, people adjust by changing their behavior, switching their smart phones to Wi-Fi access when at home, for example.

Also, data from Ericsson suggests a bit of both processes might be at work. It appears that, over time, virtually all users consume more bandwidth.


But “typical usage” remains a far different issue from “average” usage. Even as overall usage grows, a small percentage of very-heavy users represents a disproportionate amount of usage. In that sense, choice of data plans follows behavior. Heavier users will seek the biggest plans. Lighter users will choose plans with less capacity, when available.

On the other hand, usage patterns are also related to the data plan that comes with a device. That is significant because it suggests people actually modify their behavior based on plan policies. In other words, the Ericsson study suggests, people use more when their plan allows it.

If so, service providers have a wide range of options for shaping end user demand, using price and other packaging mechanisms. Generally speaking, people use more data when they buy bigger buckets of usage.

But it is a nuanced matter. It can’t be precisely determined whether people use more data because they have bigger plans, or have bigger plans because they use more data over time.

Also, since new devices aside from phones  tend to get used over time (notebooks and tablets), and since usage profiles for those other devices are different from phones, consumer usage and shaping of retail plans also will tend to change over time.

On the other hand, one might argue, given any set range of plans, users will virtually always fall into a distribution that is stable and predictable.


“Nearly all communications traffic, including Internet traffic, can be approximated with high accuracy by the log-normal distribution,” says Phoenix Center Chief Economist Dr. George S. Ford. That’s important, as it means we generally can predict overall end user behavior when we actually know only a couple of key data points.

Among the practical implications are estimates of what is likely to happen when  a broadband service provider imposes a monthly usage cap of 250 gigabytes. The log-normal distribution suggests how many customers would hit the limit.

The log-normal distribution also generally allows some estimation of how consumption will vary across the entire customer base, knowing only the consumption of the top one percent, and the consumption of the top 10 percent of users, an analysis by Dr. Ford suggests.

The point is that “averages” (the arithmetic mean) don’t tell an observer very much when any service has an asymmetric distribution, as always seems to be the case for Internet consumption by consumers.

Cisco’s Visual Networking Index reports that the top one percent of users accounted for more than 20 percent  of Internet traffic and that the top 10 percent of users accounted for 60 percent
of traffic.

That means a Pareto distribution, which would ideally show that 20 percent of instances account for 80 percent of the impact would also likely hold.

Ford notes that Comcast’s 250 GByte  per month usage cap on its residential broadband
customers, taken with Comcast’s own statements that 99 percent of its residential customers will not approach that cap suggests that only one percent of Comcast’s residential users consume 250 GBytes per month or more.

Comcast also indicated that its median customer consumes about 8 GBytes to 10 GBytes per month.

The log-normal distribution could well inform many other sorts of policies, such as what amount of consumption a “typical” user requires.

“My approach to approximating usage patterns may be useful for variety of policy issues,” says Ford. “ For example, when addressing universal service for broadband, the level of service that qualifies as ‘broadband’ will have to be parameterized.”

Knowledge of the usage distribution may aid in establishing these service level definitions that can be described as “reasonably comparable to those services provided in urban areas, for example.

The relationship between “typical” usage and “heavy” usage seems to be internally consistent, no matter what the “heavy” consumption levels might be.