Smartphone Trends

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Recent Smartphone Trends & the Extreme Data User

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Arieso Commercial-in-Confidence Copyright © 2012 Arieso Ltd

Copyright © 2012 Arieso Ltd

Author: Michael Flanagan

Version: Final

Issued: 6 January 2012

The information contained in this document and any documentation referred to herein or attached hereto, is of a confidential nature and is supplied for the purpose of discussion only and for no other purpose.

This information should only be disclosed to those individuals directly involved with consideration and evaluation of any proposals, all of who shall be made aware of this requirement for confidentiality.

All trademarks are hereby acknowledged.

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Executive Summary

Recent Tier-1 market information reveals increasingly sophisticated devices with user demands

that continue to grow over time. This represents a double-threat to the industry: smartphone

penetration rates continue to climb while smartphone user demands reach new heights. As

shown in earlier studies, the demands of prior smartphone subscribers are formidable and well

known, especially with regards to iPhone 3G data volumes and numbers of data calls. A

comparison of newer smartphones with the benchmark iPhone 3G reveal that the latest breed

of subscriber has a more insatiable demand for data on a per-subscriber basis than ever seen

before. The iPhone 4S released in 2011 is measured to be the most voracious smartphone with

unprecedented increases in uplink and downlink data demands on a per subscriber basis. In a

per-subscriber study of the eighteen hungriest smartphones across six manufacturers we find:

iPhone 4S users demand three times as much data as the benchmark iPhone 3G users

iPhone 4S users demand twice as much data as iPhone 4 users (who were most demanding last year)

Google Nexus One users make twice as many data calls than iPhone 3G users (consistent with last year)

Other observations include:

Devices like the iPhone 4S will proliferate the market within the next 12-18 months

The extreme 1% of all users consume half of the downlink data

Strategies to deal with these extreme users are considered and a subscriber-centric, location-

aware technique is shown to not only provide requisite off-loading from 3G systems in the near

term, but is also shown to satisfy longer-term theoretical limits on system performance. This

constitutes an important SON (Self-Optimizing Networks) use case involving optimal site

placement, and network operators will require this type of technique in order to satisfy the

inexorable data demands that are expected to continue into the foreseeable future.

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Introduction

Recent smartphone launches continue to reveal new breeds of data subscribers with

increasingly voracious appetites. The demands of prior smartphone subscribers are formidable

and well known in the industry, especially with regards to iPhone 3G data volumes and numbers

of data calls. However, the introduction of new data-intense features on recent smartphones

(such as dual-core A5 processor, 8-Megapixel camera, 1080p HD video, and iCloud processing to

increase ease of data access on the iPhone 4S) raises the expectation that the users of these

new smartphones will be even more intense data consumers. The purpose of this paper is to

quantify this increased usage behaviour as seen in a variety of popular smartphones in order to

continue the analysis performed a year ago.

This paper addresses the recent data demands of over 1.1 million distinct subscribers over a

single, 24-hour weekday in a Tier-1 UMTS market1 with a mixture of urban and suburban

morphologies. The following comparative analysis focuses on popular devices which were

represented by at least 1000 subscribers (and the most popular devices were represented by

well over 10,000 subscribers). While any device could be used as a point of reference, the

iPhone 3G is chosen due to its historical and statistical significance (since it constituted both a

past pinnacle in user network demand as well as exhibiting a “typical” demand across all current

devices). Therefore, increases in demand over the iPhone 3G continue to constitute a new

standard for subscriber behaviour that network operators must prepare for.

The remainder of this paper is broken into three parts: device demand, extreme user behaviour

and network operator response.

1 It should be noted that this is a different Tier-1 market than that considered in 2010’s study. While this market difference impacts absolute quantities (such as Mbytes/subscriber and total data calls), the comparative approach in this report is seen to be largely robust in spite of this difference. For example, relative to the iPhone 3G, the iPhone 4 is seen to have similar data demands as those seen last year.

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Part I: Device Demand

Comparative results

The per-subscriber demands of eighteen smartphones are compared to the iPhone 3G in Table 1

below. The devices are organised by manufacturer and then by release date. Non-voice devices

(such as a collection of 3G modems and the iPad) are also compared. The results were

normalized in each category so that the iPhone 3G score would be 100%. The highest

smartphone score in each category is high-lighted in red, as are other scores of interest.

Table 1: Comparative Results by device2

2 The voice calls/subscriber is not studied this year since the results of last year showed no significant changes in voice calling patterns. The data Minutes of Use/subscriber is not studied this year data volumes are the best measure of aggregate data demands while signalling demands are addressed by the data calls/subscriber category. It should also be noted that several devices from 2010’s study do not appear in this table due to the decreased popularity of those devices (i.e., <1000 subscribers seen).

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The Google Nexus One has twice the data calls per subscriber

compared to the iPhone 3G

The number of data calls per subscriber is 121% higher for the HTC Google Nexus One than for

the iPhone 3G. This device held the distinction of the most data calls per subscriber in the

analysis performed last year by a similar amount. By way of comparison, the iPhone 4S shows

54% more data calls per subscriber than the iPhone 3G. Part of this increase may be due to the

relative novelty of the iPhone 4S, but this can also be consistent with the greater ease-of-use of

the iPhone 4S over the iPhone 3G. Similar ease-of-use arguments may apply to the Google

Nexus One. The applications predominantly used on the Google Nexus One and the iPhone 4

(especially automated applications) may also contribute to this increase. Finally, there is also

the question of the impact of operating system and the related, (potentially excessive) signalling

demand of the smartphone on the number of data calls. This remains a topic of on-going study

by network operators in partnership with smartphone vendors.

Uplink data volumes per subscriber increased by up to 223% The HTC Desire S revealed a dramatic 223% increase in uplink data volumes per subscriber

compared to the iPhone 3G. This is greater than the 126% increase of the Samsung Galaxy

reported last year (which shows a somewhat reduced, yet comparable 95% increase this year in

Table 1). The iPhone 4S was in a virtual tie with the HTC Desire S with a 220% increase. Several

other smartphones (including the HTC Desire and the Samsung Galaxy S II) also showed

substantial gains in this category. While subscribers with newer smartphones are generally still

a minority compared to the more numerous iPhone 3G subscribers, their relative numbers are

going up each day and it is only a matter of time before they are responsible for greater

aggregate uplink data volumes than all iPhone 3G users combined.

Increases in uplink data volumes are largely expected to be due to corresponding increases in

user generated content. HD video recorders and 5-Megapixel cameras (or better) are common

features in the smartphones that show gains in uplink data volumes. The use of image and

video editing applications will also result in larger amounts of uplink data volume to be

transmitted by the subscriber to the network. It should still be noted, however, that each

smartphone in this study still consumed substantially more downlink data than it generated

uplink data (by a ratio of almost 7-to-1, which is very slightly lower than the ratio seen last year).

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Downlink data volumes per subscriber increased by up to 176%

The iPhone 4S showed an increase of 176% in downlink data volumes over the iPhone 3G. Since

the downlink-to-uplink data volume ratio was almost 7-to-1 on average for the devices under

study, this downlink increase of 176% corresponds to a larger total volume of data than a 220%

uplink increase (discussed in the last section). As noted earlier regarding the increases in total

numbers of data calls, it remains a topic for further study to characterise the root cause of this

downlink data volume increase. But regardless of the cause, quantifying this increase is still

important for purposes of network planning and optimisation (including forecast trending).The

iPhone 4S increase of 176% is more than four times the largest downlink data volume increase

seen last year in the iPhone 4. Therefore, taken together, the increases in downlink and uplink

data volumes seen in the iPhone 4S are unprecedented and marks the iPhone 4S as the

hungriest handset on the market. It should be noted that these increases are not just due to the

hungriest iPhone 4 users migrating to the latest device. This is because the relative data

volumes for the iPhone 4 are virtually unchanged from last year’s study (i.e., if the hungriest

iPhone 4 users left in droves then the average would have plummeted). While there is no

question that hungry users are attracted to the iPhone 4S, the device appears to unleash data

consumption behaviours that have no precedent.

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Quantifying the evolving data demand of devices

The downlink data volume demands per subscriber (relative to the iPhone 3G) versus release

date of the devices under study are shown in Figure 1 below.

Figure 1: Average downlink data volume/sub vs. device release date

There are three periods of interest in Figure 1: pre-1H2009, post-1H2010, and the interval from

1H2009 to 1H2010, inclusive. Prior to 1H2009, the average demand is seen to be about 50% of

the iPhone 3G demand. While the iPhone 3G had been released during this first period, many

of the devices released in this period were not as demanding: this resulted in an overall average

of about 50%. The industry began to catch up to the standard that was set by the iPhone 3G

between 1H2009 and 1H2010, inclusive. During this period, the average demand was

comparable to the iPhone 3G (i.e., near 100%) and nearly flat. Starting in 2H2010, we see a

climb in demand that exceeds that of the iPhone 3G. Overall, this provides an important

measure of the rate at which comparable devices are introduced into the market by the

smartphone manufacturers. This trend suggests a growth of 40% per annum starting in 2H2010.

Based on this growth rate and prior “catch-up” performance, Arieso predicts a proliferation of

devices with demands similar to the iPhone 4S within the next 12 to 18 months.

This analysis focuses on downlink data volumes due to its dominance compared to uplink data

volumes (by a ratio of nearly 7-to-1). However, similar overall trends in uplink data volumes

versus device release dates can be seen here as well.

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The iPhone 3G as a benchmark

The iPhone 3G is often used as a benchmark for mobile devices. In part this is due to the

historical significance of the iPhone. The advent of the iPhone in 2007-2008 heralded the start

of the “big data” challenge that the wireless industry still works to meet. The iconic design of

the iPhone is still an important point of reference that continues to be imitated. But Figure 2

below reveals another reason to employ the iPhone 3G as a benchmark: it is a “median” device.

Figure 2: Distribution of average downlink data volume/user by device type

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In a study of over 100 smartphone and non-smartphone devices (each represented by more

than 1000 users per device), nearly half of the devices had a downlink data volume per user less

than the iPhone 3G (normalized to unity on the x-axis above). As such, the iPhone 3G

represents the median demand for the collection of devices (smartphones and non-

smartphones) under study. It should be noted that the x-axis above is plotted logarithmically: as

seen in Table 1, many devices (especially USB dongles) will have demands that are substantially

greater than that of the iPhone 3G. The Nokia E71 is shown as an example of a lower-demand

device (39% of the iPhone 3G); only 15% of the reported devices had a lesser demand. As

noted earlier in this study, the iPhone 4S is located at pinnacle of smartphone demand; only USB

dongles and data cards are situated above the iPhone 4S in Figure 2. Similar results can be

observed for uplink data volumes and numbers of data calls.

The iPad is still more like a smartphone than a PC

As noted in earlier studies, 3G modems are generally noteworthy for two aspects: 1) their

relatively low volumes of subscribers (compared to smartphones and other devices) and 2) their

remarkably high volumes of data per subscriber. The product of these two items results in the

aggregate data volume across all 3G modems and is typically competitive with (and sometimes

in excess of) the aggregate data volume across all smartphones. Table 1 shows a considerable

23-to-24 times increase in the data volume per 3G modem subscriber over the iPhone 3G

reference. This is achieved by making nearly one-seventh the number of data calls per

subscriber.

In contrast, the typically more numerous iPads reveal per-subscriber scores in Table 1 that are

well within the range of the smartphone scores for each category. As such, the iPad appears to

be more like a smartphone from a 3G network demand perspective than the more voracious 3G

modem. This information about the impact of tablet devices on the network is critically

important not only for network planners but also for marketing departments who must price

data plans for tablet devices: tablets are in a completely different usage range than the 3G

modems with which they are often grouped. We conjecture that the vast majority of tablet data

transfers occur on Wi-Fi networks (although this is outside the scope of our UMTS study).

By way of tablet evolution, it is interesting to note that the advent of the iPad 2 has done little

to increase downlink data demand or the number of data calls compared to the original iPad.

However, the uplink data demand is notably increased from 173% to 261% (relative to the

iPhone 3G). As there were no cameras on the original iPad, this increase is likely due to the

introduction of front-facing and rear-facing cameras on the iPad 2, and the attendant increase in

user-generated content.

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Part II: The Extreme Data User

Extreme Usage Overview

Consideration of the device demands is of paramount importance for an integral understanding

of how aggregate network demands will evolve over time. This is because the uptake rate of

different devices in a given geographical area can be translated into demands across those

different devices to assist in traffic forecasting and capacity analysis. In addition to this

aggregate demand (which relies on the analysis of averaged quantities), it is also important to

understand the demand in extreme cases (for example, for the hungriest of users). This is

because the presence of sufficiently non-uniform demands across users dictate that different

actions be taken in order to cope with this demand.

The distribution of downlink data volume as a function of user fraction for the market under

study is shown in Figure 3.

Figure 3: Downlink data fraction versus user fraction

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It is remarkable that the hungriest 1% of all subscribers consume half of the downlink data

volume. By way of comparison, the hungriest 0.1% consume one-fifth of the downlink data

while the hungriest 10% consume 90% of the downlink data. In 2009, it was reported that 3% of

the users were consumed 40% of the data3. For this (different) Tier-1 market under study, 3% of

the users consume 70% of the data, suggesting that the hungry are getting hungrier. This

remains a topic for further study. Migration of these extreme users off of the UMTS macro

network provides an enormous opportunity for UMTS capacity relief.

The distribution of device types for the hungriest 1% of all users is shown in Figure 4.

Figure 4: Extreme user device breakdown

The hungriest 1% of all subscribers were predominantly using USB dongles or 3G Modems. This

suggests highly stationary behaviour at home or places of business. Even the smartphone and

tablet users are often seen to make use of the network from a small number of discrete

locations.

This disparity in data consumption suggests a location-aware, subscriber-centric approach to

managing future data demand. This is explored in the next part of this paper.

3http://www.pcworld.com/article/173320/atandt_wireless_ceo_hints_at_managing_iphone_data_usage.html)

http://www.pcworld.com/article/173320/atandt_wireless_ceo_hints_at_managing_iphone_data_usage.html

http://www.pcworld.com/article/173320/atandt_wireless_ceo_hints_at_managing_iphone_data_usage.html

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Part III: Network Operator Response

The “Extreme” Response

There are many strategies to deal with the demands of extreme users. One of the more popular

approaches of late has to do with data throttling. This results in the reduction of data transfer

rates once a data volume limit is exceeded. Another approach makes use of overage pricing.

This results in increased fees once a data volume is exceeded. Yet another approach involves

the use of policy management where certain applications or visits to ‘over-the-top’ websites will

result in reduced data rates. These can all be valid approaches given the circumstances of the

network operator and the relationship with the extreme customer. In a sense, each of these

techniques is a “controlled churn” strategy: if the extreme user is unhappy with the service

offered by the network operator then that user is free to go away and be extreme on the

network of a competitor. Indeed, this can be a situation where churn is viewed in a favourable

light.

There is still a need to go beyond these “controlled churn” mechanisms for the extreme

customers that a network operator wishes to keep. As noted in the prior section, a small

percentage of extreme users will consume the majority of their data from one or a small number

of discrete locations (per user). One customer-centric, location-aware strategy is then to outfit

the extreme user with a 4G device (to replace their existing 3G device) AND to install a 4G small

cell as close as possible to where they regularly consume data. This conjunction is extremely

important: it is not enough to provide a 4G device without ensuring that the data offload will

occur by judicious placement of a new small cell. The good news is that there exist location-

aware products that can determine the correct place for the new small cell. In addition, the

number of subscribers that this needs to be done for is small in order to accomplish substantial

offloads. As noted in the previous section, offloading just 1% of subscribers would double the

effective network capacity.

This approach has benefits beyond near-term offloading. It is also theoretically optimal for

longer-term network design. This is because the capacity of a network is driven by three

components:

1. The amount of available spectrum

2. The air interface performance

3. The network design (including site placement)

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Spectrum is fundamentally limited, subject to licensing costs, and made available in an

infrequent manner. Since the capacity performance typically scales with the amount of

spectrum, the capacity of a network is typically expressed per unit of spectrum (e.g.,

bits/sec/Hz).

The performance of any air interface (such as GSM, UMTS, LTE) is subject to the Shannon Limit

as shown in Figure 5 below. Progress in coding and modulation results in modern

communications systems that are within a few deciBels (dB) of the Shannon Limit. While there

have been substantial gains going from GSM to UMTS and from UMTS to LTE, there are

diminishing opportunities for gains in the future.

Figure 5: Capacity versus Signal-to-Noise (SNR)

This leaves network design as the final frontier in maximising the capacity of wireless networks.

The operating points P1, P2, P3 and P4 can be viewed as four different network designs where

sites are placed increasingly closer to where the subscriber is located (going from the low SNR

values of P1 to the higher SNR values of P4). These gains can only be accomplished by knowing

where subscribers are located. Surgical placement of small cells not only results in the desired

data off-load in the near-term, but also satisfies long-term optimality criteria.

While much of the emphasis has been on migration to LTE, offloading can also be accomplished

using UMTS strategies. This is done, as before, by placing small cells in the immediate vicinity of

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where the extreme data users are located. In a recent case study with a Tier-1 network

operator, 250 microcells were added to a macro cell network in order to offload data traffic. A

location-aware product was used to determine where data traffic demands were located in

order to best place the new microcells, as shown in Figure 6 below.

Figure 6: Microcell placement using data and voice maps

In this particular case study, the location of a microcell was moved 75 meters to the west in

order to better serve an area of intense high-speed (HS) data demand. As a result of this overall

effort, there were 20% improvements in customer experience metrics associated with data

usage and 250% increases in capacity per square-kilometre. It is important to note that these

network design decisions were effectively driven by the customers themselves (and not by

drive-testing or traditional switch statistics). This effectively constitutes a key SON (Self-

Organizing Networks) use case involving the placement of base station infrastructure.

This example illustrates the utility of customer-centric, location-aware solutions for the

following groups within the network operator:

Radio Access Network (RAN) planning

Performance engineering

Customer experience assurance These solutions also provide actionable insights to geo-marketing intelligence teams. For example, knowledge of which devices perform better in different areas of the network allows better marketing to customers who are planning to move from feature phones to smartphones.

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Summary

Recent Tier-1 market information reveals increasingly sophisticated devices that are unleashing

unprecedented levels of user demand. The iPhone 4S, in particular, was seen to effectively be

the “hungriest handset” according to per-subscriber uplink and downlink data demands. The

iPhone 4S was seen to have twice the demand of 2010’s “hungriest handset”, the iPhone 4.

Other recent devices, including the HTC Desire S, were seen to have dramatic gains in data

demand. The average device demand was seen to be increasing at a rate of 40% per annum,

suggesting a proliferation of devices rivalling the iPhone 4S in the next 12-18 months. As noted

in last year’s study, tablet users were seen to be more like smartphone users than 3G Modem

users; this motivates new data plan pricing strategies for tablets.

The study of extreme data devices motivated the study of extreme data users and the hungriest

1% were seen to consume half of the transmitted data. The most extreme data users mostly

made use of USB dongles, some smartphones and a few tablets. The disparity in consumption

appears to be increasing over time compared to earlier, well-publicised reports.

The extreme data user problem triggers a variety of network operator responses, including use

of customer-centric, location-aware techniques to surgically place a relatively-limited number of

small cells. This SON (Self-Optimizing Network) use case not only satisfies near-term offload

objectives, but also longer-term design objectives in a theoretically optimal manner. Case

studies involving the targeted application of small cells illustrated the benefits of customer-

centric, location-aware products for the following groups within the network operator:

RAN Planning

Performance Engineering

Customer Experience Assurance

Geo-Marketing Intelligence

It must be noted that the particular results in this paper correspond to the specific market that

was studied, and that these results can vary depending on a number of circumstances (including

morphologies, available devices, regional customer behaviours, and socio-economic user

factors). As such, these results are intended to be illustrative rather than definitive. Each

network operator should embark upon a similar subscriber and network evaluation programme

in order to determine the clear and present data demands being placed on their network as well

as the most appropriate response strategies to best satisfy this demand.

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Arieso Ltd

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USA

Tel: +1 678 904 2424

Fax: +1 678 904 2429

Email: [email protected]

Web: www.arieso.com

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