Understanding exactly how consumers are using their smartphones presents a
greater challenge than previous generations of handset.
We used on-device trackers to gather device usage data from a panel of
consumer smartphone users in France, Germany, Spain, the UK and the USA during
August and September 2011. In this Report, we consider how consumers use their
smartphones and the impact that this has on cellular data traffic, as well as
Wi-Fi connectivity.
This Report provides answers to the following questions.
How much data are they generating and over which network?
Which apps are generating data and how does the quantity of that data vary
between Wi-Fi and cellular networks?
Who are the consumers who do not use cellular data on their handset and
are OTT apps cannibalising core voice and messaging spend for these customers?
Are non-data-using consumers worth investing in? How can they be
encouraged to use data?
Who are the heaviest smartphone users?
What role do handset types and Wi-Fi have to play in the volume of traffic?
How do consumers use Wi-Fi hotspots with their smartphone?
What type of hotspots do they use, and how many hotspots?
How does usage behaviour change when consuemrs are abroad and which
customers are operators capturing/losing?
Figure 36: The distribution of per-minute data traffic generated by
web browsers on Android and iOS-based smartphones
[Source: Analysys Mason and Arbitron Mobile, 2012]
1 n = 1007; some respondents may have had cellular or
Wi-Fi enabled but did not use the connectivity within the observation period.
About the author
Martin Scott (Principal Analyst) co-ordinates Analysys Mason's primary
research report series, including reports related to consumer smartphone usage
and the Connected Consumer series. Martin also leads Analysys Mason's Fixed
Broadband research programme and contributes regularly to the Mobile Broadband
and Devices programme. His primary areas of specialisation include customer
satisfaction and consumer-facing marketing strategy, broadband retail pricing
and bundling. Martin also specialises in statistics, surveys and the analysis
of primary research. He has produced research for Analysys Mason on different
aspects consumer demand for present and next-generation services, the business
case for value-added services, such as videotelephony and three-screen
advertising and broadband (next-generation) access. Martin has a Master's
degree in Mathematics from Oxford University.
Table of Contents
Table of Contents
6. Executive summary
7. Understanding consumer smartphone data traffic and network usage
through on-device measurement
8. Mobile video is not the killer app (yet): the browser generates the
greatest quantity of smartphone Wi-Fi data traffic and email dominates cellular
9. 18% of smartphone users in our panel did not use cellular data: OTT
communication apps may begin to erode core revenue in this segment
10. A minority of heavy users distort total traffic levels: these are
almost all iPhone users
11. Recommendations
12. Implications and recommendations
13. Implications and recommendations
14. Real-world usage: we measured consumer smartphone usage via an
on-device monitoring application, in partnership with Arbitron Mobile
15. The two leading operating systems - Android and iOS - are
over-represented in the smartphone user panel
16. Key questions and issues discussed in this report
17. Wi-Fi versus cellular: gaining a full picture of smartphone data
usage
18. On-device measurement offers a clear view of the split between
cellular and Wi-Fi traffic among our smartphone user panellists
19. Cellular data users in the USA are slightly ‘hungrier' than in
France, Germany and the UK
20. Younger consumers generate more smartphone data traffic on cellular
and Wi-Fi networks than older consumers
21. Connectivity is linked to usage: Wi-Fi-only panellists were light
users, while panellists who used both Wi-Fi and cellular were heavier users
22. Half of our smartphone panellists generated less than 221MB of
smartphone data (across both cellular and Wi-Fi) per month
23. Wi-Fi had a greater number of very light and very heavy users whereas
cellular data usage patterns are distributed more regularly
24. More than 40% of Android-using panellists connected to two or more
Wi-Fi hotpots, and almost a third connected to public hotspots
25. More panellists used roaming cellular data than Wi-Fi hotspots when
travelling abroad
26. Apps and traffic: identifying the apps that drive data traffic
27. Our analysis of traffic generated by specific apps indicates that rich
media - in particular video - is not driving the majority of smartphone traffic
28. The combination of browser and email apps on a smartphone generated
almost four times the quantity of data traffic as social networking
29. The browser generates the greatest amount of smartphone Wi-Fi traffic,
whereas email dominates cellular data use
30. Cellular data traffic peaks at 09.00 and 19.00, whereas Wi-Fi traffic
builds to 22.00
31. Non-cellular data users: understanding Wi-Fi-only smartphone
users
32. 18% of smartphone users in our panel did not use cellular data
33. Consumers who own a smartphone but do not use cellular data are not
demographically easy to identify
34. Android users are less likely to use cellular data than iPhone users,
and US panellists are less likely to use cellular data than European panellists
35. Smartphone users who do not use cellular data tend to be either light
or heavy voice users; heavy voice users should be a key target for selling data
36. Wi-Fi-only smartphone users often use OTT communications apps: this
may erode spending on the core services of SMS and voice
37.‘Power users': traffic and usage among the heaviest data
users
38. 1% of panellists generated almost 20% of traffic in the study: such
consumers should be moderated or monetised
39. iPhone users are heavier users of data than other consumers, and
increasing iPhone penetration could stimulate data revenue growth
40. Heavier cellular data users are not necessarily also heavier Wi-Fi
users - ‘power users' are not that common
41. Operators' different approaches to data allowances appear to have a
significant affect on the number of heavy cellular data users in a country
42. Web browsing on an iPhone generates twice the per-minute traffic of
browsing on an Android smartphone
43. Methodology and definitions
44. Methodology and definitions [1]
45. Methodology and definitions [2]
46. Methodology and definitions [3]
47. About Arbitron Mobile
48. About the author and Analysys Mason
49. About the author
50. About Analysys Mason
51. Research from Analysys Mason
52. Consulting from Analysys Mason
List of figures
Figure 1: Distribution of total smartphone traffic across all panellists
Figure 2: Relative traffic volumes generated by different apps, by network
Figure 3: Distribution of smartphone panellists, by type of data
connectivity
Figure 4: The average monthly data usage of the ten heaviest individual
data users in the panel of smartphone users
Figure 5: Illustration of Analysys Mason - Arbitron smartphone data
analysis process
Figure 6: Smartphone panellists included in the data traffic analysis, by
country
Figure 7: Smartphone panellists included in the data traffic analysis, by
age
Figure 8: Smartphone panellists included in the data traffic analysis, by
OS
Figure 9: Structure of this report and key issues addressed
Figure 10: Distribution of total smartphone traffic in the panel
Figure 11: Average monthly cellular data consumption, by country
Figure 12: Average monthly cellular and Wi-Fi smartphone data consumption,
by age range
Figure 13: Panellists' average smartphone data usage
Figure 14: Distribution of total average monthly smartphone data traffic
for panellists, by percentile
Figure 15: Distribution of total monthly smartphone cellular data traffic,
by respondent's data usage percentile
Figure 16: Distribution of total monthly smartphone Wi-Fi traffic, by
percentile
Figure 17: Distribution of Wi-Fi hotspot usage for Android users during
the two-month observation period
Figure 18: Percentage of panellists who used Wi-Fi that connected to
different types of Wi-Fi hotspot
Figure 19: Types of data connectivity used abroad by panellists who spent
any time away from their home country during the observation period
Figure 20: Data traffic generated by smartphone panellists, by foreground
or background apps
Figure 21: Relative volumes of smartphone data traffic generated by
different apps
Figure 22: Relative volumes of smartphone data traffic generated by
different apps, by network
Figure 23: Cellular and Wi-Fi data volumes as a percentage of total data
traffic generated, by time of day
Figure 24: Distribution of smartphone panellists, by type of data
connectivity
Figure 25: Percentage of panellists who used different data connectivity
options, by age, relative to the total panel
Figure 26: Percentage of panellists who used different data connectivity
options, by gender, relative to the total panel
Figure 27: Percentage of panellists who used different data connectivity
options, by country, relative to the total panel
Figure 28: Percentage of panellists who used different data connectivity
options, by OS, relative to the total panel
Figure 29: Distribution of average monthly outgoing voice minutes, by data
usage profile
Figure 30: Distribution of average monthly SMS sent, by data usage profile
Figure 31: Percentage of panellists who use OTT communications apps, by
type of data connectivity
Figure 32: The average monthly data usage of the ten heaviest data users
in the smartphone panel
Figure 33: Distribution of monthly data traffic percentiles by smartphone
operating system
Figure 34: The relationship between the relative heaviness of Wi-Fi and
cellular data usage by panellist percentile
Figure 35: Demographic breakdown of the top-10% heaviest cellular data
users relative to the total panel
Figure 36: The distribution of per-minute data traffic generated by web
browsers on Android and iOS-based smartphones
Figure 37: Utility app categorisation examples
Figure 38: Utility app categorisation examples
Consumer smartphone usage: data traffic and network usage patterns published by Analysys Mason in June 25, 2012. This report price starts from US $ 4999.
