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消費者のスマートフォン利用 2014年:モバイルデータ利用

Consumer Smartphone Usage 2014: Mobile Data Usage

発行 Analysys Mason 商品コード 328151
出版日 ページ情報 英文 39 Slides
納期: 即日から翌営業日
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消費者のスマートフォン利用 2014年:モバイルデータ利用 Consumer Smartphone Usage 2014: Mobile Data Usage
出版日: 2015年04月10日 ページ情報: 英文 39 Slides
概要

スマートフォントラフィックの19%はセルラーネットワークに持ち越され、81%はWi-Fiに持ち越されました。

当レポートでは、フランス、ドイツ、英国および米国における消費者の実際の端末データ利用パターンについて分析し、端末データ利用の変化とデータ消費を促進するアプリケーション、スクリーンサイズやLTEサポートなど端末データ利用に影響を及ぼすデバイス機能の役割、Wi-Fiの役割変化と実世界の消費者がスマートフォンで接続するホットスポットのタイプ、および利用の変化に関連した人口統計動向などをまとめ、お届けいたします。

エグゼクティブサマリー

提言

端末データ利用の変化

  • モバイルデータは西側オペレーターにとって収益成長の主要なエンジンであり、端末のデータ利用がどのように変化しているかを理解することは不可欠
  • 米国におけるLTE浸透率は2015年末までに58%へ達する
  • 端末データトラフィックは、大部分はWi-Fiに持ち越されており、今後も続く見込み
  • 端末データの多くはWi-Fi上で利用されているため、オペレーターはセルラーデータ料金に適応する必要、ほか

変化の促進因子:LTE・デバイスの機能

  • 米国におけるLTE利用者は非LTE利用者と類似したWi-Fi利用プロファイルを持つが、セルラーデータ利用は事実上、3倍の大きさ
  • デバイスのサイズとセルラー・Wi-Fiデータ量の関係は、強力かつ明らか
  • LTE利用者は非LTE利用者の平均よりも1日あたり63%長くデバイスを使用し、2.7倍のセルラーデータトラフィックを生み出す
  • オンラインビデオは端末データ利用のほとんどを占めるが、ゲーム・メッセージングは利用が高いにも関わらずデータ率が比較的低い、ほか

変化の促進因子:Wi-Fiの役割

  • Wi-Fiの役割はセルラーデータサービスの収益化に直接影響を及ぼす
  • スマートフォンWi-Fiトラフィックの多くは家庭で生成される
  • 公共の場におけるWi-Fiの利用は、強力なコミュニティWi-Fiおよび小売り店舗における高いWi-Fi利用によって主に英国で発展

データ利用の人口統計分析

  • 「若い利用者がより多くのセルラーデータを消費する」という初期多数派の人口統計プロファイルは崩壊しつつある
  • 4Gサービスのアップセリングの強力な潜在的ターゲットである4G対応端末の利用者は、未開拓の人口統計
  • Wi-Fiとセルラーデータ流通間の大きな相互関係は無く、人口統計は重要な役割を果たさないと思われる

調査手法・定義

著者・Analysys Masonについて

図表リスト

目次

19% of all smartphone traffic observed in the panel was carried over the cellular network and 81% was carried over Wi-Fi.


This report analyses the real-world handset data usage patterns of consumers in France, Germany, the UK and the USA. Deep analysis of mobile data usage is important because mobile data is now the main engine of revenue growth for operators in developed markets.


The analysis is based on data from 3Q and 4Q 2013 provided by Nielsen, using an app developed by Arbitron Mobile.

This report provides information about:

  • changes in handset data usage and the apps that drive data consumption.
  • the role that device capabilities, such as screen size and LTE support, play in affecting handset data usage
  • the changing role of Wi-Fi and the types of hotspots to which 'real-world' consumers connect their smartphones
  • the demographic trends associated with changes in usage.

GEOGRAPHICAL COVERAGE

Data is provided for the following individual countries:

  • France
  • Germany
  • UK
  • USA

ABOUT THE AUTHORS

Martin Scott (Practice Head) is the head of Analysys Mason's Consumer Services research practice, which includes the Fixed Broadband and Multi-Play, Next-Generation Services, Mobile Services, Mobile Devices and Digital Economy research programmes. His primary areas of specialisation include the bundling and pricing of multi-play services, including quadruple-play bundling, customer satisfaction and consumer-facing marketing strategy. He also specialises in statistics, surveys and the analysis of primary research; he co-ordinates Analysys Mason's Connected Consumer and Consumer smartphone usage series of research.

Aris Xylouris (Research Analyst) focuses on data modelling and collection for Analysys Mason's Consumer Services research practice, contributing to the Fixed Broadband and Multi-Play, Mobile Services, Digital Economy and Mobile Devices research programmes. Before joining Analysys Mason, he held internships as an economic analyst in the media sector, working on market analysis, financial evaluation, profitability analysis and business plan development. His wider experience includes quantitative forecast modelling and computer simulations using agent-based models.

Table of Contents

  • 6. Executive summary
  • 7. Executive summary: 81% of handset data generated on smartphones in our panel was carried over Wi-Fi
  • 8. Executive summary: Online video accounts for most handset data usage, and LTE and high-specification devices will encourage greater use
  • 9. Recommendations
  • 10. Recommendations
  • 11. Changes in handset data usage
  • 12. Mobile data is the main engine of revenue growth for Western operators and understanding how handset data use is changing is vital
  • 13. LTE take-up in the USA will reach 58% by the end of 2015
  • 14. Handset data traffic is, and will continue to be, predominantly carried over Wi-Fi
  • 15. Most handset data usage is on Wi-Fi so operators must adjust cellular data pricing
  • 16. The price and monthly allowance constraints of cellular data potentially inhibit cellular data usage from being used in the same way as Wi-Fi
  • 17. Drivers of change: LTE and device capabilities
  • 18. LTE users in the USA had similar Wi-Fi usage profiles to non-LTE users, but their cellular data usage was effectively three times higher
  • 19. The relationship between the size of the device and the amount of cellular and Wi-Fi data that it generates is strong and clear
  • 20. LTE users used their devices for 63% longer per day than average and generated 2.7 times as much cellular data traffic as non-LTE users
  • 21. Online video accounts for most handset data usage, but gaming and messaging have relatively low data rates despite high usage
  • 22. Operators have ‘zero rated' many categories of app and this could be applied to other categories
  • 23. Drivers of change: the role of Wi-Fi
  • 24. The role of Wi-Fi directly affects the monetisation of cellular data services
  • 25. Most smartphone Wi-Fi traffic was generated in the home
  • 26. The use of Wi-Fi in public locations is particularly developed in the UK with strong community Wi-Fi and high Wi-Fi use in retail establishments
  • 27. Demographic analysis of data use
  • 28. The early majority demographic profile of ‘young users consume more cellular data' may be being disrupted
  • 29. There is an untapped demographic of 4G-capable handset users that are strong potential targets for upselling 4G services
  • 30. There is not a significant correlation between Wi-Fi and cellular data distribution, and demographics do not appear to play a significant role
  • 31. Methodology and definitions
  • 32. Methodology and definitions [1]
  • 33. Methodology and definitions [2]
  • 34. About the authors and Analysys Mason
  • 35. About the authors
  • 36. About Analysys Mason
  • 37. Research from Analysys Mason
  • 38. Consulting from Analysys Mason

List of figures

  • Figure 1: Distribution of total smartphone traffic across all panellists
  • Figure 2: App sub-categories by average percentage of time and average percentage of data traffic
  • Figure 3: Mobile data as a percentage of service revenue for residential customers, by country or region, 2010-2019
  • Figure 4: Percentage of panel that had an LTE-capable handset and that used LTE
  • Figure 5: LTE-capable handsets as a percentage of all handsets, by country or region, 2010-2019
  • Figure 6: Distribution of total smartphone traffic across all panellists
  • Figure 7: Distribution of smartphone panellists, by type of data connectivity
  • Figure 8: Distribution of total average monthly smartphone cellular data traffic, by percentile
  • Figure 9: Distribution of total average monthly smartphone Wi-Fi traffic, by percentile
  • Figure 10: Average monthly data usage for customers who did and did not use LTE, by network type, USA
  • Figure 11: Median data usage by network type and smartphone screen size
  • Figure 12: Average MoU by app category for panellists
  • Figure 13: Top-ten apps by handset traffic
  • Figure 14: App sub-categories by average percentage of time and average percentage of data traffic
  • Figure 15: Data traffic by app category and network type
  • Figure 16: Illustration of access technologies used for mobile data and voice coverage in an ‘inside-out' MNO model
  • Figure 17: Percentage of panellists that connected to Wi-Fi, by hotspot category, and the average amount of their Wi-Fi data usage attributable to that category, Android users
  • Figure 18: Percentage of respondents who connected to Wi-Fi, by hotspot category and country
  • Figure 19: Monthly cellular data usage by age group, 2011 and 2013
  • Figure 20: Monthly cellular and Wi-Fi data usage by country and subscription type
  • Figure 21: Percentage of panellists who use 4G services, by gender and age group
  • Figure 22: Percentage of panellists who own a 4G-capable handset but did not use 4G services, by gender and age group
  • Figure 23: Distribution of panellists by Wi-Fi and cellular data percentile
  • Figure 24: Panellists' gender, by country of observation
  • Figure 25: Panellists' age, by country of observation
  • Figure 26: Panellists' handset OS, by country of observation
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