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市場調查報告書

消費者的智慧型手機利用 2014年:行動數據利用

Consumer Smartphone Usage 2014: Mobile Data Usage

出版商 Analysys Mason 商品編碼 328151
出版日期 內容資訊 英文 39 Slides
商品交期: 最快1-2個工作天內
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消費者的智慧型手機利用 2014年:行動數據利用 Consumer Smartphone Usage 2014: Mobile Data Usage
出版日期: 2015年04月10日 內容資訊: 英文 39 Slides
簡介

智慧型手機流量有19%透過蜂巢式網路,81%透過Wi-Fi。

本報告提供法國,德國,英國及美消費者的實際終端資料利用模式相關分析,提供您終端資料利用的變化及促進資料消費的應用,螢幕尺寸和LTE支援等影響終端資料利用的設備功能所扮演的角色,彙整Wi-Fi作用的變化和實際世界的消費者智慧型手機連接的熱點類型,還有利用的變化相關之人口統計趨勢等資料,為您概述為以下內容。

摘要整理

建議

終端資料利用的變化

  • 行動數據是對電信業者而言收益成長的主要推動力,所以理解終端資料利用的變化是不可或缺的
  • 美國的LTE滲透率到2015年末為止達到58%
  • 終端資料流量,大部分透過Wi-Fi,預計將繼續發展
  • 由於很多終端資料用於Wi-Fi,業者必須適應行動電話資料費、其他

變化的促進要素:LTE·設備的功能

  • 美國的LTE使用者雖然使用Wi-Fi的情況與非LTE使用者類似,但行動電話資料實際上用量卻是三倍
  • 設備的尺寸和行動電話·Wi-Fi資料數量的關係,是強大且明顯的嗎?
  • LTE使用者比非LTE使用者平均一天長63%使用設備,創造出2.7倍的行動電話資料流量
  • 線上影片幾乎佔大部分的終端資料利用,儘管遊戲訊息利用率高但資料量卻低,其他

變化的促進要素:Wi-Fi所扮演的角色

  • Wi-Fi所扮演的角色直接影響行動電話資訊服務的收益化
  • 很多智慧型手機Wi-Fi流量在家庭內產生
  • 公共場所的Wi-Fi利用,由於強力的地方機關Wi-Fi及零售商店Wi-Fi的高度利用率,主要在英國發屉

資料利用的人口分佈分析

  • 「年輕的使用者更常消費行動電話資料」這種初期多數派的人口分佈情形正在崩潰
  • 具有強力提升4G服務銷售量目標的支援4G終端使用者是未開發的人口分佈
  • 一認為Wi-Fi和行動電話資料流通間並無巨大的相互關係,人口分佈也未必具有重要作用

調查手法·定義

關於著者·Analysys Mason

圖表清單

目錄

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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