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モバイルコンテキストと位置サービス 2014-2019年: ナビゲーション、追跡、ソーシャル、ローカル検索

Mobile Context & Location Services

Navigation, Tracking, Social & Local Search 2014-2019

 

出版社 出版年月価格
Juniper Research
ジュニパーリサーチ社
2014年8月お問い合わせください

※この調査レポートには、付属のExcelデータセットがございます。
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サマリー

この調査レポートは、モバイルコンテキストと位置サービス市場を調査し、2019年までの市場予測やモバイル位置情報サービス (MLBS) 対応スマートフォンとタブレットアプリケーションサービスに関する分析や収益化展望も掲載しています。また、勢力を強めている、アプリケーションで利用できる補助コンテキストについても詳述しています。

主な掲載内容

  1. コンテキストの台頭、位置情報
  2. モバイル位置情報サービス
  3. モバイルコンテキストアウェアサービス
  4. 市場促進要因と阻害要因
  5. モバイル位置情報サービスとコンテキストベースサービスの予測サマリー
  6. スマートフォン&タブレットナビゲーション予測
  7. スマートフォン&タブレットソーシャルアプリ予測
  8. スマートフォン&タブレット追跡アプリ予測
  9. スマート&タブレットローカル検索と探索アプリ予測
  10. スマートフォンとタブレットのその他のモバイル位置情報サービスアプリ予測
  11. ベンダ情報

Overview

•    New Context-aware Focus
•    6 Exclusive Forecast Chapters
•    Business Model & Monetisation Strategies

This third edition of Juniper’s market-leading report on consumer Mobile Context & Location Services provides definitive insight into the market, together with a comprehensive suite of 5 year calculated projections which assess the anticipated market opportunity to 2019. It includes an extensive analysis of the MLBS (Mobile Location Based Services) smartphone and tablet app service and monetisation landscape, along with an in-depth focus on the increasing power of supplementary contexts leveraged by apps. These enable providers to deliver targeted, relevant content and services to consumers based not only on who and where they are, but on when and why they are at those locations.

The report’s associated IFxl (Interactive Forecast Excel) contains all regional and sector forecasts from this report, together with additional country level forecasts for Canada, Germany, Japan, South Korea, US, and UK.

What This Report Covers

Context Awareness

This report explores how, through the combination of rapid 3G and 4G uptake and the multitude of sensors and data produced by mobile devices, stakeholders can leverage these assets in order to drive revenues and consumer engagement. It also analyses the technological advances and drivers that will transform the manner by which mobile services will be used.

With particular focus on the Navigation, Social, Tracking, Local Search and Other MLBS app landscape, Juniper investigates market drivers, hurdles and monetisation opportunities facing stakeholders. It examines 3 app revenue models and the strategies that vendors are currently employing to maximise revenues.

New & Updated Forecast Suite

With 32 forecast tables in the report, this updated study provides an extensive breakdown for the consumer Context and Location Services market:

•    Smartphone and Tablet Installed Base

•    Smartphone & Tablet Navigation Apps: Apps in Use, Pay-to-Download Revenues, In-App Purchase Spend, Ad-Supported Ad Spend

•    Smartphone & Tablet Social Apps: Apps in Use, Pay-to-Download Revenues, In-App Purchase Spend, Ad-Supported Ad Spend

•    Smartphone & Tablet Tracking Apps: Apps in Use, Pay-to-Download Revenues, In-App Purchase Spend, Ad-Supported Ad Spend

•    Smartphone & Tablet Local Search Apps: Apps in Use, Pay-to-Download Revenues, In-App Purchase Spend, Ad-Supported Ad Spend

•    Smartphone & Tablet Other MLBS Apps: Apps in Use, Pay-to-Download Revenues, In-App Purchase Spend, Ad-Supported Ad Spend

Expert Stakeholder Analysis

The report includes 14 case studies & 12 vendor profiles detailing the work of stakeholders across the MLBS landscape. These case studies and profiles are supplemented with exclusive interviews with key enablers, including Banjo, Fiksu, Nexage, Sensor Platforms, Syniverse and TeleCommunication Systems, offering expert insight into the MLBS market.

Mobile Context & Location Services IFxl 2014-2019

Juniper Research’s highly granular IFxls enable clients to manipulate Juniper’s forecast data and charts to test their own assumptions, perform what-if analysis; and compare select markets side by side in customised charts and tables. IFxls greatly increase clients’ ability both to understand a particular market and to integrate their own views into the model.

This IFxl, with 291 Tables; 30 What-if-analyses; and over 26,000 data points, provides additional market data for the following countries: Canada, Germany, Japan, South Korea, UK and US.

Companies Referenced

Interviewed: Banjo, Fiksu, Nexage, Sensor Platforms, Syniverse, TCS (TeleCommunication Systems).


Profiled: Apple, Banjo, Facebook, Fiksu, Garmin, Google, Nexage, Sensor Platforms, Skyhook Wireless, Syniverse, TCS (TeleCommunication Systems), TomTom.


Case Studied: Coca-Cola, Cota, Google, Nest Labs, Nexeon, Quixey, Sensor Platforms, Spotify, The Literary Digest, University of California, Berkeley, Waze, WifiSLAM, Wolfram Language.


Mentioned: 3, 3M, AdTruth, Aeroscan Ltd, Akamai, Amprius, AOL, ARM, AT&T, avast!, BBC, BlackBerry, BlueKai, Cisco, Coca-Cola, Commscope, Cota, DeepMind Technologies, Disney, Distimo, emoderation, Encyclopaedia Britannica, esri, Estimote, Experian, Factual, Foursquare, FOX, Foxconn, Gimbal, Glympse, GMI, Groupon, HERE, IndoorAtlas, Instagram, Ipsos Mori, IREP, LG, Liberty Media Corp, Line, LinkedIn, Macy’s, MapQuest, MasterCard, Match.com, Micello, Microsoft, Mindmeld, Mora Systems Ltd, Navicom, NBC, Nokia, OpenTable, Panasonic, Paypal, PayPal, Pew Internet Research, PlentyofFish, Proximic, Qualcomm, QuickLogic, Quiznos, Route 66, Samsung, Scout, Scoutmob, Sony, Spotify, Sprint, Swarm, SwiftKey, Sygic, Tele Atlas, Telefonica, Telenav, Telus, Tinder, T-Mobile, Toshiba, Tripadvisor, Trojan, TruePosition, TRUSTe, Twitter, US Cellular, Verizon Wireless, Viber, Wikipedia, Yelp, YouTube.

Extra Info

◊8 Key Regions include: North America; Latin America; Western Europe; Central & Eastern Europe; Far East & China; Indian Subcontinent; Rest of Asia Pacific and Africa & Middle East.

Questions

•    How much will Mobile Location & Context services be worth by 2019?

•    Which app revenue streams will generate the greatest revenue in each segment?

•    How are service providers monetising MLBS and contextual applications?

•    What opportunities are there for service providers to monetise contextual services?

•    What are the key trends shaping the MLBS/contextual services market?

•    What are the technologies powering MLBS/contextual services, and how can service providers leverage these?

•    How can operators leverage MLBS/contextual services?



目次

Table of Contents

Executive Summary

1. The Rise of Context, Location

1.1 Introduction
Figure 1.1: Cartesian 3D Positioning System
Figure 1.2: Trilateration
Table 1.1: Mobile Positioning Technologies
1.1.1 Mobile Positioning Developments
i. GLONASS
ii. Proximity Detection with Bluetooth Low Energy
iii. Dead Reckoning: Case Study – Sensor Platforms
iv. Ambient Wi-Fi signal analysis: Case Study - WiFiSLAM
1.2 Battery Life
1.2.1 Silicon Anode Batteries
i. Marketing Silicon Anode - Case Study: Nexeon
1.2.2 Wireless Charging – Case Study: Cota
1.2.3 Conclusion
1.3 Moving towards Context
1.3.1 Ambient Intelligence
1.3.2 MLBS
Figure 1.4: MLBS Application Categories
i. Local Search & Discovery
ii. Navigation
iii. Social
iv. Tracking
v. Other
1.4 Location & Context Intelligence
1.4.1 The Simpson-Yule Effect
i. Case Study: University of California, Berkeley
Table 1.2: UC Berkeley Admissions 1973
Table 1.3: UC Berkeley Admissions, 6 Largest Departments 1973
1.4.2 Sample Size vs Sample Bias
i. Case Study: The Literary Digest
1.5 Service Monetisation
1.5.1 Free Distribution
i. Gamification Case Study: Coca-Cola
1.5.2 Freemium/Ad-Supported
i. Soft Sell Advertising Can Enhance The User Experience: Case Study - Waze
Figure 1.6: Waze Taco Bell Ad
ii. Hard Sell Ads Make For Uncertain Returns – Case Study: Spotify
1.5.3 Paid Download
1.5.4 Subscription Based

2. Mobile Location Based Services

2.1 Introduction
2.2 Monetising MLBS
2.2.1 Operators
i. MLBS Drives the Data Plan
ii. Wi-Fi & Small Cells
Figure 2.1 EAP-SIM Seamless Wi-Fi
iii. Location Data Aggregators
iv. Network Optimisation
2.2.2 Marketers
i. The Active Approach
ii. The Passive Approach
iii. The Discount Model: Context Augments Location
2.3 Mapping & Positioning
2.3.1 Outdoor
i. Google
ii. HERE
iii. OpenStreetMap
2.3.2 Indoor
i. Mapping: Google
ii. Mapping: HERE
Table 2.1: HERE Indoor Venue Maps by Region and Skew (% of total)
iii. Mapping: Micello
iv. Positioning: Gimbal
v. Positioning: Skyhook Wireless
vi. Positioning: IndoorAtlas

3. Mobile Context-Aware Services

3.1 Introduction
Table 3.1: Augmentation of Apps through Context
3.2 Natural Language Processing
Figure 3.1: Keyword Search – Finding Barack Obama’s Age
Figure 3.2: Context Aware Search – Finding Barack Obama’s Age
3.2.1 Building a Knowledge Graph – Case Study: Google
3.2.2 NLP Hints at Future App Development – Case Study: Wolfram Language
3.3 Contextual Advertising
3.3.1 In App Tracking Is Possible
i. Device Fingerprinting
ii. IDFA (iOS)
iii. Advertising ID (Android)
3.3.2 The Emergence of Deep Linking
3.4 Persistent Search and the Fall of The App As We Know It
3.4.1 Persistent Search - Case Study: Google Now
Figure 3.3: Google Now Cards
3.4.2 Apps Melt into the Background: Case Study - Quixey
3.5 Context Driven Security
3.5.1 From a Business Perspective
Table 3.2: Typical System Access Security Model
3.5.2 From a Consumer Perspective
i. Biometrics
3.6 In the Smart Home
3.6.1 Ecosystem Integration
i. Case Study: Nest Labs

4. Market Drivers & Hurdles

4.1 Introduction
4.2 Market Drivers
Figure 4.1: Global 3G & 4G Adoption (% Total Subscriber Base) 2014-2019
4.3 Market Hurdles

5. Mobile Location and Context Based Services Forecast Summary

5.1 Introduction
5.2 Methodologies
5.2.1 Determining the User Base
Figure 5.1: Total Apps in Use Methodology
5.2.1 Pay-to-Download Model
Figure 5.2: Pay-to-Download Model Methodology
5.2.2 Ad-Supported Model
Figure 5.3: Ad-Supported Model Methodology
5.2.3 In App Purchase Model
Figure 5.4: IAP Model Methodology
5.3 Forecast Summary
5.3.1 The Smartphone MLBS Market
Figure 5.5 & Table 5.1: Global Smartphone MLBS Forecast Revenues ($m) Split by App Type
2014-2019
i. Revenue Splits by Distribution Model
Figure 5.6 & Table 5.2: Global Smartphone MLBS Forecast Revenues ($m) Split by Monetisation Model 2014-2019
Figure 5.7 & Table 5.3: Global Smartphone MLBS Revenue Share (% Total) Split by App Distribution Model 2014-2019
5.3.2 The Tablet MLBS Market
Figure 5.8 & Table 5.4: Global Tablet MLBS Forecast Revenues ($m) Split by App Type 2014-2019
i. Revenue Splits by Distribution Model
Figure 5.9 & Table 5.5: Global Tablet MLBS Forecast Revenues ($m) Split by Monetisation Model 2014-2019
Figure 5.10 & Table 5.6: Global Tablet MLBS Revenue Share (% Total) Split by App Distribution Model 2014-2019
5.3.1 The Total MLBS Market
Figure 5.11 & Table 5.7: Global Smartphone & Tablet MLBS Forecast Revenues ($m) Split by App Type 2014-2019
i. Revenue Splits by Distribution Model
Figure 5.12 & Table 5.8: Global Smartphone & Tablet MLBS Revenue Share (% Total) Split by App Distribution Model 2014-2019

6. Smartphone & Tablet Navigation Forecasts

6.1 Introduction
6.2 Assumptions
6.3 Smartphone & Tablet App Navigation Forecasts
6.3.1 Smartphone & Tablet Apps in Use
Figure 6.1 & Table 6.1: Smartphone Navigation Apps in Use (m) Split by 8 Key Regions 2014-2019
Figure 6.2 & Table 6.2: Tablet Navigation Apps in Use (m) Split by 8 Key Regions 2014-2019
6.3.2 Smartphone & Tablet Navigation Revenues
Figure 6.3 & Table 6.3: Smartphone Navigation Apps Revenue ($m) Split by 8 Key Regions
2014-2019
Figure 6.4 & Table 6.4: Tablet Navigation Apps Revenue ($m) Split by 8 Key Regions 2014-2019
Figure 6.5 & Table 6.5: Smartphone & Tablet Navigation Apps Revenue ($m) Split by 8 Key Regions 2014-2019
6.3.3 Total Smartphone & Tablet Navigation Revenue Forecast by Revenue Model
i. Pay-to-Download
ii. In-App-Purchases
iii. Ad-Supported
iv. Global Forecast Figures & Tables for Smartphones & Tablets
Figure 6.6 & Table 6.6: Total Smartphone & Tablet Navigation Revenue ($m) Split by Revenue Model

7. Smartphone & Tablet Social App Forecasts

7.1 Introduction
7.2 Assumptions
7.3 Smartphone & Tablet Social App Forecasts
7.3.1 Smartphone & Tablet Apps in Use
Figure 7.1 & Table 7.1: Smartphone Social Apps in Use (m) Split by 8 Key Regions 2014-2019
Figure 7.2 & Table 7.2: Tablet Social Apps in Use (m) Split by 8 Key Regions 2014-2019
7.3.2 Smartphone & Tablet Social App Revenues
Figure 7.3 & Table 7.3: Smartphone Social App Revenues ($m) Split by 8 Key Regions 2014-2019
Figure 7.4 & Table 7.4: Tablet Social App Revenues ($m) Split by 8 Key Regions 2014-2019
Figure 7.5 & Table 7.5: Smartphone & Tablet Social Revenues ($m) Split by 8 Key Regions
2014-2019
7.33 Total Smartphone & Tablet Social App Revenue Forecast by Revenue Model
i. Pay-to-Download
ii. In-App Purchases
iii. Ad-Supported
iv. Global Forecast Figures & Tables for Smartphones & Tablets
Figure 7.6 & Table 7.6: Total Smartphone & Tablet Social App Revenue ($m) Split by Revenue Model 2014-2019

8. Smartphone & Tablet Tracking App Forecasts

8.1 Introduction
8.2 Assumptions
8.3 Smartphone & Tablet Tracking App Forecasts
8.3.1 Smartphone & Tablet Apps in Use
Figure 8.1 & Table 8.1: Smartphone Tracking Apps in Use (m) Split by 8 Key Regions 2014-2019
Figure 8.2 & Table 8.2: Tablet Tracking Apps in Use (m) Split by 8 Key Regions 2014-2019
8.3.2 Smartphone & Tablet Tracking App Revenues
Figure 8.3 & Table 8.3: Smartphone Tracking App Revenue ($m) Split by 8 Key Regions 2014-2019
Figure 8.4 & Table 8.4: Tablet Tracking App Revenue ($m) Split by 8 Key Regions 2014-2019
Figure 8.5 & Table 8.5: Smartphone & Tablet Tracking App Revenue ($m) Split by 8 Key Regions 2014-2019
8.3.3 Total Smartphone & Tablet Tracking App Revenues Forecast
i. Pay-to-Download
ii. In-App Purchases
iii. Ad-Supported
iv. Global Forecast Figures & Tables for Smartphones & Tablets
Figure 8.6 & Table 8.6: Smartphone & Tablet Tracking App Revenue ($m) Split by Revenue Model 2014-2019

9. Smartphone & Tablet Local Search & Discovery App Forecasts

9.1 Introduction
9.2 Assumptions
9.3 Smartphone & Tablet Local Search & Discovery App Forecasts
9.3.1 Smartphone & Tablet Apps in Use
Figure 9.1 & Table 9.1: Smartphone Local Search & Discovery Apps in Use (m) Split by 8 Key Regions 2014-2019
Figure 9.2 & Table 9.2: Tablet Local Search & Discovery Apps in Use (m) Split by 8 Key Regions 2014-2019
9.3.2 Smartphone & Tablet Local Search & Discovery App Revenues
Figure 9.3 & Table 9.3: Smartphone Local Search & Discovery Apps Revenues ($m) Split by 8 Key Regions 2014-2019
Figure 9.4 & Table 9.4: Tablet Local Search & Discovery Apps Revenues ($m) Split by 8 Key Regions 2014-2019
Figure 9.5 & Table 9.5: Smartphone & Tablet Local Search & Discovery Apps Revenues ($m) Split by 8 Key Regions 2014-2019
9.3.3 Total Smartphone & Tablet Local Search & Discovery App Revenue Forecast
i. Pay-to-Download
ii. In-App Purchases
iii. Ad-Supported
iv. Global Forecast Figures & Tables for Smartphones & Tablets
Figure 9.6 & Table 9.6: Smartphone & Tablet Local Search & Discovery App Revenue ($m) Split by Revenue Model 2014-2019

10. Smartphone & Tablet Other MLBS App Forecasts

10.1 Introduction
10.2 Assumptions
10.3 Smartphone & Tablet Other MLBS App Forecasts
10.3.1 Smartphone & Tablet Apps in Use
Figure 10.1 & Table 10.1: Smartphone Other MLBS Apps in Use (m) Split by 8 Key Regions
2014-2019
Figure 10.2 & Table 10.2: Tablet Other MLBS Apps in Use (m) Split by 8 Key Regions 2014-2019
10.3.2 Smartphone & Tablet Other MLBS App Revenues
Figure 10.3 & Table 10.3: Smartphone Other MLBS Apps Revenues ($m) Split by 8 Key Regions 2014-2019
Figure 10.4 & Table 10.4: Tablet Other MLBS Apps Revenues ($m) Split by 8 Key Regions 2014-2019
Figure 10.5 & Table 10.5: Smartphone & Tablet Other MLBS Apps Revenues ($m) Split by 8 Key Regions 2014-2019
10.3.3 Total Smartphone & Tablet Local Search & Discovery App Revenue Forecast
i. Pay-to-Download
ii. In-App Purchases
iii. Ad-Supported
iv. Global Forecast Figures for Smartphone & Tablets
Figure 10.6 & Table 10.6: Smartphone & Tablet Other MLBS App Revenue ($m) Split by Revenue Model 2014-2019

11. Vendor Profiles

11.1 Introduction
11.2 Vendor Matrix
Table 11.1: Vendor Capability Assessment Criteria
11.2.1 Limitations and Interpretation
11.2.2 Positioning Matrix Results
Figure 11.1: MLBS Vendor Positioning Matrix
11.2.3 Vendor Groupings
i. Summary
ii. On Track Vendors
iii. Vendors Exceeding Expectations
iv. Vendors with Further Potential
11.2.4 Conclusion
11.3 Vendor Profiles
11.3.1 Apple
11.3.2 Banjo
11.3.3 Facebook
Figure 11.2: Facebook Revenues ($m) 2011-2013
Figure 11.3: Facebook Ad Targeting
11.3.4 Fiksu
11.3.5 Garmin
Figure 11.4: Garmin Net Sales ($m) 2011-2013
11.3.6 Google
11.3.7 Nexage
11.3.8 Sensor Platforms
Figure 11.5: Sensor Platforms Sensor Fusions & Context Aware Library
11.3.9 Skyhook Wireless
11.3.10 Syniverse
Figure 11.6: Syniverse Revenues 2011-2013 ($m)
11.3.11 TeleCommunication Systems
Figure 11.7: TeleCommunication Systems Revenue 2011-2013 ($m)
11.3.12 TomTom

 

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プレスリリース

[日本語訳]

位置情報サービス市場はモバイルコンテキストアウェアサービスによって促進され、収益は2019年までに433億ドルに到達へ

広告支援アプリが全収益の71%を占める

英国ハンプシャー、2014年8月13日

ジュニパーリサーチ社の新刊レポートは、モバイルコンテキストと位置サービスの市場は2014年では122億ドルの収益が見込まれるが、2019年までには433億ドルへ到達する成長を見せるであろうと伝えている。また同レポートでは、予測期間終盤までには収益の3分の2以上は、的を絞ったコンテキストアウェアな広告支援アプリによって促進されていくであろうと強調している。

ソーシャルアプリが主導

ジュニパーリサーチ社の調査レポート「モバイルコンテキストと位置サービス Mobile Context & Location Services Navigation, Tracking, Social & Local Search 2014-2019」は、ソーシャルアプリケーションが広告支援アプリによる収益の主要な促進要因になることが見込まれると伝えている。Facebookのようなソーシャルアプリケーションは、マーケティングリサーチャーらが位置情報やユーザー生成コンテキストを利用しながら特定のオーディエンスセグメント へ注目を向けることを可能にさせる。

広告費の面から成長をみせてくるのはローカル検索 (ローカルサーチ) アプリであろう。ローカル検索アプリではモバイルウェブ検索と同様の検索結果は得られないが、Googleがディープリンクのあるアプリケーションプログラミングインタフェース  (API)を開発事業者へ公開していることで、その差は間違えなく埋められていくだろう。

人気が高まるアプリ内課金(IAP)

また、同レポートではアプリ内課金 (IAP) モデルは従来の有料ダウンロードモデルに対し、平均で3倍以上の急激な年間成長をみせていくであろうと伝えている。アプリ内課金 (IAP)は、ユーザーにとっては費用を無料もしくは低額に抑えることができ、開発事業者はアプリケーション自体を販売するよりもLTV(顧客生涯価値)を得られることから、特にナビゲーション、ソーシャル、および追跡アプリケーションにおいて普及していくとみられている。

「ナビゲーションアプリについては、依然として有料版モデル が人気だが、Garminのような大手企業はこの収益モデルへ注目し始めている。」とレポート著者のSteffen Sorrell氏は述べている。

その他の特記事項:

  • モバイル広告の提供と配信は、制限要因として挙げられる消費者と情緒面で適切な状態で繋がることができなくなることで、他のメディア形態で成功が再現される前に成熟に達していなければならない。
  • モバイルアプリが受け入れられ方は、永続検索 (パーシスタントサーチ)、ディープリンク、そしてモバイル環境知能 (アンビエントインテリジェンス) により、変容を遂げようとしている。

[プレスリリース原文]

Location Based Services Market to Reach $43.3Bn by 2019, Driven by Context Aware Mobile Services

Ad-supported Apps to Account for 71% of the Total Revenue

Hampshire, UK: 13th August 2014: A new report from Juniper Research forecasts that the Mobile Context and Location Services market will reach $43.3bn in revenue by 2019, rising from an estimated $12.2bn in 2014. The report highlighted that over two-thirds of revenues will be driven through highly targeted and contextually aware ad-supported apps by the end of the forecast period.

Social Leads the Way

According to the report, Mobile Context & Location Services: Navigation, Tracking, Social & Local Search 2014-2019, Social apps are forecast to be the primary driver behind app ad-supported revenues. Apps such as Facebook enable marketers to focus on specific audience segments using location and other user-generated contexts.

Local Search apps will follow in terms of ad-spend, and although these will not generate the equivalent of mobile web search, Google’s opening of its deep linking API (application programming interface) to developers will undoubtedly close that gap.

Users Prefer In-App Purchases

Additionally, the report found that the IAP (in-app purchase) model will grow on average over three times as rapidly per annum as the classic pay-to-download model. IAP will be particularly prevalent in Navigation, Social and Tracking apps, with consumers preferring the low- to zero-entry cost and developers leveraging LTV (Lifetime Value) rather than one-off sales. 

“Even in the case of navigation apps where paid-for apps have remained popular,  large brands such as Garmin are beginning to emphasise this revenue model”, added report author Steffen Sorrell.

Other key findings include:

  • Mobile ad presentation and delivery must still mature before the success of other media forms can be replicated, with a failure to connect at the correct emotional level with the consumer cited as a limiting factor.
  • The manner by which mobile apps are accessed is set to be transformed due to the rise of persistent search, deep linking and the birth of mobile ambient intelligence.

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