Cover Image
市場調查報告書

數位廣告的全球市場

Worldwide Digital Advertising

出版商 Juniper Research 商品編碼 328287
出版日期 內容資訊 英文
商品交期: 最快1-2個工作天內
價格
Back to Top
數位廣告的全球市場 Worldwide Digital Advertising
出版日期: 2016年05月11日 內容資訊: 英文
簡介

這個調查服務,調查全球數位廣告市場現況與展望,主要趨勢與課題、策略,主要流通管道 (線上、行動網際網路、App內、訊息、穿戴式) 各的收益預測,地區/各主要國家趨勢分析,案例研究,競爭環境,主要經營者簡介等彙整資料。

提供內容

  • 市場趨勢、競爭環境 (PDF)
  • 5年市場預測 (PDF & Excel)
  • 摘要整理&主要調查結果 (PDF)

資料&互動預測

  • 全球8地區及以下的各主要國家分析
    • 美國
    • 加拿大
    • 英國
    • 德國
  • 線上廣告收益
    • 網際網路展示廣告
    • 網際網路搜尋廣告
    • 網際網路視訊廣告
  • 行動網際網路廣告收益
    • 網際網路展示廣告
    • 網際網路搜尋廣告
  • 行動App內廣告收益
    • 展示廣告
    • 利基廣告
  • 通訊廣告
    • SMS通訊
    • MMS通訊
  • 線上廣告封鎖&收入損失
    • 網際網路展示廣告
    • 網際網路搜尋廣告
    • 網際網路視訊廣告
  • 行動網際網路廣告封鎖
    • 網際網路展示廣告
    • 網際網路搜尋廣告
  • 互動Excel方案工具
  • 全預測資料的存取權

本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

目錄

Overview

Juniper Research's highly regarded Digital Advertising research provides actionable insights, market intelligence and strategic recommendations for stakeholders on the rapidly evolving mobile and online advertising markets.

It investigates the role of smartphones, tablets, smartwatches, and PCs (laptops and desktops) as a delivery and engagement channel, how it is positioned by brands and retailers within an omnichannel strategy, and what hurdles must be overcome if it is to achieve its full potential. For the first time, the research investigates the impact that ad blockers are set to have on the total digital advertising market and publisher revenues.

This research suite includes:

  • Market Trends & Competitive Landscape (PDF)
  • 5 Year Market Sizing & Forecast (PDF & Excel)
  • Executive Summary & Core Findings (PDF)

Key Features

  • Extensive analysis of the advertising value network, as new formats and payment mechanisms emerge.
  • Sector analysis and forecasts for advertising channels:
    • Online (Display, Search)
    • Mobile Internet (Display and Search)
    • In-App (Display and Rich Media)
    • Message-Based (SMS and MMS)
    • Wearables (Smartwatch).
  • Interviews with leading players across the value chain, including:
    • The Rubicon Project
    • Eyeo
    • DemandBase
    • Fiksu
    • Secret Media
    • Rocketship Apps
    • Airpush
    • Shine Technologies
  • Strategic assessment of key industry trends including the rise of wearable advertising, and the increasing threat of ad blocking technologies.
  • Key player capability and capacity assessment, together with vendor market positioning matrix.
  • Benchmark forecasts for advertising revenues split by advertising channel and device type. New market forecasts for loss revenues arising from ad blocking technologies, split by:
    • Online Advertising
    • Mobile Advertising

Key Questions

  • 1. What new platforms will see the strongest growth in digital advertising?
  • 2. What are the future opportunities that advertisers will be presented with in the mobile and digital space?
  • 3. To what degree will ad-blocking technology impact the revenues of digital advertising?
  • 4. Which strategies will be the most successful in countering the effect ad-blocking?
  • 5. What innovations are happening in the digital advertising industry with regards to rich media, ad formats, programmatic buying etc?

Companies Referenced

  • Interviewed: AirPush, Demandbase, Eyeo GmbH, Fiksu, Rocketship Apps, Rubicon Project, Shine Technologies.
  • Profiled: AirPush, Alphabet, Amobee, Demandbase, Eyeo GmbH, Facebook, Fiksu, OpenX, Rocketship Apps, Rubicon Project, Shine Technologies, xAd.
  • Case Studied: ABP (Ad Block Plus), Been Choice, MediaMath, Rubicon Project, Shine Technologies, VirtualSKY, WIRED.

Mentioned:

10x, Adconian Direct, Adelphic, Adenyo, Adjitsu, Admoove, Adobe, AMI (American Media Inc), AOL, Apple, Aprimo, Bell Labs, Blogger, Box Buzzfeed, Cadreon, Captivate Network, Carnival.io, Clickability, Crownpeak, Digicel, Disconnect, DocuSign, Dunkin' Donuts, E.piphany, EFF (Electronic Frontier Foundation), Ektron, Exact Target, Flipboard, Forbes, Foursquare, Fox, GE, Globespan, Google, Gradient X, Hubbi, Hubspot, Hutchison Group, IAB (Interactive Advertising Bureau), IBM, Instagram, ISITE Design, JumpTime, Kontera, L90, LiftDNA, LiveRamp, Lucent Technologies, Marketing Tech Blog, MediaOcean, Microsoft, News Corp, Ofcom', Opentext, Oracle, OthersOnline, Outback Steakhouse, OutFront Media, PageFair, Pedowitz Group, Philips, Picasa, Pinterest, Pubmatic, Razorfish, ReaXions, RingRing Media, RunKeeper, SailThru, Salesforce, Samsung, SAP, SDL, SecretMedia, Silverpop, Simon Fraser University, SingTel, Site Scout, Softbank, SonoTrend, StrongMail Systems, Strongview, Telefonica, Three, Turkcell, Twitter, , Verizon, Vodafone, Webtrends, WordPress, Workday, Yahoo, Yellow Pages, YouTube, Zondingo.

Data & Interactive Forecast

Juniper's Worldwide Digital Advertising forecast suite includes:

  • Regional data splits for 8 key regions as well as country level splits for:
    • US
    • Canada
    • UK
    • Germany
  • Online advertising revenues, split by:
    • Internet display advertising
    • Internet search advertising
    • Internet video advertising
  • Mobile internet advertising revenues, split by:
    • Internet display advertising
    • Internet search advertising
  • Mobile in-app advertising revenues, split by:
    • Display advertising
    • Rich media advertising
  • Messaging Advertising, split by:
    • SMS Messaging
    • MMS Messaging
  • Online ad blocking and lost revenues, split by:
    • Internet display advertising
    • Internet search advertising
    • Internet video advertising
  • Mobile internet ad blocking, split by:
    • Internet display advertising
    • Internet search advertising
  • Interactive Excel Scenario tool allowing user the ability to manipulate Juniper's data for 10 different metrics.
  • Access to the full set of forecast data of 317 tables and over 27,700 data points.

Juniper Research's highly granular interactive Excels enable clients to manipulate Juniper's forecast data and charts to test their own assumptions using the Interactive Scenario Tool; and compare select markets side by side in customised charts and tables. IFxls greatly increase clients' ability to both understand a particular market and to integrate their own views into the model.

Table of Contents

Market Trends & Competitive Landscape

1. Defining Digital Advertising

  • 1.1. Introduction
    • Figure 1.1:Juniper Research Digital Advertising Research Report Scope
    • Figure 1.2: Online Advertising Channels
    • 1.1.1. Online Display Advertisements
      • i. Banner/Frame Advertisements
      • ii. Pop-up/Pop-under
      • iii. Trick Banners
      • iv. Floating Advertisements
      • v. Expanding Advertisements
      • vi. Native Advertisements
    • 1.1.2. Mobile Advertisements
      • i. SMS Advertising
        • Figure 1.3: Example of SMS Advertising

2. The Advertising Value Chain

  • 2.1. Digital Advertising Cost Models
    • Figure 1.3: Example of SMS Advertising
      • ii. MMS (Multimedia Messaging Service) Advertising
      • iii. LBA (Location-Based Advertising)
      • iv. In-Content/In-App Advertisements
        • Figure 1.4: Three Network In-App Advertisement
      • v. Mobile Internet Advertising
      • vi. Ringback Tone Advertising
    • 2.1.1. Cost Models
  • 2.2. Drivers for Digital Ad Growth
  • 2.3. The Digital Advertising Ecosystem
    • Figure 2.1: Juniper Research Digital Advertising Ecosystem
    • 2.3.1. DSP (Demand Side Platforms)
      • i. Preferred Deal
      • ii. Programmatic Guaranteed
      • iii. Open Exchange
      • iv. Private Exchange
      • v. Case Study: MediaMath
    • 2.3.2. SSPs (Supply Side Platforms)
      • i. Case Study: Rubicon Project
    • 2.3.3. Advertising Exchanges
      • i. RTB (Real Time Bidding)
    • 2.3.3. Advertising Exchanges
      • i. RTB (Real Time Bidding)
  • 2.4. The Surge of Mobile Advertising
    • 2.4.1. The Current Mobile Market
      • Figure 2.2: The Mobile Advertising Ecosystem
      • i. SMS Advertising
      • ii. MMS (Multimedia Messaging Service) Advertising
      • iii. In-Content/In-Application Advertising
    • 2.4.2. Mobile Advertising Market Drivers
      • Figure 2.3: Mobile Advertising: Trends, Driver& Constraints.
    • 2.4.3. Mobile Advertising Market Trends
    • 2.4.4. Constraints

3. Future Prospects for Digital Advertising

  • 3.1. Future Platforms for Advertising
    • 3.1.1. Wearables Advertising - 0ne for the Future
      • i. Drawbacks of Advertising on Wearables
    • 3.1.2. Virtual Reality -Immersive Advertising
      • i. Best Places for VR Advertising
      • ii. Drawbacks of VR Advertising
      • iii. Case Study: VirtualSKY
  • 3.2. Desktop Ad Blocking
    • 3.2.1. 0nline Ad Blockers
      • i. Consumer Incentives
        • Figure 3.1: Demographics of Ad Blocker Adoptees
      • ii. Click Bait Advertisers
    • 3.2.2. Drivers for Adoption
      • Figure 3.2: Drivers for Desktop Ad Blocking Adoption
    • 3.2.3. The Current State of Ad Blocking
      • Figure 3.3:Juniper Research Ad Blocking Phased Evolution Model
    • 3.2.4. Combative Strategies
      • ii. Case Study: WIRED.com
      • iv. Case Study: ABP ( Adblock Plus)
  • 3.3. Mobile Ad Blockers
    • 3.3.1. Dedicated Browsers
    • 3.3.2. Dedicated Applications
    • 3.3.3. The Power of the 0S (Operating System) Developers
    • 3.3.4. Mobile Ad Blocking at a Network Level
      • i. The Impact on Browsing
      • ii. Case Study: Shine Technologies
      • iii. Future Implications of In-Application Ad Blocking
      • iv. Case Study: Been Choice
      • v. Arguments for Net Neutrality
  • 3.4. The Future for Ad Blocking

4. Digital Advertising: The Competitive Landscape

  • 4.1. Introduction
    • 4.2.1. Vendor Assessment Methodology
  • 4.2. Vendor Assessment
    • Table 4.1: Player Capability Criteria
    • 4.2.2. Limitations & Interpretations
    • 4.2.3. Positional & Matrix Results
      • Figure 4.2: Digital Advertising Stakeholder Positioning
      • Table 4.3: Juniper Heatmap for Digital Advertising Stakeholders
      • Figure 4.4: Juniper Competitive Web - Amobee, Fiksu & the Rubicon Project
    • 4.2.5. Movers & Shakers
  • 4.3. Player Profiles
    • 4.3.1. Airpush
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.2. Alphabet
      • i. Corporate
        • Table 4.5: Alphabet Selected Financial Information
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
        • Figure 4.6: Google Partner Logo
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.3. Amobee
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.4. Demandbase
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.5. Eyeo GmbH
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.6. Facebook
      • i. Corporate
        • Table 4.7: Facebook Selected Financial Information 2014-2015
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.7. Fiksu
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.8. OpenX
      • i. Corporate
        • Table 4.8: OpenX Investment Rounds
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.9. Rocketship Apps
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.10. Shine Technologies
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.11. The Rubicon Project
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Strengths & Development Opportunities
        • Table 4.9: The Rubicon Project Selected Financial Information
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development Opportunities
    • 4.3.12. xAd
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper View: Key Strengths & Development

Market Sizing & Forecasts

1. Introduction to Digital Advertising

  • 1.1. Types of Digital Advertising
    • 1.1.1. Dis play Advertisements
      • i. Banner/Frame Advertisements
      • ii. Pop-up/Pop-under
      • iii. Trick Banners
      • iv. Floating Advertisements
      • v. Expanding Advertisements
      • vi. Native Advertisements
  • 1.2. Digital Advertising Cost Models
    • 1.2.1. Cost Models

2. Digital Advertising: Market Forecast Summary

  • 2.1.1. Total Spend on Digital Advertising: Mobile, Online & Wearables
    • Figure & Table 2.1:TotalS pend on Digital Advertising: Mobile, Online & Wearables ($bn) Split by 8 Key Regions 2015-2020
  • 2.1.2. Total Spend on Desktop/Notebook Advertising
    • Figure & Table 2.2: Total Spend on Desktop/Notebook Advertising ($bn) Split by 8 Key Regions 2015-2020
  • 2.1.3. Total Spend on Mobile Advertising
    • Figure & Table 2.3: Total Spend on Mobile (Smartphones, Feature phones, Tablets) Advertising ($bn) Split by 8 Key Regions 2015-2020
  • 2.1.4. Total Spend on Smartwatch Advertising
    • Figure & Table 2.4: Total Spend on Smartwatch Ads ($m) Split by 8 Key Regions 2015-2020

3. Online Advertising: Forecasts & Key Takeaways

  • 3.1. Online Advertising
    • 3.1.1. Methodology
      • Figure 3.1: Online Advertising Forecast
    • 3.1.2. Total Spend on Desktop/Notebook Display Advertising
      • Figure & Table 3.2: Total Spend on Desktop/Notebook Display Advertising ($bn) Split by 8 Key Regions 2015-2020
    • 3.1.3. Total Spend on Desktop/Notebook Search Advertising
      • Figure & Table 3.3: Total Spend on Desktop/Notebook Search Advertising ($bn) Split by 8 Key Regions 2015-2020
    • 3.1.4. Total Spend on Desktop/Notebook Video Advertising
      • Figure & Table 3.4: Total Spend on Desktop/Notebook Video Advertising ($bn) Split by 8 Key Regions 2015-2020

4. Mobile Advertising: Market Forecasts & Key Takeaways

  • 4.1. The Mobile Market
    • 4.1.1. Methodology
      • Figure 4.1: Mobile Advertising Forecast Methodology
    • 4.1.2. Total Spend on Mobile Internet Browsing Advertising
      • Figure & Table 4.2: Total Mobile Internet Ad Spend Split by Device ($bn) Split by 8 Key Regions 2015-2020
    • 4.1.3. Total Smartphone Mobile Browsing Internet Spend Split by Ad Format
      • Figure & Table 4.3: Total Smartphone Mobile Browsing Internet Spend Split by Ad Format ($bn) Split by Ad Format 2015-2020
    • 4.1.4. Total Featurephone Mobile Browsing Internet Spend Split by Ad Format
      • Figure & Table 4.4: Total Featurephone Mobile Browsing Internet Spend Split by Ad Format ($m) Split by Ad Format 2015-2020
    • 4.1.5. Total Tablet Browsing Internet Advertising Revenue Split by Ad Format
      • Figure & Table 4.5: Total Tablet Browsing Internet Advertising Spend ($bn) Split by Ad Format 2015-2020

5. In-App & Messaging Advertising: Market Trends & Key Takeaways

  • 5.1. Mobile Advertisements
    • Figure 5.1:The Components of Mobile Advertising
    • 5.1.1. SMS Advertising
    • 5.1.2. MMS (Multimedia Messaging Service) Advertising
      • i. LBA (Location-Based Advertising)
    • 5.1.3. Methodology
      • Figure 5.2: In-Application Forecast Methodology
    • 5.1.4. In-application Mobile Ad Spend Split by Device Type
      • Figure & Table 5.3: In-application Mobile Ad Spend ($m) Split by Device Type 2015-2020
    • 5.1.5. Number of Smartphone Apps Downloaded which Feature Advertising
      • Figure & Table 5.4: Number of Smartphone Apps Downloaded which Feature Advertising (m) Split by 8 Key Regions 2015-2020
    • 5.1.6. Total Smartphone App Display Ad Spend
      • Figure & Table 5.5: Total Smartphone App Display Ad Spend ($m) Split by 8 Key Regions 2015-2020
    • 5.1.7. Total Smartphone App Rich Media Ad Spend
      • Figure & Table 5.6: Total Smartphone App Rich Media Ad Spend ($m) Split by 8 Key Regions 2015-2020
    • 5.1.8. Number of Tablet Apps Downloaded which Feature Advertising
      • Figure & Table 5.7: Number of Tablet Apps Downloaded which Feature Advertising (m) Split by 8 Key Regions 2015-2020
    • 5.1.9. Total Tablet In-App Display Ad Spend
      • Figure & Table 5.8: Total Tablet App Display Ad Spend ($m) Split by 8 Regions 2015-2020
    • 5.1.10. Total Tablet App Rich Media Ad Spend
      • Figure & Table 5.9: Total Tablet App Rich Media Spend ($m) Split by 8 Key Regions 2015-2020
    • 5.1.11. Total Message-Based Mobile Advertising Spend ($m)
      • Figure & Tables 5.10: Total Message-Based Mobile Advertising Revenues ($m) Split by 8 Key Regions & Type 2015-2020

6. Ad Blocking Technologies: Key Trends & Market Takeaways

  • 6.1. Ad Blocking
    • 6.1.1. 0nline Ad Blockers
    • 6.1.2. Mobile Ad Blockers
    • 6.1.3. Methodology
      • Figure 6.1: Desktop and Mobile Ad Blocking Forecast Methodology
    • 6.1.4. Mobile & 0nline Ad Blocking Summary
      • Figure & Table 6.2: Total Revenue Loss Mobile & Online Ad Blocking ($bn) Split by 8 Key Regions 2015-2020
  • 6.2. 0nline Ad Blocking
    • 6.2.1. Number of Internet Display Adverts Blocked by Ad Blockers
      • Figure & Table 6.3: Number of Internet Display Adverts Blocked by Ad Blockers (m) Split by 8 Key Regions 201 5-2020
    • 6.2.2. Potential Revenue Loss Resulting from the Use of Ad Blocking Software on Internet Display Ads
      • Figure & Table 6.4: Potential Revenue Loss Resulting from the Use of Ad Blocking Software on Internet Display Ads ($m) Split by 8 Key Regions 2015-2020
    • 6.2.3. Number of Internet Search Adverts Blocked by Ad Block
      • Figure & Table 6.5: Number of Internet Search Adverts Blocked by Ad Blockers (m) Split by 8 Key Regions 201 5-2020
    • 6.2.4. Potential Revenue Loss Resulting from the Use of Ad Blocking Software on Internet Search Ads
      • Figure & Table 6.6: Potential Revenue Loss Resulting from the Use of Ad Blocking Software on Internet Search Ads ($m) Split by 8 Key Regions 2015-2020
    • 6.2.5. Number of Internet Video Adverts Blocked by Ad Blockers
      • Figure & Table 6.7: Number of Internet Video Adverts Blocked by Ad Blockers (m) Split by 8 Key Regions 2015-2020
    • 6.2.6. Potential Revenue Loss Resulting from the Use of Ad Blocking Software on Video Display Ads
      • Figure & Table 6.8: Potential Revenue Loss Resulting from the Use of Ad Blocking Software on Video Display Ads ($m) Split by 8 Key Regions 2015-2020
  • 6.3. Mobile Ad Blocking
    • 6.3.1. Number of Smartphone Searches which would have Resulted in a Sponsored Click Through
      • Figure & Table 6.9: Number of Smartphone Searches which would have Resulted in a Sponsored Click Through (m) Split by 8 Key Regions 2015-2020
    • 6.3.2. Potential Lost Smartphone Internet Search Revenue due to Ad Blocking Software
      • Figure & Table 6.10: Potential Lost Smartphone Internet Search Revenue due to Ad Blocking Software ($m) Split by 8 Key Regions 2015-2020
    • 6.3.3. Number of Tablet Searches which would have Resulted in Sponsored Click Through
      • Figure & Table 6.11: Number of Tablet Searches which would have Resulted in a Sponsored Click Through (m)Split by 8 Key Regions 2015-2020
    • 6.3.4. Potential Lost Tablet Internet Search Revenue due to Ad Blocking Software
      • Figure & Table 6.12: Potential Lost Tablet Internet Search Revenue due to Ad Blocking Software ($m) Split by 8 Key Regions 2015-2020
    • 6.3.5. Number of Smartphone Display Ads which would have Resulted in a Sponsored Click Through
      • Figure & Table 6.13: Number of Smartphone Display Ads which would have Resulted in a Sponsored Click Through (m) Split by 8 Key Regions 2015-202
    • 6.3.6. Potential Lost Smartphone Internet Display Ad Revenue due to Ad Blocking Software
      • Figure & Table 6.14: Potential Lost Smartphone Internet Ad Revenue due to Ad Blocking Software ($m) Split by 8 Key Regions 2015-2020
    • 6.3.7. Number of Tablet Display Ads which would have Resulted in a Sponsored Click Through
    • 6.3.8. Potential Lost Tablet Internet Display Ad Revenue due to AD Blocking Software
      • Figure & Table 6.16: Potential Lost Tablet Internet Display Ad Revenue due to Ad Blocking Software ($m) Split by 8 Key Regions 2015-2020
Back to Top