デジタル広告詐欺：市場展望＆戦略予測 2019-2023年
Digital Advertising Fraud
このレポートは世界のデジタル広告のエコシステムを調査し、デジタル広告詐欺（アドフラウド）の今後やデジタル広告詐欺による損失を減らすための市場戦略について分析しています。
主な掲載内容
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サマリー
このレポートは世界のデジタル広告のエコシステムを調査し、デジタル広告詐欺（アドフラウド）の今後やデジタル広告詐欺による損失を減らすための市場戦略について分析しています。
主な掲載内容
国別市場予測
配信されたデジタル広告詐欺の数と広告主の総損失
-
オンライン広告
-
モバイルブラウザ向け広告
-
アプリ内広告
-
インストールアトリビュ－ション
Overview
OverviewJuniper’s Digital Advertising Fraud research provides a must-read analysis of the current digital advertising ecosystem, the future of digital advertising fraud and market strategies for mitigating the loss to fraudulent activities. The research also provides a comprehensive evaluation of the types of fraud, future innovation in fraud tactics and the leading fraud mitigation platforms.
This research suite includes:
-
Market Trends & Opportunities (PDF)
-
5 Year Market Sizing & Forecast Spreadsheet (Excel)
-
12 months' access to harvest online data platform
-
Market Landscape: Extensive analysis of the future outlook of the digital advertising market, and the role emerging technologies will play in its development.
-
Future opportunities in Mobile, Online and OTT TV Advertising
-
Key market trends, drivers and constraints acting on the digital advertising market
-
Benchmark Industry Forecasts: Market forecasts for the prevalence of fraud in the industry and total potential loss to advertising fraud. Forecasts also include the amount of ad spend that can be recovered through the adoption of fraud mitigation solutions. This is split across:
-
Online advertising
-
Mobile browsing advertising
-
In-app advertising
-
Install attribution fraud
-
Digital Advertising Platform Analysis: 5 year forecasts for digital advertising platform revenues and market share analysis for leading advertising networks, including:
-
Amazon
-
Baidu
-
Bing
-
Facebook
-
Google
-
Tencent
-
Twitter
-
Juniper Leaderboard: 15 leading MMPs (Mobile Measurement Platforms) and anti-fraud solution providers compared, scored and positioned on the Juniper Leaderboard.
-
Interviews with leading players across the value chain, including:
-
App Samurai
-
ClickGUARD
-
Codewise
-
GumGum
-
Kochava
-
mGage
-
Pixalate
-
Singular
-
Telecoming
-
TrafficGuard
-
How much will the digital advertising ecosystem be worth by 2023?
-
Who are the leading digital advertising platforms?
-
Who are the leading mobile measurement platforms and fraud mitigation solutions?
-
How much will advertisers lose to digital advertising fraud over the next 5 years?
-
Which strategies can be adopted to maximise the mitigation of losses to advertising fraud?
Interviewed: Adjust, Affle, App Samurai, ClickGUARD, Codewise, GumGum, Kochava, mGage, Pixalate, Singular, Telecoming, TrafficGuard.
Profiled: Adjust, Affle, App Samurai, AppsFlyer, ClickGUARD, Codewise, Comscore, Impact, Kochava, Machine Advertising, Pixalate, Singular, Telecoming, TrafficGuard, TUNE.
Case Studied: Amazon.
Mentioned: A4D, Aarki, Acquired.io, Acxiom, Adalyser, Adcolony, Adconnect, Adform, Ad‑juster, Adobe, Adveritas, Aeron, Affluent, Airbnb, Alphabet, Altitude, Amobee, Apartment List, Appfuel, Apple, Appier, AppLift, AppNexus, Apsalar, Artisan, ATIS (Alliance for Telecommunications Industry), Baidu, BBC Worldwide, Bennett, Bing, Branch, Button, CAAF (Coalition Against Ad Fraud), Cassandra, CBS, Centro, Centurion Corporation, Chartboost, Chewy, ClearPier, ClearSaleing, Coleman & Co, CreditSesame, D2C, Dataxu, elex, Eureka & Docker, Eyeball Division, Facebook, Fiksu, Fitplan, Forbes, Forensiq, FreeWheel, Fyber, GFK, Goodway Group, Google, Grammarly, Gripati Digital Entertainment, Groupon, GS Stat Counter, HIS Markit, Hotel Tonight, IMDB, InferSystems, InMobi, Innovid, Innvervate, Inovalon, Investing.com, ironSource, Itochu, Kabbage, Kayzen, Leadpoint, Liftoff, LinkedIn, Lyft, M&C Saatchi, Managed Objects, Markt.ooo, Marriott Hotels, Match, McDonald’s, M-Code, MediaOcean, MediaRadar, Mediarails, Microsoft, Migros, MobileDevHQ, MobileRQ, Mobimasta, MoPub, mParticle, Mpire, MRC (Media Rating Council), Netty, Neustar, Nike, NinthDecimal, Olymp Trade, Omnicom Media Group, Onavo, optimob, Oracle, Pandora, Paperclip, Partnerize, PlaceIQ, PLAYXPERT, Pocket Media, Pubmatic, Rakuten, Rappi, Razer, RhythmOne, Roku, runtastic, Sabre Holdings, Savings.com, Segment, Shazam, Shoffr, Shopcom, Shopify, Simmons, Sizmek, Skillz, Snap, SoundCloud, Spotify, Spring Boot on Java 8, Symantec, Tappx, Telaria, TVSquare, Tvty, Twitter, Unbotify, Verizon, Vizury, Vungle, WyWy, Yelp, Yodas.com, Zynga.
Data & Interactive Forecast
Juniper’s Digital Advertising Fraud forecast suite includes:
-
Regional data splits for 8 key regions and country level splits for:
-
Total number of fraudulent ads delivered, and total advertiser loss to fraud, split by
-
Online advertising
-
Mobile browsing advertising
-
In-app advertising
-
Install attribution fraud
-
Total potential recovered ad spend via fraud mitigation solutions
-
Interactive Excel Scenario tool allowing user the ability to manipulate Juniper’s data for 3 different metrics.
-
Access to the full set of forecast data of 44 tables and over 3,600 datapoints.
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.
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目次
Table of Contents
1. Digital Advertising Channels: Engagement & Reach
1.1 The Future of Digital Advertising . 6
Figure 1.1: Total Spend on Desktop/Notebook Advertising ($bn) in 2023 . 6
1.2 Service Reach & Scarcity of Attention. 7
Figure 1.2: Global Reach of Select Consumer Devices for Advertising by 2023. 7
1.2.1 The Reach of SMS Advertising . 7
1.3 The Future Impact of Amazon on the Digital Advertising Market . 8
Figure 1.3: Amazon’s Annual Revenue Attributable to Other Services (including
Advertising Services) ($m) 2016-2018. 8
Case Study: Amazon’s Advertising Services . 9
1.3.1 The Impact on Google & Facebook.10
Figure & Table 1.4: Total Digital Advertising Ad Spend ($m), Split by Leading
Advertising Platforms 2018-2023. 10
i. Google’s Advertising Activities . 11
Figure 1.5: Leading Advertising Netw ork Ad Revenues ($bn) by 2023. 11
ii. Facebook’s Advertising Activities . 11
iii. Baidu ’s Advertising Activities. 12
1.4 Strategic Recommendations for Digital Advertising Stakeholders.12
Figure 1.6: Net In-App OTT TV Advertising Spend in 2023, Split by 8 Key Regions. 13
2. Digital Advertising Fraud
2.1 Introduction to Advertising Fraud .16
2.1.1 The Direct Cost of Digital Advertising Fraud .16
Figure 3.1: Total Loss to Mobile & Online Advertising Fraud in 2019 & 2023 ($m)
Split by 8 Key Regions .16
2.1.2 Indirect Costs of Fraudulent Advertising Spend.17
Table 3.2: Select Types of Advertising Fraud .18
2.2 Quantifying the Loss to Advertising Fraud .19
2.2.1 Online Browsing Advertising Fraud.19
Figure 3.3: Total Potential Lost Online Brow sing Ad Spend through Advertising
Fraud w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key Regions 2019 & 2023 .19
2.2.2 Mobile Browsing Fraud.20
Figure 3.4: Total Potential Lost Mobile Brow sing Ad Spend through Advertising
Fraud w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key Regions 2019 & 2023 .20
2.2.3 In-app Browsing Fraud .21
Figure 3.5: Total Potential Lost In-app Ad Spend through Advertising Fraud
w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key Regions 2019 & 2023.21
2.2.4 Install Attribution Fraud Level .22
Figure 3.6: Total Potential Lost App Advertising Spend through Advertising Fraud
w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key Regions 2019 & 2023.22
2.3 Future Strategy Analysis .23
Table 3.7: High Level View of Mobile Measurement Platforms & Fraud Detection
Platform Strategies .23
2.4 The Cat-and-Mouse Nature of Advertising Fraud & Future Antifraud Strategies.24
Figure 3.8: The Advertising Fraud Innovation Cycle & AI.24
Figure 3.9: Attribution Window Process .26
3. Digital Advertising: The Competitive Landscape
3.1 Introduction to Advertising Attribution Platforms .28
3.1.1 Vendor Assessment Methodology.28
Table 4.1: Player Comparison Scoring Criteria - Digital Advertising Attribution Platforms . 29
Figure 4.2: Juniper Leaderboard - Digital Advertising Attribution Platforms . 30
Table 4.3: Juniper Leaderboard Heatmap Results - Digital Advertising Attribution Platforms . 31
3.2 Vendor Groupings - Digital Advertising Attribution Platforms 32
i. Established Leaders . 32
ii. Leading Challengers . 32
iii. Disruptors & Emulators . 34
3.3 Limitations & Interpretations .36
3.4 Digital Advertising: Movers & Shakers .37
3.5 Player Profiles .39
3.5.1 Adjust .39
i. Corporate. 39
ii. Geographical Spread . 39
iii. Key Clients & Strategic Partnerships. 39
iv. High Level View of Offerings . 40
v. Juniper View: Key Strengths & Strategic Opportunities . 40
3.5.2 Affle.41
i. Corporate. 41
Table 4.4: Aff le Acquisitions 2012-present . 41
ii. Geographical Spread . 41
iii. Key Clients & Strategic Partnerships. 41
iv. High Level View of Offerings .41
v. Juniper View: Key Strengths & Strategic Opportunities.42
3.5.3 App Samurai .42
i. Corporate .42
ii. Geographical Spread .42
iii. Key Clients & Strategic Partnerships .42
iv. High Level View of Offerings .42
v. Juniper View: Key Strengths & Strategic Opportunities.43
3.5.4 AppsFlyer .43
i. Corporate .43
ii. Geographical Spread .43
iii. Key Clients & Strategic Partnerships .43
iv. High Level View of Offerings .43
v. Juniper View: Key Strengths & Strategic Opportunities.44
3.5.5 ClickGUARD.45
i. Corporate .45
ii. Geographical Spread .45
iii. Key Clients & Strategic Partnerships .45
iv. High Level View of Offerings .45
v. Juniper View: Key Strengths & Strategic Opportunities.46
3.5.6 Codewise .46
i. Corporate .46
ii. Geographical Spread .46
iii. Key Clients & Strategic Opportunities.46
iv. High Level View of Offerings .46
v. Juniper View: Key Strengths & Strategic Opportunities.47
3.5.7 Comscore .47
i. Corporate. 47
Table 4.5: Comscore Select Financial Information ($m) 2015-2018 . 47
ii. Geographical Spread . 47
iii. Key Clients & Strategic Partnerships. 48
iv. High Level View of Offerings . 48
v. Juniper View: Key Strengths & Strategic Opportunities . 48
3.5.8 Impact Technologies .49
i. Corporate. 49
ii. Geographical Spread . 49
iii. Key Clients & Strategic Partnerships. 49
iv. High Level View of Offerings . 49
v. Juniper View: Key Strengths & Strategic Opportunities . 50
3.5.9 Kochava .50
i. Corporate. 50
ii. Geographical Spread . 50
iii. Key Clients & Strategic Partnerships. 50
iv. High Level View of Offerings . 50
v. Juniper View: Key Strengths & Strategic Opportunities . 51
3.5.10 Machine Advertising .52
i. Corporate. 52
ii. Geographical Spread . 52
iii. Key Clients & Strategic Partnerships. 52
iv. High Level View of Offerings . 52
v. Juniper View: Key Strengths & Strategic Opportunities . 52
3.5.11 Pixalate .52
i. Corporate .52
ii. Geographical Spread .53
iii. Key Clients & Strategic Opportunities.53
iv. High Level View of Offerings .53
v. Juniper View: Key Strengths & Strategic Opportunities.54
3.5.12 Singular .54
i. Corporate .54
Table 4.6: Singular’s Funding Rounds July 2014-present .54
ii. Geographical Spread .54
iii. Key Clients & Strategic Partnerships .54
iv. High Level View of Offerings .55
v. Juniper View: Key Strengths & Strategic Opportunities.55
3.5.13 Telecoming .56
i. Corporate .56
ii. Geographical Spread .56
iii. Key Clients & Strategic Partnerships .56
iv. High Level View of Offerings .56
v. Juniper View: Key Strengths & Strategic Opportunities.56
3.5.14 TrafficGuard .57
i. Corporate .57
ii. Geographical Spread .57
iii. Key Clients & Strategic Opportunities.57
iv. High Level View of Offerings .57
v. Juniper View: Key Strengths & Strategic Opportunities.58
3.5.15 TUNE .58
i. Corporate .58
Table 4.7: TUNE Acquisitions 2012-present . 58
ii. Geographical Spread . 58
iii. Key Clients & Strategic Opportunities . 58
iv. High Level View of Offerings . 58
v. Juniper View: Key Strengths & Strategic Opportunities . 59
4. Market Forecasts & Key Takeaways: Digital Advertising Fraud
4.1 Introduction to Digital Advertising Fraud .61
4.1.1 Digital Advertising Fraud Forecast Methodology .61
Figure 6.1: Digital Advertising Fraud Forecast Methodology . 62
4.1.2 Total Online Fraudulent Clicks .63
Figure & Table 6.2: Number of Online Ad Clickthrough Ads that are Due to
Fraudulent Clicks (m) Split by 8 Key Regions 2018-2023. 63
4.1.3 Total Loss to Online Advertising Fraud .64
Figure & Table 6.3: Total Actual Lost Online Brow sing Ad Spend through
Advertising Fraud w ithout AI-based Fraud Mitigation Solutions ($m) Splut by 8
Key Regions 2018-2023 . 64
4.1.4 Mobile Browsing Advertising Fraud .65
Figure & Table 6.4: Number of Mobile Ad Clickthrough Ads that are Due to
Fradulent Clicks (m) Split by 8 Key Regions 2018-2023. 65
4.1.5 Total Loss to Mobile Browsing Advertising Fraud .66
Figure & Table 6.5: Total Actual Lost Mobile Brow sing Ad Spend through
Advertising Fraud w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key
Regions 2018-2023 . 66
4.1.6 Number of Fraudulent Mobile In-app Clicks .67
Figure & Table 6.6: Total Number of In-App Ad Impressions that are Delivered
Fraudulently (m) Split by 8 Key Regions 2018-2023. 67
4.1.7 Total Loss to In-app Advertising Fraud.68
Figure & Table 6.7: Total Actual Lost In-app Ad Spend through Advertising Fraud
w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key Regions 2018-2023.68
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Summary
このレポートは世界のデジタル広告のエコシステムを調査し、デジタル広告詐欺（アドフラウド）の今後やデジタル広告詐欺による損失を減らすための市場戦略について分析しています。
主な掲載内容
国別市場予測
配信されたデジタル広告詐欺の数と広告主の総損失
-
オンライン広告
-
モバイルブラウザ向け広告
-
アプリ内広告
-
インストールアトリビュ－ション
Overview
OverviewJuniper’s Digital Advertising Fraud research provides a must-read analysis of the current digital advertising ecosystem, the future of digital advertising fraud and market strategies for mitigating the loss to fraudulent activities. The research also provides a comprehensive evaluation of the types of fraud, future innovation in fraud tactics and the leading fraud mitigation platforms.
This research suite includes:
-
Market Trends & Opportunities (PDF)
-
5 Year Market Sizing & Forecast Spreadsheet (Excel)
-
12 months' access to harvest online data platform
-
Market Landscape: Extensive analysis of the future outlook of the digital advertising market, and the role emerging technologies will play in its development.
-
Future opportunities in Mobile, Online and OTT TV Advertising
-
Key market trends, drivers and constraints acting on the digital advertising market
-
Benchmark Industry Forecasts: Market forecasts for the prevalence of fraud in the industry and total potential loss to advertising fraud. Forecasts also include the amount of ad spend that can be recovered through the adoption of fraud mitigation solutions. This is split across:
-
Online advertising
-
Mobile browsing advertising
-
In-app advertising
-
Install attribution fraud
-
Digital Advertising Platform Analysis: 5 year forecasts for digital advertising platform revenues and market share analysis for leading advertising networks, including:
-
Amazon
-
Baidu
-
Bing
-
Facebook
-
Google
-
Tencent
-
Twitter
-
Juniper Leaderboard: 15 leading MMPs (Mobile Measurement Platforms) and anti-fraud solution providers compared, scored and positioned on the Juniper Leaderboard.
-
Interviews with leading players across the value chain, including:
-
App Samurai
-
ClickGUARD
-
Codewise
-
GumGum
-
Kochava
-
mGage
-
Pixalate
-
Singular
-
Telecoming
-
TrafficGuard
-
How much will the digital advertising ecosystem be worth by 2023?
-
Who are the leading digital advertising platforms?
-
Who are the leading mobile measurement platforms and fraud mitigation solutions?
-
How much will advertisers lose to digital advertising fraud over the next 5 years?
-
Which strategies can be adopted to maximise the mitigation of losses to advertising fraud?
Interviewed: Adjust, Affle, App Samurai, ClickGUARD, Codewise, GumGum, Kochava, mGage, Pixalate, Singular, Telecoming, TrafficGuard.
Profiled: Adjust, Affle, App Samurai, AppsFlyer, ClickGUARD, Codewise, Comscore, Impact, Kochava, Machine Advertising, Pixalate, Singular, Telecoming, TrafficGuard, TUNE.
Case Studied: Amazon.
Mentioned: A4D, Aarki, Acquired.io, Acxiom, Adalyser, Adcolony, Adconnect, Adform, Ad‑juster, Adobe, Adveritas, Aeron, Affluent, Airbnb, Alphabet, Altitude, Amobee, Apartment List, Appfuel, Apple, Appier, AppLift, AppNexus, Apsalar, Artisan, ATIS (Alliance for Telecommunications Industry), Baidu, BBC Worldwide, Bennett, Bing, Branch, Button, CAAF (Coalition Against Ad Fraud), Cassandra, CBS, Centro, Centurion Corporation, Chartboost, Chewy, ClearPier, ClearSaleing, Coleman & Co, CreditSesame, D2C, Dataxu, elex, Eureka & Docker, Eyeball Division, Facebook, Fiksu, Fitplan, Forbes, Forensiq, FreeWheel, Fyber, GFK, Goodway Group, Google, Grammarly, Gripati Digital Entertainment, Groupon, GS Stat Counter, HIS Markit, Hotel Tonight, IMDB, InferSystems, InMobi, Innovid, Innvervate, Inovalon, Investing.com, ironSource, Itochu, Kabbage, Kayzen, Leadpoint, Liftoff, LinkedIn, Lyft, M&C Saatchi, Managed Objects, Markt.ooo, Marriott Hotels, Match, McDonald’s, M-Code, MediaOcean, MediaRadar, Mediarails, Microsoft, Migros, MobileDevHQ, MobileRQ, Mobimasta, MoPub, mParticle, Mpire, MRC (Media Rating Council), Netty, Neustar, Nike, NinthDecimal, Olymp Trade, Omnicom Media Group, Onavo, optimob, Oracle, Pandora, Paperclip, Partnerize, PlaceIQ, PLAYXPERT, Pocket Media, Pubmatic, Rakuten, Rappi, Razer, RhythmOne, Roku, runtastic, Sabre Holdings, Savings.com, Segment, Shazam, Shoffr, Shopcom, Shopify, Simmons, Sizmek, Skillz, Snap, SoundCloud, Spotify, Spring Boot on Java 8, Symantec, Tappx, Telaria, TVSquare, Tvty, Twitter, Unbotify, Verizon, Vizury, Vungle, WyWy, Yelp, Yodas.com, Zynga.
Data & Interactive Forecast
Juniper’s Digital Advertising Fraud forecast suite includes:
-
Regional data splits for 8 key regions and country level splits for:
-
Total number of fraudulent ads delivered, and total advertiser loss to fraud, split by
-
Online advertising
-
Mobile browsing advertising
-
In-app advertising
-
Install attribution fraud
-
Total potential recovered ad spend via fraud mitigation solutions
-
Interactive Excel Scenario tool allowing user the ability to manipulate Juniper’s data for 3 different metrics.
-
Access to the full set of forecast data of 44 tables and over 3,600 datapoints.
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.
ページTOPに戻る
Table of Contents
Table of Contents
1. Digital Advertising Channels: Engagement & Reach
1.1 The Future of Digital Advertising . 6
Figure 1.1: Total Spend on Desktop/Notebook Advertising ($bn) in 2023 . 6
1.2 Service Reach & Scarcity of Attention. 7
Figure 1.2: Global Reach of Select Consumer Devices for Advertising by 2023. 7
1.2.1 The Reach of SMS Advertising . 7
1.3 The Future Impact of Amazon on the Digital Advertising Market . 8
Figure 1.3: Amazon’s Annual Revenue Attributable to Other Services (including
Advertising Services) ($m) 2016-2018. 8
Case Study: Amazon’s Advertising Services . 9
1.3.1 The Impact on Google & Facebook.10
Figure & Table 1.4: Total Digital Advertising Ad Spend ($m), Split by Leading
Advertising Platforms 2018-2023. 10
i. Google’s Advertising Activities . 11
Figure 1.5: Leading Advertising Netw ork Ad Revenues ($bn) by 2023. 11
ii. Facebook’s Advertising Activities . 11
iii. Baidu ’s Advertising Activities. 12
1.4 Strategic Recommendations for Digital Advertising Stakeholders.12
Figure 1.6: Net In-App OTT TV Advertising Spend in 2023, Split by 8 Key Regions. 13
2. Digital Advertising Fraud
2.1 Introduction to Advertising Fraud .16
2.1.1 The Direct Cost of Digital Advertising Fraud .16
Figure 3.1: Total Loss to Mobile & Online Advertising Fraud in 2019 & 2023 ($m)
Split by 8 Key Regions .16
2.1.2 Indirect Costs of Fraudulent Advertising Spend.17
Table 3.2: Select Types of Advertising Fraud .18
2.2 Quantifying the Loss to Advertising Fraud .19
2.2.1 Online Browsing Advertising Fraud.19
Figure 3.3: Total Potential Lost Online Brow sing Ad Spend through Advertising
Fraud w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key Regions 2019 & 2023 .19
2.2.2 Mobile Browsing Fraud.20
Figure 3.4: Total Potential Lost Mobile Brow sing Ad Spend through Advertising
Fraud w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key Regions 2019 & 2023 .20
2.2.3 In-app Browsing Fraud .21
Figure 3.5: Total Potential Lost In-app Ad Spend through Advertising Fraud
w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key Regions 2019 & 2023.21
2.2.4 Install Attribution Fraud Level .22
Figure 3.6: Total Potential Lost App Advertising Spend through Advertising Fraud
w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key Regions 2019 & 2023.22
2.3 Future Strategy Analysis .23
Table 3.7: High Level View of Mobile Measurement Platforms & Fraud Detection
Platform Strategies .23
2.4 The Cat-and-Mouse Nature of Advertising Fraud & Future Antifraud Strategies.24
Figure 3.8: The Advertising Fraud Innovation Cycle & AI.24
Figure 3.9: Attribution Window Process .26
3. Digital Advertising: The Competitive Landscape
3.1 Introduction to Advertising Attribution Platforms .28
3.1.1 Vendor Assessment Methodology.28
Table 4.1: Player Comparison Scoring Criteria - Digital Advertising Attribution Platforms . 29
Figure 4.2: Juniper Leaderboard - Digital Advertising Attribution Platforms . 30
Table 4.3: Juniper Leaderboard Heatmap Results - Digital Advertising Attribution Platforms . 31
3.2 Vendor Groupings - Digital Advertising Attribution Platforms 32
i. Established Leaders . 32
ii. Leading Challengers . 32
iii. Disruptors & Emulators . 34
3.3 Limitations & Interpretations .36
3.4 Digital Advertising: Movers & Shakers .37
3.5 Player Profiles .39
3.5.1 Adjust .39
i. Corporate. 39
ii. Geographical Spread . 39
iii. Key Clients & Strategic Partnerships. 39
iv. High Level View of Offerings . 40
v. Juniper View: Key Strengths & Strategic Opportunities . 40
3.5.2 Affle.41
i. Corporate. 41
Table 4.4: Aff le Acquisitions 2012-present . 41
ii. Geographical Spread . 41
iii. Key Clients & Strategic Partnerships. 41
iv. High Level View of Offerings .41
v. Juniper View: Key Strengths & Strategic Opportunities.42
3.5.3 App Samurai .42
i. Corporate .42
ii. Geographical Spread .42
iii. Key Clients & Strategic Partnerships .42
iv. High Level View of Offerings .42
v. Juniper View: Key Strengths & Strategic Opportunities.43
3.5.4 AppsFlyer .43
i. Corporate .43
ii. Geographical Spread .43
iii. Key Clients & Strategic Partnerships .43
iv. High Level View of Offerings .43
v. Juniper View: Key Strengths & Strategic Opportunities.44
3.5.5 ClickGUARD.45
i. Corporate .45
ii. Geographical Spread .45
iii. Key Clients & Strategic Partnerships .45
iv. High Level View of Offerings .45
v. Juniper View: Key Strengths & Strategic Opportunities.46
3.5.6 Codewise .46
i. Corporate .46
ii. Geographical Spread .46
iii. Key Clients & Strategic Opportunities.46
iv. High Level View of Offerings .46
v. Juniper View: Key Strengths & Strategic Opportunities.47
3.5.7 Comscore .47
i. Corporate. 47
Table 4.5: Comscore Select Financial Information ($m) 2015-2018 . 47
ii. Geographical Spread . 47
iii. Key Clients & Strategic Partnerships. 48
iv. High Level View of Offerings . 48
v. Juniper View: Key Strengths & Strategic Opportunities . 48
3.5.8 Impact Technologies .49
i. Corporate. 49
ii. Geographical Spread . 49
iii. Key Clients & Strategic Partnerships. 49
iv. High Level View of Offerings . 49
v. Juniper View: Key Strengths & Strategic Opportunities . 50
3.5.9 Kochava .50
i. Corporate. 50
ii. Geographical Spread . 50
iii. Key Clients & Strategic Partnerships. 50
iv. High Level View of Offerings . 50
v. Juniper View: Key Strengths & Strategic Opportunities . 51
3.5.10 Machine Advertising .52
i. Corporate. 52
ii. Geographical Spread . 52
iii. Key Clients & Strategic Partnerships. 52
iv. High Level View of Offerings . 52
v. Juniper View: Key Strengths & Strategic Opportunities . 52
3.5.11 Pixalate .52
i. Corporate .52
ii. Geographical Spread .53
iii. Key Clients & Strategic Opportunities.53
iv. High Level View of Offerings .53
v. Juniper View: Key Strengths & Strategic Opportunities.54
3.5.12 Singular .54
i. Corporate .54
Table 4.6: Singular’s Funding Rounds July 2014-present .54
ii. Geographical Spread .54
iii. Key Clients & Strategic Partnerships .54
iv. High Level View of Offerings .55
v. Juniper View: Key Strengths & Strategic Opportunities.55
3.5.13 Telecoming .56
i. Corporate .56
ii. Geographical Spread .56
iii. Key Clients & Strategic Partnerships .56
iv. High Level View of Offerings .56
v. Juniper View: Key Strengths & Strategic Opportunities.56
3.5.14 TrafficGuard .57
i. Corporate .57
ii. Geographical Spread .57
iii. Key Clients & Strategic Opportunities.57
iv. High Level View of Offerings .57
v. Juniper View: Key Strengths & Strategic Opportunities.58
3.5.15 TUNE .58
i. Corporate .58
Table 4.7: TUNE Acquisitions 2012-present . 58
ii. Geographical Spread . 58
iii. Key Clients & Strategic Opportunities . 58
iv. High Level View of Offerings . 58
v. Juniper View: Key Strengths & Strategic Opportunities . 59
4. Market Forecasts & Key Takeaways: Digital Advertising Fraud
4.1 Introduction to Digital Advertising Fraud .61
4.1.1 Digital Advertising Fraud Forecast Methodology .61
Figure 6.1: Digital Advertising Fraud Forecast Methodology . 62
4.1.2 Total Online Fraudulent Clicks .63
Figure & Table 6.2: Number of Online Ad Clickthrough Ads that are Due to
Fraudulent Clicks (m) Split by 8 Key Regions 2018-2023. 63
4.1.3 Total Loss to Online Advertising Fraud .64
Figure & Table 6.3: Total Actual Lost Online Brow sing Ad Spend through
Advertising Fraud w ithout AI-based Fraud Mitigation Solutions ($m) Splut by 8
Key Regions 2018-2023 . 64
4.1.4 Mobile Browsing Advertising Fraud .65
Figure & Table 6.4: Number of Mobile Ad Clickthrough Ads that are Due to
Fradulent Clicks (m) Split by 8 Key Regions 2018-2023. 65
4.1.5 Total Loss to Mobile Browsing Advertising Fraud .66
Figure & Table 6.5: Total Actual Lost Mobile Brow sing Ad Spend through
Advertising Fraud w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key
Regions 2018-2023 . 66
4.1.6 Number of Fraudulent Mobile In-app Clicks .67
Figure & Table 6.6: Total Number of In-App Ad Impressions that are Delivered
Fraudulently (m) Split by 8 Key Regions 2018-2023. 67
4.1.7 Total Loss to In-app Advertising Fraud.68
Figure & Table 6.7: Total Actual Lost In-app Ad Spend through Advertising Fraud
w ithout AI-based Fraud Mitigation Solutions ($m) Split by 8 Key Regions 2018-2023.68
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