ad fraud in mobile research by augustine fou

“is ad fraud higher or lower in mobile?”

April 2017

Augustine Fou, PhD.

acfou [@] mktsci.com

212. 203 .7239

“it’s more lucrative and less measurable… hmm,

what do you think?”

April 2017 / Page 2marketing.scienceconsulting group, inc.

linkedin.com/in/augustinefou

NONE of the bot lists work

user-agents.org

bad guys’ bots

2% and “on the wane”Source: GroupM, Feb 2017

bot list-matching

4% Source: IAB Australia, Mar 2017

400 bot names in list

“not on any list”disguised as popular browsers – Internet Explorer; constantly

adapting to avoid detection

10,000bots observed

in the wild

http://www.mediapost.com/publications/article/294527/groupm-marketers-ignore-ai-at-their-peril-ad-fra.html

https://mumbrella.com.au/ad-fraud-real-problem-publishers-brands-agencies-tell-iab-429978



NONE of current js detection worksIn-Ad

(ad iframes)On-Site

(publishers’ sites)

• Used by advertisers to

measure ad impressions

• Limitations – tag is in foreign iframe, cannot look outside itself

ad tag / pixel(in-ad measurement)

javascript embed(on-site measurement)

In-Network(ad exchange)

• Used by publishers to

measure visitors to pages

• Limitations – most detailed and complete analysis of visitors

• Used by exchanges to

screen bid requests

• Limitations – relies on blacklists or probabilistic algorithms, least info

ad served

bot

human

fraud site

good site



Bad acting apps load more ad impressionsApp Name

Source: Forensiq



Fake mobile devices install appsDownload and Install

Launch and Interact



Suspicious apps (CTRs too high)

suspicious mobile apps



Magnitude of the ad fraud problem

websites

Source: Verisign, Q4 2016

329Mdomains

mobile apps

159 million“sites that carry ads”

11 milion“sites you’ve heard of”

WSJESPNNYTimes

EconomistReuters

Elle

10,000“apps you’ve heard of”

FacebookSpotifyPandora

ZyngaPokemonYouTube

96%“apps that carry ads”

3%

no adsno ads

7Mapps

Source: Statista, March 2017

http://www.verisign.com/en_US/domain-names/dnib/index.xhtml

https://www.statista.com/statistics/276623/number-of-apps-available-in-leading-app-stores/



Top 25 apps used by humans



Top 10 apps by time spent by humans



Top apps used by humans by category



Half of humans download 0 apps /mo



Apps have 1/3rd uniques, 20X time spent



Primary mobile revenue is from ads

In-App Advertising

App Store



Mobile ad revenue mostly from games



Top apps April 2017

“do you think bad guys will install your fraud detection SDK in their apps?”

“your CPI campaigns are not immune to fraud”

“it’s not lower in mobile, you just can’t measure it.”



About the Author



Dr. Augustine Fou – Independent Ad Fraud Researcher

2013

2014

Follow me on LinkedIn (click) and on Twitter @acfou (click)

Further reading:http://www.slideshare.net/augustinefou/presentationshttps://www.linkedin.com/today/author/augustinefou

2016

2015

http://www.linkedin.com/today/author/augustinefou

http://twitter.com/acfou

http://www.slideshare.net/augustinefou/presentations

https://www.linkedin.com/today/author/augustinefou



Harvard Business Review

Excerpt:

Hunting the Bots

Fou, a prodigy who earned a Ph.D. from MIT at 23, belongs to the generation that witnessed the rise of digital marketers, having crafted his trade at American Express, one of the most successful American consumer brands, and at Omnicom, one of the largest global advertising agencies. Eventually stepping away from corporate life, Fou started his own practice, focusing on digital marketing fraud investigation.

Fou’s experiment proved that fake traffic is unproductive traffic. The fake visitors inflated the traffic statistics but contributed nothing to conversions, which stayed steady even after the traffic plummeted (bottom chart). Fake traffic is generated by “bad-guy bots.” A bot is computer code that runs automated tasks.