Post on 21-Apr-2017
Better Media Means Better Outcomes
April 2017Augustine Fou, PhD.acfou@mktsci.com 212. 203 .7239
“Are you buying ‘traffic’ or ‘inventory’? There’s plenty of
that … at low cost, even.”
“Real human audiences are scarce and valuable.”
Case Examples for Advertisers
April 2017 / Page 4marketing.scienceconsulting group, inc.
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Reduce bots/NHT in display campaignsPeriod 1 Period 3Period 2
Initial baseline measurement
Measurement after first optimization
Eliminating several “problematic” networks
April 2017 / Page 5marketing.scienceconsulting group, inc.
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Improve outcomes by shifting spendMeasure
AdsMeasure Arrivals
Measure Conversions
clean, good media
low-cost media, ad exchanges
346
1743
5
156
30X better outcomes
• More arrivals• Better quality
A
B
April 2017 / Page 6marketing.scienceconsulting group, inc.
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Make analytics more accurate and clean
7% conversion rate 13% conversion rateartificially low actually correct
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Assess “humanness” of media channels
Organic sources have more humans (dark blue)
Conversion actions (calls) are well correlated to humans
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Higher quality means lower cost per human
Lower quality paid sources mean higher cost per human – like 11X higher cost.
Sources of different quality send widely different amounts of humans to landing pages.
Ad Fraud Background
April 2017 / Page 10marketing.scienceconsulting group, inc.
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Digital ad fraud is profitable and scalable
Source: https://hbr.org/2015/10/why-fraudulent-ad-networks-continue-to-thrive
“the profit margin is 99% … [especially with pay-for-use cloud services ]…”
“highly lucrative, and profitable… with margins from 80% to 94%…”
“why stop at 10 ads on the page; why
not load 13,000 ads on the page”
131 ads on pageX
100 iframes=
13,100 ads /page
Source: Digital Citizens Alliance Study, Feb 2014
April 2017 / Page 11marketing.scienceconsulting group, inc.
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Example – 92% of impressions cleaned
Increased CPM prices by 800%
Decreased impression volume by 92%
Source: http://adexchanger.com/ad-exchange-news/6-months-after-fraud-cleanup-appnexus-shares-effect-on-its-exchange/
260 billion
20 billion
> $1.60
< 20 cents
April 2017 / Page 12marketing.scienceconsulting group, inc.
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Methbot stayed hidden for years
Source: Dec 2016 WhiteOps Discloses Methbot Research
“the largest ad fraud discovered to date, a single botnet, Methbot, steals $3 - $5 million per day, $2 billion annualized.”
1. Targets video ad inventory$13 average CPM, 10X higher than display ads
2. Disguised as good publishersPretending to be good publishers to cover tracks
3. Simulated human actionsActively faked clicks, mouse movements, page scrolling
4. Obfuscated data center originsData center bots pretended to be from residential IP addresses
Where is Ad Fraud Concentrated?
April 2017 / Page 14marketing.scienceconsulting group, inc.
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CPM/CPC (91% of spend) is most targeted
Impressions(CPM/CPV)
Clicks(CPC)
Search27%
91% digital spend
Display10%
Video7%
Mobile47%
Leads(CPL)
Sales(CPA)
Lead Gen$2.0B
Other$5.0B
• classifieds• sponsorship• rich media
(89% in 2015)Source: IAB 1H 2016 Report
(86% in 2014)
April 2017 / Page 15marketing.scienceconsulting group, inc.
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Two key ingredients of CPM and CPC FraudImpression (CPM) Fraud
(includes mobile display, video ads)
1. Put up fake websites and load tons of ads on the pages
Search Click (CPC) Fraud
(includes mobile search ads)
2. Use fake users (bots) to repeatedly load pages to generate fake ad impressions
1. Put up fake websites to participate in search networks
2. Use fake users (bots) to type keywords and click on them to generate the CPC revenue
screen shots of fake sites
Fake Websites(cash-out sites)
April 2017 / Page 17marketing.scienceconsulting group, inc.
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99% human pageviews on “sites you’ve heard of”
100% botpageviews on
“fraud sites”
99% of human pageviews are on
“sites you’ve heard of”
“real content that real humans want to read”
WSJESPN
NYTimesReuters
CBSSports
1% of human pageviews are on
“long tail sites”
“niche content that some humans want
to read”
top 1 million sitesnext 10 million sites318 million sites
Verisign reports 329 million domains registered by Q4 2016Source: http://www.verisign.com/en_US/domain-names/dnib/index.xhtml
April 2017 / Page 18marketing.scienceconsulting group, inc.
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Countless fraud sites made by template
100% bot
Fake Visitors(bots)
April 2017 / Page 20marketing.scienceconsulting group, inc.
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Bots are automated browsers used for ad fraud
Headless BrowsersSeleniumPhantomJSZombie.jsSlimerJS
Mobile Simulators35 listed
Bots are made from malware compromised PCs or headless browsers (no screen) in datacenters.
Bots
April 2017 / Page 21marketing.scienceconsulting group, inc.
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Bots range in sophistication, and therefore cost
Javascript installed on webpage
Malware on PCsData Center BotsOn-Page BotsHeadless browsers
in data centersMalware installed on
humans’ devices
Less sophisticated Most sophisticated
Source: AdAge/Augustine Fou, Mar 2014 Source: Forensiq Source: Augustine Fou, Oct 2015
“the official industry lists of bots catch NONE of these bots”
1 cent CPMsLoad pages, click
10 cent CPMsFake scroll, mouse movement, click
1 dollar CPMsReplay human-like mouse movements, clone cookies
“The equation of ad fraud is simple: buy traffic for $1 CPMs, sell ads for $10 CPMs; pocket $9 of pure profit.”
April 2017 / Page 23marketing.scienceconsulting group, inc.
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How Ad Fraud HarmsAdvertisers
April 2017 / Page 24marketing.scienceconsulting group, inc.
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Messes up your analytics
click on links
load webpages tune bounce rate
tune pages/visit
“bad guys’ bots are advanced enough to fake most metrics”
April 2017 / Page 25marketing.scienceconsulting group, inc.
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Messes up your KPIsProgrammatic display
(18-45% clicks from advanced bots)Premium publishers(0% clicks from bots)
0.13% CTR(18% of clicks by bots)
1.32% CTR(23% of clicks by bots)
5.93% CTR(45% of clicks by bots)
Campaign KPI: CTRs
April 2017 / Page 26marketing.scienceconsulting group, inc.
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Fake clicks mess up CTRsLine item details
Overall average 9.4% CTR
“fraud hides easily in averages”
April 2017 / Page 27marketing.scienceconsulting group, inc.
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Fake demographic information
April 2017 / Page 28marketing.scienceconsulting group, inc.
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Fake languages declared by bots
April 2017 / Page 29marketing.scienceconsulting group, inc.
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Want 100% viewability? 0% NHT (bots)?
Bad guys cheat and stack ALL ads above the fold to make 100% viewability.
“100% viewability? Sure, no problem.”
AD • IAS filtered traffic, • DV filtered traffic• Pixalate filtered traffic, • MOAT filtered traffic, • Forensiq filtered traffic
“0% NHT? Sure, no problem.”
April 2017 / Page 30marketing.scienceconsulting group, inc.
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Bot activity has higher signal“Humans are hard to predict …
… but bots give you beautiful signals.”
Source: Claudia Perlich, PhD. Data Scientist, Dstilllery
Current State of NHT Detection
April 2017 / Page 32marketing.scienceconsulting group, inc.
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Fraud bots are NOT on any list
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
April 2017 / Page 33marketing.scienceconsulting group, inc.
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Limitations due to where measurement is done
In-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
April 2017 / Page 34marketing.scienceconsulting group, inc.
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In-ad measurements could be entirely wrong
Publisher Webpagepublisher.com
Foreign Ad iFramesadserver.com
Cross-domain (XSS) security restrictions mean iframe cannot:• read content in parent frame• detect actions in parent frame• see where it is on the page
(above- or below- fold)• detect characteristics of the
parent page
1x1 pixeljs ad tags ride along
inside iframe
incorrectly reported as 100% viewable
parent frameforeign iframes
April 2017 / Page 35marketing.scienceconsulting group, inc.
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10% bots doesn’t mean 90% humans
volume bars (green)
Stacked percentBlue (human)Red (bots)
red v blue trendlines
“Some of the data is simply not measurable – e.g. the white is not measurable, and gray is ‘not enough info’.”
“Fraud detection that only reports bots is telling half the story.”
“Having fraud DETECTION is not the same as having fraud PROTECTION.”
What about Mobile?
“it’s more lucrative and less measurable… hmm, what do you think?”
April 2017 / Page 39marketing.scienceconsulting group, inc.
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Bad acting apps load more ad impressionsApp Name
Source: Forensiq
April 2017 / Page 40marketing.scienceconsulting group, inc.
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Fake mobile devices from data centers do thisDownload and Install
Launch and Interact
“do you think bad guys install fraud detection SDKs in their apps?”
“No. Your CPI campaigns are not immune to fraud”
“it’s not lower in mobile, you just can’t measure it.”
“Let’s go fight some bad guys together!”
April 2017 / Page 43marketing.scienceconsulting group, inc.
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About the Author
April 2017Augustine Fou, PhD.acfou@mktsci.com 212. 203 .7239
April 2017 / Page 44marketing.scienceconsulting group, inc.
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Dr. Augustine Fou – Independent Ad Fraud Researcher2013
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
April 2017 / Page 45marketing.scienceconsulting group, inc.
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Harvard Business ReviewExcerpt:
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.