Q1 2022 Update on ad fraud for AMM

February 2022 / Page 0
FouAnalytics.com
Q1 2022 Update on
Digital Ad Fraud
February 2022
Augustine Fou, PhD.
acfou@mktsci.com

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A Decade of Fraud and
Harm to Publishers

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Not humans, something else
Humans’ usage of the Internet, social, and mobile all plateaued; but digital ads shot upward

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Ad fraud is more than bots
Other forms of fraud by sites and mobile apps are not IVT(bots), are under-reported
1.3%
fake traffic
(bots)
severely
under-reported
overall fraud
bot detection
sees this
bot detection
misses all these
58%
57%
+ =
other fraud
(sites/apps)

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Industry Associations’ View
Trade associations only look at invalid traffic (IVT), reported by fraud detection tech
“The 11 percent decline in
two years is particularly
noteworthy” – ANA (2019)
“IVT is between
1 – 3%”
– GroupM (2019)
“IVT is 1.05%”
– TAG (2020)
https://www.ana.net/miccontent/s
how/id/rr-2019-bot-baseline
https://www.tagtoday.net/pressrel
eases/2020-us-fraud-study-release
https://www.nexttv.com/news/groupm-
puts-risk-of-ad-fraud-at-22-4b-globally

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All parties are harmed by badtech
Good Publishers
(lower revenue, CPMs)
Consumers
(privacy violations)
Advertisers
(ad fraud, no outcomes)
Badtech Industrial Complex
Badtech Industrial Complex

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Impact on Publishers

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“For years, publishers have been convinced
by badtech – aka “ad tech” – that there
were vast new riches to be made with their
tech …
… but the reality is publishers have seen
revenue decline, lower CPMs, audience
theft, and new risks like ad fraud.”

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Prior Supply Chain Transparency Studies
Supply chain studies since 2014 corroborate that only 40 – 60% of dollars go to pubs for showing ads
ISBA 2020: 51%
Source: ISBA, May 2020
“went
missing”
WFA 2014: 40% ANA 2017: 60%
Source: ANA, May 2017
Source: WFA, Sep 2014
MAGNA 2018: 40%

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Publishers’ own experiments
Publisher only gets 30-60c on the dollar after middlemen fees
https://mediatel.co.uk/newsline/2016/10/04/where-did-the-m
oney-go-guardian-buys-its-own-ad-inventory
2016
The Guardian
“for every pound an advertiser
spends programmatically on the
Guardian only 30 pence actually
goes to the publisher.”
2017
BusinessInsider
“$40,000 worth of ad inventory
through the open exchanges,
the publication only saw $97.”
http://adage.com/article/digital/business-insider-york-times-sh
ed-details-ad-industry-s-biggest-problem/311081/

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Research from Adalytics
Data from Q1 2022 shows large variance in “take rates” by adtech middlemen

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Brand safety tech defunds legit news
The tech is no better than keyword lists, examples below of defunding legit news

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Local news killed by fake news
Local news sites relied on ad revenue; fake sites stole ad budgets
McClatchy Bankruptcy 1,300 local news “deserts”
2,000 newspapers died

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Impact on Marketers

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P&G: cut $200M, no impact
“Once we got transparency, it
illuminated what reality was,” said
Mr. Pritchard. P&G then took matters
into its owns hands and voted with
its dollars, he said.”
“As we all chased the Holy Grail of
digital, self-included, we were
relinquishing too much
control—blinded by shiny objects,
overwhelmed by big data, and ceding
power to algorithms,” Mr. Pritchard
said.
Source: WSJ, March 2018

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Chase: -99% reach, no impact
“JPMorgan had already decided
last year to oversee its own
programmatic buying operation.
Advertisements for JPMorgan
Chase were appearing on about
400,000 websites a month. [But]
only 12,000, or 3 percent, led to
activity beyond an impression.
[Then, Chase] limited its display
ads to about 5,000 websites. We
haven’t seen any deterioration on
our performance metrics,” Ms.
Lemkau said.”
“99% reduction in ‘reach’ … Same
Results.”
Source: NYTimes, March 29, 2017
(because it wasn’t real, human reach)

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Uber: cut 80%, no change
• Uber cut $120M from $150M, signups remained steady; paid
signups dropped, and organic signups increased to replace it.
• Uber sued 100 mobile exchanges for falsifying placement
reports and fabricating transparency reports.

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Uber Lawsuit Details
Mobile exchanges were ripping off Uber in broad daylight
https://www.reedsmith.com/en/news/2021/
01/reed-smith-wins-multimillion-dollar-advert
ising-fraud-suit-for-uber
“Phunware had falsely billed Uber for ad
clicks they did not deliver.
Phunware employees wrote: “Guys it’s…
time to spin some more BS to Uber to
keep the lights on.”
https://www.linkedin.com/pulse/stone-meet-
glass-house-significance-ubers-second-ad/
Uber is suing 100 mobile exchanges for
“falsifying placement reports, fabricating
reports when no ads were run, 95 exchanges
are ‘john doe’ (unidentified).”

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Other Similar Fraud Cases
Ad fraud looks the same, because it is the same
Criteo vs Steelhouse
• “Criteo sued SteelHouse for a "counterfeit click fraud scheme" that “counterfeited clicks
to trick e-tailers into attributing sales to SteelHouse that should have been attributed to
Criteo, other competitors and partners, or direct traffic.
Outcome Health settles with OIG
• “Outcome executives inflated patient engagement metrics regarding how frequently
patients engaged with Outcome’s devices. Outcome further admitted that its
under-delivery on advertising campaigns resulted in a material overstatement of its
revenue for the years 2015 and 2016. The company’s executives fabricated data to
conceal the under-deliveries from the outside auditor.“
Facebook Sues LionMobi, JediMobi
• "The app developers LionMobi in Hong Kong and JediMobi in Singapore released apps --
which make it appear as if users clicked on Facebook ads when they didn’t. LionMobi
released "Power Clean - Antivirus and Phone Cleaner App“ (100 million downloads) and
JediMobi created "Calculator Plus" (5 million downloads).”

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Why digital appears to perform
Bot activity has enabled large quantities, low prices, high clicks
Bot activity has fueled:
- large quantities of ads
- low prices
- high clicks

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Impact on Consumers

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What Should Good
Publishers Do?

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Recommended Actions
Publishers can turn the tables on ad tech and start thriving again
- analyze log data to see how much you’re
losing to ad tech middlemen
- sell as much direct as possible and on share
of voice where possible
- “show” don’t tell buyers how clean /
human your traffic is
- rip out as much ad tech from sites as
possible; protect your users
- educate buyers to focus on humans, hCPM

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Research from Adalytics
Data from Q1 2022 shows large variance in “take rates” by adtech middlemen

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Recommended Actions
- sell as much direct as possible and on
share of voice where possible

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Risk of ad fraud - spoofing
Because… “Bad guys must pretend to be
good sites (by passing domain into bid
request) in order to get bids.”
publisherA.com
What portion of
the inventory is
real? vs spoofed
If you sell on exchanges, bad guys can pretend to
be your domain and steal ad dollars from you.

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Lower CPMs, MORE unsold
“How can you maintain $10 CPMs on your site
when buyers know they can get ads on your
site by buying it on exchanges for $1 CPMs?
They will wait till it goes
remnant, and then buy it.
You are CAUSING more inventory to go unsold.

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Recommended Actions

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Pubs wrongly accused of IVT
Good publishers are blacklisted while bad guys continue fraud
Domain (spoofed) % SIVT
esquire.com 77%
travelchannel.com 76%
foodnetwork.com 76%
popularmechanics.com 74%
latimes.com 72%
reuters.com 71%
bid request
fakesite123.com
esquire.com
passes blacklist
passes whitelist
✅
✅
declared
1. fakesite123.com has to pretend
to be esquire.com to get bids;
2. fraud measurement shows high
IVT b/c it is measuring the fake
site with fake traffic
3. Fake esquire.com gets mixed with
real so average fraud rates
appear high.
4. Real esquire.com gets backlisted;
bad guy moves on to another
domain.
incorrect
measurement

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What is a good publisher?

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Recommended Actions

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Users’ privacy being violated
Source: https://freedom-to-tinker.com/2017/11/15/no-boundaries-exfiltration-of-personal-data-by-session-replay-scripts/
“Exfiltration of personal
data by session-replay
scripts; and recording of
user actions on the site.”

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PageXray Shows Tree Graph
all the ads and trackers loaded by javascript on the page
https://pagexray.fouanalytics.com/q/wsj.com

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Good publishers protect users
42 trackers
24.3s load time
8 trackers
1.3s load time
“minimize 3rd
party javascript trackers on pages”

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Unique audience stolen
Example good publisher “cookie matching”
Unique audience of
oncologists – 100% share
of voice every month
Buyers think they can
target oncologists
elsewhere for much
cheaper; don’t buy from
you any more.

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Malware ads hijack users
Malicious javascript can break out of ad
iframe -- take over the page, redirect user
Source:
https://digiday.com/media/every-vendor-problem-we
bsite-redirects-keep-plaguing-publishers/
Source:
https://blog.confiant.com/nov-22-25-attack-o
f-the-cyber-turkey-7a57a1ed498f

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Recommended Actions

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Pubs have good business practices
“good business practices lead to good looking data”
Good Publishers “sites that carry ads”
• source traffic
• audience extension
• auto-refresh
• traffic laundering
• don‘t source traffic
• protect advertisers
• protect consumers

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Good publishers act to reduce bots
Publisher 1 – stopped buying traffic
Publisher 2 – filtered data center traffic

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Relative Quality – adjust budgets

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Key Points, Looking Ahead
- privacy regulations making shockwaves
- loss of 3P cookies and Apple IDs
- ad tech targeting was very inaccurate
- UUID, RampID, fingerprinting are stop-gaps
- coming full circle to “contextual”
- buying from good publishers = good digital
marketing

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APPENDIX

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How Marketers Use
FouAnalytics

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Site
Analytics
Media
Analytics
“see fou yourself”
• alternative to Google Analytics
• secure, hardened against attack
• shows all details, no black box
• innovated w/ practitioners
• verify your own media/ads
• secure, hardened against attack
• shows details for decisioning
• recommended optimizations
for #publishers for #marketers

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How is FouAnalytics different?
• Privacy compliant – FouAnalytics collects only anonymous
javascript parameters from the browser; no PII (personally
identifiable information) is ever collected; no 3rd
party cookies
are ever set or used
• Secure/tamper-proof – FouAnalytics has security measures
built into every part of the system so false data cannot be
written into it; analytics cannot be altered or tampered-with
• Analytics w/ detailed supporting data – unlike other black box
fraud detection tech firms, FouAnalytics provides detailed
supporting data so clients understand why something is
marked fraudulent, so they can troubleshoot and take action

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More examples of fake sites
Readable or non-sensical domains are all disguised at bid-time
analyzecanceradvice.com
analyzecancerhelp.com
bestcanceropinion.com
bestcancerproducts.com
bestcancerresults.com
besthealthopinion.com
bettercanceradvice.com
bettercancerhelp.com
betterhealthopinion.com
findcanceropinion.com
findcancerresource.com
findcancertopics.com
findhealthopinion.com
finestcanceradvice.com
finestcancerhelp.com
finestcancerresults.com
getcancerproducts.com
06f09b1008ae993a5a.com
fbfd396918c60838.com
97ff623306ff4c26996.com
b1f6fe5e3f0c3c8ba6.com
23205523023daea6.com
6068a17eed25.com
b1fe8a95ae27823.com
f4906b7c15ba.com
eac0823ca94e3c07.com
1f7de8569ea97f0614.com
21c9a53484951.com
24ad89fc2690ed9369.com
efd3b86a5fbddda.com
34c2f22e9503ace.com
0926a687679d337e9d.com
6a40194bef976cc.com
Fraud sites Fake sites
02aa19117f396e9.com
f8260adbf8558d6.com
9376ec23d50b1.com
pushedwebnews.com
a0675c1160de6c6.com
0f461325bf56c3e1b9.com
850a54dbd2398a2.com
8761f9f83613.com
20a840a14a0ef7d6.com
31a5610ce3a8a2.com
5726303d87522d05.com
3ac901bf5793b0fccff.com
b014381c95cb.com
2137dc12f9d8.com
33ae985c0ea917.com
153105c2f9564.com
Fake sites

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More fake bids than real
Largest sites are also the most spoofed – 40X more fake than real

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Daily impression volumes
Unknown sites sell more “inventory” than large, well-known sites
Highlighted
domains are
interspersed with
large sites that
you know many
humans go to.
These are DAILY
quantities of
impressions.
Notice the large
quantities; some
are larger than
mainstream sites.

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Ad serving volume by hour
With hourly detail, you see most ads are blown out before 4 am
What to look for?
If all of the volume is spent in
the first hour or during
sleeping hours, you have no
impressions left for the day
What to do?
Turn off overnight hours and
frequency cap by site and user
to avoid massive spikes in
volume.

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Where do visitors click?
Click patterns on desktop and mobile screens, vs bots

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Measure quality of traffic from paid sources
Marketer 1
• Blue means humans
• Red means bots
Marketer 2
“recommendation is to increase spend on
sources driving more humans (blue); reduce
spend on sources with more bots (red)”

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Detect / reduce fraud in-flight
Launch Week 3
onward
Weeks
1-2
Initial baseline
measurement
Measurement after
first optimization
After eliminating several
“problematic” networks
Stacked percent chart
Blue (human)
Red (bots)

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Full Funnel Measurement
In-Ad
(ad tag)
On-Site
(embed code)
Measure
Ads
Measure
Arrivals
Measure
Conversions
1743 156
A
more humans (blue)
good publishers
On-Site
(embed code)
social
native
video
display
affiliate

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Improve outcomes by shifting spend
Measure
Ads
Measure
Arrivals
Measure
Conversions
clean, good media
low-cost media,
ad exchanges
346
1743
5
156
30X better
outcomes
• More arrivals
• Better quality
A
B

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Display 4
2,036 humans
human conversion rate
Comparing 4 paid display sources
Site Traffic Conversions
8,482 818
4,216 humans
5%
14,539 193
225 humans
9%
2,248 23
168 humans
5%
1,527 9
Display 3
Display 2
Display 1
Humans
40%

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Measure conversions, correlate w/ humans
Organic sources
have more humans
(dark blue)
Conversion actions
(calls) show straight line
correlation to humans

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+92%
Conversion Rate
4,248
1,812,906
= 0.23%
4,248
942,711
= 0.45%
Increase Conversion Rates
Make more accurate by subtracting bot traffic from denominator
Subtracted bots (red),
and unmeasured (white)

FouAnalytics.com
About the Author
Augustine Fou, PhD.
acfou [@] mktsci.com

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Dr. Augustine Fou
2013
2014
2016
2015
2017
2019
2018
2020
2021

FouAnalytics.com
Dr. Augustine Fou
213 Articles for Forbes CMO Network
https://www.forbes.com/sites/augustinefou
268 LinkedIn Articles on Ad Fraud and Digital Marketing
https://www.linkedin.com/today/author/augustinefou
786 Slide Decks on Ad Fraud and Digital Marketing
http://www.slideshare.net/augustinefou/presentations
Taught digital marketing at New York University and Rutgers
University Center for Management Development

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