State of Digital Ad Fraud August 2017

State of
Digital Ad Fraud
August 2017
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239

“It has gotten so bad, I can’t
even wait a quarter to edit
the estimates of fraud.”
It’s 60 – 100% fraud, with an
average of 90%; but it is
not distributed evenly.

August 2017 / Page 2marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Fraud on such a massive scale…
May 26 Forbes “Judy Malware”
• 40 bad apps to load ads
• 36 million fake devices to load
bad apps
• e.g. 30 ads per device /minute
• 30 ads per minute = 1 billion
fraud impressions per minute
June 1 Checkpoint “Fireball”
• 250 million infected computers
• primary use = traffic for ad
fraud
• 4 ads /pageview (2s load time)
• fraudulent impressions at the
rate of 30 billion per minute

Fraudsters successfully sell ads… how?
100% viewability
(but, it’s fake)
AD
Stack ads all
above the fold to
trick detection
0% NHT
(but, it’s fake)
Buy traffic that is
guaranteed to
pass fraud filters
clean placement
(but, it’s fake)
Pass fake source
to trick reports of
placement details
http://www.olay.co
m/skin-care-
products/OlayPro-
X?utm_source=elle
&utm_medium=dis
play
+ +
“by tricking measurement and reporting”

Current detection cannot catch it
In-Ad
(billions of ads)
• Limitations –
tag is in foreign
iframe, cannot look
outside itself
ad tag / pixel
(in-ad measurement)
In-Network
(trillions of bids)
On-Site
(millions of pageviews)
javascript embed
(on-site measurement)
• Limitations –
most detailed
analysis of visitors,
bots still get by
• Limitations –
relies on blacklists
or probabilistic
algorithms, least info
ad
served
bot
human
fraud site
good site

Fraud diverts ad spend to fraudsters
Good Publishers “sites that carry ads”
• No content
• Few humans
• Low CPMS
$40 Search Spend Display Spend $40
$21$30
$3
Google Search FB+Google Display
$29
(outside Google/Facebook)
$83 Digital Spend Source: eMarketer March 2017
47%
programmatic

$29
There’s 160X more “sites with ads”
Good Publishers “sites with ads”
Source: Verisign, Q4 2016
329M
domains
est. 164 million
“sites that carry ads”
“sites you’ve heard of”
WSJ
ESPN
NYTimes
Economist
Reuters
Elle
3%
no ads
carry ads
160X more
47%
programmatic
est. 1 million

700X more
There’s 700X more fake apps
7M
apps
Source: Statista, March 2017
6.99 million
96% “apps that carry ads”
10,000
“apps you’ve heard of”
Facebook
Spotify
Pandora
Zynga
Pokemon
YouTube
$29
47%
programmatic
Facebook, 2015
Users use 8 – 15 apps on their
phones.
Spotify, 2016
People have 25 apps on their
phones, use 5-8 regularly
Forrester Research, May 2017
Humans “use 9 apps per day, 30
per month”

Examples of fake sites, fake apps
Fake Sites (10s of millions)
Source: Sadbottrue.com
Fake Apps (millions)

Just because you can’t measure it
100% fraud
> 50% fraud
… doesn’t mean it’s not there.

Plainly incorrect measurements
Incorrect IVT
Measurement
Sources 1 and 2
measured on-page
Source 3
in foreign iframe
1x1 pixel
incorrectly reported as
100% viewable
Incorrect Viewability
Measurement

“just because you can’t detect
it (fraud), doesn’t mean it’s
not there….”
… “or worse, you detect it
wrong and think it’s clean.”

Marketers’ Own
Experiments ..

Chase: 99% reach had 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)

P&G: $140M in digital, no impact
“Procter & Gamble's concerns
about where its ads were
showing up online contributed
to a $140 million cutback in
the company's digital ad
spending last quarter, the
company said Thursday. That
helped the world's biggest
advertiser beat earnings
expectations. Perhaps even
more noteworthy, however,
organic sales outperformed
both analyst forecasts and key
rivals at 2% growth despite
the drop in ad support.
Source: AdAge, July 2017

Digital Ad Productivity - $1 spent
Good Publishers Ad Networks Open Exchange
91% viewable
40% fees 40% fees
30% NHT 70% NHT
No fees
3% NHT
97% Not NHT
70% Not NHT
30% Not NHT
66% viewable
41% viewable
75% confirmed human
17%confirmed human
3% confirmed human
68¢
7¢
1¢
“human
viewable ads”
“human
viewable ads” “human
viewable ads”
“not working media” “not working media”

60¢ / $1 goes towards “working media”
“When buying programmatic exchanges, for every $1 only
57 – 63 cents goes towards working digital media.”
“mark up”
“working media”
“working media”
“mark up”

Corroborated by ANA, WFA Studies
Source: WFA, April 2017
Source: ANA, May 2017

Humans (dark blue) vs Bots (dark red)
75% 2% 17% 30% 3% 72%

Directly measured viewability, by type
“Taking viewability as 50% of the pixels in view or greater, we
can see statistically different rates of viewability by network.”
91% viewable 66% viewable 41% viewable

Not Productive = Naked, Bots, Unviewable
Naked ad calls + Not viewable + Confirmed bots
= Not productive
Ad Networks Open Exchanges
47% avg
77% avg
11% avg
Good Publishers
Naked ad calls Naked ad calls

Stop buying poop water (bad ads)
“Which? 1) start with ‘poop water’ and filter it
before you drink it?, or 2) start with fresh water?”
“fraud detection can’t filter it for you”

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

Shift budgets to quality (high human)
Lower quality paid sources
mean higher cost per human
acquired – like 11X the cost.
Sources of different quality send
widely different amounts of
humans to landing pages.
“mitigation doesn’t even
require technology!”

Optimize for real human conversions
Organic sources
have even more
humans (dark blue)
Conversion actions
(phone calls) are well
correlated to humans;
bots don’t call

Measure every point of the funnel
Measure
Ads
Measure
Arrivals
Measure
Conversions
346
1743
5
156
A
B
30X more human
conversion events
• More arrivals
• Better quality
more humans (blue)
good publishers
low-cost media,
ad exchanges

About the Author
August 2017
Augustine Fou, PhD.
acfou [@] mktsci.com
212. 203 .7239

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/presentations
https://www.linkedin.com/today/author/augustinefou
2016
2015

State of Digital Ad Fraud August 2017

State of Digital Ad Fraud August 2017

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