Digital Ad Fraud - Betcha Didn't Know

Digital Ad Fraud
Betcha Didn’t Know…
June 2017
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239

June 2017 / Page 1marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
How Ad Fraud Continues to Thrive

Digital Media Imperative: Stop
Bot Fraud Before It Can Happen
By: Mike Donahue, Mediapost

Stop buying poop water…
“Which? 1) start with ‘poop water’ and filter it
before you drink it?, or 2) start with fresh water?”
Good publishers are “fresh water.”

Bifurcate good pubs from “other”
100% bot traffic
“fake (cash out) sites”
• No content
• Stolen content
• Fake content
“sites with real content that
real humans want to read”
Source: DCN/ WhiteOps 2015
“sites you’ve heard of”
WSJ
ESPN
NYTimes
Economist
Reuters
Elle
Good Publishers
(good business practices)
“sites that carry ads”

How ad fraud works … very simply
Source: Distil Networks 2017
1. Start with lots of bots
2X more data center browsers
than malware on PCs at home
2. Launder using tech tools
Randomize referrer to look legit,
user agent, and IP address location
3. Sell traffic to willing buyers
“Sites that carry ads” want to buy
traffic to increase ad revenues
4. Sell low cost CPMs on exchanges
Massive quantities of low cost inventory
sold to marketers, fully laundered
Source: Ratko Vidakovic, May 2017
Publishers who want it Advertisers who want it

“just because you can’t
detect it (fraud), doesn’t
mean it’s not there.”

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

Walled gardens are fine, on-site …
Google
Search
Facebook
Display
“bots can’t make money
when ads load here”
GDN FBX
less bots | more humans
first-party IDs, people-based marketing
facebook.comgoogle.com
facebook app

$29
There’s 15X more “sites with ads”
Good Publishers “sites with ads”
Source: Verisign, Q4 2016
329M
domains
159 million
11 million
WSJ
ESPN
NYTimes
Economist
Reuters
Elle
3%
no ads
ads
15X more

Mobile fraud steals ad dollars too
159 million
11 milion
WSJ
ESPN
NYTimes
Economist
Reuters
Elle
3%
no ads
7M
apps
Source: Statista, March 2017
96%
“apps that carry ads”
Search Spend
$40 $40
Display Spend Other
$21$30
$3
Google Search FB+Google Display$29
Source: eMarketer March 2017
Source: Verisign, Q4 2016
329M
domains
10,000
“apps you’ve heard of”
Facebook
Spotify
Pandora
Zynga
Pokemon
YouTube

Fake sites, fake apps -- examples
Fake Sites
Source: Sadbottrue.com
Fake Apps
… they can sell ad
“inventory” at low prices

The most profitable criminal activity
1000% return
11% returns1% interest
digital ad fraud
stock marketbank interest
“buy traffic for $1, sell
ads for $10 CPMs”

(2015) Display ads …
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

(2016) Video ads …
Source: Dec 2016 WhiteOps Discloses Methbot Research
“Methbot, steals $2 billion annualized;
and it avoided detection for years.”
1. Targeted video ad inventory
$13 average CPM, 10X
higher than display ads
2. Disguised as good publishers
Pretending to be good
publishers to cover tracks
3. Simulated human actions
Actively faked clicks, mouse
movements, page scrolling
4. Obfuscated data center origins
Data center bots pretended to
be from residential IP addresses
1 botnet eats 15% of video inventory

(2017) Mobile ads …Source: June 2017, Tune
average 20% fraud
100% fraud
50% fraud24 billion clicks on
700 mobile networks

Our own data on mobile…
1 2
66% avg fraud
18% avg fraud
1. 9% of the apps (blue dots) caused 80% of fraudulent impressions
2. Remaining 91% of apps caused the remaining 20% of fake impressions
• 1 billion mobile display impressions
• Nearly 1,000 apps cross referenced with SDK

Case File: 3 bad apps eat most of budget
com.jiubang com.flashlight com.latininput
75% of the
dark red

Ad 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

Fake sites 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”

Fake inventory sold on exchanges
publisherA.com
… but, PublisherA
does NOT sell ads
on open exchanges!
“Dark Revenue” is ad revenue diverted away from
publishers, so they don’t even see it’s missing.
• Large pubs – “dark” is 1-2X ad revenue
• Medium pubs - “dark” is 5-10X ad revenue
• Small pubs - “dark” is 20-100X ad revenue

Case in point… Chase (try this)
“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)

Bad guys exploit gaps
in detection

Current detection is severely limited
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 and
complete analysis
of visitors
• Limitations –
relies on blacklists
or probabilistic
algorithms, least info
ad
served
bot
human
fraud site
good site

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

Tag placement matters ... A LOT
Tag
(in foreign iframe)
Tag
(on page)
window sizes detected
as 0x0 or 0x8 pixels correct window sizes
for ads detected
0% humans
60% bots
60% humans
3% bots
“fraud measurements could be entirely wrong, depending on
where the tag is placed and where the measurement is done.”

Actively review and scrub campaigns
Launch Week 3 and beyondWeek 2
Initial baseline
measurement
Measurement after
first optimization
After eliminating several
“problematic” networks

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

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!”

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

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

Good publishers protect advertisers
On-Site measurement,
bots are still coming
In-Ad measurement, bots
and data centers filtered
11% red
-9% (filtered GIVT
and data centers)
2% red
“Filter data center traffic and not call the ads”

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

Good publishers have good 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

How can we tell “good” from “other?”
“Business practice review by independent 3rd party
provides the trust and assurance that distinguishes
good publishers from ‘sites that carry ads’.”

The “new deal” benefits all parties
Good Publishers
(more revenue)
Consumers
(better experience)
Advertisers
(better outcomes)

Trust & Transparency Manifesto
Good Publishers
(more revenue)
Consumers
(better experience)
Advertisers
(better outcomes)
Agencies
(better business)
• Protect
advertisers
• Protect
Consumers
• Whitelist
good
publishers
• Protect
themselves
from malware
and privacy
invasion
• Be an agency;
charge for
work done
• Don’t
arbitrage
undisclosed
margins
• Optimize for
outcomes not
quantity
• Pay Fairly
and Timely
“every party has a role to help make the industry better”

About the Author
June 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

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.

Digital Ad Fraud - Betcha Didn't Know

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