State of NOT Ad Fraud Q2 2019

April 2019 / Page 0marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
State of Ad Fraud
Q2 2019
April 30, 2019
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239

“I’ve been asked this a lot recently –
‘how big is ad fraud?’
I can’t say for the industry; I can
only say for the client campaigns
I’ve measured.”

Agenda
1. How big is ad fraud?
2. How much is NOT fraud (i.e. known)?
3. How marketers are solving fraud
themselves

How Big is Ad Fraud?

“you tell me”
(look at the evidence)

New “largest ever” botnet every year
Vast botnets targeting high-value video ads, disguising/hiding

Millions of apps on millions of phones
Big companies openly committing fraud; in-app is far less measurable
October 2018
https://www.buzzfeednews.com/
article/craigsilverman/how-a-
massive-ad-fraud-scheme-
exploited-android-phones-to
November 2018
article/craigsilverman/android-
apps-cheetah-mobile-kika-
kochava-ad-fraud
March 2019
article/craigsilverman/in-banner-
video-ad-fraud
April 2019
article/craigsilverman/google-
play-store-ad-fraud-du-group-
baidu

Hundreds of billions of redirects
Page redirects/pop-unders evade fraud detection easily
a.k.a. “zero-click” “pop-under”
“forced-view” “auto-nav”
Source: https://www.buzzfeed.com/craigsilverman/remember-tom

100% fraud, 99% location fraud
Big companies openly committing fraud; in-app is far less measurable
https://blog.branch.io/mobile-ad-fraud-
what-24-billion-clicks-on-700-ad-networks-
reveal/
“Top 8 networks were
100% fraud and the top
34 were all greater
than 50% fraud.”
“90 – 99% of
geolocation data is
bad or outright faked.”

99% of ads were never served
Hidden cost: marketers pay for bids won; ads served is
less, sometimes by a lot, than what they paid for
Campaign 1
• For every bid won, there should be an ad
served – a 1:1 relationship
• Comparing DSP data vs ad server data
shows that for certain domains the
discrepancy is large
• Marketers pay on bids won; the discrepancy
is the hidden cost of ads not being served
Low CPM
Campaign 2

Billions of fake users purged
February 2018
http://www.adweek.com/digital/lotame-
purges-its-data-exchange-after-identifying-
400-million-user-profiles-as-bots/
May 2018
https://www.inc.com/bill-murphy-
jr/facebook-says-it-disabled-almost-13-
billion-fake-accounts-and-numbers-only-
get-more-insane-from-there.html
July 2018
https://www.engadget.com/2018/07/06/t
witter-reportedly-suspended-70-million-
fake-accounts-in-may-and/

Luminati[.]io Oxylabs[.]io Smartproxy[.]com
Millions of IPs to disguise bots
Use: to disguise data center bots to appear to come from
residential IP addresses, avoid detection
40 million
IP addresses
10 million
IP addresses
30 million
IP addresses

700X more bad apps than good ones
$46 Search Display/Video $46
$32$39
$8
Google Search FB+Google Display$29
(outside Google/Facebook)
$100 Billion Digital SpendSource: IAB 2H 2018 Report
Source: Verisign, Q4 2016
329M
domains
est. 1 million est. 164 million
7M
apps
Source: Statista, March 2017
est. 10,000 est. 6.99 million
1% of
impressions
99% of
impressions
$10B $19B
Good Publishers “sites/apps with ads”

Traffic sellers openly selling
Many sources to buy “traffic,” tune “quality” level, host bots
Google
“buy real human traffic”
Select vendor and
“traffic quality level”
Host your own bots
(cost $3.99/mo)

Massive quantities sold by unknowns
Highlighted
domains and apps
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.

Missing media dollars, fraud?
Comparing DSP logs vs invoices, unspent dollars go missing
Display Campaign 1
$3,300,000 (+10%)
(agency invoice to advertiser)
$3,000,000
DSP invoice to agency (paid by
advertiser)
$1,800,000 (-40%)
DSP log total (bids won)
On average, 24% of advertisers’ media spend was wasted/missing
Mobile Campaign 2
$950,000
(campaign spend)
$330,000 (35%)
(invalid devices/fraud)
Display Campaign 3
$7,900,000
(campaign spend)
$1,500,000 (19%)
Display Campaign 3
$3,900,000
(campaign spend)
$550,000 (14%)
Display Campaign 4
$7,000,000
(campaign spend)
$1,700,000 (24%)
Source: Method Media Intelligence
DSP logs show only $1.8 million of bids
won, compared to the $3.0 million paid;
$1.2 million went “missing” (not
returned/credited to advertiser)

Other forms of fraud, not IVT
If you’re only looking for bots/IVT, you will find just bots
1.3% + 57% = 58%
bots site/app fraud overall fraud
bot detection sees this
bot detection misses this

Fraud, waste eats most budgets
$1 spent by Marketer
blocked
see slide 1
site/app fraud
see slides 2-4
ad/user fraud
see
https://www.slideshare.net/augustinefou/presentations
not viewable
see slides 5-7
10% 30% 40% 20%
• fake sites, spoofing
• auto-reload/refresh
• ad stacking
• tag manipulation
• redirect/sourced traffic
• pop-ups/unders
• naked/invisible ads
• bots/fake users
• fake devices
• background load
• fake apps
• deviceID rotation
• random deviceID
• retargeting fraud
• fake profiles/data
• malware/adware
• fake analytics
• fake attribution
• click injection
• incentivized views
• residential proxy
• app is not in use
• tab is not active
• browser minimized
• 50% ad in viewport
• misrepresented
• (1s display, 2s
video)
10% 70% 20%
blocked fraud useless
productive digital ads for Marketer
1% ?

10 – 90
dollars
1 - 99
impressions
unknown
unknown

Fake botnet for PR purposes
“used highly sophisticated techniques
to fraudulently load ads on the
affected sites without the site
owners' consent, leveraging a new
methodology that allows it to
monetize inventory on premium
domains.”
“none of this actually happened; it was completely
fabricated for the press release announcing their
new algo – ‘dramatic improvements to its
automated traffic detection .. primarily through
…machine learning methodologies’. The failure was
due to their analyzing only pre-bid data, which was
faked. There were no ads injected into any of the
sites they named in the press release. This was
confirmed by each of the good publishers, falsely
accused.”

Trade associations think…
Ads fraud is virtually
“non-existent”
– IAB Australia
“Ad fraud is $6.5
billion or 9% of
display ad spend”
-- Assoc. of Nat’l Advertisers
“@tag_today says it
has a ‘monumental’
breakthrough on a
method that can cut
online fraud by more
than 83%”

“the problem with an industry-
wide extrapolation is …
you don’t know what you
don’t know.”

“there are always new threats
and exploits – called ‘zero-
day’s in the cyber security.
Better to focus on what is
known or knowable.”

What is NOT ad fraud?
(i.e. known)

10 – 90
dollars
1 - 99
impressions
known
known
“$10 billion out of $100 billion of
digital spend in the U.S. is spent
with good publishers.” DCN
“1% of impressions can be
confirmed to be shown to
humans, and viewable”

Confirmed humans: in-ad, on-site
IN-AD
3% confirmed humans
(tag placed in display ads, in
ad iframe; used to determine
quality of sites and apps that
loaded them)
ON-SITE
82% confirmed humans
(embed code placed directly
on the page to determine
quality of visitors arriving on
the site)

Good publishers - magazines
Directly measured on-site, 77% confirmed humans

Good publishers – local TV

Good publishers – local radio

Measured viewability: 3-17%
Viewability must be measured in-ad, using intersectionObserver
Exchange A, All CPMs exclude app app only blended
hasFocus:1 4.2% 5% 0.5% 4.8% 4.7% 5%
-hasFocus:1 84.7% 95% 10.6% 95.2% 95.3% 95%
88.9% 11.1% 100.0%
visibilityState:visible 69.9% 79% 9.6% 86.1% 79.5% 80%
-visibilityState:visible 19.0% 21% 1.5% 13.9% 20.5% 20%
88.9% 11.1% 100.0%
intersection >50% 20.1% 23% 4.2% 37.4% 24.2% 24%
-intersection >50% 68.8% 77% 7.0% 62.6% 75.8% 76%
88.9% 11.1% 100.0%
viewable 0.8% 1.5% 0.9%
(excl hasFocus:1) viewable 17.8% 32.2% 19.3%
Exchange B, All CPMs exclude app app only blended
hasFocus:1 0.4% 0% 0.0% 0.0% 0.4% 0%
-hasFocus:1 95.1% 100% 4.5% 100.0% 99.7% 100%
95.5% 4.5% 100.0%
visibilityState:visible 34.3% 36% 4.2% 92.2% 38.4% 38%
-visibilityState:visible 61.2% 64% 0.4% 7.8% 61.6% 62%
95.5% 4.5% 100.0%
intersection >50% 8.0% 8% 0.5% 10.9% 8.5% 9%
-intersection >50% 87.5% 92% 4.0% 89.1% 91.5% 91%
95.5% 4.5% 100.0%
viewable 0.0% 0.0% 0.0%
(excl hasFocus:1) viewable 3.0% 10.0% 3.3%

Measured ad blocking: 10-18%
Ad blocking must be measured on-site, mobile separated, no bots
B2C (Consumer) Jan 2019 EXCLUDE BOTS
RAW (percent of data) percent
NOT Blocked Blocked
mobile 61.8% 0.4% 0.7% blocking rate
desktop 27.6% 3.1% 10.2% blocking rate
not measured 4.8%
97.8% 2.2% bots
B2B Jan 2019 EXCLUDE BOTS
RAW (percent of data) percent
NOT Blocked Blocked
mobile 34.5% 0.5% 1.3% blocking rate
desktop 39.9% 9.0% 18.5% blocking rate
not measured 7.9%
91.8% 8.2% bots

Known: -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)

Known: -$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

Case Examples of
Marketers Solving Fraud
THEMSELVES

Small businesses found/killed fraud
“Both of these small businesses used their own analytics and gut
instinct; they resolved ad fraud without using any expensive tech.”
Small Business A
• Noticed a 118,600% increase in Android devices hitting her site
during campaign – AND no additional goal completions
• Compiled additional data that corroborated it was fraud;
presented to ad network and got refund for entire campaign
Small Business B
• Year over year, marketer noticed the discrepancy between counts
reported by ad network versus his own Google Analytics shot up
dramatically (even though cost-per-action remained similar).
• Conversions also dropped dramatically. With deeper digging, he
found the ratio of audience network inventory grew from 5% to
65% of total impressions. Solved by turning off audience network.

How a marketer beat fraud
2016
Still buying
through exchanges
Measure In-Ad
and arrivals On-Site
2017
Buy their own ads
through DSP
Took buying in-
house
2018
Started serving
their own ads
Took ad serving
in-house
2019 …
Buying direct from good publishers

Measure in-ad and on-site
In-Ad
measurement
• Blue means humans
• Red means bots
On-Site
measurement
“increase spend on sources driving more humans
(blue); reduce spend on sources with more bots (red)”

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

Better media = better outcomes
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

Humans click and convert
IN-AD ON-SITE CLICKED
1.2% Human
CTR on ads
44% Human Clicks
to Claim Form

Display 4
2,036 humans
human conversion rate
More humans = more outcomes
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%

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

Dr. Augustine Fou – Researcher
2013
2014
Published slide decks and posts:
http://www.slideshare.net/augustinefou/presentations
https://www.linkedin.com/today/author/augustinefou
2016
2015
2017
20192018

State of NOT Ad Fraud Q2 2019

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