Digital ad fraud impact on class action notice industry

October 2018 / Page 0
linkedin.com/in/augustinefou
linkedin.com/in/jeanne-finegan
marketing.scienceconsulting group, inc.
Digital Ad Fraud
Impact on Class Action Notice
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239
Jeanne Finegan, APR
jfinegan [at] hfmediallc.com
503. 579. 0746

Context and
Background

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

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 billion Digital Spend Source: eMarketer March 2017
$10
1% of
impressions
$19
99% of
impressions

$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
0.3%
no ads
carry ads
160X more
78%
programmatic
est. 1 million

$29
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
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”
78%
programmatic

Examples of fake sites/apps
1221e236c3f8703.com
62b70ac32d4614b.com
a6f845e6c37b2833148.com
da60995df247712.com
d869381a42af33b.com
a1b1ea8f418ca02ad4e.com
1de10ecf04779.com
2c0dad36bdb9eb859f0.com
a6be07586bc4a7.com
fe95a992e6afb.com
42eed1a0d9c129.com
da6fda11b2b0ba.com
afa9bdfa63bf7.com
739c49a8c68917.com
baa2e174884c9c0460e.com
d602196786e42d.com
153105c2f9564.com
8761f9f83613.com
20a840a14a0ef7d6.com
31a5610ce3a8a2.com
5726303d87522d05.com
3ac901bf5793b0fccff.com
b014381c95cb.com
2137dc12f9d8.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
33ae985c0ea917.com
02aa19117f396e9.com
f8260adbf8558d6.com
9376ec23d50b1.com
pushedwebnews.com
a0675c1160de6c6.com
0f461325bf56c3e1b9.com
850a54dbd2398a2.com
com.dxnxbgj.mkridqxviiqaogw
com.obugniljhe.fptvznqwhmcjm
com.bpo.ksuhpsdkgvbtlsw
com.rlcznwgouw.vvtexstbfttngc
com.kasbgf.sbzwtgpcbjexi
com.bprlgbl.vbze
com.zka.lzhsoueilo
com.alxsavx.mizzucnlb
com.jxknvk.lrwfdfirdzpsw
com.tvwvqbt.wbshaguqy
com.iwnxtpahcu.leyuehdwdbb
com.okf.rhvemtykfibzpxj
com.obpmirzste.ldsjpv
com.zmm.shmxvjxnsagndui
com.nqzwr.leusrmpmsq
com.rced.zcdsglptpdlwpu
com.kerms.ehlsgnc
com.cmia.iabhheltm
com.skggynmtx.tyyjnwpefvqtll
com.kgdtltnuv.hayvfhob
com.ztzsiqg.dyojlxdscxws
com.xlwuqe.ddrdhsuosbn
com.rkrhmzee.wjcoznxu
com.ebhzb.hbzvomzpcctovj
Fake sites Fake sites Fake apps
… they can sell ad
“inventory” at low prices

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

Fraud Comes in Large
Numbers

How bad guys commit ad fraud
1. set up
FAKE SITES
2. buy
FAKE TRAFFIC
3. sell
FAKE ADS

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 app install fraudSource: October 2018,
Tune
average 20% fraud
100% fraud
50% fraud24 billion clicks on
700 mobile networks

(2017) Mobile display ad fraud
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

(2018) Mobile display ad fraud
One example was an Android app called
MegaCast, which was found to be displaying the
unique ID of others apps to attract bids for ads.
[Google] "confirmed the traffic from the apps
"seems to be a blend of organic user traffic and
artificially inflated ad traffic, including traffic
based on hidden ads".
The scheme reportedly involved 125 Android apps
and websites. … the fraudsters buy legitimate
Android apps with an established reputation and
then … blend bot- and human-generated traffic
to evade ad-fraud detection.
The TechSnab malware is usually bundled with
free, third-party apps and is installed as a
browser extension. Users would discover an
infection if they see pop-ups, pop-unders and
various other ads marked 'TechSnab'.
Source: Buzzfeed News, Oct 2018

Fraud is successful …

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”

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)

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

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

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.”

Measurements entirely wrong
Incorrect IVT Measurement
Source 3 - in ad iframe, badly sampled
Sources 1 and 2
corroborate

Legit sites incorrectly marked
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.

Beware mobile!

Mobile is 57% of digital spend
Source: IAB Full-year 2017 Digital Advertising Report

Beware mobile… MORE rampant fraud
Bad apps load impressions
in background, not in use
Source: Forensiq
Fake mobile devices install
apps and interact w/ them
Download and Install
Launch and Interact
“more money; less measurable”

9% of apps caused 80% of fake impressions
1 (52% of impressions) 2 (48% of impr)
66% avg fraud
18% avg fraud
1. 9% of the apps caused 52% of impressions; 66% outright fraud
2. Remaining 91% of apps caused 48% of impressions, 18% outright fraud

3 bad apps eat 75% of mobile budget
com.jiubang com.flashlight com.latininput
75% of the
dark red

Mobile apps loading webpages
“fraud sites’ traffic comes from apps that load hidden webpages”
Openly selling on LinkedIn

Top mobile apps by ad revenue
Top mobile apps
by ad revenue
Are entirely
different than
ones humans
spend the most
time with

Productivity of Digital Ads

Fraud, lack of viewability limit productivity of ads
100 million impressions
- 36%
- 54%
- 23%
- 41%
bot impressions
Source: WSJ One-Third of Traffic is Bogus
not-in-view
Source: comScore June 2013
ad-blocked by user
Source: PageFair, August 2013
not on target
Source: Nielsen Sept 2014

Top priorities for increasing digital ad productivity
Served ad impressions
-11%Display ads
-23%Video ads
-52%
NHT(“bots”)
Sourced traffic
Source: WhiteOps / ANA Dec 2014
Display ads
Source: Google Nov 2014
-36% Average NHT (bots)
Viewability
-56%Video ads
-80%Views Source: RealVu 2014 “1 in 5 ads are viewable”
-26%
Ad Block usage
Source: Marketing Science 2014
-34.5%
Display ads
Source: PageFair 2014
AdBlocking
-54% Source: comScore Jun 2013
-60% Average Viewability

Case Study: Class Action
Notice Campaign

“humans fill out claim forms;
bots don’t”

Executive Summary
• It is imperative to make sure that digital ads are seen by
humans, not bots; because only humans fill out settlement
claim forms, bots don’t.
• Getting ads in front of humans starts with good media
buying; the quality is evident in the data/charts
• By measuring for ad fraud and bots and quickly optimizing
for more human audiences, the performance of the digital
campaigns yield better outcomes
• Not measuring digital campaigns inevitably result in wasted
ad spend and ads not being shown to humans and class
members are deprived of Due Process.

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

Compare quality across channels
Marketer 1
• Blue means humans
• Red means bots
Marketer 2
“what is the quality of traffic arriving on your site
from various sources – organic and paid?”

Case Study – cleaning up a campaign in-flight
Campaign Launch Week 3Week 2
Benchmarking the start of
the campaign.
Further optimization to
reduce specific bots (red)
Eliminating problematic
ad networks
30% bots
15% bots
3% bots

Facebook human audience vs Ad Networks
We measured the landing page; click throughs from each media source.
Source=Facebook Source=Twitter Source=Network A
Source=Network C Source=Network DSource=Network B

Measuring clicks to the claim site
Period 2
Good media planning
Buying on open exchanges
“Clean media
buying leads to
more humans
(blue) arriving on
the settlement
claim site.”
Humans (blue)
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 more humans
(dark blue)
Conversion actions (calls)
are well correlated to
humans; bots don’t call

Display 4
2,036 humans
human conversion rate
Adjust budgets to real conversions
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%

“Best Practicable” already in practice
• Measure every ad impression to determine quality (human
audience) of media sources
• Measure landing pages to re-verify quality of users that click
through; DON’T assume fraud detection tech catches all fraud
• Using 1-2x per day frequency cap to minimize repeat bot
impressions; friendlier for humans too [Not a campaign cap
of 1x or 2x]
• Turn off offending domains and apps that are sending
irregularly high amounts of traffic or bots
• Reduce spend on ad networks that do not allow complete
tracking and measurement of impressions
• Optimize toward highest real conversion sources

About the Authors

Jeanne Finegan, APR – Class Action Notice Expert
Follow me on LinkedIn
Further Reading:
Creating a Class Notice Program that Satisfies Due Process,
Law360, New York, (February 13, 2018 12:58 PM ET).
https://www.linkedin.com/pulse/dont-turn-blind-eye-bots-ad-
fraud-reality-digital-jeanne-finegan-apr/
https://www.linkedin.com/pulse/class-action-notice-brand-
safety-why-both-sides-v-jeanne-finegan-apr/
https://www.linkedin.com/pulse/notice-any-changes-jeanne-
finegan-apr/
Jeanne Finegan, APR, is President and Chief Media Officer of HF
Media, LLC. is a member of the Board of Directors for the
prestigious Alliance for Audited Media (“AAM “) and a
distinguished legal notice and communications expert with more
than 30 years of communications and advertising experience.
During her tenure, she has planned and implemented over 1,000
high-profile, complex legal notice communication programs with
extensive international notice experience spanning more than 140
countries and over 40 languages.
Ms. Finegan has provided expert testimony before Congress on
issues of notice, and expert testimony in both state and federal
courts regarding notification campaigns. She has lectured,
published and has been cited extensively on various aspects of
legal noticing, product recall and crisis communications. She has
served the Consumer Product Safety Commission (CPSC) as an
expert to determine ways in which the Commission can increase
the effectiveness of its product recall campaigns. Further, she has
planned and implemented large-scale government enforcement
notice programs for the Federal Trade Commission (FTC) and the
Securities and Exchange Commission (SEC). Most recently, she was
a lead contributing author for Duke University's School of Law,
"Guidelines and Best Practices Implementing 2018 Amendments
to Rule 23 Class Action Settlement Provisions."

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

Digital ad fraud impact on class action notice industry

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Similar a Digital ad fraud impact on class action notice industry

Similar a Digital ad fraud impact on class action notice industry (20)

Más de Dr. Augustine Fou - Independent Ad Fraud Researcher

Más de Dr. Augustine Fou - Independent Ad Fraud Researcher (12)

Último

Último (20)

Digital ad fraud impact on class action notice industry