DMA_PPT_Analytics FINAL Sept 2017

DMA Analytics Community Monthly Webinar Series:
Update on Ad Fraud: How Bots are Skewing
Your Analytics and ROI
Led by
Dr. Augustine Fou

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Update on Ad Fraud
First , The Good News
September 2017
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239

“there is light at the end of the
tunnel … as long as we stay vigilant,
work hard, and dispel assumptions.”

September 2017 / Page 5marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Some marketers stopped buying poop water
“1) start with ‘poop water’ and filter it before
you drink it?, or 2) start with fresh water?”
Good publishers are “fresh water.”

P&G: slash $140M, 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

Chase: cut 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)

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”

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%
programmati
c

$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%
programmati
c
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%
programmati
c
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)

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.c
om/skin-care-
products/OlayPro-
X?utm_source=elle
&utm_medium=di
splay
+ +
“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

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

Tag placement yields opposite results
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.”

… if you don’t have 100%
measurement and very
detailed reports.

You’re NOT getting
what you paid for…

Thought you bought ESPN? Nope
publisherA.com
ALL fake inventory because, PublisherA
does NOT sell any ads on any exchanges!
“Fake sites must pretend to be mainstream
ones in order to sell inventory.”

Thought you bought reach? Nope
$1 CPM
Top 10 sites = 66% of
imps
$5 CPM
imps
$0.50 CPM
imps
$10 CPM
imps
Majority of your ads ran on 5-10 sites/apps

Thought your ads ran during waking hours?
Most of budget wasted
between 12a – 4a; to bots
98% impressions blown
in 1st hour (12a-1a)
HOURLY CHART

Thought your ads were geotargeted?
Not Normal – in both campaigns
1. 100% mobile apps; 100% Android; same top 15 apps in both markets
2. 100% of impressions generated between 4a – 5a local time
3. 100% fake devices; 15 unique devices generated top 95% impressions
4. 100% data center traffic, randomized through residential proxies

Thought fraud filters reduced fraud? Nope
1. Fraud filters are no better
than manual blacklists
2. In some cases it’s worse
when filter is on
3. Using fraud filters adds 20
– 24% to costs; manual
blacklists are free

End of month traffic/impressions fulfillment
Caused by bots
Caused by humans
A
B

Paid for mobile, 3 bad apps ate most of budget
com.jiubang com.flashlight com.latininput
3 bad apps
on fake
devices at
75% of your
budget

1 billion mobile display ads – 43% overall fraud
66% avg fraud
18% avg fraud
1. 9% of the apps (blue dots) caused 52% of total impressions, 80% of fraud impr.
2. 91% of apps caused 48% of the total impressions, 20% of fraud impressions
3.Overall average – 43% of impressions were fraudulent
• 1 billion mobile display impressions
• Nearly 1,000 apps cross referenced with SDK
1 (52% of imps) 2 (48% of imps)

Humans (dark blue) vs Bots (dark red)
Good Publishers Ad Networks Open Exchange
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

Thought your $1 was “working media”? Nope
“When buying programmatic exchanges, only 57 – 63 cents
of every $1 spent goes towards working digital media.”
“mark up”
“working media”
“working media”
“mark up”

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

Digital Ad Productivity – for every $1 spent
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”

Bots Mess Up Your Analytics

Conversion rates artificially depressed
7% conversion rate 13% conversion rate
artificially low actually correct

Quantity metrics are easily “tuned”
click on links
load webpages tune bounce rate
tune pages/visit
“bad guys’ bots are advanced enough to fake most metrics”

Conversion metrics easily “hacked”
Programmatic display
(18-45% clicks from advanced bots)
Premium publishers
(0% clicks from bots)
0.13% CTR
(18% of clicks by bots)
1.32% CTR
5.93% CTR
Campaign KPI: CTRs

Fake clicks mess up CTRs, hide in averages
Line item details
Overall average
9.4% CTR
“fraud hides easily
in averages”

Fake demographic information

Suspicious web and mobile placements
.xyz domains suspicious mobile apps

Bots easily trick AI/ML algorithms
“Humans are hard to predict …
… but bots give you beautiful signals.”
Source: Claudia Perlich, PhD. Data Scientist, Dstilllery

What Savvy Data-Driven
Marketers do …

Do a digital media health check
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?”

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

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

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’.”

Savvy data-driven marketers …
1. Stay vigilant
• Check your own analytics for anything suspicious
2. Work hard
• Actively scrub your own campaigns when you see
something suspicious
3. Dispel assumptions
• Don’t’ assume data-driven marketers are immune to
ad fraud; know how the analytics can be messed up
and run your own experiments to prove value.

About the Author
September 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/presentation
s
https://www.linkedin.com/today/author/augustinefo
u
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.

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QUESTIONS?
Use the “Questions” feature on the control panel.
Many thanks to our speaker for sharing his knowledge with us today.
Dr. Augustine Fou augustine.fou3@gmail.com
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based story to share? We have sponsorship opportunities, too. Contact us at
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DMA_PPT_Analytics FINAL Sept 2017

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DMA_PPT_Analytics FINAL Sept 2017