Case Studies of Reducing Bots Fraud by Augustine Fou

Case Studies of
Reducing Bots/Fraud
April 2017
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239

April 2017 / Page 2marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Advertiser myths about ad fraud
1. Fraud is 2% and ‘on the wane’
No. It’s because you’re catching less of it, because bots are
better at hiding (think Methbot)
2. Fraud is ‘priced in’
No. You may be paying 1/10th the CPM, but you’re buying
10x more impressions; an ad shown to a bot is useless.
3. We’ve got fraud-free guarantees
Nice. But this assumes the detection tech can detect it;
what if it can’t detect it, or it gets tricked? Is it fraud free?

Reduce bots/NHT in display campaigns
Period 1 Period 3Period 2
Initial baseline
measurement
Measurement after
first optimization
After eliminating several
“problematic” networks

“Humanness” of different sources
Organic sources
have more humans
(dark blue)
Conversion actions (calls)
are well correlated to
humans; bots don’t call

Higher quality means lower cost per 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.

Better outcomes from better media
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

Make analytics more accurate and clean
7% conversion rate 13% conversion rate
artificially low actually correct

Publisher myths about ad fraud
1. Fraud doesn’t affect us, there’s low bots on our site
Bots don’t come in large quantities to your sites; they just
collect a cookie and go elsewhere to create ad impressions
2. We have bot protection on our site
Nice. But what if bad guys pretend to be your site by
passing fake data, and put your brand reputation at risk?
3. We have high quality traffic
Great. We believe you. But what if bot detection tech
accuses you of high bots (falsely)?

Good publishers take action 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
10% red
-7% (filter data
center traffic)
3% red
“Filter data center traffic and not call the ads”

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

Defend against incorrect measurements
NHT counts higher
than ad impressions
1. In-ad measurement
can’t look outside iframe
ad tag / pixel
(in-ad measurement)
Challenges to
measurement accuracy
2. Extrapolations from
small sample or short
time-based sample
Sources 1 and 2
corroborate
Source 3
completely off

Good publishers reduce data leakage
specialized audience:
oncologists
Journal of Clinical Oncology
specialized audience can
be targeted elsewhere
“cookie matching”
(by placing javascript on your site)
ad revenue diverted away

$83B digital spend (2017)
CPM and CPC fraud in context of digital spend
Source: eMarketer March 2017
Search Spend
$40 $40
Display Spend Other
$16$30
$3
Google Search FB Display
$4E $11E
What’s left for good publishers
CPC Fraud
$5 Google Display
CPM Fraud
(75% of search) (40% of display)
$8$6
60% fraud
$29
(outside Google/Facebook)
(display opportunity)
Source: eMarketer March 2017
• $7.2B out of $12B (2016)
• $11B out of $19B (2017)40%

“sites that
carry ads”
Top good domains vs “sites that carry ads”
Source: Verisign, Q4 2016
329M
domains
$80B
search + display
Google
Search
FB+GOOG
Display
$29
billion
“mainstream sites
you’ve heard of”
WSJ
ESPN
NYTimes
Economist
Reuters
Elle
top 1 million + next 10 million
159 million unknown sites
100% bot
pageviews on
“fraud sites”
99% human
pageviews are on
“sites you’ve heard of”
3%
carry adsno ads

Supply chain costs, ad blockingAdvertisers
Publishers
are left with 30%
Bad Guys
siphon dollars OUT
of the ecosystem
30% ($6B)
60% ($11B)
Ad Blocking
users use ad blocking to
protect themselves
10% ($2B)
Ad Tech
“plumbing” and verification
Source: The Guardian, Oct 2016
$5B to Google Display
$16B to Facebook Display
Display Spend$40B
DisplaySpend

Example – 92% impressions cleaned up
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

Single botnet steals 15% of video ad spend
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

About the Author
April 2017
Augustine Fou, PhD.
acfou [at] 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 – October 2015
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.

Case Studies of Reducing Bots Fraud by Augustine Fou

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Case Studies of Reducing Bots Fraud by Augustine Fou