B2C Marketers Anti Ad-Fraud Playbook

October 2018 / Page 0marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
B2C MARKETER’S
ANTI-AD FRAUD PLAYBOOK
Augustine Fou, PhD.
acfou [at] mktsci.com

“Digital ad fraud is literally the
bad guys’ ATM – it spits out cash.
And every year $300 billion of
marketers’ digital ad budgets refills it.”

Executive Summary
Marketers can take control and fight fraud with analytics/insights
1. Marketers can run one or more of the following
“plays” themselves without any specialized fraud
detection tech; see if they are exposed to ad fraud.
2. Each “play” tells marketers where to look and what
to look for in their own reports and analytics.
3. Marketers can then take specific actions if they find
fraud and decide they no longer want to buy it.

Ad fraud is at all-time highs
There’s $100B in digital ad spend to steal from, year after year
U.S. Digital Ad Spend
($ billions)
Actuals Projected
Digital Ad Fraud
($ billions)
($300B worldwide)

Bad guys easily avoid detection
Blocking of tags, altering measurement to avoid detection
Detection Tag Blocking— analytics
tags/fraud detection tags are accidentally
blocked or maliciously stripped out
“malicious code manipulated data to
ensure that otherwise unviewable ads
showed up in measurement systems
as valid impressions, which resulted in
payment being made for the ad.”
Source: Buzzfeed, March 2018

Easy to buy traffic, impressions
Many sources to buy “traffic” that appears human, not invalid
Choose Your “Traffic Quality Level”
“Valid traffic” goes
for higher prices

Anti-Fraud Playbook
(Tech + Technique)

Super high win rates
Normal “win rates” (impressions won / bids) are in the 10% range
Where to find this?
DSP report (your buying
interface); have your
agency provide on domain-
by-domain basis.
What to look for?
Win rates that are
significantly above 50%
with substantial volume.
What to do?
Examine sites and see if
they appear fraudulent in
other ways; if so, turn off
line item in the DSP.

Bids won vs ads served
For each “bid won,” an “ad impression” should be served
Where to find this?
Compare your DSP reports for
bids won, by domain, to your
ad server reports, by domain.
What to look for?
Data discrepancies where the
impressions served is far lower
than the bids won, by domain.
The more fraudulent the site
the higher this disparity.
What to do?
Identify domains that have
greater than 10% discrepancy
and study them further; turn
off if you agree it is fraud.

Ad serving volume by hour
With hourly detail, you see most ads are blown out before 4 am
Where to find this?
Your ad server report; be sure
quantities are reported
by hour.
What to look for?
If all of the volume is spent in
the first hour or during
sleeping hours, you have no
impressions left for the day –
your budget is wasted.
What to do?
Turn off overnight hours and
frequency cap by site and user
to avoid massive spikes in
volume that eat up all your
budget.

Ad serving volume by site
Details show you if your ads are too concentrated on a few sites
Top 10 sites = 66%
of impressions
Top 10 sites = 74%
of impressions
Top 5 sites = 100%
of impressions
Where to find this?
Your ad server report; be sure
quantities are reported
by domain.
What to look for?
If most of your impression
volume is concentrated in a
small number of sites or apps,
then you did not achieve
reach.
What to do?
Add frequency and quantity
caps by site to ensure your ads
are more widely distributed to
other sites where your target
users are.

Over-frequency to devices
When tons of ads are served to the same device, it’s likely fraud
Where to find this?
In your own analytics, look for
anomalies like massive changes or
high concentrations (like all Android
8.0.0 devices – see chart to the left)
What to look for?
If a small number of devices get a
large number or high percentage of
ad impressions (too concentrated)
something is suspicious.
What to do?
If you identify over-frequency, check
that you have frequency caps set by
user, by domain, or by time period. If
it continues to happen, identify the
domains/sellerIDs and turn off.

Ads.txt – who got paid?
Domain-based reports are useless, insist on sellerID based reports
Where to find this?
You usually get domain-
based placement reports;
insist on getting sellerID-
based reports.
What to look for?
Is the sellerID the correct one
for the domain? (verify
against ads.txt)
What to do?
• Insist on getting sellerID-
based reports.
• Turn off that line item to
stop buying from that
sellerID.
bid request
fakesite123.com cookie
ft.com
bid won
ad served
PROGRAMMATIC SEQUENCE
spoofed domain
156400
real sellerID

Abnormal traffic patterns
All sites show abnormal surge in traffic on last day of the month
Where to find this?
Ad server or campaign
reports; be sure to isolate
individual sites
What to look for?
Are there abnormal surges
in volume on the last day
of the month? Tell-tale sign
of sourced traffic (non-
human)
What to do?
Identify the sites that
exhibit these patterns and
turn them off in your
campaign interface.

Abnormal data consistency
Sites with 100% “social traffic”, 100% Android, 100% same browser
Where to find this?
Your own ad server records or
log files; also fraud detection
data files with supporting details
What to look for?
Abnormal consistency like 100%
Android, 100% same browser
version, 100% of traffic is from
Facebook, etc.
What to do?
These sites use these methods of
cheating that are not typically
caught by bot detection; decide
whether you want to keep
buying from them. Turn off in
campaign interface.

Progressively clean campaigns
Obvious fraud still gets through; analyze and turn off the fraud
Launch Week 3 and beyondWeek 2
Initial baseline
measurement
Measurement after
first optimization
After turning off more “sites
that cheat”
30% bots
15% bots
3% bots

Shift budgets to higher quality
Some channels have more humans, shift budget to these
• Blue means humans
• Red means bots
Where to find this?
Your own bots versus humans
measurement, with detailed
and complete supporting data.
What to look for?
Be sure to measure for both
bots and humans; rank order
channels, sources, or sites by
highest humans first.
What to do?
Some channels have more
humans (blue) and others have
more bots (red); shift budgets
during campaign to more
human channels.

About the Author

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

B2C Marketers Anti Ad-Fraud Playbook

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