You're Not Getting What You Bought

You’re Not Getting
What You Paid For
September 2017
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239

Marketers run their
own ROI experiments

September 2017 / Page 2marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Chase: 99% reach had 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: $140M in digital, 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

Restoration Hardware: cut all keywords
“[W]e’ve found out that 98%
of our business was coming
from 22 words. So, wait, we’re
buying 3,200 words and 98%
of the business is coming from
22 words. What are the 22
words? And they said, well, it’s
the word Restoration
Hardware and the 21 ways to
spell it wrong, okay?
Immediately the next day, we
cancelled all the words,
including our own name.”
Source: BusinessInsider, Sept 2017

Uber Sues Mobile Agency for Ad Fraud
“Between 2015 and the first quarter of 2017,
Uber paid more than $82.5 million for
the ad effort coordinated by Fetch, court
documents show.
Uber alleges to have found… a Fetch
transparency report that showed the number of
weekly reported clicks on Uber ads on one
website was nearly equal to the site’s monthly
active users.
Uber was spending millions of dollars a week on
mobile ad inventory that was “purportedly
attributable to hundreds of thousands (even
millions) of Uber App installs per week,”
according to the complaint. However, when the
mobile ad effort was suspended, Uber said it
saw “no material drop in total installations.”
Source: WSJ, Sept 2017

If you don’t have 100%
measurement or 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
$0.50 CPM
$10 CPM
Majority of your ads ran on 5-10 sites/apps

In daily reports, you wont notice…
Most of budget wasted
between 12a – 4a; to bots
98% impressions blown
in 1st hour (12a-1a)
HOURLY CHART

Fraud filters don’t reduce fraud
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

Paid extra for geotargeting, but it’s faked
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

Paid extra for targeting, but didn’t work
“Verified Bots” “Verified Humans”
“Fraud-free Apps”Control: No Targeting
+$0.25 data CPM
+$0.25 data CPM+$0.25 data CPM

Targeting recent purchasers, didn’t work
“Frequent Buyers” “Heavy Buyers”
“Recent Purchaser - Books”Control: No Targeting
+$1.00 data CPM
+$1.00 data CPM+$1.75 data CPM

90-99% of geolocation bad or faked
Source: Placed
Source: SafeGraph

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

34 Mobile Networks >50% Fraud
Source: June 2017, Tune
average 20% fraud
100% fraud
> 50% fraud

Some ads are called without webpages
“Naked ad calls” are ad impressions served without webpages

Fraud on such a massive scale…
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

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%
programmatic

$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%
programmatic
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%
programmatic
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)

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

Just because you can’t measure it
100% fraud
> 50% fraud
… doesn’t mean it’s not there.

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/presentations
https://www.linkedin.com/today/author/augustinefou
2016
2015

You're Not Getting What You Bought

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