This document discusses ad fraud and ad blocking. It begins with background on the rise of programmatic digital ad spending and fraud. It then defines the two main types of ad fraud as impression fraud and click fraud, both of which use bots. The document discusses how bots range in sophistication and why they are difficult to identify. It also addresses the impact of ad blocking and how fraud and blocking pollute analytics metrics. The document concludes with recommendations for advertisers to measure fraud directly and focus on metrics like conversions that cannot be easily faked.
1. Ad Fraud / Ad Blocking
and Polluted Analytics
December 2015
Augustine Fou, PhD.
acfou@mktsci.com
212.203.7239
2. November 2015 / Page 1marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Brief Agenda
• Ad Fraud Background
• What is Ad Fraud
• Impact of Ad Blocking
• How Fraud Pollutes Analytics
• Low Hanging Fruit – You Can Do NOW!
4. November 2015 / Page 3marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Percent of digital ad spend in programmatic: 70 - 75%
1995
Hundreds of major
sites.
2005
Thousands of
mainstream blogs.
2015
Millions of “long-tail”
websites.
5. November 2015 / Page 4marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Fraud continues upward as digital ad spend goes up
Digital ad fraud
High / Low Estimates
plus best-guess
Published estimates
Digital ad spend
Source: IAB 2014 FY Report
$ billions
E
6. November 2015 / Page 5marketing.scienceconsulting group, inc.
Dr. Augustine Fou
UPDATED: Full Year 2014 Digital Ad Spend – $50B
Impressions
(CPM/CPV)
Clicks
(CPC)
Leads
(CPL)
Sales
(CPA)
Search 38%
$18.8B
Video 7%
$3.5B
Lead Gen 4%
$2.0B
10% Other
$5.0B
Source: IAB, FY 2014 Internet Advertising Report, May 2015
$42.5B
Display 16%
$7.9B
Mobile 25%
$6.2B$6.2B
CPM Performance
• classifieds
• sponsorship
• rich media
$7.0B
7. November 2015 / Page 6marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Bad guys go where the money is – impressions/clicks
Impressions
(CPM/CPV)
Clicks
(CPC)
Search
$18.8B
86% digital spend
Display
$7.9B
Video
$3.5B
Mobile
$6.2B$6.2B
Leads
(CPL)
Sales
(CPA)
Lead Gen
$2.0B
Other
$5.0B
• classifieds
• sponsorship
• rich media
estimated fraud
not at risk
(up from 84% in 2013)
8. November 2015 / Page 7marketing.scienceconsulting group, inc.
Dr. Augustine Fou
0
10
20
30
40
50
60
70
80
90
100
retail finance automotive telecom CPG entertainment pharma travel cons.
electronics
indexed spend share
indexed fraud rate
Ad fraud impacts every industry vertical
High CPC
industries
Source: Ad spend share data from IAB, May 2015 | Fraud rate data from Integral Ad Science Q2 2014 Fraud Report
10. November 2015 / Page 9marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Two main types of fraud and how each is generated
Impression
(CPM) Fraud
(includes mobile display, video ads)
1. Put up fake websites and load
tons of ads on the pages
Search Click
(CPC) Fraud
(includes mobile search ads)
2. Use bots to repeatedly load
pages to generate fake ad
impressions (launder the origins
of the ads to avoid detection)
1. Put up fake websites and
participate in search networks
2a. Use bots to type keywords to
cause search ads to load
2b. Use bots to click on the ad to
generate the CPC revenue
11. November 2015 / Page 10marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Bots are the cause of all automated ad fraud
Headless Browsers
Selenium
PhantomJS
Zombie.js
SlimerJS
Mobile Simulators
35 listed
Bots are made from malware
compromised PCs or headless
browsers (no screen) in datacenters.
12. November 2015 / Page 11marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Bots range from simple to advanced; do different tasks
Malware (on PCs)Botnets (from datacenters)
Toolbars (in-browser)Javascript (on webpages)
13. November 2015 / Page 12marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Bot fraud observed as high as 100%
Source: ANA / White Ops Study Published December 2014 [PDF]
display ads
11%
25%
video ads
23%
50%
sourced traffic
52%
100%
ANA/WhiteOps Study
What We’ve Seen
Case 1 Case 2
15. November 2015 / Page 14marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Bad guy’s advanced bots are not on any industry list
10,000
bots observed
in the wild
user-agents.org
bad guys’ bots
3%
Dstillery, Oct 9, 2014_
“findings from two independent third parties,
Integral Ad Science and White Ops”
3.7%
Rocket Fuel, Sep 22, 2014
“Forensiq results confirmed that ... only 3.72% of
impressions categorized as high risk.”
2 - 3%
comScore, Sep 26, 2014
“most campaigns have far less; more in the
2% to 3% range.”
detect based on
industry bot list
“not on any list”
disguised as normal browsers –
Internet Explorer; constantly
adapting to avoid detection
16. November 2015 / Page 15marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Example of filtering using bot lists – good, but not enough
Google Analytics filters visits
using official bot lists
Bad guy bots are not on those lists
and don’t declare themselves
honestly; they pretend to be browsers
like Internet Explorer, Safari, etc.
“bad guy bots are
not on industry lists”
17. November 2015 / Page 16marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Humans vs “honest” bots vs fraudulent bots
Confirmed humans
• found page via search
• observed events (mouse
click with coordinates)
“Honest” bots
• search engine crawlers
• declare user agent honestly
• observed to be 1 – 5% of
websites’ traffic
Fraud bots
• come from data centers
• malware compromised PCs
• deliberately disguise user
agent as human users
18. November 2015 / Page 17marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Mitigation does not require developers or statisticians
Sites or ad networks that have high
percentage of confirmed bots are
blacklisted from ad-serving or ad
spend to those sites is reduced
In-ad (display ads served)On-site (clients’ websites)
Sources of traffic that have high
incidence of bots are added to ad-
serving blacklists and filtered in
analytics reports
20. November 2015 / Page 19marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Ad blocking user growth continues to soar
Source: PageFair / Adobe Aug 2015
21. November 2015 / Page 20marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Ad blocking as a percent of users
Source: PageFair / Adobe Aug 2015
Europe: 8% - 38%U.S.: 8% - 17%
22. November 2015 / Page 21marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Estimated economic impact of ad blocking
Source: PageFair / Adobe Aug 2015
Global economic impact: $41BU.S. economic impact: $20B
23. November 2015 / Page 22marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Directly measured ad blocking rate
Non-mobile Mobile
Ad Block
No Ad Block 53.6%
15.4%
25.6%
5.4%
29% 21%
Overall Average
79.2%
20.8%
26% Ad Blocking Rate
* percentages represent portion of data from N = 10 million sample
69.0% 31.0%Column Totals
25. November 2015 / Page 24marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Bot activity pollutes quantity metrics
• Bots can be programmed to send as much traffic and generate as many impresisons
and clicks as the advertiser wants
By systematically reducing ad
spend to ad networks and sites
that had the highest bots, and
increasing allocation to premium
publishers, the advertiser
increased ad impressions served
to humans, and lowered those
served to bots.
26. November 2015 / Page 25marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Bot activity pollutes quality metrics
• Bots can manipulate bounce rates, click through rates, time on site, pages per visit;
These engagement metrics appear to be tuned to 47 – 63%; pages per session
averaged 2.03; and time on site was 1 – 2 minutes.
28. November 2015 / Page 27marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Bad guys hide fraud by passing fake parameters
Click thru URL
passing fake source
“utm_source=msn”
fake campaign
“utm_campaign=Olay_Search”
http://www.olay.com/skin-care-
products/OlayPro-
X?utm_source=msn&utm_medium=
cpc&utm_campaign=Olay_Search_D
esktop_Category+Interest+Product.P
hrase&utm_term=eye%20cream&ut
m_content=TZsrSzFz_eye%20cream_
p_2990456911
29. November 2015 / Page 28marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Bad guys fake KPIs, trick measurement systems
Bad guys have higher CTR Bad guys have higher viewability
AD
Bad guys stack
ads above the
fold to fake
100% viewability
Good guys have to
array ads on the
page – e.g. lower
average viewability.
31. November 2015 / Page 30marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Recommendations
1. Don’t panic; but also don’t be complacent – directly
measure the amount of fraud that is impacting your
digital ad spend and continuously mitigate.
2. Focus on the details – don’t assume someone else has
taken care of the problem; take small, simple steps at
low to no cost – e.g. look in analytics for referring sites
that have 100% bounce and 0:00 time on site.
3. Update KPIs to focus on things that are not easily faked
(i.e. don’t focus on number of impressions, clicks, or
visits); focus on “conversion events” like purchases or
other human actions.
32. November 2015 / Page 31marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Normal Weekday vs Weekend Traffic Patterns
weekends weekends weekends weekends
weekdays weekdays weekdays weekdays
Natural website pattern is weekends have lower traffic
33. November 2015 / Page 32marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Typical Hour-of-Day Pattern
humans sleeping
humans awake;
visiting websites
34. November 2015 / Page 33marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Humans Sleep At Night
Hourly traffic charts show lower
traffic at night (as expected
because humans sleep at night)
Unusual traffic patterns with no
normal night time trends visible,
likely due to bot activity
35. November 2015 / Page 34marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Humans Visit via Search
humans find sites via
search, during waking
hours
Bot traffic adds
anomalous spikes to
pattern
36. November 2015 / Page 35marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Search vs Non-Human Traffic
notice the timing
hour-of-day pattern
37. November 2015 / Page 36marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Closeup by Hour of Day
6 am5 am 2 am 3 am 3 am 2 am 3 am
18396 sessions 162 184 178 159 156
Sunday
85% avg bounce rate; 100% peak bounce rate
38. November 2015 / Page 37marketing.scienceconsulting group, inc.
CONFIDENTIAL
These advanced bots also faked some Goal Events
Goal events that are based on page visits and video plays, could be (and were) faked.
page visit goal page visit goal video play goal
39. November 2015 / Page 38marketing.scienceconsulting group, inc.
CONFIDENTIAL
But, there was no motive to fake other goals – e.g. pledges
Other goals like pledges and downloads were not faked (faking downloads would cost
them server resources).
make a pledge curriculum download
“Bots don’t make donations!”
40. November 2015 / Page 39marketing.scienceconsulting group, inc.
CONFIDENTIAL
Despite traffic loss, real human goals did not change
Despite losing all of the traffic from these fake/fraud sites, there was no change to the
number of pledges and downloads, during the same period of time.
102,231 sessions
0 sessions
Conversion event 1
Conversion event 2
42. November 2015 / Page 41marketing.scienceconsulting group, inc.
CONFIDENTIAL
Dr. Augustine Fou – Recognized Expert on Ad Fraud
2013
2014
2015
SPEAKING ENGAGEMENTS / PANELS
4A’s Webinar on Ad Fraud – October
Digital Ad Fraud Podcast – January
Programmatic Ad Fraud Webinar – March
AdCouncil Webinar on Ad Fraud - April
TelX Marketplace Live – June
ARF Audience Measurement – June
IAB Webinar on Ad Fraud / Botnets - September acfou@mktsci.com | 212.203.7239
43. November 2015 / Page 42marketing.scienceconsulting group, inc.
CONFIDENTIAL
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.