I stumbled upon the world of Adfraud recently while doing some research on a personal assignment and was amazed at the magnitude of the issue is.
This presentation touches upon my findings, my understandings and finally my attempt to visualize a tool which can help deal with adfraud.
Feel free to use the presentation. Although giving due credit would be appreciated.
8. Ghost Sites Ad Stacking Pixels
Stuffing
Ad Injection
Tiny 1x1 pixels which are
virtually not visible
Fake websites created just for
the reason of diverting traffic
and displaying ads
Injecting ads through
toolbars and extensions
Placing multiple ads on the
top of each other in a single
ad slot.
9. Click Farms Brower
Frauds
Domain and
Site Spoofing
Re-targeting
fraud
Infecting browsers with
malicious code and forcing
them to load certain
webpages
Hiring people to click on ads and
fil forms
Changing the url codes of the
websites to a fraudulent
website
Bots mimicking consumer
behaviour by visiting
websites and then being
shown retargeted ads
10. Ad Fraud in Mobile Advertising
The Next
Battleground
11. 34% of programmatic mobile ad
impressions are “at risk of being
fraudulent”.
Mobile botnets are costing
advertisers $1 billion in ad fraud,
study shows
The Common Types
Mobile ad spending is set to reach
$100 billion in 2016, according to an
eMarketer report.
ClickImpression
App Install In-App
13. < With the increase in digital
marketing budgets there are higher
chances of fraudulent activities
resulting in loses and wrong KPIs.
Creating a need for a systematic tool
to deal with it >
14. Highest concern rate in
between marketers and
agencies
Ad Fraud takes $1
for every $3 spent
on Digital Ads
25% of video ads
impressions is
fraudulent
25% of publishers
have no way to detect
fraudulent traffic
19. < Insights > < Identification > < Elimination >
Complex algorithms to verify traffic and
identify manipulative bot-like activity
Identifying and creating databases
consisting of fraudulent IPs and
webpages
Scanning insights, to understand
fraudulent activities
Identifying non human traffic through
minute by minute verifications and
evaluation
Finally trying to eliminate the frauds
Going through the past campaigns
for similar learnings
Reporting the fraud detected
Recommendations on the
measures that can be taken for
future campaigns
20. < Product Features >
< APIs and
Tags >
< IP Blocklists >
< Multi-stage
Evaluation >
< Analytics
Integration >
< Traffic Pattern
Recognition >
< Human
Verification >
21. < Resources >
< Developers
And Engineers >
< Data Scientists
and Analytics >
< Digital
Marketing
Executives >
< Designers/UI/UX
Team >
< Managers > < Support >
