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Context of Fraud in Digital Advertising Ecosystem

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So much misinformation out there about digital ad fraud. Most numbers come without context. And that is a problem. Here are some charts updated with the latest data; internally consistent.

Publié dans : Marketing
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Context of Fraud in Digital Advertising Ecosystem

  1. 1. Context of Fraud in Digital Ad Ecosystem April 2017 Augustine Fou, PhD. acfou [at] mktsci.com 212. 203 .7239
  2. 2. Industry Context
  3. 3. April 2017 / Page 2marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Total digital opportunity: search + display display spend left for good publishers $83B digital spend (2017) Source: eMarketer March 2017 Search Spend $40 $40 Display Spend Other $21$30 $3 Google Search FB+Google Display $4E $11E CPC Fraud CPM Fraud (75% of search) (52% of display) $8$6 $29 (outside Google/Facebook) $33 programmatic $24 private exchange$9 open
  4. 4. April 2017 / Page 3marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Top good domains vs “sites that carry ads” 100% bot pageviews on “fraud sites” “sites that carry ads” Source: Verisign, Q4 2016 329M domains $83B digital Google Search FB+GOOG Display $29 billion “sites you’ve heard of” WSJ ESPN NYTimes Economist Reuters Elle top 1 million + next 10 million 159 million carry adsno ads 3% “Digital spend outside of Google/Facebook”
  5. 5. April 2017 / Page 4marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Display opportunity for good publishersAdvertisers 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
  6. 6. April 2017 / Page 5marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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 Every industry is affected, CPC vs CPM High CPC industries Hit with CPC Fraud Source: Ad spend share data from IAB, May 2015 | Fraud rate data from Integral Ad Science Q2 2014 Fraud Report High Spend industries Hit with CPM Fraud
  7. 7. Fraud comes in large numbers
  8. 8. April 2017 / Page 7marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Ad fraud comes in large numbers… Increased CPM prices by 800% Decreased impressions 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
  9. 9. April 2017 / Page 8marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Single botnet steals 15% of video 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
  10. 10. April 2017 / Page 9marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 40-50% web traffic is NHT (Non-Human Traffic) Distil Networks March 2017 – 39% botsIncapsula Dec 2016 – 52% bots
  11. 11. “The equation of ad fraud is simple: buy traffic for $1 and sell ads for $10 you make $9 of pure profit.”
  12. 12. Main targets of ad fraud
  13. 13. April 2017 / Page 12marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou CPM and CPC buckets are most targeted Leads (CPL) Sales (CPA) Lead Gen $2.0B Other $5.0B • classifieds • sponsorship • rich media Impressions (CPM/CPV) Clicks (CPC) Search 27%Display 10% Video 7% 60% fraud 40% fraud 80% fraud Mobile 47% 50% fraud 91% digital ad spend Source: IAB 1H 2016 Report mobile display mobile search
  14. 14. April 2017 / Page 13marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Two key ingredients of CPM and CPC Fraud 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 fake users (bots) to repeatedly load pages to generate fake ad impressions 1. Put up fake websites to participate in search networks 2. Use fake users (bots) to type keywords and click on them to generate the CPC revenue screen shots of fake sites
  15. 15. Fake Websites (cash-out sites)
  16. 16. April 2017 / Page 15marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fake sites have no content, no humans Identical sites made by template Alphanumeric domains They can sell ad “inventory” at low prices; still make huge profits Source: Sadbottrue.com
  17. 17. April 2017 / Page 16marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Ad dollars diverted to very low CPMs $40 Display Spend $16 FB Display $11E $5Google Display CPM Fraud $8 $33 programmatic $24 private exchange$9 open 100% bot traffic “fraud (cash out) sites” • No content • Stolen content • Fake content low CPMs higher CPMs “sites with real content that real humans want to read” Source: DCN/ WhiteOps 2015
  18. 18. Fake Visitors (bots)
  19. 19. April 2017 / Page 18marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Bots are automated browsers used for fraud Headless Browsers Selenium PhantomJS Zombie.js SlimerJS Mobile Simulators 35 listed
  20. 20. April 2017 / Page 19marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Bots range in sophistication, and therefore cost Javascript on page or scripts Sophisticated (29%)Moderate (46%)Simple (25%) Headless browsers in data centers Malware on humans’ devices (residential) Less sophisticated More sophisticated Source: AdAge/Augustine Fou, Mar 2014 Source: Forensiq Source: Augustine Fou, Oct 2015 1 cent CPMs 10 cent CPMs 1 dollar CPMs Source: Distil Networks 2017
  21. 21. April 2017 / Page 20marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Mobile fraud doesn’t even need bots Bad apps load tons of impressions in background Source: Forensiq Fake mobile devices install apps and interact w/ them Download and Install Launch and Interact
  22. 22. Directly measured examples
  23. 23. April 2017 / Page 22marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Some campaigns have very little humans (blue) Phone calls as conversion events Comparing five paid display sources
  24. 24. April 2017 / Page 23marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou But only humans convert, bots don’t … 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
  25. 25. April 2017 / Page 24marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou About the Author April 2017 Augustine Fou, PhD. acfou [at] mktsci.com 212. 203 .7239
  26. 26. April 2017 / Page 25marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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
  27. 27. April 2017 / Page 26marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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.