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State of Digital Ad Fraud August 2017

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Digital ad fraud is so bad, I could not wait another quarter to update the numbers, based on recent research. The difference in quality is so dramatic, it is worthwhile just buying clean to begin with. Savvy marketers are already starting to do this.

Published in: Marketing

State of Digital Ad Fraud August 2017

  1. 1. State of Digital Ad Fraud August 2017 Augustine Fou, PhD. acfou [at] mktsci.com 212. 203 .7239
  2. 2. “It has gotten so bad, I can’t even wait a quarter to edit the estimates of fraud.” It’s 60 – 100% fraud, with an average of 90%; but it is not distributed evenly.
  3. 3. August 2017 / Page 2marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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
  4. 4. August 2017 / Page 3marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fraudsters successfully sell ads… how? 100% viewability (but, it’s fake) AD Stack ads all above the fold to trick detection 0% NHT (but, it’s fake) Buy traffic that is guaranteed to pass fraud filters clean placement (but, it’s fake) Pass fake source to trick reports of placement details http://www.olay.co m/skin-care- products/OlayPro- X?utm_source=elle &utm_medium=dis play + + “by tricking measurement and reporting”
  5. 5. August 2017 / Page 4marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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
  6. 6. Context and Sizing
  7. 7. August 2017 / Page 6marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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
  8. 8. August 2017 / Page 7marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou $29 (outside Google/Facebook) 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
  9. 9. August 2017 / Page 8marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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 (outside Google/Facebook) 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”
  10. 10. August 2017 / Page 9marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Examples of fake sites, fake apps Fake Sites (10s of millions) Source: Sadbottrue.com Fake Apps (millions)
  11. 11. August 2017 / Page 10marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Just because you can’t measure it 100% fraud > 50% fraud … doesn’t mean it’s not there.
  12. 12. August 2017 / Page 11marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Plainly incorrect measurements Incorrect IVT Measurement Sources 1 and 2 measured on-page Source 3 in foreign iframe 1x1 pixel incorrectly reported as 100% viewable Incorrect Viewability Measurement
  13. 13. “just because you can’t detect it (fraud), doesn’t mean it’s not there….” … “or worse, you detect it wrong and think it’s clean.”
  14. 14. Marketers’ Own Experiments ..
  15. 15. August 2017 / Page 14marketing.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)
  16. 16. August 2017 / Page 15marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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
  17. 17. Three buckets …
  18. 18. August 2017 / Page 17marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Digital Ad Productivity - $1 spent Good Publishers Ad Networks Open Exchange 91% viewable 40% fees 40% fees 30% NHT 70% NHT No fees 3% NHT 97% Not NHT 70% Not NHT 30% Not NHT 66% viewable 41% viewable 75% confirmed human 17%confirmed human 3% confirmed human 68¢ 7¢ 1¢ “human viewable ads” “human viewable ads” “human viewable ads” “not working media” “not working media”
  19. 19. August 2017 / Page 18marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 60¢ / $1 goes towards “working media” “When buying programmatic exchanges, for every $1 only 57 – 63 cents goes towards working digital media.” “mark up” “working media” “working media” “mark up”
  20. 20. August 2017 / Page 19marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Corroborated by ANA, WFA Studies Source: WFA, April 2017 Source: ANA, May 2017
  21. 21. August 2017 / Page 20marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Humans (dark blue) vs Bots (dark red) Good Publishers Ad Networks Open Exchange 75% 2% 17% 30% 3% 72%
  22. 22. August 2017 / Page 21marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Directly measured viewability, by type “Taking viewability as 50% of the pixels in view or greater, we can see statistically different rates of viewability by network.” Good Publishers Ad Networks Open Exchange 91% viewable 66% viewable 41% viewable
  23. 23. August 2017 / Page 22marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Not Productive = Naked, Bots, Unviewable Naked ad calls + Not viewable + Confirmed bots = Not productive Ad Networks Open Exchanges 47% avg 77% avg 11% avg Good Publishers Naked ad calls Naked ad calls
  24. 24. What Savvy Marketers do
  25. 25. August 2017 / Page 24marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Stop buying poop water (bad ads) “Which? 1) start with ‘poop water’ and filter it before you drink it?, or 2) start with fresh water?” “fraud detection can’t filter it for you”
  26. 26. August 2017 / Page 25marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Measure relative quality of traffic Marketer 1 • Blue means humans • Red means bots Marketer 2 “increase spend on sources driving more humans (blue); reduce spend on sources with more bots (red)”
  27. 27. August 2017 / Page 26marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Shift budgets to quality (high human) Lower quality paid sources mean higher cost per human acquired – like 11X the cost. Sources of different quality send widely different amounts of humans to landing pages. “mitigation doesn’t even require technology!”
  28. 28. August 2017 / Page 27marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Optimize for real human conversions Organic sources have even more humans (dark blue) Conversion actions (phone calls) are well correlated to humans; bots don’t call
  29. 29. August 2017 / Page 28marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Measure every point of the funnel 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
  30. 30. August 2017 / Page 29marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou About the Author August 2017 Augustine Fou, PhD. acfou [@] mktsci.com 212. 203 .7239
  31. 31. August 2017 / Page 30marketing.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

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