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Good Publishers Will Save Digital

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Good publishers have real human audiences. Advertisers should value real human audiences because their ads will be shown to humans. By focusing on good publishers, marketers can get more ROI from their digital campaigns; good publishers will earn what they deserve; and ad fraud will be significantly reduced.

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Good Publishers Will Save Digital

  1. 1. Good Publishers Will Save Digital Advertising May 2017 Augustine Fou, PhD. acfou [at] mktsci.com 212. 203 .7239
  2. 2. Context of Ad Fraud
  3. 3. May 2017 / Page 2marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fraud diverts revenue to fake sites Good Publishers “sites that carry ads” • No content • No humans • Low CPMS Search Spend $40 $40 Display Spend Other $21$30 $3 Google Search FB+Google Display$29 (outside Google/Facebook) Source: eMarketer March 2017
  4. 4. May 2017 / Page 3marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou $29 (outside Google/Facebook) There’s 16X more “sites with ads” Good Publishers “sites with ads” Source: Verisign, Q4 2016 329M domains 159 million “sites that carry ads” 11 million “sites you’ve heard of” WSJ ESPN NYTimes Economist Reuters Elle 3% no ads ads 16X more
  5. 5. May 2017 / Page 4marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Mobile fraud steals ad dollars too 159 million “sites that carry ads” 11 milion “sites you’ve heard of” WSJ ESPN NYTimes Economist Reuters Elle 3% no ads 7M apps Source: Statista, March 2017 96% “apps that carry ads” Search Spend $40 $40 Display Spend Other $21$30 $3 Google Search FB+Google Display$29 (outside Google/Facebook) Source: eMarketer March 2017 Source: Verisign, Q4 2016 329M domains 10,000 “apps you’ve heard of” Facebook Spotify Pandora Zynga Pokemon YouTube
  6. 6. May 2017 / Page 5marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fake sites have no content, no humans Identical sites made by template Alphanumeric domains So they can sell ad “inventory” at low prices Source: Sadbottrue.com
  7. 7. Fraud comes in large numbers
  8. 8. May 2017 / Page 7marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Display ads … Increased CPM prices by 800% Decreased impression 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. May 2017 / Page 8marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Video ads … 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. May 2017 / Page 9marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Mobile ads … Source: May 2017, Tune 23% had over 20% fraud 100% fraud 24 billion clicks on 700 mobile networks
  11. 11. Good publishers have done everything they could …
  12. 12. May 2017 / Page 11marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Publishers added lots of measurement Publishers put tags on site Advertisers send tags in ads … but advertisers still didn’t trust them.
  13. 13. May 2017 / Page 12marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Wrongly accused by bad measurement Wrongly accused of high bots “In-ad measurement can’t look outside iframe” Sources 1 and 2 corroborate Source 3 completely off 1x1 pixel incorrectly reported as 100% viewable
  14. 14. May 2017 / Page 13marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Tech, standards have not solved … standards (MRC) “are useful, but haven’t solved fraud” detection tech (Moat) + “ ‘self-certification’ certainly won’t work. We’ll be the laughing stock of bad guys.”
  15. 15. Fraud is successful …
  16. 16. May 2017 / Page 15marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fake sites successfully sell ads… how? 100% viewability (fake) AD Stack ads all above the fold to trick detection 0% NHT (fake) Buy traffic that is guaranteed to pass fraud filters clean placement (fake) Pass fake source or forge fake placement details http://www.olay.co m/skin-care- products/OlayPro- X?utm_source=elle &utm_medium=dis play + + “by tricking measurement and reporting”
  17. 17. May 2017 / Page 16marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Current detection is severely limited In-Ad (ad iframes) • Limitations – tag is in foreign iframe, cannot look outside itself ad tag / pixel (in-ad measurement) In-Network (ad exchange) On-Site (publishers’ sites) javascript embed (on-site measurement) • Limitations – most detailed and complete analysis of visitors • Limitations – relies on blacklists or probabilistic algorithms, least info ad served bot human fraud site good site
  18. 18. May 2017 / Page 17marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fake audiences, divert via retargeting Journal of Clinical Oncology “cookie matching” Bots pretend to be oncologists by collecting cookie Attract ad dollars to fake sites when retargeted
  19. 19. May 2017 / Page 18marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fake inventory sold on exchanges publisherA.com … but, PublisherA does NOT sell ads on open exchanges! “Dark Revenue” is ad revenue diverted away from publishers, so they don’t even see it’s missing. • Large pubs – “dark” is 1-2X ad revenue • Medium pubs - “dark” is 5-10X ad revenue • Small pubs - “dark” is 20-100X ad revenue
  20. 20. May 2017 / Page 19marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou The “dark revenue” problem publisher.com (real) does not sell any ads on open exchanges publisher.com (fake) pretend to be good publisher to attract ad dollars direct buy private market open exchange 20X more fake inventory than real “Advertisers think they bought ads for cheap; but it was fake. When fraud is detected, they call up real publisher and accuse them of high bots/fraud.” (small publisher)
  21. 21. “The problem is not the lack of measurement…. … it’s the lack of trust.”
  22. 22. What if … ?
  23. 23. May 2017 / Page 22marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou We bifurcate good from “other” 100% bot traffic “fake (cash out) sites” • No content • Stolen content • Fake content “sites with real content that real humans want to read” Source: DCN/ WhiteOps 2015 “sites you’ve heard of” WSJ ESPN NYTimes Economist Reuters Elle Good Publishers (good business practices) “sites that carry ads”
  24. 24. May 2017 / Page 23marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Why? It boils down to intentions Bad guys cheat and stack ALL ads above the fold to make 100% viewability. “100% viewability? Sure, no problem.” AD • IAS filtered traffic, • DV filtered traffic • Pixalate filtered traffic, • MOAT filtered traffic, • Forensiq filtered traffic “0% NHT? Sure, no problem.” Bad guys cheat and use bots proven to get through ALL fraud filters. “bad guys will always find tech to defeat your detection”
  25. 25. May 2017 / Page 24marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publishers have good practices “good business practices lead to good looking data” Good Publishers “sites that carry ads” • source traffic • audience extension • auto-refresh • traffic laundering • don‘t source traffic • protect advertisers • protect consumers
  26. 26. May 2017 / Page 25marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou How can we tell “good” from “other?” “Business practice review by independent 3rd party provides the trust and assurance that distinguishes good publishers from ‘sites that carry ads’ ”
  27. 27. May 2017 / Page 26marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publishers have human audiences Humans (blue) “convert” Conversion actions (calls) correlate perfectly to humans
  28. 28. May 2017 / Page 27marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Advertisers value humans that convert 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
  29. 29. May 2017 / Page 28marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Case in point… Chase “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)
  30. 30. Good publishers already have good practices
  31. 31. May 2017 / Page 30marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publishers act to reduce bots Publisher 1 – stopped buying traffic Publisher 2 – filtered data center traffic
  32. 32. May 2017 / Page 31marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publishers protect advertisers On-Site measurement, bots are still coming In-Ad measurement, bots and data centers filtered 11% red -9% (filtered GIVT and data centers) 2% red “Filter data center traffic and not call the ads”
  33. 33. May 2017 / Page 32marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publishers protect users 42 trackers 24.3s load time 8 trackers 1.3s load time “minimize 3rd party javascript trackers on pages”
  34. 34. May 2017 / Page 33marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publishers reduce data leakage specialized audience: oncologists Journal of Clinical Oncology specialized audience can be targeted elsewhere “cookie matching” (by placing javascript on your site) ad revenue diverted away
  35. 35. May 2017 / Page 34marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Real human audiences are valuable 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.
  36. 36. May 2017 / Page 35marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Next steps … 1. Measurement - DONE On-site measurement with Bot Analytics toolset, complete details of every visit; bot mitigation recommendations 2. Research – IN PROCESS ROI study to show advertisers more humans means better outcomes and lower costs per outcome, vs cheap media 3. Certification Certification by independent 3rd party, AAM, which focuses on good business practices bifurcates good from “other”
  37. 37. “this is how good publishers can save all of digital.”
  38. 38. May 2017 / Page 37marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou About the Author May 2017 Augustine Fou, PhD. acfou @ mktsci. com 212. 203 .7239
  39. 39. May 2017 / Page 38marketing.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
  40. 40. May 2017 / Page 39marketing.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.

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