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How Brands are Solving Ad Fraud Themselves


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Ad fraud is very bad. But no matter how big the number reported, brands often don't think it affects them -- i.e. it's someone elses' problem. Here are 3 case studies of marketers taking a look for themselves and solving ad fraud by putting in place best practices and processes to continuously monitor and reduce fraud, without using fraud detection tech.

Published in: Internet

How Brands are Solving Ad Fraud Themselves

  1. 1. February 2019 / Page 0marketing.scienceconsulting group, inc. How Brands Are Solving Ad Fraud Themselves February 2019 Augustine Fou, PhD. acfou [at] 212. 203 .7239
  2. 2. February 2019 / Page 1marketing.scienceconsulting group, inc. Agenda • What is ad fraud? • How did it happen? • Why is it not detected? • What have some marketers done?
  3. 3. February 2019 / Page 2marketing.scienceconsulting group, inc. What is Ad Fraud?
  4. 4. February 2019 / Page 3marketing.scienceconsulting group, inc. What is digital ad fraud ? Ad Fraud = ad impressions caused by bots, not seen by humans Impression Fraud (CPM) Fraud (includes mobile display, video ads) Click Fraud (CPC) Fraud (includes mobile search ads)
  5. 5. February 2019 / Page 4marketing.scienceconsulting group, inc. Why? Largest spend buckets Leads (CPL) Sales (CPA) Lead Gen $2.0B Other $5.0B • classifieds • sponsorship • rich media Impressions (CPM/CPV) Clicks (CPC) Search 46% Display 31% Video 14% 91% digital ad spend Source: IAB FY 2017 Report Estimated >$300B in 2018 9% spend
  6. 6. February 2019 / Page 5marketing.scienceconsulting group, inc. How? Three step process 1. set up FAKE SITES 2. buy FAKE TRAFFIC 3. sell FAKE ADS
  7. 7. February 2019 / Page 6marketing.scienceconsulting group, inc. Why is ad fraud bad? Advertisers Publishers Bad Guys 1/3 2/3 Ads are not shown to humans, wasted ad dollars Ad revenue declines because dollars are stolen by bad guys. Steal money using fake ads; siphon dollars out of ecosystem.
  8. 8. February 2019 / Page 7marketing.scienceconsulting group, inc. Ad dollars fund child abuse sites “Using a variety of sophisticated techniques to avoid detection, offenders are exploiting online advertising networks to monetise their distribution of child sexual abuse material.” Source: The Drum Nov 6, 2018 Source: CNN, Feb 2019
  9. 9. February 2019 / Page 8marketing.scienceconsulting group, inc. (2013) Ad dollars fund piracy sites “Highly Lucrative, Profitable The aggregate ad revenue for the sample of 596 sites was an estimated $56.7 million for Q3 of 2013, projecting out to $226.7 million dollars annually, with average profit margins of 83%, ranging from 80% to as high as 94%.” Source: Digital Citizens Alliance Study brands-supporting-music-piracy-its-big-business/
  10. 10. February 2019 / Page 9marketing.scienceconsulting group, inc. DDoS traffic for ad revenueDDoS attacks overwhelm with traffic; now use traffic to make ad revenue Google Digital Attack Map
  11. 11. February 2019 / Page 10marketing.scienceconsulting group, inc. Economics of botnets explained Source: MIT Tech Review, May 2018 “distributed denial-of-service attacks using a network of 30,000 bots can generate around $26,000 a month. Spam advertising with 10,000 bots generates around $300,000 a month, and bank fraud with 30,000 bots can generate over $18 million per month. But the most profitable undertaking is click fraud, which generates well over $20 million a month of profit.” Botnets can be used for a variety of things
  12. 12. February 2019 / Page 11marketing.scienceconsulting group, inc. Ad Tech Gave Rise to Ad Fraud
  13. 13. February 2019 / Page 12marketing.scienceconsulting group, inc. Adtech enabled siphoning PublishersAdvertisers Human Audience Advertisers Publishers Human Audience Fake Users Fake Sites 1995 2015
  14. 14. February 2019 / Page 13marketing.scienceconsulting group, inc. Badtech Tax: 60-70% extracted Source: WFA, April 2017 Source: ANA, May 2017
  15. 15. February 2019 / Page 14marketing.scienceconsulting group, inc. Case examples of this … Publisher only gets 30-60c on the dollar after middlemen fees money-go-guardian-buys-its-own-ad-inventory 2016 The Guardian “for every pound an advertiser spends programmatically on the Guardian only 30 pence actually goes to the publisher.” 2017 BusinessInsider “$40,000 worth of ad inventory through the open exchanges, the publication only saw $97.” shed-details-ad-industry-s-biggest-problem/311081/
  16. 16. February 2019 / Page 15marketing.scienceconsulting group, inc. U.S. Digital Ad Spend Distribution $46 Search Display/Video $46 $32$39 $8 Google Search FB+Google Display$29 (outside Google/Facebook) $100 Billion Digital SpendSource: IAB 2H 2018 Report Source: Verisign, Q4 2016 329M domains est. 1 million est. 164 million 7M apps Source: Statista, March 2017 est. 10,000 est. 6.99 million 1% of impressions 99% of impressions $10B $19B Good Publishers “sites/apps with ads”
  17. 17. February 2019 / Page 16marketing.scienceconsulting group, inc. Scarcity … vs unlim fake ads Infinite quantities of digital ads can be created on real or fake sites Unlike real billboards that people actually drive by in the physical world … Limitless quantities of digital ads can be created on fake sites that humans never visit.
  18. 18. February 2019 / Page 17marketing.scienceconsulting group, inc. Myth of the long tail Most people visit sites they know most; occasionally long tail ones “There are numerous pieces of research on how even as people accumulate hundreds of TV channels, they only watch seven. It's rather commonly accepted that in a sea of millions of mobile apps, most people stick to half a dozen.”
  19. 19. February 2019 / Page 18marketing.scienceconsulting group, inc. Myth of Hypertargeting After 3 parameters, the matching audience gets really tiny Female Male 18-25 13-17 25-34 35-49 50+ 1. gender 2. age range 3. geographic location 50% 10% 2% 100 params? 300 params? Starting Audience 100% ? ? % of AudienceTargeting parameters
  20. 20. February 2019 / Page 19marketing.scienceconsulting group, inc. Myth of behavioral targeting Ad tech sold the idea of deriving intent from web history Outdoor enthusiast?Male? Female? “This works on simplistic examples, like the above. But when the list of sites grows longer and more diverse, the assumptions used to derive data points, even gender, are going to be less and less accurate. In fact, a recent study of online identifiers determined that over 80% of the records were designated as BOTH male and female.” Source: Yeah, Your Data’s Screwed
  21. 21. February 2019 / Page 20marketing.scienceconsulting group, inc. hypertargeting behavioral targeting “Badtech” harms all parties Good Publishers (lower revenue, CPMs) Consumers (privacy violations) Advertisers (ad fraud, no outcomes) Badtech Industrial Complex Badtech Industrial Complex long tail sites
  22. 22. February 2019 / Page 21marketing.scienceconsulting group, inc. Gross Failures of Fraud Detection Tech
  23. 23. February 2019 / Page 22marketing.scienceconsulting group, inc. Bad guys easily avoid detection Blocking of tags, altering measurement to avoid detection Detection Tag Blocking— analytics tags/fraud detection tags are accidentally blocked or maliciously stripped out “malicious code manipulated data to ensure that otherwise unviewable ads showed up in measurement systems as valid impressions, which resulted in payment being made for the ad.” Source: Buzzfeed, March 2018
  24. 24. February 2019 / Page 23marketing.scienceconsulting group, inc. Traffic sellers’ “high quality traffic” Many sources to buy “traffic” and even tune “quality” level Choose Your “Traffic Quality Level” “Valid traffic” goes for higher prices
  25. 25. February 2019 / Page 24marketing.scienceconsulting group, inc. (2017) Pop-Unders / Redirects These forms of fraud typically get by current fraud detection tech a.k.a. “zero-click” “pop-under” “forced-view” “auto-nav” Source:
  26. 26. February 2019 / Page 25marketing.scienceconsulting group, inc. (2018) Cheetah was cheating “Eight apps with a total of more than 2 billion downloads in the Google Play store have been exploiting user permissions as part of an ad fraud scheme that could have stolen millions of dollars.” Source: Buzzfeed News, Nov 2018
  27. 27. February 2019 / Page 26marketing.scienceconsulting group, inc. Fake sites/apps NOT detected com.dxnxbgj.mkridqxviiqaogw com.obugniljhe.fptvznqwhmcjm com.bpo.ksuhpsdkgvbtlsw com.rlcznwgouw.vvtexstbfttngc com.kasbgf.sbzwtgpcbjexi com.bprlgbl.vbze com.zka.lzhsoueilo com.alxsavx.mizzucnlb com.jxknvk.lrwfdfirdzpsw com.tvwvqbt.wbshaguqy com.iwnxtpahcu.leyuehdwdbb com.okf.rhvemtykfibzpxj com.obpmirzste.ldsjpv com.zmm.shmxvjxnsagndui com.nqzwr.leusrmpmsq com.rced.zcdsglptpdlwpu com.kerms.ehlsgnc com.cmia.iabhheltm com.skggynmtx.tyyjnwpefvqtll com.kgdtltnuv.hayvfhob com.ztzsiqg.dyojlxdscxws com.xlwuqe.ddrdhsuosbn com.rkrhmzee.wjcoznxu com.ebhzb.hbzvomzpcctovj Fake sites Fake sites Fake apps
  28. 28. February 2019 / Page 27marketing.scienceconsulting group, inc. Chase: -99% reach, 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)
  29. 29. February 2019 / Page 28marketing.scienceconsulting group, inc. P&G: cut $200M, no impact “Once we got transparency, it illuminated what reality was,” said Mr. Pritchard. P&G then took matters into its owns hands and voted with its dollars, he said.” “As we all chased the Holy Grail of digital, self-included, we were relinquishing too much control— blinded by shiny objects, overwhelmed by big data, and ceding power to algorithms,” Mr. Pritchard said. Source: WSJ, March 2018
  30. 30. February 2019 / Page 29marketing.scienceconsulting group, inc. Just because you can’t measure it … doesn’t mean it’s not there.
  31. 31. February 2019 / Page 30marketing.scienceconsulting group, inc. Marketer 1 Case Study
  32. 32. February 2019 / Page 31marketing.scienceconsulting group, inc. Which site would you buy from? A B
  33. 33. February 2019 / Page 32marketing.scienceconsulting group, inc. Which site would you buy from? A B
  34. 34. February 2019 / Page 33marketing.scienceconsulting group, inc. What we measured – ads and site IN-AD tag placed in display ads, to determine quality of sites and apps that loaded them ON-SITE embed code placed on a brand site to determine quality of visitors arriving on the site; cross check clicks from the ads
  35. 35. February 2019 / Page 34marketing.scienceconsulting group, inc. Confirmed Bots – 2.3%, not bad Good media buying limited fraud to < 3% in impressions
  36. 36. February 2019 / Page 35marketing.scienceconsulting group, inc. Datacenter visits – 1% (half of bots) Data center bots made up about half of the confirmed bots
  37. 37. February 2019 / Page 36marketing.scienceconsulting group, inc. Confirmed Bots – Supporting Data The data confirmed servers (Linux x86_64), from data centers
  38. 38. February 2019 / Page 37marketing.scienceconsulting group, inc. Confirmed Bots – Supporting Data Bots had 0x0 window sizes and a good portion was hidden
  39. 39. February 2019 / Page 38marketing.scienceconsulting group, inc. Select high volume site(s) to investigate
  40. 40. February 2019 / Page 39marketing.scienceconsulting group, inc. Why s*********** is suspicious Note, all of the site’s traffic is from Android 8.0.0 devices, strange.
  41. 41. February 2019 / Page 40marketing.scienceconsulting group, inc. Sites to turn off
  42. 42. February 2019 / Page 41marketing.scienceconsulting group, inc. Top apps to turn off
  43. 43. February 2019 / Page 42marketing.scienceconsulting group, inc. Marketer 2 Case Study
  44. 44. February 2019 / Page 43marketing.scienceconsulting group, inc. Would you buy from this site? Unique devices loading ads 100% Android 8.0.0 visitors
  45. 45. February 2019 / Page 44marketing.scienceconsulting group, inc. Fraud type: Apps load webpages “fraud sites’ traffic comes from apps that load hidden webpages” Openly selling on LinkedIn
  46. 46. February 2019 / Page 45marketing.scienceconsulting group, inc. Mobile apps loading webpages Almost all disguised to be from Facebook app; nearly 100% Android
  47. 47. February 2019 / Page 46marketing.scienceconsulting group, inc. Top 10 apps ate 57% of volume Turned off the line items of these apps in the campaign interface within the first day; rest of the campaign ran more cleanly.
  48. 48. February 2019 / Page 47marketing.scienceconsulting group, inc. Overall fraud is more than just bots Sites and apps that cheat may look fine in bot detection reports 1.3% + 57% = 58% bot fraud site/app fraud overall fraud bot detection sees this bot detection misses this
  49. 49. February 2019 / Page 48marketing.scienceconsulting group, inc. Marketer 3 Case Study
  50. 50. February 2019 / Page 49marketing.scienceconsulting group, inc. Would you buy from these sites? Bad guys may not even wait till the ad is served since they are already paid based on the number of impressions won. From the data, the more fraudulent the site, the greater the discrepancy – e.g. 80 – 100% DSP says Adserver says
  51. 51. February 2019 / Page 50marketing.scienceconsulting group, inc. Taking control of digital media 2016 Still buying through exchanges Measure In-Ad and arrivals On-Site 2017 Buy their own ads through DSP Took buying in- house 2018 Started serving their own ads Took ad serving in-house 2019 … Buying direct from good publishers
  52. 52. February 2019 / Page 51marketing.scienceconsulting group, inc. Detect and reduce fraud in-flight Launch Week 3 onwardWeeks 1-2 Initial baseline measurement Measurement after first optimization After eliminating several “problematic” networks Stacked percent chart Blue (human) Red (bots)
  53. 53. February 2019 / Page 52marketing.scienceconsulting group, inc. Better media = better outcomes 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
  54. 54. February 2019 / Page 53marketing.scienceconsulting group, inc. Local TV websites - clean Great consistency in the data over long periods of time
  55. 55. February 2019 / Page 54marketing.scienceconsulting group, inc. Local radio websites - clean Great consistency in the data over long periods of time
  56. 56. February 2019 / Page 55marketing.scienceconsulting group, inc. Magazine websites - clean Great consistency in the data over long periods of time
  57. 57. February 2019 / Page 56marketing.scienceconsulting group, inc. Human CPM, not just CPM Low CPM sources result in higher cost per human – like 11X the cost. Sources of different quality send widely different amounts of humans to landing pages.
  58. 58. February 2019 / Page 57marketing.scienceconsulting group, inc. Display 4 2,036 humans human conversion rate More humans = more outcomes Site Traffic Conversions 8,482 818 4,216 humans 5% human conversion rate 14,539 193 225 humans 9% human conversion rate 2,248 23 168 humans 5% human conversion rate 1,527 9 Display 3 Display 2 Display 1 Humans 40%
  59. 59. February 2019 / Page 58marketing.scienceconsulting group, inc. #defendthespend “marketers can (and should) reduce the flow of dollars to cybercriminals that are committing ‘major economic crimes’.” Then, and only then, will we get back to REAL digital marketing.”
  60. 60. February 2019 / Page 59marketing.scienceconsulting group, inc. Digital Marketing circa 2018
  61. 61. February 2019 / Page 60marketing.scienceconsulting group, inc. About the Author Augustine Fou, PhD. acfou [@] 212. 203 .7239
  62. 62. February 2019 / Page 61marketing.scienceconsulting group, inc. Dr. Augustine Fou – Researcher 2013 2014 Published slide decks and posts: 2016 2015 2017 20192018