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Low-Cost, No-Tech Ways to Fight Fraud vMiMA


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Ad fraud background, a deeper look at analytics and how marketers can spot tell-tale signs of fraud and stop it.

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Low-Cost, No-Tech Ways to Fight Fraud vMiMA

  1. 1. Low-Cost, No-Tech Ways to Fight Digital Ad Fraud February 2017 Augustine Fou, PhD. 212. 203 .7239
  2. 2. Ad Fraud is VERY Lucrative, VERY Scalable
  3. 3. February 2017 / Page 2marketing.scienceconsulting group, inc. How profitable is ad fraud? EXTREMELY Source: networks-continue-to-thrive “the profit margin is 99% … [especially with pay-for-use cloud services ]…” Source: Digital Citizens Alliance Study, Feb 2014 “highly lucrative, and profitable… with margins from 80% to as high as 94%…”
  4. 4. February 2017 / Page 3marketing.scienceconsulting group, inc. How scalable are fraud operations? MASSIVELY Cash out sites are massively scalable 131 ads on page X 100 iframes = 13,100 ads /page One visit redirected dozens of times Known blackhat technique to hide real referrer and replace with faked referrer. Example how-to: m/blackhat-seo/cloaking- content-generators/36830- cloaking-redirect-referer.html Thousands of requests per page Single mobile app calling 10k impressions Source: Forensiq
  5. 5. February 2017 / Page 4marketing.scienceconsulting group, inc. AppNexus cleaned up 92% of impressions Increased CPM prices by 800% Decreased impression volume by 92% Source: 260 billion 20 billion > $1.60 < 20 cents “pity those advertisers who bought before the cleanup”
  6. 6. February 2017 / Page 5marketing.scienceconsulting group, inc. Methbot eats $1 in $6 of $10B video ad spend Source: Dec 2016 Whiteops Discloses Methbot Research “the largest ad fraud discovered to date, a single botnet, Methbot, steals $2 billion annualized.” 1. Targets video ads $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, page scrolling, mouse movements 4. Obfuscated data center origins Data center bots pretended to be from residential IP addresses
  7. 7. February 2017 / Page 6marketing.scienceconsulting group, inc. Ad fraud is now the largest form of crime $20 billion Counterfeit Goods U.S. $18 billion Somali pirates 44% of digital ad spend $70B 2016E Source: IAB H1 2016 Bank robberies $38 million $31 billion U.S. alone $1 billion ATM Malware Payment Card Fraud 2015 $22 billion Source: Nilson Report Dec 2016 Source: ICC, U.S. DHS, et. al Source: World Bank Study 2013 Source: Kaspersky 2015 $7 in $100$3 in $100 “this is a PER YEAR number”
  8. 8. Where is Ad Fraud Concentrated?
  9. 9. February 2017 / Page 8marketing.scienceconsulting group, inc. CPM/CPC buckets (91% of spend) is most targeted Impressions (CPM/CPV) Clicks (CPC) Search 27% 91% digital spend Display 10% Video 7% Mobile 47% Leads (CPL) Sales (CPA) Lead Gen $2.0B Other $5.0B • classifieds • sponsorship • rich media (89% in 2015) Source: IAB 1H 2016 Report (86% in 2014)
  10. 10. February 2017 / Page 9marketing.scienceconsulting group, inc. 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 display 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 and 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
  11. 11. Fake Websites (cash-out sites)
  12. 12. February 2017 / Page 11marketing.scienceconsulting group, inc. Websites – spectrum from bad to good Ad Fraud Sites Click Fraud Sites 100% bot mostly human Piracy Sites Premium Publishers Sites w/ Sourced Traffic “fraud sites” “sites w/ questionable practices” “good guys” “real content that real humans want to read”
  13. 13. February 2017 / Page 12marketing.scienceconsulting group, inc. Identical sites – fraud sites made by template 100% bot
  14. 14. February 2017 / Page 13marketing.scienceconsulting group, inc. Countless fraud domains used to commit ad fraud 100M+ more sites like these, designed to profit from high value display, video, and mobile ads
  15. 15. Fake Visitors (bots)
  16. 16. February 2017 / Page 15marketing.scienceconsulting group, inc. Bots are automated browsers used for ad fraud Headless Browsers Selenium PhantomJS Zombie.js SlimerJS Mobile Simulators 35 listed Bots are made from malware compromised PCs or headless browsers (no screen) in datacenters. Bots
  17. 17. February 2017 / Page 16marketing.scienceconsulting group, inc. Bots range in sophistication, and therefore cost Javascript installed on webpage Malware on PCsData Center BotsOn-Page Bots Headless browsers in data centers Malware installed on humans’ devices Less sophisticated Most sophisticated Source: AdAge/Augustine Fou, Mar 2014 Source: Forensiq Source: Augustine Fou, Oct 2015 “the official industry lists of bots catch NONE of these bots, not one.” 1 cent CPMs Load pages, click 10 cent CPMs Fake scroll, mouse movement, click 1 dollar CPMs Replay human-like mouse movements, clone cookies
  18. 18. February 2017 / Page 17marketing.scienceconsulting group, inc. Any device with chip/connectivity can be used as a bot Traffic cameras used as botnet (Engadget, Oct 2015) mobile devices connected traffic lights connected cars thermostat connected fridge Security cams used as DDoS botnet (Engadget, Jun 2016) (TechTimes, Sep 2016)
  19. 19. “The equation of ad fraud is simple: buy traffic for $1 CPMs, sell ads for $10 CPMs; pocket $9 of pure profit.”
  20. 20. February 2017 / Page 19marketing.scienceconsulting group, inc. How Ad Fraud Harms Advertisers
  21. 21. February 2017 / Page 20marketing.scienceconsulting group, inc. How many clicks/sessions/views do you want? click on links load webpages tune bounce rate tune pages/visit “bad guys’ bots are advanced enough to fake most metrics”
  22. 22. February 2017 / Page 21marketing.scienceconsulting group, inc. What click through rates are you shooting for? Programmatic display (18-45% clicks from advanced bots) Premium publishers (0% clicks from bots) 0.13% CTR (18% of clicks by bots) 1.32% CTR (23% of clicks by bots) 5.93% CTR (45% of clicks by bots) Campaign KPI: CTRs
  23. 23. February 2017 / Page 22marketing.scienceconsulting group, inc. Want 100% viewability? 0% NHT (bots)? 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.” Source: Shailin Dhar
  24. 24. Current State of NHT Detection
  25. 25. February 2017 / Page 24marketing.scienceconsulting group, inc. Fraud bots are NOT on any list 10,000 bots observed in the wild bad guys’ bots3% Dstillery “findings from two independent third parties, Integral Ad Science and White Ops” 3.7% Rocket Fuel “Forensiq results confirmed that ... only 3.72% of impressions categorized as high risk.” 2 - 3% comScore “most campaigns have far less; more in the 2% to 3% range.” bot list-matching “not on any list” disguised as popular browsers – Internet Explorer; constantly adapting to avoid detection
  26. 26. February 2017 / Page 25marketing.scienceconsulting group, inc. Three main places for NHT detection In-Ad (ad iframes) On-Site (publishers’ sites) • Used by advertisers to measure ad impressions • Limitations – tag is in foreign iframe, severe limits on detection ad tag / pixel (in-ad measurement) javascript embed (on-site measurement) In-Network (ad exchange) • Used by publishers to measure visitors to pages • Limitations – most detailed and complete analysis of visitors • Used by exchanges to screen bid requests • Limitations – relies on blacklists or probabilistic algorithms, least info ad served bot human fraud site good site
  27. 27. February 2017 / Page 26marketing.scienceconsulting group, inc. 5% bots doesn’t mean 95% humans good publishers ad exchanges/networks volume bars (green) Stacked percent Blue (human) Red (bots) red v blue trendlines
  28. 28. “Having fraud DETECTION is not the same as having fraud PROTECTION.”
  29. 29. Case Examples
  30. 30. February 2017 / Page 29marketing.scienceconsulting group, inc. Differences in quality, arrivals, conversions Measure Ads Measure Arrivals Measure Conversions good publishers ad exchanges/networks 346 1743 5 156
  31. 31. February 2017 / Page 30marketing.scienceconsulting group, inc. Stepwise improvement using our data Period 1 Period 3Period 2 Initial baseline measurement Measurement after first optimization Eliminating several “problematic” networks
  32. 32. February 2017 / Page 31marketing.scienceconsulting group, inc. More accurate analytics when data is clean 7% conversion rate 13% conversion rate artificially low actually correct
  33. 33. February 2017 / Page 32marketing.scienceconsulting group, inc. Best Practices of Savvy Advertisers/Agencies • Challenge all assumptions – don’t assume someone else “took care of it.” Verify, by demanding line-item detailed reports, because fraud hides easily in averages • Check your Google Analytics - question anything that looks suspicious; more details that can reveal fraud and waste • Corroborate measurements – measure different parameters together and see if they still make sense together; reduce false positives or negatives • Use conversion metrics – CPG client uses click-and-print digital coupons; pharma client uses doctor finder zip code searches, plus clicks to doctor pages; retailers use sales
  34. 34. Bot Fraud Game Show
  35. 35. February 2017 / Page 34marketing.scienceconsulting group, inc. Where would you prefer to place your ads? A B
  36. 36. February 2017 / Page 35marketing.scienceconsulting group, inc. Which chart shows real human traffic surges? A B
  37. 37. February 2017 / Page 36marketing.scienceconsulting group, inc. Traffic surges caused by bots vs real humans Caused by bots Caused by humans A B
  38. 38. February 2017 / Page 37marketing.scienceconsulting group, inc. Which chart shows fake/sourced traffic? A B
  39. 39. February 2017 / Page 38marketing.scienceconsulting group, inc. Which chart shows fake/sourced traffic? A B
  40. 40. February 2017 / Page 39marketing.scienceconsulting group, inc. Which chart shows human segment? A B ON-SITE measurement • Scroll: 57% • Mouse: 67% • Click: 56% ON-SITE measurement • Scroll: 2% • Mouse: 2% • Click: 2%
  41. 41. February 2017 / Page 40marketing.scienceconsulting group, inc. Which chart shows human segment? A B ON-SITE measurement • Scroll: 57% • Mouse: 67% • Click: 56% ON-SITE measurement • Scroll: 2% • Mouse: 2% • Click: 2%
  42. 42. February 2017 / Page 41marketing.scienceconsulting group, inc. Which chart shows fraudulent mobile apps? A B
  43. 43. February 2017 / Page 42marketing.scienceconsulting group, inc. Which chart shows fraudulent mobile apps? A B
  44. 44. February 2017 / Page 43marketing.scienceconsulting group, inc. What’s wrong with this picture (chart)?
  45. 45. February 2017 / Page 44marketing.scienceconsulting group, inc. What’s wrong with this picture (chart)?
  46. 46. February 2017 / Page 45marketing.scienceconsulting group, inc. Would you buy more media from this site? 102,231 sessions 0 sessions goal events YES NO
  47. 47. February 2017 / Page 46marketing.scienceconsulting group, inc. Would you buy more media on this site? NO! 102,231 sessions 0 sessions goal event – no change
  48. 48. February 2017 / Page 47marketing.scienceconsulting group, inc. Would you continue search ad placements? Line item details Overall average 9.4% CTR “fraud hides easily in averages”
  49. 49. February 2017 / Page 48marketing.scienceconsulting group, inc. Your ads on .xyz domains, these mobile apps? .xyz domains suspicious mobile apps
  50. 50. February 2017 / Page 49marketing.scienceconsulting group, inc. Mix and Match – which goes with which? A B C video entertainment sports info site investment info site
  51. 51. “Let’s go fight some bad guys together!”
  52. 52. February 2017 / Page 51marketing.scienceconsulting group, inc. About the Author February 2017 Augustine Fou, PhD. 212. 203 .7239
  53. 53. February 2017 / Page 52marketing.scienceconsulting group, inc. Dr. Augustine Fou – Independent Ad Fraud Researcher 2013 2014 Follow me on LinkedIn (click) and on Twitter @acfou (click) Further reading: 2016 2015
  54. 54. February 2017 / Page 53marketing.scienceconsulting group, inc. 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.