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State of digital ad fraud 2017 by augustine fou

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Overview of digital ad fraud, state of the industry by Augustine Fou

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State of digital ad fraud 2017 by augustine fou

  1. 1. State of Digital Ad Fraud January 1, 2017 Update January 2017 Augustine Fou, PhD. acfou@mktsci.com 212. 203 .7239
  2. 2. Ad Fraud is VERY Lucrative and Scalable
  3. 3. January 2017 / Page 2marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou How profitable is ad fraud? EXTREMELY Source: https://hbr.org/2015/10/why-fraudulent-ad- 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. January 2017 / Page 3marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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: http://www.blackhatworld.co 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. January 2017 / Page 4marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Example – AppNexus cleaned up 92% of impressions 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 “pity those advertisers who bought before the cleanup”
  6. 6. January 2017 / Page 5marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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 $3 - $5 million per day, $2 billion annualized.” 1. Targets 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
  7. 7. Ad Fraud Harms The Digital Ad Ecosystem
  8. 8. January 2017 / Page 7marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Digital Ad Fraud is Hitting All Time Highs Digital ad SPEND Source: IAB 2016 F1H Report $ billions
  9. 9. January 2017 / Page 8marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Ad fraud is now the largest form of crime $20 billion Counterfeit Goods U.S. $18 billion Somali pirates $70B 2016E Digital Ad Spending 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” Digital Ad Fraud Source: IAB H1 2016 $44 in $100
  10. 10. January 2017 / Page 9marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou CPM/CPC buckets (91% of spend) are 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)
  11. 11. January 2017 / Page 10marketing.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 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
  12. 12. Fake Websites (cash-out sites)
  13. 13. January 2017 / Page 12marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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”
  14. 14. January 2017 / Page 13marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Identical fraud sites made by template 100% bot
  15. 15. January 2017 / Page 14marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Countless fraud domains used to commit ad fraud http://analyzecanceradvice.com http://analyzecancerhelp.com http://bestcanceropinion.com http://bestcancerproducts.com http://bestcancerresults.com http://besthealthopinion.com http://bettercanceradvice.com http://bettercancerhelp.com http://betterhealthopinion.com http://findcanceropinion.com http://findcancerresource.com http://findcancertopics.com http://findhealthopinion.com http://finestcanceradvice.com http://finestcancerhelp.com http://finestcancerresults.com http://getcancerproducts.com 100M+ more sites like these, designed to profit from high value display, video, and mobile ads
  16. 16. Fake Visitors (bots)
  17. 17. January 2017 / Page 16marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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
  18. 18. January 2017 / Page 17marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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 “not many people know that 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
  19. 19. January 2017 / Page 18marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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)
  20. 20. “The equation of ad fraud is simple: buy traffic for $1 CPMs, sell ads for $10 CPMs; pocket $9 of pure profit.”
  21. 21. Current Bot/Fraud Detection
  22. 22. January 2017 / Page 21marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fraud bots are NOT on any list user-agents.org bad guys’ bots 2% Source: GroupM, Feb 2017 bot list-matching 4% Source: IAB Australia, Mar 2017 400 bot names in list “not on any list” disguised as popular browsers – Internet Explorer; constantly adapting to avoid detection 10,000 bots observed in the wild
  23. 23. January 2017 / Page 22marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Three main types of bot / fraud 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
  24. 24. January 2017 / Page 23marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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
  25. 25. “Having fraud DETECTION is not the same as having fraud PROTECTION.”
  26. 26. January 2017 / Page 25marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou How Fraud Harms Good Publishers
  27. 27. January 2017 / Page 26marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Significant ad revenue stolen from publishers 1. Bots collect “cookie” 2. Bots cause ad impressions on fake sites. www.nejm.org healthsiteproductionalways.com
  28. 28. January 2017 / Page 27marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou http://www.olay.co m/skin-care- products/OlayPro- X?utm_source=msn &utm_medium=cpc &utm_campaign=Ol ay_Search_Desktop Bad guys pretend to be good publishers’ sites Click thru URL passes fake source “utm_source=msn” buy eye cream online (expensive CPC keyword) 1. Fake site that carries search ads Olay.com ad in #1 position 2. search ad served, fake click Destination page fake source declared 3. Click through to destination page
  29. 29. January 2017 / Page 28marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Bad measurements wrongly accuse publishers Publisher clearly does not have 90% bots and never had “you have low viewability” “you have 90% bots” • We want a refund • We won’t pay • We want make-goods
  30. 30. January 2017 / Page 29marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Best Practices of Good Publishers 1. Reduce/eliminate shortcuts – mainstream publisher never sources traffic, never uses audience extension or other practices that artificially inflate impressions 2. Protect data and reputation – news publisher purged 30+ trackers from their sites to minimize “data leakage” and stopped selling remnant/unsold inventory on exchanges 3. Consistently prove ROI – specialty publisher limited ads to 3 per page, lazy loads all ads, filters all known bots by name; better business outcomes proven over time “hard work and consistency will pay off”
  31. 31. January 2017 / Page 30marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou How Fraud Harms Advertisers
  32. 32. January 2017 / Page 31marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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”
  33. 33. January 2017 / Page 32marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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
  34. 34. January 2017 / Page 33marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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.”
  35. 35. January 2017 / Page 34marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Best Practices of Savvy Advertisers “don’t assume your agency took care of it” • 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
  36. 36. January 2017 / Page 35marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Implications for Digital Media
  37. 37. January 2017 / Page 36marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Humans block ads; fraud bots don’t Comparing high human vs high bot samples 96% bots sample 42% ad blocked 1% ad blocked 93% human sample Comparing ad blocking vs non-ad blocking samples ad blocking ON ad blocking OFF
  38. 38. January 2017 / Page 37marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Ad impressions served mostly to bots Total Human Users – 115 million Visitors (U.S. Only) U.S. Internet – 285 million Source: eMarketer 2016 estimate Source: Distil Networks 2015 Adblock Users (humans) – 45 million Source: PageFair / Adobe 2015 “subtracting adblocking humans, your open exchange ad impressions are being served to a population that is disproportionally non-human.” Non-Human Traffic (NHT) HUMAN VISITORS ads served “fraud sites” “sites w/ questionable practices” “good guys” Websites 3% IVT caught by industry lists 39%Ad blocking humans 71% 29%
  39. 39. January 2017 / Page 38marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou No matter how much traffic, bots don’t convert 102,231 sessions 0 sessions goal events – no change bot traffic turned off bot traffic turned off
  40. 40. January 2017 / Page 39marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Other Hidden Dangers
  41. 41. January 2017 / Page 40marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Analytics are messed up by fake data 7% conversion rate 13% conversion rate artificially low actually correct
  42. 42. January 2017 / Page 41marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Real human audiences stolen from publishers specialized audience: human oncologists jco.ascopubs.org specialized audience can be targeted elsewhere “cookie matching” (by placing javascript on your site)
  43. 43. January 2017 / Page 42marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou In-ad measurements could be entirely wrong Publisher Webpage publisher.com Foreign Ad iFrames adserver.com Cross-domain (XSS) security restrictions mean iframe cannot: • read content in parent frame • detect actions in parent frame • see where it is on the page (above- or below- fold) • detect characteristics of the parent page 1x1 pixel js ad tags ride along inside iframe incorrectly reported as 100% viewable
  44. 44. January 2017 / Page 43marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou On-site Javascript poses gaping security risks Source: https://www.exchangewire.com/blog/2016/05/19/%E2%80%8Bon-site-javascript-trackers-open-gaping-security-holes/
  45. 45. January 2017 / Page 44marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou From our First-hand Data
  46. 46. January 2017 / Page 45marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Traffic surges caused by bots vs real humans Caused by bots Caused by humans “end of month traffic fulfillment” News site
  47. 47. January 2017 / Page 46marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Publishers taking action to reduce bots Publisher 1 – stopped buying traffic Publisher 2 – filtered data center traffic
  48. 48. January 2017 / Page 47marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Stepwise improvement using our data Period 1 Period 3Period 2 Initial baseline measurement Measurement after first optimization Eliminating several “problematic” networks
  49. 49. January 2017 / Page 48marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Advertisers buying low vs high quality media Traffic to Site from Buying LOW quality media Traffic to Site from Buying HIGH quality media
  50. 50. January 2017 / Page 49marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Better media leads to way better outcomes Measure Ads Measure Arrivals Measure Conversions clean, good media low-cost media, ad exchanges 346 1743 5 156 30X better outcomes• More arrivals • Better quality
  51. 51. January 2017 / Page 50marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou About the Author January 2017 Augustine Fou, PhD. acfou@mktsci.com 212. 203 .7239
  52. 52. January 2017 / Page 51marketing.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
  53. 53. January 2017 / Page 52marketing.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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