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State of Digital Ad Fraud Q1 2015 Update by Augustine Fou

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Digital ad fraud continues to increase, as more ad dollars shift into digital. This is a recap of current forms of ad fraud and current techniques and technologies being used to combat it.

Published in: Marketing
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State of Digital Ad Fraud Q1 2015 Update by Augustine Fou

  1. 1. The State of Digital Ad Fraud Q1 2015 Update May 2015 Augustine Fou, PhD. acfou [at] mktsci. com 212. 203. 7239
  2. 2. The Trend Continues
  3. 3. May 2015 / Page 2marketing.scienceconsulting group, inc. Dr. Augustine Fou UPDATED: Full Year 2014 Digital Ad Spend – $50B Impressions (CPM/CPV) Clicks (CPC) Leads (CPL) Sales (CPA) Search 38% $18.8B Video 7% $3.5B Lead Gen 4% $2.0B 10% Other $5.0B Source: IAB, FY 2014 Internet Advertising Report, May 2015 $42.5B Display 16% $7.9B Mobile 25% $6.2B$6.2B CPM Performance • classifieds • sponsorship • rich media $7.0B
  4. 4. May 2015 / Page 3marketing.scienceconsulting group, inc. Dr. Augustine Fou Impressions and clicks remain the biggest targets Impressions (CPM/CPV) Clicks (CPC) Search $18.8B 86% digital spend Display $7.9B Video $3.5B Mobile $6.2B$6.2B Leads (CPL) Sales (CPA) Lead Gen $2.0B Other $5.0B • classifieds • sponsorship • rich media estimated fraud not at risk (84% in 2013)
  5. 5. May 2015 / Page 4marketing.scienceconsulting group, inc. Dr. Augustine Fou Two main types of fraud and how each is generated Impression (CPM) Fraud (includes mobile display, video ads) 1. Put up fake websites and load tons of ads on the pages Search Click (CPC) Fraud (includes mobile search ads) 2. Use bots to repeatedly load pages to generate fake ad impressions (launder the origins of the ads to avoid detection) 1. Put up fake websites and participate in search networks 2a. Use bots to type keywords to cause search ads to load 2b. Use bots to click on the ad to generate the CPC revenue
  6. 6. May 2015 / Page 5marketing.scienceconsulting group, inc. Dr. Augustine Fou Fraud goes up as digital ad spend continues upward Digital ad fraud High / Low Estimates plus best-guess (36%) Published estimates Digital ad spend Source: IAB Annual Reports $ billions E
  7. 7. May 2015 / Page 6marketing.scienceconsulting group, inc. Dr. Augustine Fou Digital ad fraud is larger than many other types of fraud $24 billion Counterfeit goods 5,000 robberies in 2011 $18 billion Somali pirates 39% of global digital ad spend ($171B 2015E) Source: eMarketer March 2015 $5 billion Tax fraud Bank robberies $38 million $67 billion globally “A third of traffic is bogus” WSJ, March 2014 $1 billion ATM Malware U.S Credit Card Fraud 2014 $32 billion $21 billion U.S.
  8. 8. May 2015 / Page 7marketing.scienceconsulting group, inc. Dr. Augustine Fou Estimates of fraud vary widely; which is right? 3% Dstillery, Oct 9, 2014_ “findings from two independent third parties, Integral Ad Science and White Ops” 3.7% Rocket Fuel, Sep 22, 2014 “Forensiq results confirmed that ... only 3.72% of impressions categorized as high risk.” 57% Telemetry, May 26, 2014 “Telemetry found that 57 per cent were “viewed” by automated computer programs rather than real people.” 25 - 50% WhiteOps, Jul 14, 2014 “digital ad fraud outbreak – one that gobbles up roughly $14 billion in advertising spend and between 25 and 50% of ad spend per campaign” 45% Solve Media, Sep 19, 2014 “suspicious web activity dropped to 45% – still a high figure, but an improvement nonetheless from the high of 61% in Q4 2013” “Whatever the number, there is no refuting that there is ad fraud... and job one is to find the fraud impacting your business and extinguish it....” -- Dr. Augustine Fou 2 - 3% comScore, Sep 26, 2014 “most campaigns have far less; more in the 2% to 3% range.”
  9. 9. May 2015 / Page 8marketing.scienceconsulting group, inc. Dr. Augustine Fou 0 10 20 30 40 50 60 70 80 90 100 retail finance automotive telecom CPG entertainment pharma travel cons. electronics indexed spend share indexed fraud rate Ad fraud impacts every industry vertical High CPC industries Source: Ad spend share data from IAB, May 2015 | Fraud rate data from Integral Ad Science Q2 2014 Fraud Report
  10. 10. May 2015 / Page 9marketing.scienceconsulting group, inc. Dr. Augustine Fou Fraud siphons 1/3 of dollars out of ad ecosystem Advertisers “ad spend” in digital is $56B in 2015 Publishers are left with only 2/3 of the dollars Bad Guys siphon 1/3 of ad spend OUT of the ecosystem • Ad dollars are being siphoned OUT of the ecosystem into the pockets of the bad guys • Advertisers have lower ROI due to fraud (fake impressions, non-humans) • Publishers have lower revenues (ad dollars stolen by bad guys) 2/3 1/3 Users use ad blocking and need to protect privacy
  11. 11. May 2015 / Page 10marketing.scienceconsulting group, inc. Dr. Augustine Fou Motive & Opportunity – More programmatic, more fraud As more digital ads are placed entirely programmatically, the opportunity for fraud continues to increase. Bad guys also fully automate their digital ad fraud operations, using programmatic tools.
  12. 12. May 2015 / Page 11marketing.scienceconsulting group, inc. Dr. Augustine Fou Viewability is not fraud; but often mentioned together Viewability (or lack thereof) is not fraud; but it is often mentioned together with ad fraud because early forms of fraud involved bad guys overloading ads on the page Bad Guys Publishers
  13. 13. May 2015 / Page 12marketing.scienceconsulting group, inc. Dr. Augustine Fou Bad guys have already “defeated” viewability In fact, bad guys’ sites may even have 100% viewability (even while the industry continues to debate its definition) by arraying all the ads above the fold to trick tracking technology to appear to be 100% viewable Source: Spider.io May 2, 2013 AD Ad Stacking
  14. 14. How Impression (CPM) Fraud Works
  15. 15. May 2015 / Page 14marketing.scienceconsulting group, inc. Dr. Augustine Fou The two main ingredients of programmatic fraud Fake Sites Fake Users 1 2 3 4 5 6 7 8 9 10 Designed with no real content; exists only to carry ads Bots are automated browsers which load webpages to load ads
  16. 16. May 2015 / Page 15marketing.scienceconsulting group, inc. Dr. Augustine Fou 19 blocked ads on page 1. Bad guys put up fake sites site = analyzecanceradvice .com site = missomoms.com
  17. 17. May 2015 / Page 16marketing.scienceconsulting group, inc. Dr. Augustine Fou 2. Load tons of ads (Display, Video) on pages http://interiorcom plex.com/ http://modernbab y.com/
  18. 18. May 2015 / Page 17marketing.scienceconsulting group, inc. Dr. Augustine Fou 3. Use bots to repeatly load pages and cause impressions Current forms of ad fraud involve virtual browsers spun up by the millions in data centers to load webpages to create ad impressions or to make fake clicks Source: Google Digital Attack Map
  19. 19. May 2015 / Page 18marketing.scienceconsulting group, inc. Dr. Augustine Fou Sites can be grouped into three “strata” Sites, networks and exchanges that cheat or look the other way • Stacked ads, auto-refresh • High ad-load, low viewability • Purchased traffic, audience expansion Sites created solely for ad fraud • Created by template/algo • Enormous quantity of ads • All traffic/impressions from bots Mainstream publishers with human + bot traffic • Legitimate publishers with real content that humans engage with, interact, read and watch • An unknown percentage of malicious bot traffic designed to enrich their cookie-profiles for later high-value re-targeting FRAUDSITE bot bot bot bot bot bot PUBLISHERuser bot user crawler user bot 2/3 1/3
  20. 20. May 2015 / Page 19marketing.scienceconsulting group, inc. Dr. Augustine Fou Examples of Fake/Fraud Websites • Algo generated using templates (e.g. The same wordpress template) • No content or plagiarized content • Stuffed with keywords ands ads • Uses keyword domains or domains with lots of numbers in them • Sends large quantities of ad impressions and search clicks
  21. 21. May 2015 / Page 20marketing.scienceconsulting group, inc. Dr. Augustine Fou Examples of Known/Observed Bots (User Agents) Headless Browsers Selenium PhantomJS Mobile Simulators 35 listed
  22. 22. How Click (CPC) Fraud Works
  23. 23. May 2015 / Page 22marketing.scienceconsulting group, inc. Dr. Augustine Fou 1. Bad guys choose expensive keywords homemadesimple.comolay.com “cosmetic face lift” $10.84 CPC “residential home cleaning” $9.95 CPC > 100,000 monthly searches avg position 1 – 10 sort by highest avg CPC Source: iSpionage
  24. 24. May 2015 / Page 23marketing.scienceconsulting group, inc. Dr. Augustine Fou 2. Their bots type keywords on sites they control buy eye cream online healthsiteproduc tionalways.com
  25. 25. May 2015 / Page 24marketing.scienceconsulting group, inc. Dr. Augustine Fou 3. Bots click on search ad to create CPC revenue Olay.com ad in #1 position
  26. 26. May 2015 / Page 25marketing.scienceconsulting group, inc. Dr. Augustine Fou 4. Pass fake URL trackers to hide the origin of the clicks Click thru URL passing fake source “utm_source=msn” http://www.olay.com/skin-care- products/OlayPro- X?utm_source=msn&utm_medium= cpc&utm_campaign=Olay_Search_D esktop_Category+Interest+Product.P hrase&utm_term=eye%20cream&ut m_content=TZsrSzFz_eye%20cream_ p_2990456911
  27. 27. May 2015 / Page 26marketing.scienceconsulting group, inc. Dr. Augustine Fou Examples of search fraud domains observed 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 36,000+ more sites like these, designed to show search ads and self-click on them to siphon CPC revenue
  28. 28. Bots Are the Main Cause of Ad Fraud
  29. 29. May 2015 / Page 28marketing.scienceconsulting group, inc. Dr. Augustine Fou Bad bots are the primary tool for stealing ad dollars Retargeting fraud • Bots come to advertisers site to collect cookie • Bots visit other sites owned by bad guys • Retargeting follows them around to show ads • When ad impression is served on bad guys sites, they earn the CPM revenue Diverting ad dollars • CTR manipulation: bots go to premium site to generate pageviews without clicks (lowers the sitewide CTR ); optimizers divert dollars to sites with higher sitewide CTR (bad guy sites) • Fake viewability: bad guys stack all ads behind each other so they are all viewable; optimizers divert dollars to sites with higher viewability (bad guy sites) Source: ANA / White Ops Study Published December 2014 [PDF] Generate fake ad impressions and clicks • Bots repeatedly load pages or ads to make impressions • Bots can also “type” search keywords and click on search ads to create fake clicks
  30. 30. May 2015 / Page 29marketing.scienceconsulting group, inc. Dr. Augustine Fou Different kinds of bots generate fake impressions/clicks Malware (on PCs)Botnets (from datacenters) Toolbars (in-browser)Javascript (on webpages)
  31. 31. May 2015 / Page 30marketing.scienceconsulting group, inc. CONFIDENTIAL Bad guys’ bots are not in ANY official bot list 10,000 bots observed in the wild user-agents.org bad guys’ bots 3% Dstillery, Oct 9, 2014_ “findings from two independent third parties, Integral Ad Science and White Ops” 3.7% Rocket Fuel, Sep 22, 2014 “Forensiq results confirmed that ... only 3.72% of impressions categorized as high risk.” 2 - 3% comScore, Sep 26, 2014 “most campaigns have far less; more in the 2% to 3% range.” industry bot list “not on any list” disguised as normal browsers – Internet Explorer; constantly adapting to avoid detection”
  32. 32. May 2015 / Page 31marketing.scienceconsulting group, inc. Dr. Augustine Fou Humans vs “honest” bots vs fraudulent bots Confirmed humans • found page via search • observed events (mouse click with coordinates) “Honest” (declared) bots • search engine crawlers • declare user agent honestly • observed to be 1 – 5% of websites’ traffic Fraud bots • come from data centers • malware compromised PCs • deliberately disguise user agent as human users
  33. 33. May 2015 / Page 32marketing.scienceconsulting group, inc. Dr. Augustine Fou Bot fraud and activity observed as high as 100% Source: ANA / White Ops Study Published December 2014 [PDF] display ads 11% 25% video ads 23% 50% sourced traffic 52% 100% ANA/WhiteOps Study What We’ve Seen Case 1 Case 2
  34. 34. May 2015 / Page 33marketing.scienceconsulting group, inc. Dr. Augustine Fou “bad bots are about 1/3rd” Multiple sources comfirm levels of bot activity and traffic Source: Incapsula 2014 http://www.incapsula.com/blog/bot-traffic-report-2014.html Source: Solve media 2013
  35. 35. May 2015 / Page 34marketing.scienceconsulting group, inc. Dr. Augustine Fou Bots morph to target verticals with highest ad spend Source: DataXu/DoubleVerify Webinar, April 2015
  36. 36. May 2015 / Page 35marketing.scienceconsulting group, inc. Dr. Augustine Fou Even “good” bots still cost advertisers money iSpionage (harvest search ads)Moat (harvest display ads)
  37. 37. Techniques for Committing Fraud
  38. 38. May 2015 / Page 37marketing.scienceconsulting group, inc. Dr. Augustine Fou Techniques to generate more impressions AD Ad stacking (dozens in one call) Pixel Stuffing (hiding more ads) Source: DoubleVerify Case Study Sourced Traffic (more pageviews) Ad Injection (replace ads) Image Source: BenEdelman.org The ANA/WhiteOps study also found rampant injection fraud, including one publisher whose site was hit with 500,000 injected ads every day for the duration of the two-month study.
  39. 39. May 2015 / Page 38marketing.scienceconsulting group, inc. Dr. Augustine Fou Techniques to hide fraudulent activity Domain / IP Spoofing Impression Laundering Source: Whiteops/ANA Study Dec 2014 Stacked Redirects Passing fake variables 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 Make it appear that the impression came from legit site http://www.olay.com/skin-care- products/OlayPro- X?utm_source=msn&utm_medium=cpc& utm_campaign=Olay_Search_Desktop_Ca tegory+Interest+Product.Phrase&utm_ter m=eye%20cream&utm_content=TZsrSzFz _eye%20cream_p_2990456911 Easily trick analytics platforms to think the impression came from legit source
  40. 40. How The Industry is Fighting Fraud
  41. 41. May 2015 / Page 40marketing.scienceconsulting group, inc. Dr. Augustine Fou Industry associations are leading the charge “We must create an all-inclusive program that identifies qualified participants, and commits them to good actions, guaranteed by continual monitoring and sanctions for non-compliance,” said Randall Rothenberg, IAB. “Together we can continue to rebuild the trust that is necessary for the interactive marketing industry to thrive,” said Nancy Hill, President and CEO, 4A’s “We’ve invested [heavily] in data and data technology and [have] been very much at the forefront of fraud verification,” said John Montgomery, GroupM Bob Liodice, President and CEO of the ANA said, “It is imperative that the ecosystem addresses online fraud and improves media measurement and transparency.” September 30, 2014 - IAB, 4A’s, and ANA announce cross-industry organization to fight ad fraud, malware, and piracy.
  42. 42. June 2015 / Page 41marketing.scienceconsulting group, inc. CONFIDENTIAL Comparison matrix of anti-fraud solutions Vendors Description Mitigation WhiteOps Forensiq Pixalate On-site • Typically javascript code installed on websites (like analytics) • Detect parameters about the user and computing environment • blacklist sites that send traffic that is mostly bots DoubleVerify Integral Ad Science In-ad exchange • Real-time decisioning in the bid- stream using rules and blacklists (e.g. if a site is on a blacklist, don’t serve the ad) • reject users and reject sites in real-time based on blacklists SolveMedia AreYouAHuman reCaptcha Challenge based • Using captchas and other challenges which ask humans to verify themselves as humans • identify and whitelist humans for use in RTB decisioning Incapsula Distil Networks Spider.io Network analysis • Analysis of network traffic and technical analysis of visitors to prevent scraping, content theft, fraudulent JS execution • deflect traffic from known sources of bots, reject individual visits
  43. 43. May 2015 / Page 42marketing.scienceconsulting group, inc. Dr. Augustine Fou Recommendations for reducing fraud Move beyond just viewability; tackle NHT/bot fraud • Even if viewability was solved, if the impression was caused to load by a bot, it is still fraud and money wasted for the advertiser Allocate budget toward mainstream, premium publishers • Allocate ad spend away from long-tail sites in ad exchanges that have the most fraud; the more direct way to reduce bot fraud is to buy mainstream sites Demand full transparency and independently verify • If they can’t show you where each ad impression was served, don’t buy it; put in place your own checks and balances Focus less on impressions served or traffic, more on observable actions • If we focus on actions that can be observed rather than tonnage of ads served, and incentivize our agencies differently, we avoid the temptation of buying lower cost (lower quality) ad inventory
  44. 44. Humanness ScoreTM
  45. 45. May 2015 / Page 44marketing.scienceconsulting group, inc. Dr. Augustine Fou Humanness ScoreTM - Peer-Reviewed, Open-Standard Definitions: J9NGT6H3 = client/entity identifier; 65 = humanness score - 0 (bots) to 100 (human); i = in-ad | o = on-site; ^9 indicates the order of magnitude of the data set - e.g. billions. Score Syntax: < J9NGT6H3 | 65 (i^9) > A higher Humanness Score means a higher proportion of confirmed humans and good policies and disclosures that go along with ensuring a human audience. A higher Humanness Score should also lead to higher premium CPM or CPC -- this rewards premium publishers for their good work and "playing by the rules" and rewards advertisers with better performance for their ad spending. https://www.linkedin.com/pulse/humanness-score-open-standard-peer-reviewed-digital
  46. 46. May 2015 / Page 45marketing.scienceconsulting group, inc. Dr. Augustine Fou Humanness ScoreTM – How it is Calculated Three Ingredients Used to Calculate Policies - 20% of score • does the entity purchase or source traffic of any kind? • does the entity have published policies protecting users' privacy and does it consistently act according to these policies (see the EFF's Privacy Badger Initiative) • does the entity sell data -- e.g. cookie matching, cookie profile, collected or derived data Data - 50% of score • continuously measured data points on ad impressions and visits to websites - a minimum of 1 billion in-ad data points required for certification; and X million on-site data points for websites (depending on natural traffic volumes). • how often data like website and cookie blacklists are updated • whether the appropriate anti-fraud vendors are used and how the technology is deployed Disclosures - 30% of score • whether the entity provides full transparency to peers by providing access to visit level data so that peer can verify parameters like placement, viewability, and other metrics • whether the entity provides access to auditors of the sites on which the ads were run, the sources of traffic, and the recipients of media payments. • whether the entity generates and shares threat data with peers
  47. 47. May 2015 / Page 46marketing.scienceconsulting group, inc. Dr. Augustine Fou Humanness ScoreTM – Ad Network Comparison Ad Network AAd Network B Ad Network C < 7N20D7NC | 69 (i^6) > < NXDK1MWR | 52 (i^9) > < MQ3K5VWT | 100 (i^7) > < MC8RNDY7 | 79 (i^6) > Ad Network D
  48. 48. May 2015 / Page 47marketing.scienceconsulting group, inc. Dr. Augustine Fou Humanness ScoreTM – Publisher/Websites Comparison < DJF6H3JW | 51 (o^7) > < MC8RNDY7 | 69 (o^7) > < 53NC7EDK | 51 (o^8) > < K39DM5HF | 61 (o^5) > < MQ0D73NG | 74 (o^5) > < 3359HIN1 | 100 (o^5) > < 5BGEWDWV | 55 (o^5) > < HURXLSUK | 62 (o^4) > < GLWTQFL8 | 64 (o^5) > < 1LN9M6U6 | 52 (o^4) >
  49. 49. Fraud Mitigation Success Stories
  50. 50. May 2015 / Page 49marketing.scienceconsulting group, inc. Dr. Augustine Fou Success stories of advertisers reducing ad fraud Impressions / Traffic (CPM/CPV) Clicks (CPC) [ pharma ] 18% Shifted paid search budgets away from fraud websites that carried search ads and sent fake clicks [ skincare ] 10% [ haircare ] 1% [ cosmetics ] 6% • Blacklisted hundreds-of-thousands more sites • Set up more frequent blacklist / whitelist update intervals • Adjusted parameters to proactively discard suspicious bids • Updated ROI measures to focus on human actions • Cut off spending in certain categories, ad types, vendors [ franchise ] 47% [ technology ] 9% [ non-profit ] 39% [ med device ] 11%
  51. 51. May 2015 / Page 50marketing.scienceconsulting group, inc. Dr. Augustine Fou Click fraud case study: allocating away from fraud sites 18% of spend shifted from fraudulent websites to “top 5” good guys BEFORE Top 2 “good guys” = 76% AFTER Top 5 “good guys” = 94%
  52. 52. May 2015 / Page 51marketing.scienceconsulting group, inc. Dr. Augustine Fou Display ad case study: allocate to better ad networks Period 1 Period 2 Period 3 20% confirmed humans 30% confirmed humans 190k 5k 220k 6k 280k 7k total goal events average daily goals 10% confirmed humans
  53. 53. May 2015 / Page 52marketing.scienceconsulting group, inc. Dr. Augustine Fou Dr. Augustine Fou – Independent Ad Fraud Researcher “I advise advertisers and their agencies on the technical aspects of fighting digital ad fraud. Using forensic technologies and techniques I help to assess the threat and the current countermeasures in order to recommend additional steps that can be taken to combat fraud and improve ROI.” FORMER CHIEF DIGITAL OFFICER, HCG (OMNICOM) MCKINSEY CONSULTANT CLIENT SIDE / AGENCY SIDE EXPERIENCE PROFESSOR AND COLUMNIST ENTREPRENEUR / SMALL BUSINESS OWNER PHD MATERIALS SCIENCE (MIT '95) AT AGE 23 Articles: https://www.linkedin.com/today/author/84444-augustinefou Slideshares: http://bit.ly/augustine-fou-slideshares Bio: http://linkd.in/augustinefou acfou@mktsci.com | 212.203.7239

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