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Digital ad fraud impact on class action notice industry

Ad fraud steals ad budgets and negatively impacts the class action notice industry - ads are not put in front of humans, but instead are shown to bots (software programs that load webpages). Bot don't complete claim forms; humans do.

Despite the use of fraud detection technologies, notice providers should use "best practicable" actions to verify the campaign analytics to see if ad fraud still gets through.

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Digital ad fraud impact on class action notice industry

  1. 1. October 2018 / Page 0 marketing.scienceconsulting group, inc. Digital Ad Fraud Impact on Class Action Notice Augustine Fou, PhD. acfou [at] 212. 203 .7239 Jeanne Finegan, APR jfinegan [at] 503. 579. 0746
  2. 2. October 2018 / Page 1 marketing.scienceconsulting group, inc. Context and Background
  3. 3. October 2018 / Page 2 marketing.scienceconsulting group, inc. “just because you can’t detect it (ad fraud), doesn’t mean it’s not there.”
  4. 4. October 2018 / Page 3 marketing.scienceconsulting group, inc. Fraud diverts ad spend to fraudsters Good Publishers “sites that carry ads” • No content • Few humans • Low CPMS $40 Search Spend Display Spend $40 $21$30 $3 Google Search FB+Google Display $29 (outside Google/Facebook) $83 billion Digital Spend Source: eMarketer March 2017 $10 1% of impressions $19 99% of impressions
  5. 5. October 2018 / Page 4 marketing.scienceconsulting group, inc. $29 (outside Google/Facebook) There’s 160X more “sites with ads” Good Publishers “sites with ads” Source: Verisign, Q4 2016 329M domains est. 164 million “sites that carry ads” “sites you’ve heard of” WSJ ESPN NYTimes Economist Reuters Elle 0.3% no ads carry ads 160X more 78% programmatic est. 1 million
  6. 6. October 2018 / Page 5 marketing.scienceconsulting group, inc. $29 (outside Google/Facebook) 700X more There’s 700X more fake apps 7M apps Source: Statista, March 2017 6.99 million 96% “apps that carry ads” 10,000 “apps you’ve heard of” Facebook Spotify Pandora Zynga Pokemon YouTube Facebook, 2015 Users use 8 – 15 apps on their phones. Spotify, 2016 People have 25 apps on their phones, use 5-8 regularly Forrester Research, May 2017 Humans “use 9 apps per day, 30 per month” 78% programmatic
  7. 7. October 2018 / Page 6 marketing.scienceconsulting group, inc. Examples of fake sites/apps 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 … they can sell ad “inventory” at low prices
  8. 8. October 2018 / Page 7 marketing.scienceconsulting group, inc. Walled gardens are fine, on-site … Google Search Facebook Display “bots can’t make money when ads load here” GDN FBX less bots | more humans first-party IDs, people-based marketing facebook app
  9. 9. October 2018 / Page 8 marketing.scienceconsulting group, inc. Fraud Comes in Large Numbers
  10. 10. October 2018 / Page 9 marketing.scienceconsulting group, inc. How bad guys commit ad fraud 1. set up FAKE SITES 2. buy FAKE TRAFFIC 3. sell FAKE ADS
  11. 11. October 2018 / Page 10 marketing.scienceconsulting group, inc. The most profitable criminal activity 1000% return 11% returns1% interest digital ad fraud stock marketbank interest “buy traffic for $1, sell ads for $10 CPMs”
  12. 12. October 2018 / Page 11 marketing.scienceconsulting group, inc. (2015) Display ads … Increased CPM prices by 800% Decreased impression volume by 92% Source: 260 billion 20 billion > $1.60 < 20 cents
  13. 13. October 2018 / Page 12 marketing.scienceconsulting group, inc. (2016) 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 1 botnet eats 15% of video inventory
  14. 14. October 2018 / Page 13 marketing.scienceconsulting group, inc. (2017) Mobile app install fraudSource: October 2018, Tune average 20% fraud 100% fraud 50% fraud24 billion clicks on 700 mobile networks
  15. 15. October 2018 / Page 14 marketing.scienceconsulting group, inc. (2017) Mobile display ad fraud May 26 Forbes “Judy Malware” • 40 bad apps to load ads • 36 million fake devices to load bad apps • e.g. 30 ads per device /minute • 30 ads per minute = 1 billion fraud impressions per minute June 1 Checkpoint “Fireball” • 250 million infected computers • primary use = traffic for ad fraud • 4 ads /pageview (2s load time) • fraudulent impressions at the rate of 30 billion per minute
  16. 16. October 2018 / Page 15 marketing.scienceconsulting group, inc. (2018) Mobile display ad fraud One example was an Android app called MegaCast, which was found to be displaying the unique ID of others apps to attract bids for ads. [Google] "confirmed the traffic from the apps "seems to be a blend of organic user traffic and artificially inflated ad traffic, including traffic based on hidden ads". The scheme reportedly involved 125 Android apps and websites. … the fraudsters buy legitimate Android apps with an established reputation and then … blend bot- and human-generated traffic to evade ad-fraud detection. The TechSnab malware is usually bundled with free, third-party apps and is installed as a browser extension. Users would discover an infection if they see pop-ups, pop-unders and various other ads marked 'TechSnab'. Source: Buzzfeed News, Oct 2018
  17. 17. October 2018 / Page 16 marketing.scienceconsulting group, inc. Fraud is successful …
  18. 18. October 2018 / Page 17 marketing.scienceconsulting group, inc. Fake sites successfully sell ads… how? 100% viewability (but, it’s fake) AD Stack ads all above the fold to trick detection 0% NHT (but, it’s fake) Buy traffic that is guaranteed to pass fraud filters clean placement (but, it’s fake) Pass fake source to trick reports of placement details m/skin-care- products/OlayPro- X?utm_source=elle &utm_medium=dis play + + “by tricking measurement and reporting”
  19. 19. October 2018 / Page 18 marketing.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)
  20. 20. October 2018 / Page 19 marketing.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
  21. 21. October 2018 / Page 20 marketing.scienceconsulting group, inc. Bad guys exploit gaps in detection
  22. 22. October 2018 / Page 21 marketing.scienceconsulting group, inc. Current detection is severely limited In-Ad (billions of ads) • Limitations – tag is in foreign iframe, cannot look outside itself ad tag / pixel (in-ad measurement) In-Network (trillions of bids) On-Site (millions of pageviews) 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
  23. 23. October 2018 / Page 22 marketing.scienceconsulting group, inc. Tag placement matters ... A LOT Tag (in foreign iframe) Tag (on page) window sizes detected as 0x0 or 0x8 pixels correct window sizes for ads detected 0% humans 60% bots 60% humans 3% bots “fraud measurements could be entirely wrong, depending on where the tag is placed and where the measurement is done.”
  24. 24. October 2018 / Page 23 marketing.scienceconsulting group, inc. Measurements entirely wrong Incorrect IVT Measurement Source 3 - in ad iframe, badly sampled Sources 1 and 2 corroborate
  25. 25. October 2018 / Page 24 marketing.scienceconsulting group, inc. Legit sites incorrectly marked Domain (spoofed) % SIVT 77% 76% 76% 74% 72% 71% bid request passes blacklist passes whitelist ✅ ✅ declared 1. has to pretend to be to get bids; 2. fraud measurement shows high IVT b/c it is measuring the fake site with fake traffic 3. Fake gets mixed with real so average fraud rates appear high. 4. Real gets backlisted; bad guy moves on to another domain.
  26. 26. October 2018 / Page 25 marketing.scienceconsulting group, inc. Beware mobile!
  27. 27. October 2018 / Page 26 marketing.scienceconsulting group, inc. Mobile is 57% of digital spend Source: IAB Full-year 2017 Digital Advertising Report
  28. 28. October 2018 / Page 27 marketing.scienceconsulting group, inc. Beware mobile… MORE rampant fraud Bad apps load impressions in background, not in use Source: Forensiq Fake mobile devices install apps and interact w/ them Download and Install Launch and Interact “more money; less measurable”
  29. 29. October 2018 / Page 28 marketing.scienceconsulting group, inc. 9% of apps caused 80% of fake impressions 1 (52% of impressions) 2 (48% of impr) 66% avg fraud 18% avg fraud 1. 9% of the apps caused 52% of impressions; 66% outright fraud 2. Remaining 91% of apps caused 48% of impressions, 18% outright fraud
  30. 30. October 2018 / Page 29 marketing.scienceconsulting group, inc. 3 bad apps eat 75% of mobile budget com.jiubang com.flashlight com.latininput 75% of the dark red
  31. 31. October 2018 / Page 30 marketing.scienceconsulting group, inc. Mobile apps loading webpages “fraud sites’ traffic comes from apps that load hidden webpages” Openly selling on LinkedIn
  32. 32. October 2018 / Page 31 marketing.scienceconsulting group, inc. Top mobile apps by ad revenue Top mobile apps by ad revenue Are entirely different than ones humans spend the most time with
  33. 33. October 2018 / Page 32 marketing.scienceconsulting group, inc. Productivity of Digital Ads
  34. 34. October 2018 / Page 33 marketing.scienceconsulting group, inc. Fraud, lack of viewability limit productivity of ads 100 million impressions - 36% 64 million impressions - 54% 17 million impressions - 23% 29 million impressions - 41% 13 million impressions bot impressions Source: WSJ One-Third of Traffic is Bogus not-in-view Source: comScore June 2013 ad-blocked by user Source: PageFair, August 2013 not on target Source: Nielsen Sept 2014
  35. 35. October 2018 / Page 34 marketing.scienceconsulting group, inc. Top priorities for increasing digital ad productivity Served ad impressions -11%Display ads -23%Video ads -52% NHT(“bots”) Sourced traffic Source: WhiteOps / ANA Dec 2014 Display ads Source: Google Nov 2014 -36% Average NHT (bots) Viewability -56%Video ads -80%Views Source: RealVu 2014 “1 in 5 ads are viewable” -26% Ad Block usage Source: Marketing Science 2014 -34.5% Display ads Source: PageFair 2014 AdBlocking -54% Source: comScore Jun 2013 -60% Average Viewability
  36. 36. October 2018 / Page 35 marketing.scienceconsulting group, inc. Case Study: Class Action Notice Campaign
  37. 37. October 2018 / Page 36 marketing.scienceconsulting group, inc. “humans fill out claim forms; bots don’t”
  38. 38. October 2018 / Page 37 marketing.scienceconsulting group, inc. Executive Summary • It is imperative to make sure that digital ads are seen by humans, not bots; because only humans fill out settlement claim forms, bots don’t. • Getting ads in front of humans starts with good media buying; the quality is evident in the data/charts • By measuring for ad fraud and bots and quickly optimizing for more human audiences, the performance of the digital campaigns yield better outcomes • Not measuring digital campaigns inevitably result in wasted ad spend and ads not being shown to humans and class members are deprived of Due Process.
  39. 39. October 2018 / Page 38 marketing.scienceconsulting group, inc. Measure every point of the funnel 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
  40. 40. October 2018 / Page 39 marketing.scienceconsulting group, inc. Compare quality across channels Marketer 1 • Blue means humans • Red means bots Marketer 2 “what is the quality of traffic arriving on your site from various sources – organic and paid?”
  41. 41. October 2018 / Page 40 marketing.scienceconsulting group, inc. Case Study – cleaning up a campaign in-flight Campaign Launch Week 3Week 2 Benchmarking the start of the campaign. Further optimization to reduce specific bots (red) Eliminating problematic ad networks 30% bots 15% bots 3% bots
  42. 42. October 2018 / Page 41 marketing.scienceconsulting group, inc. Facebook human audience vs Ad Networks We measured the landing page; click throughs from each media source. Source=Facebook Source=Twitter Source=Network A Source=Network C Source=Network DSource=Network B
  43. 43. October 2018 / Page 42 marketing.scienceconsulting group, inc. Measuring clicks to the claim site Period 2 Good media planning Buying on open exchanges “Clean media buying leads to more humans (blue) arriving on the settlement claim site.” Humans (blue) Bots (red)
  44. 44. October 2018 / Page 43 marketing.scienceconsulting group, inc. Shift budgets to quality (high human) 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. “mitigation doesn’t even require technology!”
  45. 45. October 2018 / Page 44 marketing.scienceconsulting group, inc. Optimize for real human conversions Organic sources have more humans (dark blue) Conversion actions (calls) are well correlated to humans; bots don’t call
  46. 46. October 2018 / Page 45 marketing.scienceconsulting group, inc. Display 4 2,036 humans human conversion rate Adjust budgets to real conversions 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%
  47. 47. October 2018 / Page 46 marketing.scienceconsulting group, inc. “Best Practicable” already in practice • Measure every ad impression to determine quality (human audience) of media sources • Measure landing pages to re-verify quality of users that click through; DON’T assume fraud detection tech catches all fraud • Using 1-2x per day frequency cap to minimize repeat bot impressions; friendlier for humans too [Not a campaign cap of 1x or 2x] • Turn off offending domains and apps that are sending irregularly high amounts of traffic or bots • Reduce spend on ad networks that do not allow complete tracking and measurement of impressions • Optimize toward highest real conversion sources
  48. 48. October 2018 / Page 47 marketing.scienceconsulting group, inc. About the Authors
  49. 49. October 2018 / Page 48 marketing.scienceconsulting group, inc. Jeanne Finegan, APR – Class Action Notice Expert Follow me on LinkedIn Further Reading: Creating a Class Notice Program that Satisfies Due Process, Law360, New York, (February 13, 2018 12:58 PM ET). fraud-reality-digital-jeanne-finegan-apr/ safety-why-both-sides-v-jeanne-finegan-apr/ finegan-apr/ Jeanne Finegan, APR, is President and Chief Media Officer of HF Media, LLC. is a member of the Board of Directors for the prestigious Alliance for Audited Media (“AAM “) and a distinguished legal notice and communications expert with more than 30 years of communications and advertising experience. During her tenure, she has planned and implemented over 1,000 high-profile, complex legal notice communication programs with extensive international notice experience spanning more than 140 countries and over 40 languages. Ms. Finegan has provided expert testimony before Congress on issues of notice, and expert testimony in both state and federal courts regarding notification campaigns. She has lectured, published and has been cited extensively on various aspects of legal noticing, product recall and crisis communications. She has served the Consumer Product Safety Commission (CPSC) as an expert to determine ways in which the Commission can increase the effectiveness of its product recall campaigns. Further, she has planned and implemented large-scale government enforcement notice programs for the Federal Trade Commission (FTC) and the Securities and Exchange Commission (SEC). Most recently, she was a lead contributing author for Duke University's School of Law, "Guidelines and Best Practices Implementing 2018 Amendments to Rule 23 Class Action Settlement Provisions."
  50. 50. October 2018 / Page 49 marketing.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