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Marketers' Playbook Questions to Ask Verification Vendors

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Do you think fraud detection tech works? Consider this. Bad guys are hackers. They have better tech and are always 1 step ahead of good guys trying to detect and catch them.

Here are some questions to ask of fraud detection vendors so you can tell if you are getting ripped off and if they can actually do what they claim to be doing.

Published in: Internet
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Marketers' Playbook Questions to Ask Verification Vendors

  1. 1. November 2018 / Page 0marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou MARKETERS’ PLAYBOOK Questions to Ask Verification Vendors Augustine Fou, PhD. acfou [at] mktsci.com
  2. 2. November 2018 / Page 1marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou “Digital ad fraud is at all time highs – both in dollar and rate. Most of the fraud is missed by fraud detection tech, because bad guys have better tech and easily trick or block them.”
  3. 3. November 2018 / Page 2marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Ad fraud is at all-time highs There’s $100B in digital ad spend to steal from, year after year U.S. Digital Ad Spend ($ billions) Actuals Projected Digital Ad Fraud ($ billions) ($300B worldwide)
  4. 4. November 2018 / Page 3marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 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
  5. 5. November 2018 / Page 4marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Executive Summary Marketers can take control and fight fraud with analytics/insights 1. Marketers should not assume that fraud verification vendors can detect fraud and stop it. There are technical limitations to what can be measured, how much is measured, and if it is measured. 2. Marketers should look at their own analytics to see if there are still tell-tale signs of fraud. 3. Marketers should ask hard and detailed questions of their verification vendors to assess whether they are even doing what they claim they can do.
  6. 6. November 2018 / Page 5marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Questions to ask your fraud detection vendor
  7. 7. November 2018 / Page 6marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Do they have in-ad vs on-site tags? Tags tuned for in-ad versus on-site measurement are needed In-Ad (rides with marketers’ ad) On-Site (installed on-site by publisher) 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.”
  8. 8. November 2018 / Page 7marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Do they measure for humans? Measuring for humans is crucial; as is reporting not-measurable volume bars (green) Stacked percent Blue (human) White (not measurable) Red (bots) red v blue trendlines “Fraud detection that only reports NHT/IVT is not correct” 10% bots does NOT mean 90% humans
  9. 9. November 2018 / Page 8marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Do they check for other fraud? How? Fraud detection looks for IVT(bots); may miss other forms of fraud % bot + % site + % mobile fraud % overall fraud = 23%, not 5% 5% 11% 7%
  10. 10. November 2018 / Page 9marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Do they detect popunders/redirects These forms of fraud typically get by current fraud detection tech Vendor openly selling 125 billion page redirects (pageviews) per month, at low CPMs) a.k.a. “zero-click” “pop-under” “forced-view” “auto-nav”
  11. 11. November 2018 / Page 10marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Do they detect mobile app fraud? “fraud sites’ traffic comes from apps that load hidden webpages” Openly selling on LinkedIn
  12. 12. November 2018 / Page 11marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Do they sample the data? Sampling can lead to large discrepancies and bad measurements WRONG IVT Measurement Source 3 - in ad iframe, badly sampled Incorrect, due to sampling
  13. 13. November 2018 / Page 12marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Can they explain their measurement? If something is marked as fraud, why?... or not fraud, why? “detailed supporting data to show client why something was marked as fraudulent, or marked as clean – not black box.”
  14. 14. November 2018 / Page 13marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Why are legit sites marked as fraud Something is wrong when legit sites are marked fraud and blocked Domain (spoofed) % SIVT esquire.com 77% travelchannel.com 76% foodnetwork.com 76% popularmechanics.com 74% latimes.com 72% reuters.com 71% bid request fakesite123.com esquire.com passes blacklist passes whitelist ✅ ✅ declared 1. fakesite123.com has to pretend to be esquire.com to get bids; 2. fraud measurement shows high IVT b/c it is measuring the fake site with fake traffic 3. Fake esquire.com gets mixed with real so average fraud rates appear high. 4. Real esquire.com gets backlisted; bad guy moves on to another domain.
  15. 15. November 2018 / Page 14marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou How much of their tags are blocked? 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
  16. 16. November 2018 / Page 15marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Why is obvious fraud getting through After fraud filters, obvious fraud is still impacting campaigns Repeatedly loading slideshow pages— log file data easily shows strange behavior, like slideshow pages loaded sequentially, or not sequentially Site with 100% Android visitors—log file data shows all devices were Android 8.0.0 and browsers were identical version
  17. 17. November 2018 / Page 16marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Why so many sellers offer valid traffic? Many sellers of “traffic” say they get by all fraud detection filters Choose Your “Traffic Quality Level” “Valid traffic” goes for higher prices Source: Shailin Dhar
  18. 18. November 2018 / Page 17marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Why are bots still getting through? Launch Week 3 and beyondWeek 2 Initial baseline measurement Measurement after first optimization After eliminating several “problematic” networks Obvious bots still get through
  19. 19. November 2018 / Page 18marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou After all flavors of“fraud filters” Obvious fraud still gets through (90- 100% win rates); but we turned off manually early in the campaign
  20. 20. November 2018 / Page 19marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou declared to be: How does brand safety tech work? They cannot read the content of the site with tags that are in ads Pre-scanned Domain List In-ad tag Ad tags that are in the foreign iframe (different domain) cannot look outside the iframe – i.e. cannot read content on the site to determine brand safety. bad word porn terrorism hate badsite123.com badsite123.com badsite123.com badsite123.com goodsite123.com goodsite123.com goodsite123.com Domain Placement Reports goodsite123.com goodsite123.com goodsite123.com goodsite123.com goodsite123.com goodsite123.com goodsite123.com FAILS because it is not directly measured; relies on domain placement reports which have declared data.
  21. 21. November 2018 / Page 20marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Why is “verified” no different than control “Verified Bots” “Verified Humans” Control: No Targeting +$0.25 data CPM +$0.25 data CPM “verified bots” and “verified humans” showed no difference in quality to each other – AND both were no different than the control where no targeting was used.
  22. 22. November 2018 / Page 21marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou “Note that I did NOT recommend asking them if they are ‘accredited’ or ‘certified against fraud’.” (they all are, so their numbers have gotta be right…. Right?)
  23. 23. November 2018 / Page 22marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou “fight ad fraud with common sense” - stop wasting money on tech that doesn’t work - insist on detailed data and look at the analytics yourself Here are some ideas to get you started (Marketers’ Anti-Ad Fraud Playbook) https://www.slideshare.net/augustinefou/b2c-marketers-anti-adfraud-playbook
  24. 24. November 2018 / Page 23marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou About the Author
  25. 25. November 2018 / Page 24marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Dr. Augustine Fou – Anti-Ad Fraud Consultant 2013 2014 Published slide decks and posts: http://www.slideshare.net/augustinefou/presentations https://www.linkedin.com/today/author/augustinefou 2016 2015 2017
  26. 26. November 2018 / Page 25marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Harvard Business Review 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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