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B2C Marketers Anti Ad-Fraud Playbook


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Marketers can look in their own analytics and detailed reports to identify ad fraud and clean it up. There is little need to use 3rd party fraud detection tech that is proven to not work.

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B2C Marketers Anti Ad-Fraud Playbook

  1. 1. October 2018 / Page 0marketing.scienceconsulting group, inc. B2C MARKETER’S ANTI-AD FRAUD PLAYBOOK Augustine Fou, PhD. acfou [at]
  2. 2. October 2018 / Page 1marketing.scienceconsulting group, inc. “Digital ad fraud is literally the bad guys’ ATM – it spits out cash. And every year $300 billion of marketers’ digital ad budgets refills it.”
  3. 3. October 2018 / Page 2marketing.scienceconsulting group, inc. Executive Summary Marketers can take control and fight fraud with analytics/insights 1. Marketers can run one or more of the following “plays” themselves without any specialized fraud detection tech; see if they are exposed to ad fraud. 2. Each “play” tells marketers where to look and what to look for in their own reports and analytics. 3. Marketers can then take specific actions if they find fraud and decide they no longer want to buy it.
  4. 4. October 2018 / Page 3marketing.scienceconsulting group, inc. 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)
  5. 5. October 2018 / Page 4marketing.scienceconsulting group, inc. 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
  6. 6. October 2018 / Page 5marketing.scienceconsulting group, inc. Easy to buy traffic, impressions Many sources to buy “traffic” that appears human, not invalid Choose Your “Traffic Quality Level” “Valid traffic” goes for higher prices
  7. 7. October 2018 / Page 6marketing.scienceconsulting group, inc. Anti-Fraud Playbook (Tech + Technique)
  8. 8. October 2018 / Page 7marketing.scienceconsulting group, inc. Super high win rates Normal “win rates” (impressions won / bids) are in the 10% range Where to find this? DSP report (your buying interface); have your agency provide on domain- by-domain basis. What to look for? Win rates that are significantly above 50% with substantial volume. What to do? Examine sites and see if they appear fraudulent in other ways; if so, turn off line item in the DSP.
  9. 9. October 2018 / Page 8marketing.scienceconsulting group, inc. Bids won vs ads served For each “bid won,” an “ad impression” should be served Where to find this? Compare your DSP reports for bids won, by domain, to your ad server reports, by domain. What to look for? Data discrepancies where the impressions served is far lower than the bids won, by domain. The more fraudulent the site the higher this disparity. What to do? Identify domains that have greater than 10% discrepancy and study them further; turn off if you agree it is fraud.
  10. 10. October 2018 / Page 9marketing.scienceconsulting group, inc. Ad serving volume by hour With hourly detail, you see most ads are blown out before 4 am Where to find this? Your ad server report; be sure quantities are reported by hour. What to look for? If all of the volume is spent in the first hour or during sleeping hours, you have no impressions left for the day – your budget is wasted. What to do? Turn off overnight hours and frequency cap by site and user to avoid massive spikes in volume that eat up all your budget.
  11. 11. October 2018 / Page 10marketing.scienceconsulting group, inc. Ad serving volume by site Details show you if your ads are too concentrated on a few sites Top 10 sites = 66% of impressions Top 10 sites = 74% of impressions Top 5 sites = 100% of impressions Where to find this? Your ad server report; be sure quantities are reported by domain. What to look for? If most of your impression volume is concentrated in a small number of sites or apps, then you did not achieve reach. What to do? Add frequency and quantity caps by site to ensure your ads are more widely distributed to other sites where your target users are.
  12. 12. October 2018 / Page 11marketing.scienceconsulting group, inc. Over-frequency to devices When tons of ads are served to the same device, it’s likely fraud Where to find this? In your own analytics, look for anomalies like massive changes or high concentrations (like all Android 8.0.0 devices – see chart to the left) What to look for? If a small number of devices get a large number or high percentage of ad impressions (too concentrated) something is suspicious. What to do? If you identify over-frequency, check that you have frequency caps set by user, by domain, or by time period. If it continues to happen, identify the domains/sellerIDs and turn off.
  13. 13. October 2018 / Page 12marketing.scienceconsulting group, inc. Ads.txt – who got paid? Domain-based reports are useless, insist on sellerID based reports Where to find this? You usually get domain- based placement reports; insist on getting sellerID- based reports. What to look for? Is the sellerID the correct one for the domain? (verify against ads.txt) What to do? • Insist on getting sellerID- based reports. • Turn off that line item to stop buying from that sellerID. bid request cookie bid won ad served PROGRAMMATIC SEQUENCE spoofed domain 156400 real sellerID
  14. 14. October 2018 / Page 13marketing.scienceconsulting group, inc. Abnormal traffic patterns All sites show abnormal surge in traffic on last day of the month Where to find this? Ad server or campaign reports; be sure to isolate individual sites What to look for? Are there abnormal surges in volume on the last day of the month? Tell-tale sign of sourced traffic (non- human) What to do? Identify the sites that exhibit these patterns and turn them off in your campaign interface.
  15. 15. October 2018 / Page 14marketing.scienceconsulting group, inc. Abnormal data consistency Sites with 100% “social traffic”, 100% Android, 100% same browser Where to find this? Your own ad server records or log files; also fraud detection data files with supporting details What to look for? Abnormal consistency like 100% Android, 100% same browser version, 100% of traffic is from Facebook, etc. What to do? These sites use these methods of cheating that are not typically caught by bot detection; decide whether you want to keep buying from them. Turn off in campaign interface.
  16. 16. October 2018 / Page 15marketing.scienceconsulting group, inc. Progressively clean campaigns Obvious fraud still gets through; analyze and turn off the fraud Launch Week 3 and beyondWeek 2 Initial baseline measurement Measurement after first optimization After turning off more “sites that cheat” 30% bots 15% bots 3% bots
  17. 17. October 2018 / Page 16marketing.scienceconsulting group, inc. Shift budgets to higher quality Some channels have more humans, shift budget to these • Blue means humans • Red means bots Where to find this? Your own bots versus humans measurement, with detailed and complete supporting data. What to look for? Be sure to measure for both bots and humans; rank order channels, sources, or sites by highest humans first. What to do? Some channels have more humans (blue) and others have more bots (red); shift budgets during campaign to more human channels.
  18. 18. October 2018 / Page 17marketing.scienceconsulting group, inc. About the Author
  19. 19. October 2018 / Page 18marketing.scienceconsulting group, inc. Dr. Augustine Fou – Anti-Ad Fraud Consultant 2013 2014 Published slide decks and posts: 2016 2015 2017