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Digital Ad Fraud FAQ Question 1

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a colleague asked this question - "Publishers claim to only have 0.2-1% IVT on their sites. Can it be possible?” I answered in 22 slides.

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Digital Ad Fraud FAQ Question 1

  1. 1. May 2020 / Page 0marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Digital Ad Fraud FAQ #1 May 2020 Augustine Fou, PhD. acfou [at] mktsci.com 212. 203 .7239
  2. 2. May 2020 / Page 1marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou FAQ #1 “Publishers claim to only have 0.2-1% IVT on their sites. Can it be possible?”
  3. 3. May 2020 / Page 2marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Answer • Yes. That is possible for “good publishers” (ones that don’t source traffic); this is because fraud bots won’t waste time loading pages on sites that don’t pay them for traffic. Fraud bots will load pages on sites that pay them for traffic • Good publishers will still have normal search engine crawlers and other “honest” bots that declare themselves (bot tells you it is “moatbot, Googlebot, facebookbot,” etc. • Be sure to also confirm for humans, because “not invalid” or “not bots” does not automatically mean “human.” (see dark blue in the next 3 slides)
  4. 4. May 2020 / Page 3marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Definitions (for charts below) What each of the labels means • Humans - 3 or more blue flags to confirm • Some blue flags but not 3 or more • Can’t label it either red or blue • Tag was called, but no data was sent back (blocked) • Tag was not called (not measurable) • Bot – Search crawler • Bot – Says its name honestly, (14,000 bot names) • Some red flags, but not 3 or more • Bots - 3 or more red flags to confirm
  5. 5. May 2020 / Page 4marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publisher 1 Great consistency in the data; lots of humans (blue), low bots
  6. 6. May 2020 / Page 5marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publisher 2 Declared (orange), search (yellow), other bots can be identified
  7. 7. May 2020 / Page 6marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publisher 3 Search engine crawlers (yellow) can account for 5-10% of traffic
  8. 8. May 2020 / Page 7marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Answer • Some good publishers also filter for bots (datacenter, declared) – this means when the visitor is from a data center, or a declared bot, the ad calls are NOT made • When the publisher filters for data center and declared bots, the resulting bot % can indeed be sub-1% - in the charts above, they would filter out the yellow (search crawlers) and orange (declared bots)
  9. 9. May 2020 / Page 8marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publisher, filter datacenter/bots 10% red 3% red “Filter for GIVT and data center; don’t call ads” 27% red 17% red -7% -10% On-Site measurement In-Ad measurement Filter applied Stopped buying traffic
  10. 10. May 2020 / Page 9marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Answer • On-site measurement is most accurate; but fake sites won’t allow measurement tags to be added to the site. So most marketers will only have in-ad measurement • Most fraud sites buy traffic that is well-disguised; that means standard IVT verification tech is not detecting it as “invalid” so ad impressions get marked as “valid” even though they are not • By analyzing for other forms of fraud (e.g. mobile apps that load webpages) we can catch a lot more fraud
  11. 11. May 2020 / Page 10marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Places to buy “valid” traffic
  12. 12. May 2020 / Page 11marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou IVT only catches bots, misses other Sites and apps that cheat may look fine in bot detection reports 1.3% + 57% = 58%IVT site/app fraud overall fraud bot detection sees this bot detection misses this
  13. 13. May 2020 / Page 12marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Campaign example Not Measurable 0.0% No Client-Side Data 6.9% DESKTOP GIVT/SIVT Humans Other Disguised Traffic Device Error App Fraud 49% 11.3% 0.1% 88.6% 0.2% 0.3% 10.1% MOBILE GIVT/SIVT Humans Other 44% 1.2% 14.5% 84.3% DEFINITIONS • Not measurable – no tags sent (this should be zero, ads are called by JS) • No Client-Side Data – no data sent back, ad blocker or browser block • Other – not enough blue or red labels to confirm • Disguised Traffic – fake traffic, bounced through residential proxies • Device Error – one or more factors indicating fake device • App Fraud – apps loading webpages and other non-IVT fraud Mobile apps using hidden webview browsers to load webpages; those appear to be mobile devices loading webpages; NOT detected by IVT verification.
  14. 14. May 2020 / Page 13marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Campaign Examples Filtered versus not filtered campaigns – basic G-IVT 3% IVT 25 - 40% IVT well managed NOT well managed
  15. 15. May 2020 / Page 14marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou High bot/fraud examples
  16. 16. May 2020 / Page 15marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Answer • Industry-reported benchmarks are in the 1 – 3% range for this reason; they are only reporting IVT, and missing other forms of fraud, which could be many times higher
  17. 17. May 2020 / Page 16marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou IAS – 1.9% (desktop display) Source: IAS Media Quality Report H1 2019
  18. 18. May 2020 / Page 17marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou IAS – 0.9% (mobile display) Source: IAS Media Quality Report H1 2019
  19. 19. May 2020 / Page 18marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou IAS – 1.1% (desktop video) Source: IAS Media Quality Report H1 2019
  20. 20. May 2020 / Page 19marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou WhiteOps - 3% IVT Source: WhiteOps Bot Baseline, May 2019
  21. 21. May 2020 / Page 20marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Answer • It is important to actually detect what sites the ads actually loaded on, instead of just assume the domain from the bid request (fake sites pass legit domains in bid request, in order to get bids) • Legit sites that are spoofed get falsely accused of high IVT; but none of the bots or fake traffic were actually on the real legit publisher’s site
  22. 22. May 2020 / Page 21marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good pubs, wrongly accused Domain spoofing causes legit pubs to get accused of high IVT Domain (spoofed) % SIVT esquire.com 77% travelchannel.com 76% foodnetwork.com 76% popularmechanics.com 74% latimes.com 72% reuters.com 71% to get bids fakesite123.com esquire.compasses blacklist passes whitelist✅ ✅ declares to be 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.

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