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You're Not Getting What You Bought


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Marketers should always insist on detailed reports; otherwise they are sure to be ripped off and not get what they thought they bought, in digital media. The charts collected over the years show that the reality is usually the opposite of what people expected.

Published in: Data & Analytics
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You're Not Getting What You Bought

  1. 1. You’re Not Getting What You Paid For September 2017 Augustine Fou, PhD. acfou [at] 212. 203 .7239
  2. 2. Marketers run their own ROI experiments
  3. 3. September 2017 / Page 2marketing.scienceconsulting group, inc. Chase: 99% reach had 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)
  4. 4. September 2017 / Page 3marketing.scienceconsulting group, inc. P&G: $140M in digital, no impact “Procter & Gamble's concerns about where its ads were showing up online contributed to a $140 million cutback in the company's digital ad spending last quarter, the company said Thursday. That helped the world's biggest advertiser beat earnings expectations. Perhaps even more noteworthy, however, organic sales outperformed both analyst forecasts and key rivals at 2% growth despite the drop in ad support. Source: AdAge, July 2017
  5. 5. September 2017 / Page 4marketing.scienceconsulting group, inc. Restoration Hardware: cut all keywords “[W]e’ve found out that 98% of our business was coming from 22 words. So, wait, we’re buying 3,200 words and 98% of the business is coming from 22 words. What are the 22 words? And they said, well, it’s the word Restoration Hardware and the 21 ways to spell it wrong, okay? Immediately the next day, we cancelled all the words, including our own name.” Source: BusinessInsider, Sept 2017
  6. 6. September 2017 / Page 5marketing.scienceconsulting group, inc. Uber Sues Mobile Agency for Ad Fraud “Between 2015 and the first quarter of 2017, Uber paid more than $82.5 million for the ad effort coordinated by Fetch, court documents show. Uber alleges to have found… a Fetch transparency report that showed the number of weekly reported clicks on Uber ads on one website was nearly equal to the site’s monthly active users. Uber was spending millions of dollars a week on mobile ad inventory that was “purportedly attributable to hundreds of thousands (even millions) of Uber App installs per week,” according to the complaint. However, when the mobile ad effort was suspended, Uber said it saw “no material drop in total installations.” Source: WSJ, Sept 2017
  7. 7. If you don’t have 100% measurement or very detailed reports… you’re NOT getting what you paid for.
  8. 8. September 2017 / Page 7marketing.scienceconsulting group, inc. Thought you bought ESPN? Nope ALL fake inventory because, PublisherA does NOT sell any ads on any exchanges! “Fake sites must pretend to be mainstream ones in order to sell inventory.”
  9. 9. September 2017 / Page 8marketing.scienceconsulting group, inc. Thought you bought reach? Nope $1 CPM Top 10 sites = 66% of imps $5 CPM Top 10 sites = 74% of imps $0.50 CPM Top 5 sites = 100% of imps $10 CPM Top 10 sites = 71% of imps Majority of your ads ran on 5-10 sites/apps
  10. 10. September 2017 / Page 9marketing.scienceconsulting group, inc. In daily reports, you wont notice… Most of budget wasted between 12a – 4a; to bots 98% impressions blown in 1st hour (12a-1a) HOURLY CHART
  11. 11. September 2017 / Page 10marketing.scienceconsulting group, inc. Fraud filters don’t reduce fraud 1. Fraud filters are no better than manual blacklists 2. In some cases it’s worse when filter is on 3. Using fraud filters adds 20 – 24% to costs; manual blacklists are free
  12. 12. September 2017 / Page 11marketing.scienceconsulting group, inc. Paid extra for geotargeting, but it’s faked Not Normal – in both campaigns 1. 100% mobile apps; 100% Android; same top 15 apps in both markets 2. 100% of impressions generated between 4a – 5a local time 3. 100% fake devices; 15 unique devices generated top 95% impressions 4. 100% data center traffic, randomized through residential proxies
  13. 13. September 2017 / Page 12marketing.scienceconsulting group, inc. Paid extra for targeting, but didn’t work “Verified Bots” “Verified Humans” “Fraud-free Apps”Control: No Targeting +$0.25 data CPM +$0.25 data CPM+$0.25 data CPM
  14. 14. September 2017 / Page 13marketing.scienceconsulting group, inc. Targeting recent purchasers, didn’t work “Frequent Buyers” “Heavy Buyers” “Recent Purchaser - Books”Control: No Targeting +$1.00 data CPM +$1.00 data CPM+$1.75 data CPM
  15. 15. September 2017 / Page 14marketing.scienceconsulting group, inc. 90-99% of geolocation bad or faked Source: Placed Source: SafeGraph
  16. 16. September 2017 / Page 15marketing.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
  17. 17. September 2017 / Page 16marketing.scienceconsulting group, inc. 3 bad apps eat 75% of mobile budget com.jiubang com.flashlight com.latininput 75% of the dark red
  18. 18. September 2017 / Page 17marketing.scienceconsulting group, inc. 34 Mobile Networks >50% Fraud Source: June 2017, Tune average 20% fraud 100% fraud > 50% fraud
  19. 19. September 2017 / Page 18marketing.scienceconsulting group, inc. Some ads are called without webpages “Naked ad calls” are ad impressions served without webpages
  20. 20. Context and Sizing
  21. 21. September 2017 / Page 20marketing.scienceconsulting group, inc. Fraud on such a massive scale… 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
  22. 22. September 2017 / Page 21marketing.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 Digital Spend Source: eMarketer March 2017 47% programmatic
  23. 23. September 2017 / Page 22marketing.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 3% no ads carry ads 160X more 47% programmatic est. 1 million
  24. 24. September 2017 / Page 23marketing.scienceconsulting group, inc. 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 $29 (outside Google/Facebook) 47% programmatic 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”
  25. 25. September 2017 / Page 24marketing.scienceconsulting group, inc. Examples of fake sites, fake apps Fake Sites (10s of millions) Source: Fake Apps (millions)
  26. 26. September 2017 / Page 25marketing.scienceconsulting group, inc. Current detection cannot catch it 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 analysis of visitors, bots still get by • Limitations – relies on blacklists or probabilistic algorithms, least info ad served bot human fraud site good site
  27. 27. September 2017 / Page 26marketing.scienceconsulting group, inc. Just because you can’t measure it 100% fraud > 50% fraud … doesn’t mean it’s not there.
  28. 28. September 2017 / Page 27marketing.scienceconsulting group, inc. About the Author September 2017 Augustine Fou, PhD. acfou [@] 212. 203 .7239
  29. 29. September 2017 / Page 28marketing.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