Ad Fraud Blocking Analytics Webinar
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This document discusses ad fraud and ad blocking. It begins with background on the rise of programmatic digital ad spending and fraud. It then defines the two main types of ad fraud as impression fraud and click fraud, both of which use bots. The document discusses how bots range in sophistication and why they are difficult to identify. It also addresses the impact of ad blocking and how fraud and blocking pollute analytics metrics. The document concludes with recommendations for advertisers to measure fraud directly and focus on metrics like conversions that cannot be easily faked.
![November 2015 / Page 12marketing.scienceconsulting group, inc.
Dr. Augustine Fou
Bot fraud observed as high as 100%
Source: ANA / White Ops Study Published December 2014 [PDF]
display ads
11%
25%
video ads
23%
50%
sourced traffic
52%
100%
ANA/WhiteOps Study
What We’ve Seen
Case 1 Case 2](https://image.slidesharecdn.com/adfraudblockinganalyticswebinar-151118134611-lva1-app6891/85/Ad-Fraud-Blocking-Analytics-Webinar-13-320.jpg)
Ad Fraud Blocking Analytics Webinar
- 1. Ad Fraud / Ad Blocking and Polluted Analytics December 2015 Augustine Fou, PhD. acfou@mktsci.com 212.203.7239
- 2. November 2015 / Page 1marketing.scienceconsulting group, inc. Dr. Augustine Fou Brief Agenda • Ad Fraud Background • What is Ad Fraud • Impact of Ad Blocking • How Fraud Pollutes Analytics • Low Hanging Fruit – You Can Do NOW!
- 4. November 2015 / Page 3marketing.scienceconsulting group, inc. Dr. Augustine Fou Percent of digital ad spend in programmatic: 70 - 75% 1995 Hundreds of major sites. 2005 Thousands of mainstream blogs. 2015 Millions of “long-tail” websites.
- 5. November 2015 / Page 4marketing.scienceconsulting group, inc. Dr. Augustine Fou Fraud continues upward as digital ad spend goes up Digital ad fraud High / Low Estimates plus best-guess Published estimates Digital ad spend Source: IAB 2014 FY Report $ billions E
- 6. November 2015 / Page 5marketing.scienceconsulting group, inc. Dr. Augustine Fou UPDATED: Full Year 2014 Digital Ad Spend – $50B Impressions (CPM/CPV) Clicks (CPC) Leads (CPL) Sales (CPA) Search 38% $18.8B Video 7% $3.5B Lead Gen 4% $2.0B 10% Other $5.0B Source: IAB, FY 2014 Internet Advertising Report, May 2015 $42.5B Display 16% $7.9B Mobile 25% $6.2B$6.2B CPM Performance • classifieds • sponsorship • rich media $7.0B
- 7. November 2015 / Page 6marketing.scienceconsulting group, inc. Dr. Augustine Fou Bad guys go where the money is – impressions/clicks Impressions (CPM/CPV) Clicks (CPC) Search $18.8B 86% digital spend Display $7.9B Video $3.5B Mobile $6.2B$6.2B Leads (CPL) Sales (CPA) Lead Gen $2.0B Other $5.0B • classifieds • sponsorship • rich media estimated fraud not at risk (up from 84% in 2013)
- 8. November 2015 / Page 7marketing.scienceconsulting group, inc. Dr. Augustine Fou 0 10 20 30 40 50 60 70 80 90 100 retail finance automotive telecom CPG entertainment pharma travel cons. electronics indexed spend share indexed fraud rate Ad fraud impacts every industry vertical High CPC industries Source: Ad spend share data from IAB, May 2015 | Fraud rate data from Integral Ad Science Q2 2014 Fraud Report
- 9. What is Ad Fraud?
- 10. November 2015 / Page 9marketing.scienceconsulting group, inc. Dr. Augustine Fou Two main types of fraud and how each is generated Impression (CPM) Fraud (includes mobile display, video ads) 1. Put up fake websites and load tons of ads on the pages Search Click (CPC) Fraud (includes mobile search ads) 2. Use bots to repeatedly load pages to generate fake ad impressions (launder the origins of the ads to avoid detection) 1. Put up fake websites and participate in search networks 2a. Use bots to type keywords to cause search ads to load 2b. Use bots to click on the ad to generate the CPC revenue
- 11. November 2015 / Page 10marketing.scienceconsulting group, inc. Dr. Augustine Fou Bots are the cause of all automated ad fraud Headless Browsers Selenium PhantomJS Zombie.js SlimerJS Mobile Simulators 35 listed Bots are made from malware compromised PCs or headless browsers (no screen) in datacenters.
- 12. November 2015 / Page 11marketing.scienceconsulting group, inc. Dr. Augustine Fou Bots range from simple to advanced; do different tasks Malware (on PCs)Botnets (from datacenters) Toolbars (in-browser)Javascript (on webpages)
- 13. November 2015 / Page 12marketing.scienceconsulting group, inc. Dr. Augustine Fou Bot fraud observed as high as 100% Source: ANA / White Ops Study Published December 2014 [PDF] display ads 11% 25% video ads 23% 50% sourced traffic 52% 100% ANA/WhiteOps Study What We’ve Seen Case 1 Case 2
- 14. Why are bad bots so hard to identify?
- 15. November 2015 / Page 14marketing.scienceconsulting group, inc. Dr. Augustine Fou Bad guy’s advanced bots are not on any industry list 10,000 bots observed in the wild user-agents.org bad guys’ bots 3% Dstillery, Oct 9, 2014_ “findings from two independent third parties, Integral Ad Science and White Ops” 3.7% Rocket Fuel, Sep 22, 2014 “Forensiq results confirmed that ... only 3.72% of impressions categorized as high risk.” 2 - 3% comScore, Sep 26, 2014 “most campaigns have far less; more in the 2% to 3% range.” detect based on industry bot list “not on any list” disguised as normal browsers – Internet Explorer; constantly adapting to avoid detection
- 16. November 2015 / Page 15marketing.scienceconsulting group, inc. Dr. Augustine Fou Example of filtering using bot lists – good, but not enough Google Analytics filters visits using official bot lists Bad guy bots are not on those lists and don’t declare themselves honestly; they pretend to be browsers like Internet Explorer, Safari, etc. “bad guy bots are not on industry lists”
- 17. November 2015 / Page 16marketing.scienceconsulting group, inc. Dr. Augustine Fou Humans vs “honest” bots vs fraudulent bots Confirmed humans • found page via search • observed events (mouse click with coordinates) “Honest” bots • search engine crawlers • declare user agent honestly • observed to be 1 – 5% of websites’ traffic Fraud bots • come from data centers • malware compromised PCs • deliberately disguise user agent as human users
- 18. November 2015 / Page 17marketing.scienceconsulting group, inc. Dr. Augustine Fou Mitigation does not require developers or statisticians Sites or ad networks that have high percentage of confirmed bots are blacklisted from ad-serving or ad spend to those sites is reduced In-ad (display ads served)On-site (clients’ websites) Sources of traffic that have high incidence of bots are added to ad- serving blacklists and filtered in analytics reports
- 19. Impact of Ad Blocking
- 20. November 2015 / Page 19marketing.scienceconsulting group, inc. Dr. Augustine Fou Ad blocking user growth continues to soar Source: PageFair / Adobe Aug 2015
- 21. November 2015 / Page 20marketing.scienceconsulting group, inc. Dr. Augustine Fou Ad blocking as a percent of users Source: PageFair / Adobe Aug 2015 Europe: 8% - 38%U.S.: 8% - 17%
- 22. November 2015 / Page 21marketing.scienceconsulting group, inc. Dr. Augustine Fou Estimated economic impact of ad blocking Source: PageFair / Adobe Aug 2015 Global economic impact: $41BU.S. economic impact: $20B
- 23. November 2015 / Page 22marketing.scienceconsulting group, inc. Dr. Augustine Fou Directly measured ad blocking rate Non-mobile Mobile Ad Block No Ad Block 53.6% 15.4% 25.6% 5.4% 29% 21% Overall Average 79.2% 20.8% 26% Ad Blocking Rate * percentages represent portion of data from N = 10 million sample 69.0% 31.0%Column Totals
- 25. November 2015 / Page 24marketing.scienceconsulting group, inc. Dr. Augustine Fou Bot activity pollutes quantity metrics • Bots can be programmed to send as much traffic and generate as many impresisons and clicks as the advertiser wants By systematically reducing ad spend to ad networks and sites that had the highest bots, and increasing allocation to premium publishers, the advertiser increased ad impressions served to humans, and lowered those served to bots.
- 26. November 2015 / Page 25marketing.scienceconsulting group, inc. Dr. Augustine Fou Bot activity pollutes quality metrics • Bots can manipulate bounce rates, click through rates, time on site, pages per visit; These engagement metrics appear to be tuned to 47 – 63%; pages per session averaged 2.03; and time on site was 1 – 2 minutes.
- 27. November 2015 / Page 26marketing.scienceconsulting group, inc. Dr. Augustine Fou Bot activity pollutes conversion metrics 378 411 357 361 512 495 525 409 595 536 552 596 437 452 380 425 532 489 592 584 403 416 415 587 570 490 463 516 400 389 418 Avg. 475 conversions /day Avg. 3,526 sessions /day Avg. 6,636 sessions /day 24% confirmed humans Avg. 473 conversions /day 40% confirmed humans 0% 5% 10% 15% 20% 25% Avg. 7.1% conversion rate Avg. 13.5% conversion rate “doubling humans, doubles conversion rates”
- 28. November 2015 / Page 27marketing.scienceconsulting group, inc. Dr. Augustine Fou Bad guys hide fraud by passing fake parameters Click thru URL passing fake source “utm_source=msn” fake campaign “utm_campaign=Olay_Search” http://www.olay.com/skin-care- products/OlayPro- X?utm_source=msn&utm_medium= cpc&utm_campaign=Olay_Search_D esktop_Category+Interest+Product.P hrase&utm_term=eye%20cream&ut m_content=TZsrSzFz_eye%20cream_ p_2990456911
- 29. November 2015 / Page 28marketing.scienceconsulting group, inc. Dr. Augustine Fou Bad guys fake KPIs, trick measurement systems Bad guys have higher CTR Bad guys have higher viewability AD Bad guys stack ads above the fold to fake 100% viewability Good guys have to array ads on the page – e.g. lower average viewability.
- 30. What you can do NOW
- 31. November 2015 / Page 30marketing.scienceconsulting group, inc. Dr. Augustine Fou Recommendations 1. Don’t panic; but also don’t be complacent – directly measure the amount of fraud that is impacting your digital ad spend and continuously mitigate. 2. Focus on the details – don’t assume someone else has taken care of the problem; take small, simple steps at low to no cost – e.g. look in analytics for referring sites that have 100% bounce and 0:00 time on site. 3. Update KPIs to focus on things that are not easily faked (i.e. don’t focus on number of impressions, clicks, or visits); focus on “conversion events” like purchases or other human actions.
- 32. November 2015 / Page 31marketing.scienceconsulting group, inc. Dr. Augustine Fou Normal Weekday vs Weekend Traffic Patterns weekends weekends weekends weekends weekdays weekdays weekdays weekdays Natural website pattern is weekends have lower traffic
- 33. November 2015 / Page 32marketing.scienceconsulting group, inc. Dr. Augustine Fou Typical Hour-of-Day Pattern humans sleeping humans awake; visiting websites
- 34. November 2015 / Page 33marketing.scienceconsulting group, inc. Dr. Augustine Fou Humans Sleep At Night Hourly traffic charts show lower traffic at night (as expected because humans sleep at night) Unusual traffic patterns with no normal night time trends visible, likely due to bot activity
- 35. November 2015 / Page 34marketing.scienceconsulting group, inc. Dr. Augustine Fou Humans Visit via Search humans find sites via search, during waking hours Bot traffic adds anomalous spikes to pattern
- 36. November 2015 / Page 35marketing.scienceconsulting group, inc. Dr. Augustine Fou Search vs Non-Human Traffic notice the timing hour-of-day pattern
- 37. November 2015 / Page 36marketing.scienceconsulting group, inc. Dr. Augustine Fou Closeup by Hour of Day 6 am5 am 2 am 3 am 3 am 2 am 3 am 18396 sessions 162 184 178 159 156 Sunday 85% avg bounce rate; 100% peak bounce rate
- 38. November 2015 / Page 37marketing.scienceconsulting group, inc. CONFIDENTIAL These advanced bots also faked some Goal Events Goal events that are based on page visits and video plays, could be (and were) faked. page visit goal page visit goal video play goal
- 39. November 2015 / Page 38marketing.scienceconsulting group, inc. CONFIDENTIAL But, there was no motive to fake other goals – e.g. pledges Other goals like pledges and downloads were not faked (faking downloads would cost them server resources). make a pledge curriculum download “Bots don’t make donations!”
- 40. November 2015 / Page 39marketing.scienceconsulting group, inc. CONFIDENTIAL Despite traffic loss, real human goals did not change Despite losing all of the traffic from these fake/fraud sites, there was no change to the number of pledges and downloads, during the same period of time. 102,231 sessions 0 sessions Conversion event 1 Conversion event 2
- 41. About the Author
- 42. November 2015 / Page 41marketing.scienceconsulting group, inc. CONFIDENTIAL Dr. Augustine Fou – Recognized Expert on Ad Fraud 2013 2014 2015 SPEAKING ENGAGEMENTS / PANELS 4A’s Webinar on Ad Fraud – October Digital Ad Fraud Podcast – January Programmatic Ad Fraud Webinar – March AdCouncil Webinar on Ad Fraud - April TelX Marketplace Live – June ARF Audience Measurement – June IAB Webinar on Ad Fraud / Botnets - September acfou@mktsci.com | 212.203.7239
- 43. November 2015 / Page 42marketing.scienceconsulting group, inc. CONFIDENTIAL Harvard Business Review – October 2015 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.