The Bot Baseline Fraud in Digital Advertising

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BOT FRAUD VARIES ACROSSCAMPAIGNS AND PLACEMENTSBecause bot percentages vary unpredictably across campaigns and placements, identifying and comparingthe real cost of the bot fraud problem is complex. One successful strategy is to examine fraud losses at theplacement level rather than looking at total losses within a brand or company-wide fraud loss.RECOMMENDATIONTroubleshoot placements that lose more ad spend than others.Using granular information (e.g., “Placements I, K, L, and M have below-average bots, but we are losing33 percent of impressions on Placement D to bots”), advertisers can quickly diagnose and eliminate largemonetary losses to bots even if the brand’s overall level of bot traffic is low.Cost-per-human (CPH) measures the actual cost of human impressions after accounting for loss due tobot fraud. Advertisers can compare CPH values across placements, campaigns, brands, and traffic sourcesto understand and communicate the real cost of reaching a human audience goal.FINDINGBOT PERCENTAGE353025201510509A3315101391011664 43 35B C D E F G H I J K L M N OPLACEMENTS WITHIN ONE PARTICIPANT CAMPAIGNCPHCost perHumanMeasures theactual cost of eachthousand humanimpressions afteraccounting for lossdue to bot trafficFigure 1: Placement Bot Percentage Can Vary Within a Single Brand12
BOT FRAUD VARIES OVER TIMENot all bot operations are created equal. Half of bots are not sophisticated enough to keepnormal waking hours.FINDINGAt night, bot percentages were higher.Total bots observed were higher during the day but were a lowerproportion of overall traffic.AVG. HOURLY BOT PERCENTAGEFOR ALL CAMPAIGNS28211470midnight 6 a.m. noon 6 p.m. midnightHOUR OF DAYFigure 2: Bot Traffic Time-of-Day Pattern Across Study Time PeriodLocal time determination was made using IP geolocation data provided by Maxmind.RECOMMENDATIONConsider day-parting to reducetime-of-day bot percentage spikes.13
Page 1 and 2: The Bot Baseline:Fraud in DigitalAd
Page 3 and 4: Special Thanks to the FollowingANA
Page 5 and 6: ABOUT THE STUDY
Page 7 and 8: BOTS ARE EVERYWHERE...BUT NOT IN EQ
Page 9 and 10: HOW WE USED THE DATAThis study is a
Page 11: BOTS WERE IN EVERY KINDOF CAMPAIGN
Page 15 and 16: BOT TRAFFIC VARIES BY DOMAIN CATEGO
Page 17 and 18: SOURCING TRAFFIC INCREASESBOT LEVEL
Page 19 and 20: BOTS GET INTO PREMIUM BUYSSignifica
Page 21 and 22: TO REDUCE BOTS IN PREMIUM CAMPAIGNS
Page 23 and 24: BOTS ARE BUILT TO FOOL ALL STAKEHOL
Page 25 and 26: ADWARE: NOT ALL ATTACKS ARE THE SAM
Page 27 and 28: BOTNET CONTROLLERS HIJACK EVERYDAYU
Page 29 and 30: BOTS FAKED ALL OF THE ENGAGEMENT AN
Page 31 and 32: BOTS ARE GETTING TARGETED AND RETAR
Page 33 and 34: HOW BOTSUPPLIERS GETAWAY WITH IT:EV
Page 35 and 36: BOT SUPPLIERS ACTIVELY DISGUISE BOT
Page 37 and 38: SITES THAT ONLYA BOT COULDLOVE37
Page 39 and 40: BOT SUPPLIERS ARE WATCHING YOUBot t
Page 41 and 42: WHEN PUBLISHERSARE VICTIMS TOO:AD I
Page 43 and 44: AD INJECTIONDEGRADES THEQUALITY OFT
Page 45 and 46: BOTNET OPERATORS ARE ALREADYDEFEATI
Page 47 and 48: Action Plan for All StakeholdersCre
Page 49 and 50: Action Plan for Buyers(Continued)Ap
Page 51 and 52: Who is your real audience:BOB SMITH
Page 53 and 54: Appendix AGlossary of Terms (Contin
Page 55 and 56: Appendix BConstraints and Limitatio
Page 57: Appendix DIllustrative Terms and Co

The Bot Baseline Fraud in Digital Advertising

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