The Bot Baseline Fraud in Digital Advertising

Appendix BConstraints and LimitationsComplexity of StudyStudy participants joined the initiative for differentperiods of time with varying platform configurations,target audiences, industry verticals, and ad agencies.Because of these differences, not all data from thestudy can be compared directly between participants.Web Framework LimitationsThe study software could only be deployed to systemsthat were JavaScript-enabled.Public Study AwarenessBecause many participants experienced administrativeand technical deployment delays during the publicstudy phase, and because bot numbers may have beenartificially decreased during the public study phase,bot numbers detected during the covert study phasemay be more representative of the numbers occurringin normal ad campaigns outside of the initiativeframework. Because the study was announcedand widely known, it is assumed that bot numbersfor the month of the public study were artificially lowerthan numbers we might otherwise observe.Coordinating With AgenciesSome study participants were not able to deploy thestudy software uniformly throughout their campaignsdue to administrative elements including legalagreements, site policies, and organizational complexity.In some cases, the study participants were not awarethat study software had not been deployed throughtheir ad agencies. When these issues became apparentduring the monitoring and data collection phases, WhiteOps worked with the study participants and their adagencies to attempt to correct the problem.Seasonal Time FrameWhite Ops expects bot numbers to be at their lowestin the late summer months and at their highest whendemand for advertising is highest near the end of thecalendar year. This seasonal snapshot from the monthsof August and September cannot predict the numberof bots in a typical month or during peak months whenadvertising volume is higher.55