Anti-Ad Blocking Strategy: Measuring its True Impact

Atanu R. Sinha (Adobe Research), Meghanath Macha (CMU), Pranav Maneriker (Adobe Research), Sopan Khosla (IIT Roorkee), Avani Samdariya (IIT Kanpur), Navjot Singh (IIT Bombay)

2017
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e increasing use of ad blocking soware poses a major threat for publishers in loss of online ad revenue, and for advertisers in the loss of audience. Major publishers have adopted various anti-ad block- ing strategies such as denial of access to website content and asking users to subscribe to paid ad-free versions. However, publishers are unsure about the true impact of these strategies [2, 3]. We posit that the real problem lies in the measurement of eectiveness be- cause the existing methods compare metrics aer implementation of such strategies with that of metrics just before implementation, making them error prone due to sampling bias. e errors arise due to dierences in group compositions across before and aer periods, as well as dierences in time-period selection for the before measurement. We propose a novel algorithmic method which mod- ies the dierence-in-dierences approach to address the sampling bias due to dierences in time-period selection. Unlike dierence- in-dierences, we choose the time-period for comparison in an endogenous manner, as well as, exploit dierences in ad block- ing tendencies among visitors’ arriving on the publisher’s site to allow cluster specic choice of the control time-period. We evalu- ate the method on both synthetic data (which we make available) and proprietary real data from an online publisher and nd good support.