Anti-Ad Blocking Strategy: Measuring Its True Impact


Publication date: August 14, 2017

Atanu R Sinha, Meghanath Macha, Pranav Maneriker, Sopan Khosla, Avani Samdariya, Navjot Singh

The increasing use of ad blocking software 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 blocking 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 effectiveness because the existing methods compare metrics after implementation of such strategies with that of metrics just before implementation, making them error prone due to sampling bias. The errors arise due to differences in group compositions across before and after periods, as well as differences in time-period selection for the before measurement. We propose a novel algorithmic method which modifies the difference-in-differences approach to address the sampling bias due to differences in time-period selection. Unlike difference-in-differences, we choose the time-period for comparison in an endogenous manner, as well as, exploit differences in ad blocking tendencies among visitors' arriving on the publisher's site to allow cluster specific choice of the control time-period. We evaluate the method on both synthetic data (which we make available) and proprietary real data from an online publisher and find good support.

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Research Area:  Adobe Research iconData Intelligence