Impact of Bias in Incrementality Measurement Created on Account of Competing Ads in Auction Based Digital Ad Delivery Platforms
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Abstract
Incrementality measurement, often conducted via randomized control trials (RCTs), is widely recognized as the gold standard for assessing digital advertising effectiveness. These measurements aim to quantify the true causal impact of advertising campaigns by isolating incremental outcomes from baseline behaviors. However, auction-based ad delivery platforms, which rely on complex real-time bidding (RTB) mechanisms, introduce substantial biases that compromise the validity of incrementality results. These biases stem from competitive dynamics, including intra-advertiser (self-competition) and inter-advertiser (rival) effects, which distort treatment-control group comparisons through mechanisms such as bid inflation, cannibalization of conversions, and contamination of control groups. Moreover, these biases are exacerbated by temporal and spatial variations in auction intensity and user behavior.
This study explores the mechanisms through which auction dynamics skew incrementality measurements, with a focus on biases affecting cost-efficiency metrics like incremental Return on Ad Spend (iROAS) and incremental conversions. Using theoretical models and empirical analyses, we highlight the role of bid overlap, sequential exposure effects, and auction pressure in distorting results. The paper also proposes methodological interventions, including advanced experimental designs, collaborative data frameworks such as cleanrooms, and auction-dynamics-aware metrics.
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