Synergy and Antagonism in Online Advertising

An advertising campaign is usually composed of a series of coordinated advertisements, with various formats and delivered through different media channels. Several existing studies have attempted to measure the individual contribution of related advertisements in a campaign, resulting in rule-based and data-driven multi-touch attribution models. However, most of these models ignored the interaction among different advertisements. We here specifically consider the synergy and antagonism interaction among advertisements and propose a data-driven model. With the interaction, the influence of different advertisements is no longer linearly additive. We choose a vector representation for the influence of an individual advertisement, which can easily model the synergy and antagonism interactions. We validate the proposed method with a real-world dataset and show that antagonism is prevalent among advertisements while synergy rarely appears. In addition, our method leads to more accurate purchase predictions.