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.
2016 IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT)