Attribution Modeling Increases Efficiency of Bidding in Display\n Advertising

Predicting click and conversion probabilities when bidding on ad exchanges is\nat the core of the programmatic advertising industry. Two separated lines of\nprevious works respectively address i) the prediction of user conversion\nprobability and ii) the attribution of these conversions to advertising events\n(such as clicks) after the fact. We argue that attribution modeling improves\nthe efficiency of the bidding policy in the context of performance advertising.\nFirstly we explain the inefficiency of the standard bidding policy with respect\nto attribution. Secondly we learn and utilize an attribution model in the\nbidder itself and show how it modifies the average bid after a click. Finally\nwe produce evidence of the effectiveness of the proposed method on both offline\nand online experiments with data spanning several weeks of real traffic from\nCriteo, a leader in performance advertising.\n