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