  We present a vertical introduction to campaign optimization; that is, the
ability to predict the user response to an ad campaign without any users'
profiles on average and for each exposed ad. In practice, we present an
approach to build a polytomous model, multi response, composed by several
hundred binary models using generalized linear models. The theory has been
introduced twenty years ago and it has been applied in different fields since
then. Here, we show how we optimize hundreds campaigns and how this large
number of campaigns may overcome a few characteristic caveats of single
campaign optimization. We discuss the problem and solution of training and
calibration at scale. We present statistical performance as {\em coverage},
{\em precision} and {\em recall} used in classification. We present also a
discussion about the potential performance as throughput: how many decisions
can be done per second streaming the bid auctions also by using dedicated
hardware.
