Abstract
A preferred deal is a special contract for selling impressions of display ad inventory. By accepting a
deal, a buyer agrees to buy a minimum amount of
impressions at a fixed price per impression, and is
granted priority access to the impressions before
they are sent to an open auction on an ad exchange.
We consider the problem of designing preferred
deals (inventory, price, quantity) in the presence of
general convex constraints, including budget constraints, and propose an approximation algorithm
to maximize the revenue obtained from the deals.
We then evaluate our algorithm using auction data
from a major advertising exchange and our empirical results show that the algorithm achieves around
95% of the optimal revenue