  In targeted online advertising, advertisers look for maximizing campaign
performance under delivery constraint within budget schedule. Most of the
advertisers typically prefer to impose the delivery constraint to spend budget
smoothly over the time in order to reach a wider range of audiences and have a
sustainable impact. Since lots of impressions are traded through public
auctions for online advertising today, the liquidity makes price elasticity and
bid landscape between demand and supply change quite dynamically. Therefore, it
is challenging to perform smooth pacing control and maximize campaign
performance simultaneously. In this paper, we propose a smart pacing approach
in which the delivery pace of each campaign is learned from both offline and
online data to achieve smooth delivery and optimal performance goals. The
implementation of the proposed approach in a real DSP system is also presented.
Experimental evaluations on both real online ad campaigns and offline
simulations show that our approach can effectively improve campaign performance
and achieve delivery goals.
