Smart Pacing for Effective Online Ad Campaign Optimization
This work addresses the problem of dynamic bid landscapes and price elasticity for advertisers in online advertising, representing an incremental improvement in pacing control methods.
The paper tackles the challenge of simultaneously achieving smooth budget pacing and maximizing performance in online ad campaigns, proposing a smart pacing approach that learns from offline and online data, with experimental results showing effective improvement in campaign performance and delivery goals.
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.