Smart Pacing for Effective Online Ad Campaign Optimization

TL;DR

This paper introduces a smart pacing method for online ad campaigns that learns from data to balance smooth delivery with performance optimization, demonstrated through real system implementation and experiments.

Contribution

It proposes a novel data-driven pacing approach that dynamically adjusts delivery to improve campaign performance and meet delivery constraints.

Findings

Improved campaign performance in real online advertising systems

Effective achievement of delivery goals through the proposed method

Demonstrated robustness in offline simulations

Abstract

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.