Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising

TL;DR

This paper introduces an online bid optimization method for real-time advertising that balances smooth budget delivery with maximizing conversions, addressing challenges of delayed feedback and rare events.

Contribution

It proposes an adaptive online algorithm that dynamically adjusts bids to optimize conversion performance while ensuring smooth budget spending over time.

Findings

Effective in real advertising campaigns

Balances budget and performance goals

Improves conversion rates

Abstract

Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for advertisers to submit a bid for each selected RTB ad request in milliseconds. Restricted by the budget, the goal is to buy a set of ad impressions to reach as many targeted users as possible. A desired action (conversion), advertiser specific, includes purchasing a product, filling out a form, signing up for emails, etc. In addition, advertisers typically prefer to spend their budget smoothly over the time in order to reach a wider range of audience accessible throughout a day and have a sustainable impact. However, since the conversions occur rarely and the occurrence feedback is normally delayed, it is very challenging to achieve both budget and performance goals at the same time. In this paper, we present an online approach to the smooth budget delivery while optimizing for the conversion performance. Our algorithm tries to select high quality impressions and adjust the bid price based on the prior performance distribution in an adaptive manner by distributing the budget optimally across time. Our experimental results from real advertising campaigns demonstrate the effectiveness of our proposed approach.