An expected win rate-based real-time bidding strategy for branding campaigns on display advertising

Abstract

For branding campaigns, the demand-side platforms (DSPs) in real-time bidding (RTB) usually need to win as many impressions as possible to inform most audiences about the product messages under constraints on budgets, campaign lifetimes and budget spending plans. In this paper, we propose a novel bidding strategy by introducing the concept of expected win rate. With the proposed expected win rate-based bidding strategy, the DSP can dynamically adjust the expected win rate for each incoming bid request based on factors such as the predicted number of bid requests in the near future, the remaining budget and the remaining lifetime of the campaign. The experimental results show that the proposed bidding strategy has a lower cost per thousand impressions and cost per clicks than existing pacing model-based bidding strategies for branding campaigns with the same budgets and budget spending plans.

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