Real-time bidding (RTB) has become one of the largest online advertising
markets in the world. Today the bid price per ad impression is typically
decided by the expected value of how it can lead to a desired action event
(e.g., registering an account or placing a purchase order) to the advertiser.
However, this industry standard approach to decide the bid price does not
consider the actual effect of the ad shown to the user, which should be
measured based on the performance lift among users who have been or have not
been exposed to a certain treatment of ads. In this paper, we propose a new
bidding strategy and prove that if the bid price is decided based on the
performance lift rather than absolute performance value, advertisers can
actually gain more action events. We describe the modeling methodology to
predict the performance lift and demonstrate the actual performance gain
through blind A/B test with real ad campaigns in an industry-leading
Demand-Side Platform (DSP). We also discuss the relationship between
attribution models and bidding strategies. We prove that, to move the DSPs to
bid based on performance lift, they should be rewarded according to the
relative performance lift they contribute.

本文提出一种新的投标策略，通过考虑广告对用户的实际影响来决定出价，并证明采用基于广告性能提升的投标策略可以使广告主获得更多的行动事件。并通过实际广告活动进行了盲目的 A/B 测试，证明了这一策略的实际性能提升，并探讨了归因模型与投标策略之间的关系。