Incrementality Bidding & Attribution
Incrementality Bidding & Attribution
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增量招标
DOI：
10.2139/ssrn.3129350
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Jeffrey Wong
中科院分区：
文献类型：
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作者：
Randall A. Lewis;Jeffrey Wong
向潜在客户展示广告与不向潜在客户展示广告的因果效应（通常称为“增量”）是广告效果的基本问题。在数字广告中，三个主要难题对于严格量化广告增量至关重要：广告购买/出价/定价、归因和实验。基于机器学习和因果计量经济学的基础，我们提出了一种方法，将这三个概念统一成一个计算上可行的出价和归因模型，涵盖广告因果效应的随机化、训练、交叉验证、评分和转化归因。实施这种方法可能会显着提高广告投资回报率。
The causal effect of showing an ad to a potential customer versus not, commonly referred to as “incrementality,” is the fundamental question of advertising effectiveness. In digital advertising three major puzzle pieces are central to rigorously quantifying advertising incrementality: ad buying/bidding/pricing, attribution, and experimentation. Building on the foundations of machine learning and causal econometrics, we propose a methodology that unifies these three concepts into a computationally viable model of both bidding and attribution which spans the randomization, training, cross validation, scoring, and conversion attribution of advertising’s causal effects. Implementation of this approach is likely to secure a significant improvement in the return on investment of advertising.
DOI：
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发表时间：
2016-03
期刊：
arXiv: Methodology
影响因子：
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作者：
S. Athey;Raj Chetty;G. Imbens;Hyunseung Kang
通讯作者：
S. Athey;Raj Chetty;G. Imbens;Hyunseung Kang