递增竞价&归因Incrementality Bidding & Attribution |
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课程网址: | http://videolectures.net/kdd2017_lewis_bidding_attribution/ |
主讲教师: | Randall A. Lewis |
开课单位: | 奈非股份有限公司 |
开课时间: | 2017-12-01 |
课程语种: | 英语 |
中文简介: | 向潜在客户展示广告与不展示广告的因果效应,通常被称为“增量效应”,是广告效果的基本问题。在数字广告中,严格量化广告增量的关键在于三大难题:广告购买/竞价/定价、归因和实验。在机器学习和因果计量经济学的基础上,我们提出了一种方法,将这三个概念统一到一个计算上可行的竞价和归因模型中,该模型涵盖了广告效果因果模型中的随机化、训练、交叉验证、评分和转换归因。得益于这种方法,奈非公司通过发现许多传统模式要么超支要么支出不足的情况而受益,从而显著提高了广告投资回报率。 |
课程简介: | 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 randomization, training, cross validation, scoring, and conversion attribution in a causal model of advertising’s effects. Thanks to this method, Netflix has benefited by identifying many cases where traditional models were either overspending or underspending, leading to a significant improvement in the return on investment of advertising. |
关 键 词: | 因果效应; 潜在客户; 增量效应; 广告效果 |
课程来源: | 视频讲座网 |
数据采集: | 2023-03-06:chenxin01 |
最后编审: | 2023-05-17:chenxin01 |
阅读次数: | 30 |