Dynamic Evaluation of Online Display Advertising with Randomized Experiments: An Aggregated Approach

被引：0

作者：

Barajas, Joel ^{[1
]}

Akella, Ram ^{[1
,2
]}

Holtan, Marius ^{[3
]}

Kwon, Jaimie ^{[3
]}

Flores, Aaron ^{[3
]}

Andrei, Victor ^{[3
]}

机构：

[1] UC, Santa Cruz, CA 95064 USA

[2] UC, Sch Informat, Berkeley, CA USA

[3] AOL Res, Palo Alto, CA USA

来源：

PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'13 COMPANION) | 2013年

关键词：

A/B Testing; Causal Attribution; DLM; Marketing;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We perform a randomized experiment to estimate the effects of a display advertising campaign on online user conversions. We present a time series approach using Dynamic Linear Models to decompose the daily aggregated conversions into seasonal and trend components. We attribute the difference between control and study trends to the campaign. We test the method using two real campaigns run for 28 and 21 days respectively from the Advertising.com ad network.

引用

页码：115 / 116

页数：2

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