Advertising on YouTube and TV: A Meta-analysis of Optimal Media-mix Planning
Venue
Google, Inc. (2015), pp. 1-28 (to appear)
Publication Year
2015
Authors
Georg M. Goerg, Christoph Best, Sheethal Shobowale, Jim Koehler, Nicolas Remy
BibTeX
Abstract
In this work we investigate under what circumstances a TV campaign should be
complemented with online advertising to increase combined reach. First, we use
probabilistic models to derive necessary and sufficient conditions. We then test
these optimality conditions on empirical findings of a large collection of TV
campaigns to answer two important questions: i) which characteristics of a TV
campaign make it favorable to shift part of its budget to online advertising?; and
ii) if it should shift, how much cost savings and additional reach can advertisers
expect? First, we use classification methods such as linear discriminant analysis,
logistic regression, and decision trees to decide whether a TV campaign should add
online advertising; secondly, we train linear and support vector regression models
to predict optimal budget allocation, cost savings, or additional reach. To train
these models we use optimization results on roughly 26,000 campaigns. We do not
only achieve excellent out-of-sample predictive power, but also obtain simple,
interpretable, and actionable rules that improve the understanding of media mix
advertising.
