Bayesian Estimation of the Effect of Television Advertising on Web Metrics

Aggregate advertising-presenting a single ad to large groups of individuals through traditional media such as television and print-presents a unique challenge to measuring efficacy because treatment and outcome are observed from two disparate sources (interaction and revenue realization). In this work, we propose a Bayesian model to estimate the impact of an ad on observable web metrics that are readily available in many modern analytics suites. The proposed model controls for three sources of possible confounding: the time, geography, and content of the advertisement. The proposed model is easily applicable to a wide variety of problems and readily generates error bounds for the estimates. We evaluate our approach on a real dataset for a set of TV ads for an advertiser.

I. Introduction

Aggregate advertising presents an ad to broad groups of potential customers. Often, without the ability to explicitly measure individual exposure. This is in contrast to personalized advertisements—email or display—where the brand may have additional information about the target customer. The most common aggregate ads are TV commercials, but other mediums include radio, traditional print media, or billboards.