Multibrand geographic experiments

TL;DR

This paper proposes a multibrand geographic experimental framework that leverages data across multiple brands and regions to improve the estimation of advertising effectiveness, reducing costs and enabling better identification of high-impact regions.

Contribution

It introduces a novel experimental design and shrinkage-based estimation methods for simultaneous multibrand geographic experiments, enhancing efficiency and regional effectiveness detection.

Findings

Data sharing across brands improves return estimates.

Simultaneous experiments reduce individual experiment costs.

Design enables identification of high-impact regions.

Abstract

In a geographic experiment to measure advertising effectiveness, some regions (hereafter GEOs) get increased advertising while others do not. This paper looks at running $B>1$ such experiments simultaneously on $B$ different brands in $G$ GEOs, and then using shrinkage methods to estimate returns to advertising. There are important practical gains from doing this. Data from any one brand helps to estimate the return of all other brands. We see this in both a frequentist and Bayesian formulation. As a result, each individual experiment could be made smaller and less expensive when they are analyzed together. We also provide an experimental design for multibrand experiments where half of the brands have increased spend in each GEO while half of the GEOs have increased spend for each brand. For $G>B$ the design is a two level factorial for each brand and simultaneously a supersaturated design for the GEOs. Multiple simultaneous experiments also allow one to identify GEOs in which advertising is generally more effective. That cannot be done in the single brand experiments we consider.