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Conducting Research in Marketing with Quasi-Experiments

Conducting Research in Marketing with Quasi-Experiments

Avi Goldfarb, Catherine Tucker and Yanwen Wang

quasi experiments

Quasi-experimental methods have been widely applied in marketing to explain changes in consumer behavior, firm behavior, and market-level outcomes. The purpose of quasi-experimental methods is to determine the presence of a causal relationship in the absence of experimental variation. A new Journal of Marketing article offers guidance on how to successfully conduct research in marketing with quasi-experiments to understand whether an action causally affects a marketing outcome.

As a vivid example, we describe a quasi-experiment that occurred when eBay shut down all the paid search advertising on Bing during a dispute with Microsoft, but lost little traffic. These quasi-experimental results inspired a follow-up field experiment where eBay randomized suspension of its branded paid search advertising and found results consistent with the quasi-experiment.
We begin by establishing various type of quasi-experimental variation at the individual, organizational, and market-levels. In each type, given the lack of an experiment, some individuals, companies, or markets receive an action or policy (i.e., treatment group) and some do not (i.e., control group). For example, some markets are affected by a new policy and some are not. The question is how the markets receiving the treatment would act if they had not received it (i.e., the counterfactual). The unobservability of the counterfactual means assumptions are required to ensure that differences (both observed and unobserved) are as untroubling as possible, thereby mimicking random assignment as closely as possible. 


We discuss how to structure an empirical strategy to identify a causal relationship using methods such as difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. We emphasize the importance of clearly communicating identifying assumptions underlying the assertion of causality and establishing the generalizability of the findings.

We examine the following topics with the goal of helping researchers and analysts use quasi-experiments more effectively.

Read the full article.

From: Avi Goldfarb, Catherine Tucker, and Yanwen Wang, “Conducting Research in Marketing with Quasi-Experiments,” Journal of Marketing.

Go to the Journal of Marketing

Avi Goldfarb is Ellison Professor of Marketing, University of Toronto, Canada.

Catherine Tucker is Sloan Distinguished Professor of Management Science, Massachusetts Institute of Technology, USA.

Yanwen Wang is Assistant Professor of Marketing, University of British Columbia, Canada.