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Reducing Biases in Advertising Effectiveness Research: New Meta-Analytic Evidence

Joseph Korkames T.D. Stanley Stefan Stremersch 4 December 2022 ABSTRACT The authors employ and compare recently developed meta-analysis methods to better correct for misspecification, aggregation, publication, and selection bias, which frequently plague metaanalyses and are often misunderstood. They apply these methods in a new meta-analysis of advertising effectiveness, using the largest database to date on business-to-consumer own-brand advertising elasticities (i.e., consisting of 538 elasticities with standard errors). The authors show that the usage of such better methods greatly reduces (more than sixfold) the meta-analytic overall estimate of advertising effectiveness relative to prior meta-analytic estimates reported before; they obtain an average short-term elasticity of .0008 and a long-term elasticity of .03. The authors also inventory how conditional estimates meaningfully differ depending on the product life cycle stage, measures, models, industries, regions, data structures, and advertising medium employed. For advertising scholars and professionals, this study provides an updated (i.e., lower) benchmark on advertising effectiveness. For analysts, it charts an improved methodological framework that should guide meta-analytic research on and beyond advertising effectiveness.