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Raj Sethuraman, Gerard J. Tellis, and Richard BrieschAbstractdataAdvertising ElasticityInfluencing FactorsPROCEDURERESULTSProduct Type: Availability of data permits us to test differences among many types of product categories – pharmaceutical, food, non-food, durable, and service goods (e.g., banks, movies). However, we do not have prior expectations for the relative m...Key Empirical GeneralizationsHow Well Does Advertising Work? Generalizations From A Meta-Analysis of Brand Advertising Elasticity RAJ SETHURAMAN, GERARD J. TELLIS, AND RICHARD BRIESCH The authors thank Don Lehmann, Mike Hanssens, the participants at the Advertising Generalization conference at the Wharton School, University of Pennsylvania and the Marketing Science conference at Ann Arbor. The authors also thank Marc Fischer for providing part of the data, Anocha Aribarg for clarifications on the elasticities, Ranga Venkatesan and Prerit Souda for assistance in data collection and data analysis. This study benefitted from a grant by Don Murray to the USC Marshall Center for Global Innovation.1 How Well Does Advertising Work? Generalizations From A Meta-Analysis of Brand Advertising Elasticity ABSTRACT This study conducts a meta-analysis of 751 short-term and 402 long-term direct-to-consumer brand advertising elasticities estimated in 56 studies published between 1960 and 2008. The study finds several new empirical generalizations about advertising elasticity. The most important generalizations are: The average short-term advertising elasticity is .12, which is substantially lower than the prior meta-analytic mean of .22 (Assmus, Farley, and Lehmann 1984); there has been a decline in the advertising elasticity over time; and advertising elasticity is higher a) for durable goods than nondurable goods, b) in the early stage of the life cycle than in the mature stage, c) for yearly data than for quarterly data, and d) when advertising is measured in Gross Rating Points than in monetary terms. The mean long-term advertising elasticity is .24, which is much lower than the implied mean in the prior meta-analysis (.41). Many of the results for short-term elasticity hold for long-term elasticity, with some notable exceptions. The authors discuss the implications of these findings. Key Words: Advertising Elasticity, Meta-analysis, Empirical generalization, Promotion, Marketing mix1 Advertising is one of the most important elements of the marketing mix. Controversy rages over whether firms are getting adequate returns on their advertising expenditures (Aaker and Carman 1982, Tellis 2004). One key element in this controversy is how effective advertising is in generating sales. The effectiveness of advertising is often measured in terms of advertising elasticity, i.e., the percentage increase in sales or market share for a one percent increase in advertising. Obtaining generalizable estimates of advertising elasticity and identifying factors that influence advertising elasticity can further our understanding of the effectiveness of advertising. Assmus, Farley, and Lehmann (1984) provide the first empirical generalizations on advertising elasticity. In particular, these authors met analyze 128 estimates of advertising elasticity from 16 studies published between 1962 and 1981 and provide useful generalizations on the patterns of advertising elasticity. Over 25 years have passed since that publication. This period (1984-2008) has witnessed significant changes on many fronts that may have an impact on the measurement and effectiveness of advertising. First, the marketing environment has changed due to greater competition, globalization, the advent of the Internet, and the ability of the consumer to opt out of TV commercials through devices such as TiVo. Second, the data and methods for estimating advertising elasticity are increasing in sophistication with the use of disaggregate scanner data and the application of New Empirical Industrial Organization (NEIO) econometric models. It would therefore appear prudent to update the empirical generalizations on advertising elasticity by including data from studies published since 1981. This study conducts a meta-analysis of 751 short-term brand-level direct-to-consumer advertising elasticities, as well as 402 long-term estimates of advertising elasticities, from 56 studies published between 1960 and 2008. Our study disconfirms a few findings from Assmus,2 Farley, and Lehmann (1984), validates some of the earlier findings, and uncovers several new empirical generalizations and insights. In this regard, this research is similar in spirit to other follow-up meta-analytic studies in recent times. For example, Bijmolt, van Heerde, and Pieters (2005) update the meta-analysis of price elasticity conducted earlier by Tellis (1988). Hu, Lodish, and Krieger (2007) provide a partial update on the meta-analytic study of Lodish et al. (1995) related to TV advertising experiments. Our study can also be viewed as a meta-analytic complement to the broad review of advertising literature by Vakratsas and Ambler (1999). They develop a taxonomy, review 250 studies, and provide insights into how advertising works. Our study performs a meta-analysis of econometric estimates of advertising elasticity and provides insights into whether advertising works, the magnitude of the effect, and the factors which influence elasticity. In the process, the study adds to Hanssens’ (2009) list of empirical generalizations about marketing’s impact. Our study complements the recent article by Fischer and Albers (2010). Both studies attempt to provide insights into the effect of marketing mix on sales. However, the foci of the two studies are quite different. Fischer and Albers provide an excellent analysis of the effect of marketing efforts (detailing, journal advertising, and consumer advertising) on primary demand (category expansion) in pharmaceutical product categories. Our analysis focuses on the effect of consumer advertising on selective demand (competitive brand sales) across a wide range of consumer products, including pharmaceuticals. Consistent with the prior meta-analysis of Assmus, Farley, and Lehmann (1984), we find that advertising elasticity is higher in Europe than in the United States and higher when lagged sales is omitted from the model. However, the contribution of this research lies in the differences in results obtained and the


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