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Quantitative Marketing and Economics

Erschienen in:

01.06.2009

The effect of advertising on brand awareness and perceived quality: An empirical investigation using panel data

verfasst von: C. Robert Clark, Ulrich Doraszelski, Michaela Draganska

Erschienen in: Quantitative Marketing and Economics | Ausgabe 2/2009

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Abstract

We use a panel data set that combines annual brand-level advertising expenditures for over three hundred brands with measures of brand awareness and perceived quality from a large-scale consumer survey to study the effect of advertising. Advertising is modeled as a dynamic investment in a brand’s stocks of awareness and perceived quality and we ask how such an investment changes brand awareness and quality perceptions. Our panel data allow us to control for unobserved heterogeneity across brands and to identify the effect of advertising from the time-series variation within brands. They also allow us to account for the endogeneity of advertising through recently developed dynamic panel data estimation techniques. We find that advertising has consistently a significant positive effect on brand awareness but no significant effect on perceived quality.

Vorheriger Artikel A generalized framework for estimating customer lifetime value when customer lifetimes are not observed

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Another way to get around this issue is to take an experimental approach, as in Mitra and Lynch (1995).

This source of endogeneity is not tied to advertising in particular; rather it always arises in estimating dynamic relationships in the presence of unobserved heterogeneity. An exception is the (rather unusual) panel-data setting where one has T→ ∞ instead of N→ ∞. In this case the within estimator is consistent (Bond 2002, p. 5).

The Brandweek Superbrands survey reports on only the top brands (in terms of sales) in each subcategory or category. The number of brands varies from 3 for some subcategories to 10 for others. We therefore use the average, rather than the sum, of competitors’ advertising.

Of course, the AR(3) test uses less observations than the AR(2) test and is therefore also less powerful.

The exact wording of the question is: “We will display for you a list of brands and we are asking you to rate the overall quality of each brand using a 0 to 10 scale, where ‘0’ means ‘Unacceptable/Poor Quality’, ‘5’ means ‘Quite Acceptable Quality’ and ‘10’ means ‘Outstanding/ Extraordinary Quality’. You may use any number from 0 to 10 to rate the brands, or use 99 for ‘No Opinion’ option if you have absolutely no opinion about the brand.” Panelists are being incentivized through sweepstakes on a periodic basis but are not paid for a particular survey.

The 2000 Superbrands survey does not separately report perceived quality and salience scores. We received these scores directly from Harris Interactive. 2000 is the first year for which we have been able to obtain perceived quality and salience scores for a large number of brands. Starting with the 2004 and 2005 Superbrands surveys, salience is replaced by a new measure called “familiarity.” For these two years we received salience scores directly from Harris Interactive. The contemporaneous correlation between salience and familiarity is 0.98 and significant with a p-value of 0.000.

The estimates use at most 317 out of 348 brands because we restrict the sample to brands with data for two years running but use third and higher lags of brand awareness respectively perceived quality and advertising expenditures as instruments. Different sample sizes are reported for the DGMM and SGMM estimators. Sample size is not a well-defined concept in SGMM since this estimator essentially runs on two different samples simultaneously. The xtabond2 routine in STATA reports the size of the transformed sample for DGMM and of the untransformed sample for SGMM.

The marginal effects are calculated at the mean, 25th, 50th, and 75th percentile for advertising for the brands in the categories judged to be stable in terms of objective quality over time.

For this analysis we take the subcategory rather than the category as the relevant competitive environment. Consider for instance the beer, wine, liquor category. There is no reason to expect the advertising expenditures of beer brands to affect the perceived quality or awareness of liquor brands. We drop any subcategory in any year where there is just one brand due to the lack of competitors.

We caution the reader against reading too much into these results: The number and identity of the brands within a subcategory or category varies sometimes widely from year to year in the Brandweek Superbrands surveys. Thus, the sum of competitors’ advertising is an extremely volatile measure of the competitive environment. Moreover, the number of brands varies from 3 for some subcategories to 10 for others, thus making the sum of competitors’ advertising difficult to compare across subcategories.

The number of observations differs slightly across specifications because the logarithm of zero is not defined. Our conclusions remain unchanged if we replace ln E _jt − 1 by ln ( c + E _jt − 1), where c > 0 is a constant, in order to be able to use all observations.

Anand and Shachar (2004) pursue a different methodology that is not limited to newly introduced brands, although the data requirement may prevent more wide-spread application. Their study of advertising for television shows in the form of previews highlights advertising as a vehicle of matching and information rather than an instrument of persuasion.

A long standing problem in estimating the effect of advertising on sales is the so-called data interval bias (Clarke 1976). The impact of advertising is misestimated to the extent that the flow of sales and the flow of advertising are not properly matched up over the course of a period. In our setting, the dependent variables are a brand’s stocks of perceived quality and awareness. This may mitigate the data interval bias because these stocks encompass all current and previous flows and thus are more likely to pick up the impact of advertising.

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Titel: The effect of advertising on brand awareness and perceived quality: An empirical investigation using panel data
verfasst von: C. Robert Clark
Ulrich Doraszelski
Michaela Draganska
Publikationsdatum: 01.06.2009
Verlag: Springer US
Erschienen in: Quantitative Marketing and Economics / Ausgabe 2/2009
Print ISSN: 1570-7156
Elektronische ISSN: 1573-711X
DOI: https://doi.org/10.1007/s11129-009-9066-z