We estimate the distribution of television advertising elasticities and the distribution of the advertising return on investment (ROI) for a large number of products in many categories…We construct a data set by merging market (DMA) level TV advertising data with retail sales and price data at the brand level…Our identification strategy is based on the institutions of the ad buying process.
Our results reveal substantially smaller advertising elasticities compared to the results documented in the literature, as well as a sizable percentage of non-statistically-significant or negative estimates. The results are robust to functional form assumptions and are not driven by insufficient statistical power or measurement error.
The ROI analysis shows negative ROIs at the margin for more than 80% of brands, implying over-investment in advertising by most firms. Further, the overall ROI of the observed advertising schedule is only positive for one third of all brands.
[Keywords: advertising, return on investment, empirical generalizations, agency issues, consumer packaged goods, media markets]
…We find that the mean and median of the distribution of estimated long-run own-advertising elasticities are 0.023 and 0.014, respectively, and 2⁄3rds of the elasticity estimates are not statistically-significantly different from zero. These magnitudes are considerably smaller than the results in the extant literature. The results are robust to controls for own and competitor prices and feature and display advertising, and the advertising effect distributions are similar whether a carryover parameter is assumed or estimated. The estimates are also robust if we allow for a flexible functional form for the advertising effect, and they do not appear to be driven by measurement error.
…First, the advertising elasticity estimates in the baseline specification are small. The median elasticity is 0.0140, and the mean is 0.0233. These averages are substantially smaller than the average elasticities reported in extant meta-analyses of published case studies ( et al 1984b, et al 2011). Second, 2⁄3rds of the estimates are not statistically distinguishable from zero. We show in Figure 2 that the most precise estimates are those closest to the mean and the least precise estimates are in the extremes.
Note: Brands are arranged on the horizontal axis in increasing order of their estimated ad effects. For each brand, a dot plots the point estimate of the ad effect and a vertical bar represents the 95% confidence interval. Results are from the baseline strategy model with δ = 0.9 (Equation 1).
…6.1 Average ROI of Advertising in a Given Week:
In the first policy experiment, we measure the ROI of the observed advertising levels (in all DMAs) in a given week t relative to not advertising in week t. For each brand, we compute the corresponding ROI for all weeks with positive advertising, and then average the ROIs across all weeks to compute the average ROI of weekly advertising. This metric reveals if, on the margin, firms choose the (approximately) correct advertising level or could increase profits by either increasing or decreasing advertising.
We provide key summary statistics in the top panel of Table III, and we show the distribution of the predicted ROIs in Figure 3a. The average ROI of weekly advertising is negative for most brands over the whole range of assumed manufacturer margins. At a 30% margin, the median ROI is −88.15%, and only 12% of brands have positive ROI. Further, for only 3% of brands the ROI is positive and statistically-significantly different from zero, whereas for 68% of brands the ROI is negative and statistically-significantly different from zero.
These results provide strong evidence for over-investment in advertising at the margin. [In Appendix C.3, we assess how much larger the TV advertising effects would need to be for the observed level of weekly advertising to be profitable. For the median brand with a positive estimated ad elasticity, the advertising effect would have to be 5.33× larger for the observed level of weekly advertising to yield a positive ROI (assuming a 30% margin).]
6.2 Overall ROI of the Observed Advertising Schedule: In the second policy experiment, we investigate if firms are better off when advertising at the observed levels versus not advertising at all. Hence, we calculate the ROI of the observed advertising schedule relative to a counterfactual baseline with zero advertising in all periods.
We present the results in the bottom panel of Table III and in Figure 3b. At a 30% margin, the median ROI is −57.34%, and 34% of brands have a positive return from the observed advertising schedule versus not advertising at all. Whereas 12% of brands only have positive and 30% of brands only negative values in their confidence intervals, there is more uncertainty about the sign of the ROI for the remaining 58% of brands. This evidence leaves open the possibility that advertising may be valuable for a substantial number of brands, especially if they reduce advertising on the margin.
…Our results have important positive and normative implications. Why do firms spend billions of dollars on TV advertising each year if the return is negative? There are several possible explanations. First, agency issues, in particular career concerns, may lead managers (or consultants) to overstate the effectiveness of advertising if they expect to lose their jobs if their advertising campaigns are revealed to be unprofitable. Second, an incorrect prior (ie. conventional wisdom that advertising is typically effective) may lead a decision maker to rationally shrink the estimated advertising effect from their data to an incorrect, inflated prior mean. These proposed explanations are not mutually exclusive. In particular, agency issues may be exacerbated if the general effectiveness of advertising or a specific advertising effect estimate is overstated. [Another explanation is that many brands have objectives for advertising other than stimulating sales. This is a nonstandard objective in economic analysis, but nonetheless, we cannot rule it out.]
While we cannot conclusively point to these explanations as the source of the documented over-investment in advertising, our discussions with managers and industry insiders suggest that these may be contributing factors.