New research concludes that TV ad spending has a positive ROI only in rare cases and even then not by much
In 2019, advertisers in the U.S. spent over $66B in commercials, so it would be safe to assume that this huge investment yielded a positive result to the ad buyers. This assumption is wrong, says new research from Anna Tuchman (Kellogg), Bradley Shapiro (Chicago Booth), and Günter Hitsch (Chicago Booth). Their newly published ad effectiveness analysis for 288 well-known consumer packaged goods should be required reading for advertisers everywhere.
Establishing the efficacy of television advertising has been a fiendishly hard task ever since the medium was invented. Over the years, different researchers have approached the problem in different ways. Some analyses focused on case studies, which were instructive but hardly indicative of overall patterns. Other researchers looked at the question from a high level, performing meta-analyses that were generally negative about the claimed impact of advertising but still left a lot of room for interpretation. New research from Tuchman and her colleagues is more promising.
Tuchman’s team started with sales data from 288 well-known brands and purchase data from 60,000 households from 2010-14, noted a recent summary of the work:
The researchers obtained data from Nielsen on the products’ sales at about 12,000 stores in the United States, as well as data on purchases by more than 60,000 American households, from 2010–14. The team also examined Nielsen data on traditional T.V. ads during the same time period. (Streaming services were not included in the data set.) For each commercial, Tuchman and her colleagues calculated the percentage of households in a particular local market that saw the ads (zones known as “designated market areas” or DMAs).
To determine how much commercials drove sales, Tuchman’s team calculated a measure called advertising elasticity, which captures how much sales change with a given increase in ad exposure. This calculation is trickier than it sounds because ads can be affected by many things, such as seasonality and spot availability of products in a given DMA. The researchers adjusted their calculations to account for such variables and arrived at what they hope is an accurate indicator of ad elasticity.
After completing their analysis, the team found that the average elasticity across all products was only 0.0233 (while the median is 0.014). In other words, doubling advertising implies a roughly 1% increase in long-run sales (for the median brand). This is a surprising result and much lower than previous elasticity estimates that ranged from 9-24%. Indeed, their new paper notes that the ROI of advertising in a given week, holding advertising in all other weeks constant, is negative for more than 80% of the brands studied. As the authors note:
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 different from zero, whereas for 68% of brands the ROI is negative and statistically different from zero. These results provide strong evidence for over-investment in advertising at the margin.
Put another way, most money spent on advertising for these kinds of consumer products is wasted, or, as Tuchman puts it, the effect of the ad spend is “essentially zero.”
Of course, we all know that many, if not most, people today either skip or tune out commercials for many reasons, not least of which is the ubiquitous smartphone. Whatever the cause, the authors are curious to understand why advertisers continue to spend so much money with such poor results:
This raises an economic puzzle. Why do firms spend billions of dollars on T.V. 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 (i.e., 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. Third, the estimated advertising effects may be inflated if confounding factors are not adequately adjusted for. The last two explanations do not assume irrational behavior, but may simply represent a cost of conducting causal inference.
Indeed, the authors conclude that some advertising is probably better than none for only about 34% of brands analyzed, which, they note, should be “a threat to the survival of media markets in their current form, once knowledge about the small degree of TV advertising effectiveness becomes common knowledge.” Indeed, their main recommendation is that, given the reality of poor returns, advertisers should be skeptical of efficacy claims from broadcasters and agencies and conduct thorough analyses of all ad spend to increase their returns.
The paper ends with the following conclusion:
While improvements in targeting technology may theoretically increase the potential for higher advertising returns, they do not solve the underlying agency problems that allow sub-optimal advertising decisions to persist in the traditional TV advertising model we evaluate in this paper. Together with past research documenting similar results in digital advertising markets, our work should motivate economists to further study the managerial and agency issues in advertising markets.
Their recommendation for further analysis holds for CMO’s and advertising buyers as well, for even after accepting that this is only one study of a limited set of brands, the dismal ROI on television ad spend should generate a serious debate within the companies driving that $66B in ad spending. This conclusion is only enhanced when one looks at reports paid for by the TV industry itself, which are notoriously vague about the specific positive effects of their medium. This “white paper” is representative in that it offers 24 pages of positive claims for TV ad spend without ever providing a clear ROI baseline for their customers.
Given the analytical alternatives, perhaps unsurprisingly, the results of this new study are getting a good reading, with almost 4500 downloads as of this morning (an excellent figure for a journal paper). I’ll take the broad readership as a good sign that this team’s analysis has struck a chord and that there is more to come on this topic in the future.
Shapiro, Bradley and Hitsch, Guenter J. and Tuchman, Anna, Generalizable and Robust TV Advertising Effects (August 2020). NBER Working Paper No. w27684, Available at SSRN: https://ssrn.com/abstract=3675236
You can listen to my brief conversation with Professor Brad Shapiro about his team’s research below.