Cases of false advertising typically revolve around two questions: (1) did the false advertising have an impact on purchasing behavior? and (2) if such an impact did indeed occur, what was the potential loss to the plaintiff’s brand? The first question addresses the presence of impact while the second is concerned with its magnitude.

In preparing for litigation, attorneys often employ consumer research to answer the first question and damages experts, i.e., economists, forensic accountants, corporate finance professionals, etc., to answer the second question.

This paper describes a new method for using consumer research methodology that can not only detect the presence of impact, but also go one step further and quantify the potential gain due to false claims. What this new method cannot do is to quantify what percent of sales has the plaintiff’s brand lost to defendant’s brand. That brand-to-brand comparison is still reserved to damages experts using economic theory and corporate finance estimates.

The Setting

Let’s take as an example a case in which the plaintiff (P) argues that the defendant (D) has been using four false claims on its packaging and on its website. In its suit, P argues that those false claims are likely to affect consumer choice behavior and to cause P to lose sales to D.

The Research Design

The design of this study presents a sample of consumers with a choice between two unidentified brands—labeled Brand W and Brand H—each described by the four attributes being litigated. Brand W lists the attributes verbatim as D has been using on the package and on the website. Brand H lists the same attributes either in what would be their accurate version or by a statement denoting that Brand H does not make any reference or claim regarding that particular attribute. Thus, Brand W replicates the marketing communication employed by D—which brought about the suit—while Brand H attempts to replicate the marketing communication of a brand that would not have caused P to file suit.

Each attribute is presented to respondents separately on a screen (if the interview is conducted online) that shows the attribute statement for Brand W and the attribute statement for Brand H. Respondents are instructed to imagine that they are shopping for the product category under study and that they are holding in their hands two packages—Brand W and Brand H—that contain the four attributes.  They are asked to choose a point on a 5-point scale that best represents their preference when faced with that information. The scale is:

  • Very likely to buy Brand W
  • Somewhat likely to buy Brand W
  • Indifferent between the two brands
  • Somewhat likely to buy Brand H
  • Very likely to buy Brand H

The attribute ratings question is followed by an importance ratings question in which respondents are asked to use a number between 0 and 100 that best reflects the importance of each of the four attributes when selecting for purchase a brand in the product category of interest.

The reason for this question is to quantify inter-personal differences of each and every category user since not all consumers go to market with the same set of requirements or values. Allowing for the real expression of attribute importance, rather than assuming that all consumers value all attributes equally, injects a high level of realism into the experiment. The greater the realism the higher is the validity of results.

Analysis of Results

The table below captures the key results of a recent study.

Importance Rating
Percent Impacted
Impact Weighted by Importance
Percent Not Impacted
Lack of Impact Weighted by Importance
A .81 92 75 8 7
B .63 75 47 25 16
C .48 53 25 47 23
D .74 53 39 47 35
Average Impact 47 39

Column [1] lists the four attributes that P deems to be false.

Column [2] shows the importance ratings mentioned earlier as a fraction ranging from 0 to 1.00. As can be seen in Column 2, the allegedly offending attributes differ in their capacity to impact consumers because not all consumers are equally susceptible to all the attributes. The “susceptibility variance” is measured by how important each attribute is to every individual member of the consuming public. In other words, not every false claim has the same “sticking power” for all consumers; consumers implicitly differ from one another.

Column [3] shows the percent of the sample that checked the “Very/Somewhat likely to buy Brand W” in the questionnaire. These are the people who were affected by the content and the phrasing of the false claim.

Column [4] is the result of weighting the percent of people impacted by the false advertising by the attribute’s relative importance. The weighted impact is obtained by multiplying the data for each attribute in Column [2] by Column [3]. In so doing we account for the very important fact that not all attributes are equally important to all the members of a market. The adjusted, or weighted, result captures interpersonal differences as well as inter-attribute differences and thus safeguards the validity of results.

Column [5] shows the percent of people who were not impacted by the false advertising, i.e., those who showed preference for the “not-misleading” brand or were indifferent between the two brands. The data in Column [5] is the complement of Column [3]; the sum of the two equals 100 percent.

Column [6] presents the weighted percentages of the non-impacted people for each attribute, just as Column [4] presented the results for the impacted people.

The last line at the bottom of the table shows the weighted average brand impact for the impacted (BI[I]) group, which equals 47, and the weighted average brand impact for the not impacted group (BI[NI]), which equals 39. The Brand Impact Indexes are calculated by summing over the four attributes and dividing by four.

The Gain Factor

Armed with the two indices we can now derive a gain factor due to false advertising using the formula:

Gain Factor = [BI(I) – BI (NI)]/BI(I)

The formula states that the gain factor is equal to the net impact of the falsely advertised brand as a proportion of its total brand impact. The Gain Factor answers the question: What proportion of the preference for the misleading brand is due to the false claims made in its marketing communication? In this case, the gain factor equals:

Gain Factor = [47 – 39]/47 = 17%

This means that the false advertising being contested here is responsible for 17 percent of the total preference for the brand that is attributable directly to the false advertising of the four attributes.


When a brand is alleged to have engaged in false advertising the plaintiff lays claim to a portion of the infringing brand’s revenue and profits arguing that ceteris paribus, if it were not for the false claims, its own sales and profits would have been higher. This formulation pits two brands against each other in a zero-sum scenario.

Consumer research cannot estimate the transfer of sales, revenue or profits from one brand to another that is due to false claims. But, as demonstrated in this paper, consumer research can estimate the overall ill-gotten gain of the alleged misleading brand.

Instead of pitting a specific brand against the alleged infringer, this estimate is pitting the infringer against the average of all brands in the product category. It does so by estimating the ill-gotten gain as compared to not having used the allegedly misleading claims.

We can employ consumer research as shown here to assess the gain from misleading communication regardless of who the loser might be, as long as the loser is a brand operating in the same product space.

The methodology discussed here provides the finder of fact with a quantitative estimate of the impact of false advertising that should be of value in finding for one of the litigants.