False Advertising
We work on matters involving allegedly false, misleading, incomplete, or otherwise deceptive claims in media environments ranging from traditional print and TV advertisements to apps, websites, and social media. Our clients rely on our ability to analyze the “mind of the consumer” to assess consumer understanding of product claims and to evaluate whether at-issue statements have an impact on purchasing behavior.
Supporting clients in such matters may involve conducting bespoke surveys, synthesizing existing market research studies, or reviewing publicly available materials. Our analyses of consumer-generated content such as online reviews and social media leverage a combination of best-practice coding methods and natural language processing (NLP) approaches to generate real-world evidence of consumer perceptions, beliefs, and purchase and repurchase intent, as well as customer satisfaction.
Our Methods
Our surveys have been accepted as evidence and relied on by courts in numerous complex litigations. These studies can be supplemented with other kinds of analysis, including:
- Test/control experiments – To measure consumer understanding or purchase intent, we apply test/control experimental designs to isolate the causal effect (if any) of a particular product characteristic, allegedly false claim, or missing disclosure on purchase decisions, beliefs, or other aspects of consumer behavior.
- Purchase funnel – We apply the purchase funnel framework and examine each step up to the purchase decision, as well as post-purchase factors such as customer satisfaction. As consumer purchases are typically preceded by information search, processing, and the shaping of a set of relevant purchase options, our analyses of the purchase journey can provide information crucial to a factfinder’s understanding of how and why certain purchases were made.
- Social media analysis – Analysis of social media, online reviews, and other data reflecting consumer sentiment can provide helpful evidence in false advertisement matters to shed light on consumer perceptions and beliefs. We apply NLP and data science models to gain further empirical insight into consumers’ voices.
