Last Friday, the Third Circuit held that a J. Crew customer lacked standing to sue the company for printing ten digits of his credit card on a receipt, in violation of the Fair and Accurate Credit Transaction Act (which provides that companies should print only the last four digits). Relying on the Supreme Court’s decision in Spokeo v. Robins, the court held that the plaintiff’s alleged injuries—a violation of the statute and the “risk of identity theft”—were merely “procedural,” and thus insufficiently “concrete” to confer standing under Article III. The Third Circuit’s rigorous application of Article III standing requirements is good news for defendants in mislabeling cases, some of which are “gotcha”-type suits arising from highly technical labeling violations.
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Misbranded is Patterson Belknap’s blog covering false advertising litigation—both consumer class actions and competitor suits—with a particular focus on FDA-regulated products (foods/beverages, pharmaceuticals, cosmetics, and dietary supplements). Writing from the industry perspective, we provide timely updates on important cases, surveys of litigation trends, and in-depth analyses of “hot” legal issues. Our firm pioneered the modern practice of false advertising law more than 40 years ago, bringing the first competitor suits under the Lanham Act. In the decades since, we have continued to practice at the cutting edge, handling many of the field’s most groundbreaking cases on behalf of the nation’s best-known businesses. Today, led by Steven A. Zalesin, our team advocates creatively, strategically, and efficiently on behalf of our clients at all phases of litigation, from pre-complaint demands to Supreme Court appeals.
Over the last few years, “conjoint analysis” has become the methodology du jour for false advertising plaintiffs seeking to demonstrate they can calculate class-wide damages. Conjoint analysis is so named because it is used to study the joint effects of multiple product attributes on consumers’ choices. At bottom, conjoint analysis uses survey data to measure the strength of consumers’ preferences for particular product features. Or, put differently, it tries to isolate how much people care about an individual product attribute in a multi-feature product (in a more scientific manner than just asking them directly).