In a legal update that could continually reshape damage assessment practices in class-wide fraud and negligent misrepresentation claims, the Ninth Circuit has upheld the denial of class certification, based on the discrepancies present in the conjoint analysis put forth by the putative class representative in Mier v. CVS Health, No. 22-55665, 2023 WL 4837851.
For those unfamiliar with the term, conjoint analysis is a statistical technique commonly employed in market research to establish how people value different aspects of a product or service. Over the last few years, it has been increasingly applied in litigation related to class-wide damages, especially in cases of consumer fraud and misrepresentation.
However, it’s crucial to note that the Ninth Circuit has criticised this approach before. As highlighted in an earlier case on January 18, 2022, the court reversed the exclusion of a conjoint survey due to its “major flaws.” The recent ruling only sharpens this stance, underscoring the need for meticulous research and robust data when presenting class-wide damage assessments in court.
The decision stands as a firm reminder for legal professionals that for class certification in fraud and misrepresentation cases, it is significant to present a bulletproof and valid conjoint analysis to substantiate class-wide damages. It also serves as a marker of the Ninth Circuit’s approach towards leniency in accepting these analysis methodologies – a leniency that is clearly dwindling.
The ruling re-emphasizes the scrutiny that courts are exerting over damage assessment models, bolstering the belief that a high degree of analytical rigour and robustness are prerequisites for their acceptance. Legal professionals are encouraged to take note of the evolving judicial approach and adjust their strategies accordingly.
In the ever-evolving legal landscape, this landmark decision signals a decisive shift. It highlights not only the importance of underpinning claims in solid, empirical evidence but also the necessity for careful consideration and implementation of statistical analysis methodologies in the presentation of class-wide damages.