Fashion retailer agrees to $4.2 million settlement with the FTC and the issuance of guidance regarding consumer reviews
Ecommerce marketers are increasingly relying on reviews to help drive sales, particularly given the impact of Amazon on retail sales. How marketers select and post online reviews have come under increasing scrutiny by federal and state regulators. A recent enforcement action and published guidance highlights these concerns.
Recently, online fashion retailer Fashion Nova, LLC agreed to pay $4.2 million to the Federal Trade Commission (“FTC”) to resolve allegations that it blocked negative customer reviews from its website. Besides the payment, Fashion Nova will also be prohibited from suppressing negative reviews in the future. The FTC alleged that the California-based retailer, which sells fast fashion products online, implicitly misrepresented that the reviews on its website reflected the views of all purchasers who submitted reviews, when in fact it suppressed reviews with ratings lower than four stars out of five. The case is the FTC’s first involving a company’s efforts to conceal negative customer reviews.
The FTC’s administrative complaint contained a single cause of action, entitled deceptive review practices. Fashion Nova used a third-party interface that automatically posted four- and five-star reviews to its website but held three-star and below reviews for review and approval. The FTC alleged that from late 2015 until November 2019, Fashion Nova never posted “hundreds of thousands” of the lower-starred reviews. According to the FTC, suppressing negative reviews deprives consumers of potentially useful information and artificially inflates the product’s average star rating.
In conjunction with this first-of-its-kind settlement, the FTC issued a set of guidelines for online retailers and review platforms to educate them about collecting and publishing reviews in ways that do not mislead consumers.
The guidance includes the following recommendations by the FTC:
> Publish all genuine reviews and don’t […]
I am a robot. This article is curated from another source (e.g. videos, images, articles, etc.). For the complete article please use the link provided to visit the original source or author. Content from other websites behaves in the exact same way as if the visitor has visited the other website.
Warning: The views and opinions expressed are those of the authors and do not necessarily reflect the official policy or position of MichelPaquin.com.