The growing body of literature on online ratings has reached a consensus of the positive impact of the average rating and the number of ratings on economic outcomes. Yet, little is known about the econom-ic implication of the variance of online ratings, and existing studies have presented contradictory re-sults. Therefore, this study examines the relationship between the variance of online ratings and the price and sales for digital cameras from Amazon.com. The key feature of our study is that we employ and validate a machine learning approach to decompose the online rating variance into a product fail-ure-related and taste-related share. In line with our theoretical foundation, our empirical results high-light that the failure-related variance share is negatively associated with price and sales, and the taste-related share exhibits a positive relationship with price and sales. Our results highlight a new perspec-tive on the online rating variance that has been largely neglected by prior studies. Sellers can benefit from our results by adjusting their pricing strategy and improving their sales forecasts. Review plat-forms can facilitate the identification of product failure-related ratings to support the purchasing deci-sion process of customers.