This paper examines the informational role of product ratings. We build a theoretical model in which ratings can help consumers figure out how much they would enjoy the product. In our model, a high average rating indicates a high product quality, whereas a high variance of ratings is associated with a niche product, one that some consumers love and others hate. Based on its informational role, a higher variance would correspond to a higher subsequent demand if and only if the average rating is low. We find empirical evidence that is consistent with the theoretical predictions with book data from Amazon.com and BN.com. A higher standard deviation of ratings on Amazon improves a book's relative sales rank when the average rating is lower than 4.1 stars, which is true for 35% of all the books in our sample. This paper was accepted by Pradeep Chintagunta, marketing.

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Consumers often need to search for product information before making purchase decisions. We consider a parsimonious model in which consumers incur search costs to learn further product information, and update their expected utility of the product at each search occasion. We characterize the optimal stopping rules to either purchase, or not purchase, as a function of search costs and the informativeness of each attribute. The paper also characterizes how the likelihood of purchase changes with the ex-ante expected utility, search costs, and informativeness of each attribute. We discuss optimal pricing, the impact of consumer search on profits and social welfare, and how the seller chooses its price to strategically affect the extent of the consumers' search behavior. We show that lower search costs can hurt the consumer because the seller may choose to then charge higher prices. The paper also considers the impact of searching for signals of the value of the product, of discounting, and of endogenizing the intensity of search.