Motivated by the proliferation of user-generated product-review information and its widespread use, this note studies a market where consumers are heterogeneous in terms of their willingness-to-pay for a new product. Each consumer observes the binary reviews (like or dislike) of consumers who purchased the product in the past and uses Bayesian updating to infer the product quality. We show that the learning process is successful as long as the price is not prohibitive and therefore at least some consumers, with sufficiently high idiosyncratic willingness-to-pay, will purchase the product irrespective of their posterior quality estimate. We examine some structural properties of the dynamics of the posterior beliefs.
Finally, we study the seller’s pricing problem, and we show that, if the set of possible prices is finite, then a stationary optimal pricing policy exists. If putting one item on the market involves a constant cost, then under this optimal policy, learning fails with positive probability.