Incorporating Social Learning into the Optimal Return and Pricing Decisions of Online Retailers

The biggest drawback of online shopping lies in consumers' uncertainty about the product. Before making a purchase decision, consumers receive product information from many sources which helps them infer whether a product meets their preferences. This information constitute a positive or a negative signal about the product resulting in positive-type consumers and negative-type consumers. Also, many retailers offer money-back guarantees (MBGs) to reduce consumers' mismatch risk. We analyze a retailer's pricing and return policy decisions in the presence of product information. We find that when information accuracy is low, it is optimal for the retailer to sell to both positive and negative-type consumers. When information accuracy is high, it is optimal for the retailer to sell to only positive-type consumers. In addition, if no returns are allowed, product information hurts the retailer when information accuracy is low while product information benefits the retailer when information accuracy is high. If a MBG is offered, product information always benefits the retailer. When consumers are heterogeneous in their belief about information accuracy, if no returns are allowed, product information benefits the retailer for a small match probability, whereas product information hurts the retailer for a large match probability. Similar to the case of homogenous consumers, product information benefits the retailer in almost the whole range of problem parameters if a MBG is offered. Moreover, we find that product information can be a driving factor to the ubiquity of MBGs regardless of homogenous or heterogeneous consumers