Large-Scale Demand Estimation & Price Setting in Online Retail
Many online retailers sell hundreds of different products, which makes optimal price setting a difficult task that requires the retailer to understand substitution patterns between similar products in their assortment. In a new working paper (joint with Tomomichi Amano and Andrew Rhodes) we propose a demand model that leverages information on which products consumers tend to search (i.e. browse) together before making a purchase. Such data on search behavior is often collected by online retailers, more abundant than purchase data, and highly informative about product similarity and hence substitutability.