Pricing a Digital Services Marketplace under Asymmetric Information

This paper addresses the pricing problem of an online service marketplace under asymmetric information. An example is an online learning platform such as Coursera that provides courses from suppliers (in this case, universities) to learners. We focus on the matching function of the marketplace whereby it engages in sequential search on behalf of a consumer using partially-observable consumer and supplier attributes. We develop the optimal pricing policies for a general distribution of the unobservable valuations. When these distributions are exponential, it is optimal to charge the same total fee for each match rather than engage in price discrimination, and this entire fee should be levied on the less elastic side of the marketplace