Design and Pricing of Probabilistic Quality

Abstract

Increasingly, sellers are offering goods characterized by probabilistic quality. In such offers, buyers receive a synthetic product comprising of a lottery between two vertically differentiated goods. Given this emerging practice, I formally investigate the design and pricing of probabilistic quality. In this dissertation, I ask: How does probabilistic selling improve seller profits in vertical markets? When probabilistic quality is optimal, how is it designed; in particular, how are the associated probability, pricing, and product set determined? Further, what is the impact of transaction costs on the design of probabilistic quality? Next, what is the impact of probabilistic selling on consumer surplus? Finally, will probabilistic quality arise under demand uncertainty? My analysis reveals that probabilistic quality can enhance seller profits via three distinct routes: profitably disposing excess capacity, better targeting of the high-quality product, and greater market coverage. In addition, transaction costs can play a critical role on the emergence and manner of emergence of probabilistic quality. Next, I find that probabilistic quality can potentially enhance consumer surplus even though its implementation necessitates a dissipative transaction cost. Finally, I find that probabilistic quality is robust to considerations of demand uncertainty.