Prof. Zizhuo Wang
University of Minnesota
Talk:
Revenue Management of Network Goods under Demand Uncertainty
Abstract:
A network good with positive network externalities is a unit of goods or services with value increasing in the number of units sold. In many today's business settings, e.g., online group buying, project crowd funding or event ticket selling, a firm faces the problem of selling network goods over a finite horizon under demand uncertainty. Without information of others, customers face uncertainty of the final number of units sold when making purchase decisions. Through social interactions, customers can reduce such uncertainty, and be willing to pay more for a massive turnout, but may also forgo the purchase if the turnout is found out to be low. We compare the optimal committed pricing ex ante with and without social interactions, and identify two countervailing effects of allowing social interactions: a pro-interaction risk seeking effect, that is, the firm has an intrinsic tendency to expose itself to market size revelation, because the benefit of a realized high market size outweighs the loss of a realized low market size up to a certain threshold, and an antiinteraction risk saturation effect, that is, for a sufficiently large expectation of network benefits, customers would make a purchase anyway even without knowing the market size realization, but may be turned away if observing a small market size realization. The tension of the two effects leads to that if the network benefit is relatively strong, curbing social interactions tends to generate higher profit than cultivating them, and if the network benefit is relatively weak, vice versa. We also show that optimal contingent pricing ex post with social interactions always generates higher profit than committed pricing ex ante with or without social interactions. Our results highlight the strategic impact of social interactions on the firm, and shed light on how the firm should optimally manage information and pricing, as important aspects of revenue management, in selling network goods subject to demand uncertainty. Extensions are made to account for heterogeneous network externalities and capacity constraints.
Biography:
Dr Zizhuo Wang is an assistant professor from the Department of Industrial and Systems Engineering (ISyE) at University of Minnesota. He received his PhD in Operations Research from Stanford University in 2012. Prior to that, he graduated from Department of Mathematical Sciences in Tsinghua University at 2007 and obtained his M.S. in Mathematical Finances in 2011 from Stanford University. His research interests mainly focus on optimization and stochastic modeling, especially with applications to pricing and revenue management. He has published several papers in top journal in the field of operations research, including Operations Research, Mathematical Programming, SIAM Journal on Optimization etc.
