Personalized Pricing and Distribution Strategies
研究了品牌制造商在个性化定价环境下选择单一分销还是双重分销的决策，发现双重分销在多数情况下最优，且个性化定价与垂直整合的互动影响行业利润。
The availability of consumer data is inducing a growing number of firms to adopt more personalized pricing policies. This affects both the performance of, and the competition between, alternative distribution channels, which in turn has implications for firms’ distribution strategies. We develop a formal model to examine a brand manufacturer’s choice between mono distribution (selling only through its own direct channel) or dual distribution (selling through an independent retailer as well). We consider different demand patterns, covering both horizontal and vertical differentiation and different pricing regimes, with the manufacturer and retailer each charging personalized prices or a uniform price. We show that dual distribution is optimal for a large number of cases. In particular, this is always the case when the channels are horizontally differentiated, regardless of the pricing regime; moreover, if both firms charge personalized prices, a well-designed wholesale tariff allows them to extract the entire consumer surplus. These insights obtained here for the case of intrabrand competition between vertically related firms are thus in stark contrast to those obtained for interbrand competition, where personalized pricing dissipates industry profit. With vertical differentiation, dual distribution remains optimal if the manufacturer charges a uniform price. By contrast, under personalized pricing, mono distribution can be optimal when the retailer does not expand demand sufficiently. Interestingly, the industry profit may be largest in a hybrid pricing regime, in which the manufacturer forgoes the use of personalized pricing and only the retailer charges personalized prices. This paper was accepted by Joshua Gans, business strategy. Funding: The financial support of the European Research Council under the European Union’s Horizon 2020 research and innovation programme [Grant Agreement 670494] and of the Agence nationale de la recherche (ANR) [Grant ANITI (ANR Grant 3IA)] and [Grant CHESS ANR-17-EURE-0010] (Investissements d’Avenir program) is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4437 .