Information Disclosure and Promotion Policy Design for Platforms

Abstract

We consider a platform facilitating trade between sellers and buyers with the objective of maximizing consumer surplus. Even though in many such marketplaces, prices are set by revenue-maximizing sellers, platforms can influence prices through (i) price-dependent promotion policies that can increase demand for a product by featuring it in a prominent position on the web page and (ii) the information revealed to sellers about the value of being promoted. Identifying effective joint information design and promotion policies is a challenging dynamic problem as sellers can sequentially learn the promotion value from sales observations and update prices accordingly. We introduce the notion of confounding promotion policies, which are designed to prevent a Bayesian seller from learning the promotion value (at the expense of the short-run loss of diverting some consumers from the best product offering). Leveraging these policies, we characterize the maximum long-run average consumer surplus that is achievable through joint information design and promotion policies when the seller sets prices myopically. We then construct a Bayesian Nash equilibrium, in which the seller’s best response to the platform’s optimal policy is to price myopically in every period. Moreover, the equilibrium we identify is platform optimal within the class of horizon-maximin equilibria, in which strategies are not predicated on precise knowledge of the horizon length and are designed to maximize payoff over the worst-case horizon. Our analysis allows one to identify practical long-run average optimal platform policies in a broad range of demand models. This paper was accepted by David Simchi-Levi, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4677 .

Dynamic competition and market structure for platform‐based products: roles of product quality and indirect network effect

“…Gur et al. (2023) design promotion policies for a platform and information disclosure on the additional demand. Dynamic pricing is explored, and a Bayesian Nash equilibrium is characterized to maximize the long‐run average consumer surplus.…”

“…They suggest that decentralized pricing creates friction in the matching process, except if supply and demand arrivals are balanced or if the agents are patient. Gur et al (2023) design promotion policies for a platform and information disclosure on the additional demand. Dynamic pricing is explored, and a Bayesian Nash equilibrium is characterized to maximize the long-run average consumer surplus.…”

Data, Competition, and Digital Platforms

“…This paper is most closely related to the literature on information gatekeepers pioneered by Baye and Morgan (2001) and on the conflict of interest between intermediaries and the consumers they serve. Many recent contributions-including Armstrong and Zhou (2011), Condorelli and Szentes (2022), De Corniere and Taylor (2019), Gomes and Pavan (2016), Gur et al (2022), Hagiu and Jullien (2011), Inderst and Ottaviani (2012a), Inderst and Ottaviani (2012b), Ke et al (2022), Rayo andSegal (2010), andShi (2022)-analyze the steering role of platforms that strategically modify search results, e.g., to match consumers with the sellers that pay the largest commissions. 6 The provision of information by a digital platform is central to the model of de Cornière and de Nijs (2016), who examine a platform's incentives to provide match-value information to differentiated sellers in a second-price auction model.…”

Seller promotion and competition on decentralized two-sided platforms

“…On the other hand, Amazon firmly controls the recommendation mechanisms, a practice that cannot help all the sellers and may even sink successful businesses in some cases (Wang et al. , 2022; Gur et al. , 2023; Rietveld et al.…”

“…On the one hand, it facilitates the sellers' success, enabling them to concentrate on product description and pricing strategy to generate sales. On the other hand, Amazon firmly controls the recommendation mechanisms, a practice that cannot help all the sellers and may even sink successful businesses in some cases (Wang et al, 2022;Gur et al, 2023;Rietveld et al, 2019). However, when the sellers set up a Shopify store, they are basically starting from scratch.…”