Revenue Management with Repeated Customer Interactions

68 Pages Posted: 26 Jun 2018 Last revised: 23 Jun 2020

See all articles by Andre Calmon

Andre Calmon

INSEAD - Technology and Operations Management

Dragos Ciocan

INSEAD

Gonzalo Romero

University of Toronto - Rotman School of Management

Date Written: June 23, 2020

Abstract

Motivated by online advertising, we model and analyze a revenue management problem where a platform interacts with a set of customers over a number of periods. Unlike traditional network revenue management, which treats the interaction between platform and customers as one-shot, we consider stateful customers who can dynamically change their goodwill towards the platform depending on the quality of their past interactions. Customer goodwill further determines the amount of budget that they allocate to the platform in the future. These dynamics create a trade-off between the platform myopically maximizing short-term revenues, versus maximizing the long-term goodwill of its customers to collect higher future revenues. We identify a set of natural conditions under which myopic policies that ignore the budget dynamics are either optimal or admit parametric guarantees; such simple policies are particularly desirable since they do not require the platform to learn the parameters of each customer dynamic and only rely on data that is readily available to the platform. We also show that, if these conditions do not hold, myopic and finite look-ahead policies can perform arbitrarily poorly in this repeated setting. From an optimization perspective, this is one of a few instances where myopic policies are optimal or have parametric performance guarantees for a dynamic program with non-convex dynamics. We extend our model to the cases where supply varies over time and where customers may not interact with the platform in every period.

Keywords: Revenue management, Analysis of algorithms, Dynamic Programming.

Suggested Citation

Calmon, Andre and Ciocan, Dragos and Romero, Gonzalo, Revenue Management with Repeated Customer Interactions (June 23, 2020). INSEAD Working Paper No. 2020/31/TOM, Available at SSRN: https://ssrn.com/abstract=3200338 or http://dx.doi.org/10.2139/ssrn.3200338

Andre Calmon (Contact Author)

INSEAD - Technology and Operations Management ( email )

Boulevard de Constance
77 305 Fontainebleau Cedex
France

Dragos Ciocan

INSEAD ( email )

Boulevard de Constance
77305 Fontainebleau Cedex
France

Gonzalo Romero

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

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