Pricing of Conditional Upgrades in the Presence of Strategic Consumers

Abstract

In this paper, we study a conditional upgrade strategy that has recently become very common in the travel industry. After a consumer makes a reservation for a product (e.g., a hotel room), she is asked whether she would like to upgrade her product to a higher-quality (more expensive) one at a discounted price. The upgrade, however, is not fulfilled immediately. The firm fulfills upgrades at check-in if higher-quality products are still available, and the upgrade fee is only charged to the consumer if she gets upgraded. Consumers decide which product type to book and whether to accept an upgrade offer or not based on the anticipated upgrade probability. We model the consumers' decisions using a Poisson-arrival game framework with incomplete information and prove the existence of Bayesian Nash equilibrium. To further study the firm's optimal upgrade pricing strategy and develop managerial insights, we also analyze a fluid model which is the asymptotic version of the stochastic model. Our numerical studies validate that our theoretical results derived from the fluid model carry through to the stochastic model. Our analysis identifies multiple benefits of conditional upgrades. First, the firm is able to capture more demand by offering conditional upgrades, i.e., the consumers who value original product types lower than the original prices but value higher-quality products higher than the discounted price with upgrades. Second, conditional upgrades enable the firm to improve its market segmentation by inducing more consumers to purchase higher-quality products. Third, conditional upgrades give the firm more flexibility in better matching fixed capacities to stochastic demands. For a firm that is a price taker, offering conditional upgrades is effective in compensating for the firm's lack of ability in setting its prices optimally, and can sometimes generate even higher revenues than being able to optimize product prices. For a firm that has the ability to optimize product prices, conditional upgrades can generate higher revenues than dynamic pricing.

The joint influence of the timing and framing of an online upselling message on consumer perceptions: the roles of construal level and reactance

“…Extant research on upselling has mainly focused on the offline setting, offering limited insights into online upselling (Denizci Guillet, 2020). Prior research on online upselling has investigated optimal pricing strategies in the presence of different consumer types (Cui et al , 2017), the effect of consumer types and market size of premium rooms on boosting revenue (Yilmaz et al , 2017) and the comparison of online and offline upselling on generating additional hotel revenue (Denizci Guillet, 2020). While these studies showcase the importance of online upselling, little is known about when and how to make online upselling successful from the consumer’s perspective.…”

“…Further, while a preliminary study on upselling has revealed the different upselling effectiveness between the time immediately after the booking and the time at check-in, the underlying reasons remained unknown (Cui et al , 2017). This research reveals the underlying mechanism that explains why consumers respond differently to upselling promotions offered at different points in time.…”

Pricing Strategies for Different Periods During Subsequent Selling Season for Seasonal Products

“…Cachon and Swinney [16] have studied purchasing, pricing, and quick response in relation to strategic consumers. Cui et al [17] have considered the pricing of conditional upgrades in relation to strategic consumers. Numerous studies have also examined strategic (forward-looking) and non-strategic (myopic) customers [12], [14], [18]- [23].…”

Dynamic Pricing and Replenishment with Customer Upgrades

“…There is also a related stream of work on pricing and upselling, in which a customer is required to make a side payment to receive a higher value product. For examples of recent work on upselling, we refer the reader to Aydin and Ziya (2008), Gallego and Stefanescu (2009), and Cui et al (2017).…”