Strategic behavior for hotel standby upgrade programs: Empirical evidence and pricing implications

Yılmaz, Övünç; Ferguson, Mark; Pekgün, Pelin; Shang, Guangzhi

doi:10.1002/joom.1177

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…These upgrades are sold in advance, but will be fulfilled if and only if capacity in higher compartments is not fully utilized at the end of the booking horizon. Exemplary papers considering conditional upgrades are Yilmaz et al (2017Yilmaz et al ( , 2022, Cui et al (2018) and Biyalogorsky et al (2005).…”

Section: Upsells In Revenue Managementmentioning

confidence: 99%

On the value of booking data for upsell decision-making in revenue management

Rauhaus,

Gönsch,

Steinhardt

2024

Flex Serv Manuf J

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In passenger aviation and many other areas of transportation, it is common practice to offer customers who have booked a ticket for a lower compartment free seats in higher compartments at a discount before departure, a practice known as upselling. For example, economy class customers are offered a seat in business class for a small surcharge a few days before take-off. Obviously, it matters to whom to offer an upsell and at what price. In this paper, we address this decision problem in a generic fashion for revenue management settings. We assume that the company has disaggregated booking data about the customer’s initial choice of a product from a provided offer set. This data contains information about individual customers’ preferences and may be leveraged to decide on upsell prices. To this end, we propose an optimization approach based on an expectation model, in which customers’ response probability is represented as a conditional probability formally consistent with their initial buying decision in a multinomial logit model. We present variants of the approach based on different levels of exploitable customer-specific booking data. In a numerical study, we investigate the value of this data usage and upselling in general to the company. Upselling in conjunction with knowledge of the customers’ original offer sets and customer segments, substantially increases revenues. Furthermore, the study demonstrates that the proposed approach can lead to larger revenue benefits than a naive benchmark approach which statistically decouples the customers’ upgrade acceptance decision from their original choice during the purchasing process.

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Section: Upsells In Revenue Managementmentioning

confidence: 99%

On the value of booking data for upsell decision-making in revenue management

Rauhaus,

Gönsch,

Steinhardt

2024

Flex Serv Manuf J

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“…Current hotel revenue management literature focuses on sophisticated issues such as optimizing standby upgrades (Yılmaz et al, 2017), pricing of conditional upgrades (Cui et al, 2018), estimating strategic customer behavior in the context of standby upgrades (Yılmaz, Ferguson, et al, 2022), predicting personalized demand with unobserved no‐purchases (Cho et al, 2022), and identifying demand elasticities by leveraging the ability of observing delayed price adjustment to demand shocks (Garcia et al, 2022). However, the aforementioned challenges that budget hotels face are often ignored.…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning approach for hotel revenue management with evidence from field experiments

Chen

et al. 2023

J of Ops Management

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We consider a budget hotel chain's revenue management problem of deciding how to dynamically allocate capacity to multiple segments of customers. Our work solves an industrial‐sized problem faced by practitioners, with the reality of implementation motivating us to develop a tailored reinforcement learning approach. Our approach proceeds in two steps. First, a recommended average discount is computed with a reinforcement learning algorithm. Then, the recommended average discount is turned into a capacity allocation through a linear program. This approach overcomes the challenges of characterizing demand and estimating cancellations, and it facilitates hotel managers' acceptance of the revenue management system. We implement this approach in the hotel chain in a pilot study and assess its effectiveness using synthetic control methods. Our approach improves the key operational performance measure—revenue per available room—by 11.80%. There is heterogeneity in how the pilot hotels improve their revenue per available room. Some mainly increase their occupancy rate, some mainly increase the average daily room rate, while others experience significant increases in both. Further analysis shows that our approach uncovers the individual sources of suboptimal performance in pilot hotels and correspondingly improves decision‐making. Our work demonstrates that a reinforcement learning approach for hotel revenue management is promising.

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“…Increasingly, advanced technologies are being used to change the services offered and delivered to customers, sometimes having unexpected outcomes for consumers or service firms. In “Strategic behavior for hotel standby upgrade programs: Empirical evidence and pricing implications,” Yılmaz et al (2022) examine the roles played by standby upgrade technologies. Such technologies open up the possibility of strategic behaviors on the part of hotel customers, that is, customers learning the technology so well that they game the technology in a manner that enhances their own service consumption (i.e., lower price) at the cost of the service firm's profit.…”

Section: Looking Forward – Introduction To the Special Issuementioning

confidence: 99%