Optimal pricing strategy based on market segmentation for service products using online reservation systems: An application to hotel rooms

Guo, Xiaolong; Ling, Liuyi; Yang, Chenchen; Li, Zhaoqiong; Liang, Liang

doi:10.1016/j.ijhm.2013.07.001

Cited by 69 publications

(51 citation statements)

References 34 publications

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“…Optimal decisions for a hotel will be affected by various factors, such as seasons, weathers, or even social activities [8,57], however, this model can be applied for various hotels with diverse capacities by assuming a fixed demand function.…”

Section: Overbooking and Expected Profitsmentioning

confidence: 99%

“…This distinguishing characteristic results in a zero salvage value to hotel rooms, because unoccupied rooms do not generate any revenue for hoteliers and cannot be reflected in the inventory for future use [6,7]. On the other hand, managing hotel rooms incurs a high fixed cost but a relatively low variable cost, leading to considerable marginal profits for hoteliers [7,8]. By contrast, hotels suffer heavy revenue loss from last-minute cancellations and customer no-shows.…”

Section: Introductionmentioning

confidence: 99%

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Hotel Overbooking and Cooperation with Third-Party Websites

Dong

Ling

2015

Sustainability

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Hotels cooperate with third-party websites to enhance their competitive position and attain sustainable development in the era of e-commerce. Furthermore, hotel managers overbook to hedge against last-minute cancellations and customer no-shows. This paper discusses pricing and overbooking strategies of a hotel in the context of cooperation with multiple third-party websites and analyzes how these strategies influence the cooperation process. The increase in profits resulting from the overbooking strategy is also examined. A model of a Stackelberg game between the hotel and third-party websites and a Nash game among the third-party websites are developed to analyze the process. Results indicate that the compensation coefficient has a slight influence on hotel pricing strategy, but causes a sharp decrease in overbooking level. As the hotel demands to lessen the probability of denying under higher compensation rate, the third-party websites would exert less sales effort to reduce the demand of online customers. Results also show that under a given market demand, hotel effort only serves to redistribute market shares among the hotel and third-party websites, without influencing hotel pricing and overbooking decisions. Specifically, the market shares of the third-party websites decrease in the effort level of the hotel resulting from an increase in the hotel market share. Finally, in the numerical example where one hotel and two websites are analyzed, hotel overbooking strategy improves hotel profit by 4.20%, whereas profit improvements for Websites 1 and 2 are as high as 5.26% and 5.21%, respectively. Managerial implications of the study are also provided.

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Section: Overbooking and Expected Profitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hotel Overbooking and Cooperation with Third-Party Websites

Dong

Ling

2015

Sustainability

Self Cite

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“…Segmentation: Pricing classifications based on customer type of business, type of hotel or room, day of week stays, and purpose of stay (Guo, Ling, Yang, Li, & Liang, 2013).…”

Section: Revenue Managementmentioning

confidence: 99%

“…Masiero and Law (2015) found that the guest profile was different for different channels. Guo, et al (2013) looked at cooperation between hotels and online travel agents and the impact that relationship has on commissions. Hua, et al (2015) examined hotel expenses incurred by the utilization of ecommerce channels.…”

Section: Channelsmentioning

confidence: 99%

“…Siguaw, Kimes, and Gassenheimer (2003, p. 543) proposed that effective revenue management must include "the ability to segment customers into those sensitive to prices and those sensitive to time. " Guo, et al, (2013) found that dynamic pricing strategies that leverage segmentation benefit both the company and the customer. As the discipline of revenue management continues its online channel evolution, segmentation based on channel selection will be central to setting optimal rates and driving occupancy (Guo, et al, 2013) with greater consideration on channel costs.…”

Section: Segmentationmentioning

confidence: 99%

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