Differentiation of cancellation policies in the U.S. hotel industry

Chen, Chih‐Chien

doi:10.1016/j.ijhm.2013.02.007

Cited by 38 publications

(40 citation statements)

References 21 publications

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“…The typical reasons include sudden illnesses, accidents, schedule conflicts, unexpected (family) obligations, and natural catastrophes. Although the option to cancel a hotel booking (preferably at a low cost) is beneficial to presumptive hotel guests, it is a less desirable and possibly revenue-diminishing factor for hotel managers to deal with (Chen and Xie, 2013). Such losses are particularly high on last-minute cancellations (Chen, Schwartz, and Vargas, 2011;Koide and Ishii, 2005).…”

Section: Introductionmentioning

confidence: 99%

Modelling the cancellation behaviour of hotel guests

Falk

Vieru

2018

IJCHM

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Purpose The purpose of this study is to provide new insights into the factors that influence cancellation behaviour with respect to hotel bookings. The data are based on individual bookings drawn from a hotel reservation system database comprising nine hotels. Design/methodology/approach The determinants of cancellation probability are estimated using a probit model with cluster adjusted standard errors at the hotel level. Separate estimates are provided for rooms booked offline, through online travel agencies and through traditional travel agencies. Findings Evidence based on 233,000 bookings shows that the overall cancellation rate is 8 per cent. Cancellation rates are highest for online bookings (17 per cent), followed by offline bookings (12 per cent) and travel agency bookings (4 per cent). Probit estimations show that the probability of cancelling a booking is significantly higher for early bookings, large groups that book offline, offline bookings during high seasons, bookings not involving children and bookings made by guests from specific countries (e.g. China and Russia). Among the factors, booking lead time and country of residence play the largest role, particularly for online bookings. Research limitations/implications The analysis is based on individual-level booking data from one hotel chain in Finland, and therefore cannot be generalised for the total population of hotels in the country under observation. Originality/value The main contribution of this paper is a thorough investigation of the factors that influence cancellation behaviour at both the theoretical and empirical levels. Detailed and unique data from a hotel reservation system allow for new empirical insights into this behaviour.

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Section: Introductionmentioning

confidence: 99%

Modelling the cancellation behaviour of hotel guests

Falk

Vieru

2018

IJCHM

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“…But, as identified by Chen and Xie (2013) and Chen, Schwartz, and Vargas (2011), nowadays, a big part of these cancellations occur because of deal-seeking customers who are determined in the search for best deals. Sometimes, these customers continue to search for better deals of the same product/service after having placed a booking.…”

Section: Introductionmentioning

confidence: 99%

“…As a way to manage the risk associated to booking cancellations, hotels implement a combination of overbooking and cancellation policies (C.-C. Chen et al, 2011;C.-C. Chen & Xie, 2013;Mehrotra & Ruttley, 2006;Smith et al, 2015;Talluri & Van Ryzin, 2004). However, both overbooking and cancellation policies can be prejudicial to the hotel.…”

Section: Introductionmentioning

confidence: 99%

Predicting hotel booking cancellations to decrease uncertainty and increase revenue

António

Almeida

Nunes

2017

TMS

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Booking cancellations have a substantial impact in demandmanagement decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on revenue and reputation.Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled.Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies.Keywords: Data science, hospitality industry, machine learning, predictive modeling, revenue management. ResumoO cancelamento de reservas tem um impacto substancial nas decisões de gestão da procura na industria hoteleira. Os cancelamentos limitam a produção de previsões precisas, uma ferramenta crítica em termos de desempenho de gestão da receita. Para limitar os problemas causados pelo cancelamento de reservas, os hotéis implementam políticas de cancelamento rígidas e estratégias de overbooking, as quais podem vir a ter influência negativa sobre a receita e reputação social. Usando conjuntos de dados de quatro hotéis de resort e abordando a previsão de cancelamento de reservas como um problema de classificação no âmbito da Data Science, os autores demonstram que é possível construir modelos para prever cancelamentos de reservas com resultados superiores a 90%. Estes resultados permitem demonstrar que apesar do que foi assumido por Morales e Wang (2010) é possível prever com alta precisão se uma reserva será cancelada. Os resultados permitem que os hoteleiros prevejam com melhor precisão a procura líquida e construam melhores previsões, melhorem as políticas de cancelamento, definam melhores táticas de overbooking e usem estratégias de alocação de inventário com preços mais assertivos.

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“…Using Levene's test to identify differences in normalised variation between the segments (p > 0.05) it was found, with regard to group and transient cancellation behaviour, that group business represents a relatively large proportion of the uncertainty in demand and cancellations. Cancellations have received wide research attention in the hotel revenue management (e.g., Chen and Xie, 2013). The recent customer choice models in hotel revenue management forecasting literature, however, do not take cancellation into account, with the exception of Sierag et al (2015, p.3) who include cancellation but use three solution methods, each with different assumptions about cancellation, as the "problems are too large to solve exactly because of the curse of dimensionality".…”

Section: Cancellationsmentioning

confidence: 99%

A call for exploratory data analysis in revenue management forecasting: a case study of a small and independent hotel in The Netherlands

Sierag

Rest

Koole

et al. 2017

IJRM

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Using five years of data collected from a small and independent hotel this case study explores RMS data as a means to seek new insights into occupancy forecasting. The study provides empirical evidence on the random nature of group cancellations, an important but neglected aspect in hotel revenue management modelling. The empirical study also shows that in a local market context demand differs significantly per point of time during the day, in addition to seasonal monthly and weekly demand patterns. Moreover, the study presents evidence on the nonhomogeneous Poisson nature of the probability distribution that demand follows, a crucial characteristic for forecasting modelling that is generally assumed but not reported in the hotel forecasting literature. This implies that demand is more uncertain for smaller than for larger hotels. The paper concludes by drawing attention to the critical and often overlooked role of exploratory data analysis in hotel revenue management forecasting.

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Differentiation of cancellation policies in the U.S. hotel industry

Cited by 38 publications

References 21 publications

Modelling the cancellation behaviour of hotel guests

Modelling the cancellation behaviour of hotel guests

Predicting hotel booking cancellations to decrease uncertainty and increase revenue

A call for exploratory data analysis in revenue management forecasting: a case study of a small and independent hotel in The Netherlands

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