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.
In the hospitality industry, demand forecast accuracy is highly impacted by booking cancellations, which makes demand-management decisions difficult and risky. In attempting to minimize losses, hotels tend to implement restrictive cancellation policies and employ overbooking tactics, which, in turn, reduce the number of bookings and reduce revenue. To tackle the uncertainty arising from booking cancellations, we combined the data from eight hotels’ property management systems with data from several sources (weather, holidays, events, social reputation, and online prices/inventory) and machine learning interpretable algorithms to develop booking cancellation prediction models for the hotels. In a real production environment, improvement of the forecast accuracy due to the use of these models could enable hoteliers to decrease the number of cancellations, thus, increasing confidence in demand-management decisions. Moreover, this work shows that improvement of the demand forecast would allow hoteliers to better understand their net demand, that is, current demand minus predicted cancellations. Simultaneously, by focusing not only on forecast accuracy but also on its explicability, this work illustrates one other advantage of the application of these types of techniques in forecasting: the interpretation of the predictions of the model. By exposing cancellation drivers, models help hoteliers to better understand booking cancellation patterns and enable the adjustment of a hotel’s cancellation policies and overbooking tactics according to the characteristics of its bookings.
Booking cancellations in the hospitality industry not only generate revenue loss and affect pricing and inventory allocation decisions, but they also, in overbooking situations, have the potential to affect the hotel's online social reputation. By employing data sets from four resort hotels and addressing this issue as a classification problem in the scope of data science, the authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This research also demonstrates that despite what was alleged by Morales and Wang (2010), it is possible to predict with high accuracy whether a booking will be canceled. Results allow hotel managers to act on bookings with high cancellation probability and contain the associated revenue losses, produce better net demand forecasts, improve overbooking/cancellation policies, and have more assertive pricing and inventory allocation strategies.
Booking cancellations have significant impact on demand-management decisions in the hospitality industry. To mitigate the effect of cancellations, hotels implement rigid cancellation policies and overbooking tactics, which in turn can have a negative impact on revenue and on the hotel reputation. To reduce this impact, a machine learning based system prototype was developed. It makes use of the hotel's Property Management Systems data and trains a classification model every day to predict which bookings are "likely to cancel" and with that calculate net demand. This prototype, deployed in a production environment in two hotels, by enforcing A/B testing, also enables the measurement of the impact of actions taken to act upon bookings predicted as "likely to cancel". Results indicate good prototype performance and provide important indications for research progress whilst evidencing that bookings contacted by hotels cancel less than bookings not contacted.
This data article describes two datasets with hotel demand data. One of the hotels (H1) is a resort hotel and the other is a city hotel (H2). Both datasets share the same structure, with 31 variables describing the 40,060 observations of H1 and 79,330 observations of H2. Each observation represents a hotel booking. Both datasets comprehend bookings due to arrive between the 1st of July of 2015 and the 31st of August 2017, including bookings that effectively arrived and bookings that were canceled. Since this is hotel real data, all data elements pertaining hotel or costumer identification were deleted. Due to the scarcity of real business data for scientific and educational purposes, these datasets can have an important role for research and education in revenue management, machine learning, or data mining, as well as in other fields.
This study examines the relationship between distance measures and a Portuguese data set consisting of 34,622 online hotel reviews extracted from Booking.com and TripAdvisor written in Portuguese, Spanish, and English. Based on the country of origin of each review author, a geographic and a psychic distance measure is calculated for Portugal. Data and text mining analysis provides additional insights into online hotel ratings. The authors confirm that online travelers’ evaluations are multifaceted constructs displaying varying patterns of rating behavior among the traveler base. By investigating the contemporary relevance of geographic and psychic distance, a key finding of this study is that travelers with less distance both in terms of psychic and geographic distance give a lower rating score than travelers with greater distance. The inclusion of psychic and geographic distance is advocated as a salient aspect for future researchers and for those practitioners who wish to enhance hotel product and service features.
Despite its benefits related to efficiency, creating better customer experiences, increasing revenue, and supporting decision-making, until the outbreak of COVID-19 digital transformation was not on many hotels’ strategic plans. However, like in many other industries, the COVID-19 physical distancing good practices and governments’ restrictions acted as catalysts and promoted hotel digitalization. To this end, a questionnaire was administered to 51 hotel managers to verify if that happened in Portuguese hotels and what processes were most impacted. Results showed that 92% of the hotel managers agreed that COVID-19 promoted the digitalization of processes, with most organizations considering that online meetings and technology productivity tools are here to stay. Hotels’ digitalization has the potential to generate high-efficiency gains both in public-facing operations and back-office operations. This study highlights these implications and intends to spur researchers to investigate the practical impact of these implications on business efficiency and social theory.
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