Big data in hotel revenue management: exploring cancellation drivers to gain insights into booking cancellation behavior

n 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.info:eu-repo/semantics/acceptedVersio

An automated machine learning based decision support system to predict hotel booking cancellations

Booking cancellations negatively contribute to the production of accurate forecasts, which comprise a critical tool in the hospitality industry. Research has shown that with today’s computational power and advanced machine learning algorithms it is possible to build models to predict bookings cancellation likelihood. However, the effectiveness of these models has never been evaluated in a real environment. To fill this gap and investigate how these models can be implemented in a decision support system and its impact on demand-management decisions, a prototype was built and deployed in two hotels. The prototype, based on an automated machine learning system designed to learn continuously, lead to two important research contributions. First, the development of a training method and weighting mechanism designed to capture changes in cancellations patterns over time and learn from previous days’ predictions hits and errors. Second, the creation of a new measure – Minimum Frequency – to measure the precision of predictions over time. From a business standpoint, the prototype demonstrated its effectiveness, with results exceeding 84% in accuracy, 82% in precision, and 88% in Area Under the Curve (AUC). The system allowed hotels to predict their net demand and thus making better decisions about which bookings to accept and reject, what prices to make, and how many rooms to oversell. The systematic prediction of bookings with high probability of being canceled allowed hotels to reduce cancellations by 37 percentage points by acting to avoid their cancellation.info:eu-repo/semantics/publishedVersio

Hotel revenue management: usingdata science to predict booking cancellations

In the hotel industry, demand forecast accuracy is highly impacted by booking cancellations. Trying to overcome loss, hotels tend to implement restrictive cancellation policies and employ overbooking tactics which in turn reduces the number of bookings and reduces revenue. To tackle the uncertainty arising from cancellations, models for the prediction of a booking's cancellation were developed. Data from hotels' reservations systems was combined with data from other sources (events, holidays, online prices/inventory, social reputation and weather). Despite data class imbalance, concept drift, and dataset shift problems, it was possible to demonstrate that to predict cancellations of bookings is not only possible but also accurate. Moreover, it helped to better understand what the cancellation drivers can be. In order to assess the models under real conditions, a prototype was developed for field tests allowing to evaluate how an automated machine learning system that predicts booking’s cancellations could be integrated into hotels' systems. The model's performance in a real environment was assessed, including the impact on the business. The prototype implementation enable an understanding of adjustments to be made in the models so that they could effectively work in a real environment, as well as fostered the creation of a new measure of performance evaluation. The prototype enabled hoteliers to act upon identified bookings and effectively decrease cancellations. Moreover, results confirmed that booking cancellation prediction models can improve demand forecast, allowing hoteliers to understand their net demand, i.e., current demand minus predicted cancellations.Na indústria hoteleira, a precisão da previsão da procura é altamente impactada pelos cancelamentos de reservas. Na tentativa de mitigar as consequências dos cancelamentos, os hotéis tendem a implementar políticas de cancelamento restritivas e táticas de "overbooking", o que, por sua vez, reduz o número de reservas e a receita. Para combater a incerteza decorrente dos cancelamentos, foram desenvolvidos modelos capazes de prever a probabilidade de cada reserva vir a ser cancelada. Neste desenvolvimento foram utilizados dados de oito sistemas de gestão de reservas de outros tantos hotéis, conjuntamente com dados de outras fontes (eventos, feriados, preços/inventário "online", reputação social e clima). Apesar dos problemas de desequilíbrio de classe de dados, desvio de conceito e variação de distribuição entre variáveis ao longo do tempo, foi possível demonstrar que prever cancelamentos de reservas não é apenas possível realizar, mas que é possível de fazer com elevada precisão. A elaboração dos modelos ajudou ainda a compreender os fatores que influenciam o cancelamento. Para avaliar os modelos em condições reais, foi desenvolvido um protótipo, o qual permitiu avaliar como um sistema automatizado baseado em aprendizagem automática para prever os cancelamentos de reservas pode ser integrado nos sistemas dos hotéis. Este protótipo permitiu ainda avaliar o desempenho dos modelos num ambiente real, incluindo o seu impacto na operação. A implementação possibilitou também compreender os ajustes a serem feitos aos modelos para que pudessem efetivamente trabalhar num ambiente real, bem como fomentou a criação de uma nova medida de avaliação de desempenho. O protótipo permitiu que os hoteleiros agissem sobre as reservas identificadas e efetivamente diminuíssem os cancelamentos. Para além disso, os resultados confirmaram que os modelos de previsão de cancelamento de reservas podem melhorar a previsão de procura, permitindo que os hoteleiros compreendam melhor a sua procura líquida, ou seja, a procura atual menos os cancelamentos previstos

How relationship norms shape moral obligation in cancelation behavior

Shuqair, S., Costa Pinto, D., Cruz-Jesus, F., Mattila, A. S., da Fonseca Guerreiro, P., & Kam Fung So, K. (2022). Can customer relationships backfire? : How relationship norms shape moral obligation in cancelation behavior. Journal of Business Research, 151(November), 463-472. https://doi.org/10.1016/j.jbusres.2022.07.008 ---Funding Information: The authors Diego and Frederico gratefully acknowledge financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020).While prior research indicates that establishing interpersonal interaction with customers is mostly beneficial, this work reveals that the impact of social ties depends on relationship norms (communal vs. exchange). In three studies, including a real-world field dataset (N = 87,615 customers), the current investigation demonstrates the conditions under which interpersonal relationships can increase or decrease customers’ cancelation behavior. The findings indicate that communal (vs. exchange) relationships can increase customers’ future cancelation behaviors. The findings also demonstrate that perceived moral obligation underlies interpersonal effects on cancelation behavior. That is, when providers develop communal (vs. exchange) ties, consumers feel that their interaction with the providers is in a closed social context, which tends to reduce their obligations towards attending their booking, thus increasing cancelation behavior. Theoretical and practical implications for business researchers and practitioners are discussed.publishersversionpublishe