Publicação
Timing is Everything: Predicting Hotel Booking Cancellations with Survival Analysis
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | António, Nuno Miguel da Conceição | |
| dc.contributor.author | Almeida, Maria André de | |
| dc.date.accessioned | 2025-11-14T11:40:15Z | |
| dc.date.embargo | 2028-10-30 | |
| dc.date.issued | 2025-10-30 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics | pt_PT |
| dc.description.abstract | Booking cancellations affect both revenue and resource allocation in the hospitality industry. To mitigate the risks associated with cancellations, hotels implement a combination of overbooking and cancellation policies. However, both negatively impact the hotel’s revenue and reputation. Therefore, it becomes crucial to anticipate cancellations and take proactive measures to prevent them. While previous research has focused primarily on predicting whether a booking will be canceled, this study investigates when cancellations are most likely to occur. As poorly timed interventions can lead to inefficiency and ineffectiveness when targeting cancellations, it is necessary to predict when cancellations are likely to occur. To address this, this study analyzes the likelihood of a booking remaining active from the time it is made until arrival using Survival Analysis (SA), which is traditionally applied in medicine to estimate a patient's probability of survival over time. This study focused on bookings made at least 30 days before arrival. Data from four hotels, two city and two resort hotels, were analyzed using three SA models: Gradient Boosting Survival Analysis, Random Survival Forest (RSF), and Extra Survival Trees. RSF produced the strongest performance across the assessed evaluation metrics (Concordance Index, Integrated Brier Score (IBS), and Integrated Area Under the Curve), particularly for one hotel, where the model achieved an IBS value of 0.04. By predicting the cancellation day, we accurately identified the exact cancellation day for 69% of canceled bookings at the same hotel. Across all hotels, when applying time windows around the predicted day, ranging from 1% to 4% of the lead time, the actual cancellation day fell within the window in 33% to 72% of cases. Survival probabilities on the arrival day were also analyzed as a complementary indicator for identifying bookings that were likely to be canceled. This is the first study to apply SA to hotel booking cancellations, estimating when cancellations are most likely to occur and introducing a new binary perspective on booking outcomes. This study provides hotels with a practical tool to support proactive customer engagement, reduce cancellations, and enhance operational planning and resource allocation. | pt_PT |
| dc.identifier.tid | 204073391 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190726 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Booking Cancellation Prediction | pt_PT |
| dc.subject | Hospitality | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Revenue Management | pt_PT |
| dc.subject | Survival Analysis | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.title | Timing is Everything: Predicting Hotel Booking Cancellations with Survival Analysis | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.embargofct | Solicito que permaneça confidencial pois existem conteúdos que vão ser publicados em artigo. | pt_PT |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Business Analytics | pt_PT |