DETERMINANTS OF HOTEL BOOKING CANCELLATIONS: EVIDENCE FROM RESERVATION ANALYTICS
Deprecated: htmlspecialchars(): Passing null to parameter #1 ($string) of type string is deprecated in /var/www/html/lib/pkp/lib/vendor/smarty/smarty/libs/plugins/modifier.escape.php on line 36
DOI:
https://doi.org/10.53555/th.v1i2.2596Keywords:
hotel booking cancellations, reservation analytics, logistic regression, revenue management, cancellation predictionAbstract
Hotel booking cancellations create substantial operational and financial challenges by disrupting demand forecasting, room inventory allocation, pricing strategies, and revenue management, while also affecting staff morale, service efficiency, and customer satisfaction. The present study explored the determinants of cancellation of hotel bookings by analysis of hotel reservations and a quantitative explanatory design. Characteristics of the customer, booking, pricing, distribution, and characteristics of the hotel were analysed. Descriptive statistics, bivariate tests and binary logistic regression were used to determine the significant predictors and model performance. The results indicated that customers' booking lead time, cancellation history, non-refundable deposit, average daily rate, group size and selected market segment were factors that positively influenced their likelihood of cancellation. On the other hand, prior non-cancelled bookings, resort hotel classification, resort modification and special requests were found to correlate with reduced risk of cancellation. The logistic regression model had a good predictive performance, with the accuracy achieved being more than 81% and the area under the receiver operating characteristic curve being 0.866. These results suggest that the information provided at the reservation level can be used effectively in order to separate high-risk bookings from more stable bookings. On a management level, hotels can take the cancellation risk estimates to enhance overbooking decisions, prioritise confirmation messages, make more accurate demand estimates and minimise revenue leakage. The study concludes that interpretable reservation analytics provides a viable framework to support evidence-based booking management.
References
Adil, M., Ansari, M. F., Alahmadi, A., Wu, J. Z., & Chakrabortty, R. K. (2021). Solving the problem of class imbalance in the prediction of hotel cancelations: A hybridized machine learning approach. Processes, 9(10), 1713.
Altin, M., Chen, C. C., Riasi, A., & Schwartz, Z. (2023). Go moderate! How hotels' cancellation policies affect their financial performance. Tourism Economics, 29(8), 2165-2182.
Andriawan, Z. A., Purnama, S. R., Darmawan, A. S., Wibowo, A., Sugiharto, A., & Wijayanto, F. (2020, November). Prediction of hotel booking cancellation using CRISP-DM. In 2020 4th International Conference on Informatics and Computational Sciences (ICICoS) (pp. 1-6). IEEE.
Atabay, L., Çizel, B., & Atabay, E. (2026). Timing is everything: Segmenting OTA's cancellations behavior through the BCR model. International Journal of Hospitality Management, 135, 104605.
Bedmutha, K. S. (2025). Hotel booking reservation: An updated 2024 dataset for hotel booking demand and cancellation analysis [Data set]. Kaggle. https://www.kaggle.com/datasets/kundanbedmutha/hotel-booking-reservation
Bhardwaj, A., Yadav, T., & Chaudhary, R. (2024, June). Predicting hotel booking cancellations using machine learning techniques. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
Boto-García, D., Zapico, E., Escalonilla, M., & Pino, J. F. B. (2021). Tourists’ preferences for hotel booking. International Journal of Hospitality Management, 92, 102726.
Chen, J., Xu, Y., Yu, P., & Zhang, J. (2023). A reinforcement learning approach for hotel revenue management with evidence from field experiments. Journal of Operations Management, 69(7), 1176-1201.
Chen, Y., Ding, C., Ye, H., & Zhou, Y. (2022, March). Comparison and analysis of machine learning models to predict hotel booking cancellation. In 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) (pp. 1363-1370). Atlantis Press.
Das, R., Chadha, H., & Banerjee, S. (2023). Multi-layered market forecast framework for hotel revenue management by continuously learning market dynamics. In Artificial Intelligence and Machine Learning in the Travel Industry: Simplifying Complex Decision Making (pp. 145-161). Cham: Springer Nature Switzerland.
Dowlut, N., & Gobin-Rahimbux, B. (2023). Forecasting resort hotel tourism demand using deep learning techniques–A systematic literature review. Heliyon, 9(7).
Guizzardi, A., Ballestra, L. V., & D’Innocenzo, E. (2024). Reverse engineering the last-minute on-line pricing practices: an application to hotels: A. Guizzardi et al. Statistical Methods & Applications, 33(3), 943-971.
Henriques, H., & Pereira, L. N. (2024). Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review. Tourism & Management Studies, 20(3), 39-51.
Hermawan, A., Saputra, A., Lailinajma, N., Julianti, R., Hartanto, T., & Daniel, T. K. (2025). Predicting Hotel Booking Cancellations Using Machine Learning for Revenue Optimization. Router: Jurnal Teknik Informatika Dan Terapan, 3(1), 37-48.
Herrera, A., Arroyo, Á., Jiménez, A., & Herrero, Á. (2024). Forecasting hotel cancellations through machine learning. Expert Systems, 41(9), e13608.
Jishan, M. A., Singh, V., Ghosh, A. K., Alam, M. S., Mahmud, K. R., & Paul, B. (2024, December). Hotel Booking Cancellation Prediction Using Applied Bayesian Models. In 2024 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 1-5). IEEE.
Kmiecik, M., & Antonio, N. (2026). Cancel, rebook, save: Revenue leakage from price cuts in hotels. International Journal of Hospitality Management, 135, 104647.
Kundu, S., Roy, S., Shukla, A., & Mateen, A. (2025). Unveiling cancellation dynamics: A two-stage model for predictive analytics. Data & Knowledge Engineering, 160, 102467.
Lei, C., & Ibrahim, M. (2024). Efficient revenue management: Classification model for hotel booking cancellation prediction. Global Perspectives in Management, 2(1), 12-21.
Liu, Z., De Bock, K. W., & Zhang, L. (2025). Explainable profit-driven hotel booking cancellation prediction based on heterogeneous stacking-based ensemble classification. European Journal of Operational Research, 321(1), 284-301.
Liu, Z., Jiang, P., Wang, J., Du, Z., Niu, X., & Zhang, L. (2023). Hospitality order cancellation prediction from a profit-driven perspective. International Journal of Contemporary Hospitality Management, 35(6), 2084-2112.
Masiero, L., Viglia, G., & Nieto-Garcia, M. (2020). Strategic consumer behavior in online hotel booking. Annals of Tourism Research, 83, 102947.
Mir, V. R., Tortella, B. D., Leoni, V., & Nicolau, J. L. (2026). Decoding booking cancellations: Quantitative insights and theoretical advances in tourism behavior. Tourism Management, 114, 105384.
Pereira, L. N., & Cerqueira, V. (2022). Forecasting hotel demand for revenue management using machine learning regression methods. Current Issues in Tourism, 25(17), 2733-2750.
Rahmawati, E. K. A., Nurohim, G. S., Agustina, C., Irawan, D., & Muttaqin, Z. (2024). Develompent of Machine Learning Model to Predict Hotel Room Reservation Cancellations. Jurnal Teknologi Informasi dan Terapan, 11(2), 58-65.
Sánchez-Medina, A. J., & Eleazar, C. (2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management, 89, 102546.
Schwartz, Z., Webb, T., van der Rest, J. P. I., & Koupriouchina, L. (2021). Enhancing the accuracy of revenue management system forecasts: The impact of machine and human learning on the effectiveness of hotel occupancy forecast combinations across multiple forecasting horizons. Tourism Economics, 27(2), 273-291.
Silvestre, P., Antonio, N., & Carrasco, P. (2026). Navigating uncertainty: enhancing hotel cancellation predictions with adaptive machine learning. Information Technology & Tourism, 28(1), 9.
Русакова, Е. И., & Радионова, М. В. (2021). Прогнозирование отмены бронирования отелей: сравнительная характеристика спецификаций моделей. Вестник Пермского университета. Серия: Экономика, 16(4), 327-345.
