Predicting Hotel Booking Cancellations Using Machine Learning for Revenue Optimization

Andy Hermawan; Aji Saputra; Nabila Lailinajma; Reska Julianti; Timothy Hartanto; Troy Kornelius Daniel

doi:10.62951/router.v3i1.400

Authors

Andy Hermawan Universitas Indraprasta PGRI
Aji Saputra Universitas Khairun
Nabila Lailinajma Purwadhika Digital School
Reska Julianti Purwadhika Digital School
Timothy Hartanto Purwadhika Digital School
Troy Kornelius Daniel Purwadhika Digital School

DOI:

https://doi.org/10.62951/router.v3i1.400

Keywords:

Hotel Booking Cancellations, Machine Learning, XGBoost, Revenue Management, Classification, LightGBM

Abstract

Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings reveal that XGBoost model outperforms other models, achieving F2-score of 0.7897. Key influencing factors include deposit type, total number of special requests, and marketing segment. The results underscore the potential of predictive modeling in optimizing hotel revenue strategies by enabling proactive measures such as dynamic pricing, targeted customer engagement, and improved overbooking policies. This study contributes to the ongoing advancements in data-driven decision-making within the hospitality industry, offering insights into how machine learning can mitigate financial risks associated with booking cancellations.

Downloads

Download data is not yet available.

References

Anderson, C. K., & Xie, X. (2016). Dynamic pricing in hospitality: Overview and opportunities. International Journal of Revenue Management, 9(2–3), 165–174.

Antonio, N., De Almeida, A., & Nunes, L. (2019). Big data in hotel revenue management: Exploring cancellation drivers to gain insights into booking cancellation behavior. Cornell Hospitality Quarterly, 60(4), 298–319.

Alexandropoulos, S. A. N., Kotsiantis, S. B., & Vrahatis, M. N. (2019). Data preprocessing in predictive data mining. The Knowledge Engineering Review, 34, e1.

Binesh, F., Belarmino, A., & Raab, C. (2021). A meta-analysis of hotel revenue management. Journal of Revenue and Pricing Management, 1–13.

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.

Ghosh, P., Gelasca, E. D., Ramakrishnan, K. R., & Manjunath, B. S. (2008). Duplicate image detection in large-scale databases. In Advances in Intelligent Information Processing: Tools and Applications (pp. 149–166).

Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques. Morgan Kaufmann Publishers.

Hermawan, A., Jayanti, N. R., Tabaruk, Z., Triadi, F. L. Y., Saputra, A., & Syachrudin, M. R. H. (2024). Membangun model prediksi churn pelanggan yang akurat: Studi kasus tentang TELCO company. Merkurius: Jurnal Riset Sistem Informasi Dan Teknik Informatika, 2(6), 67–81. https://doi.org/10.61132/merkurius.v2i6.398

Kimes, S. E. (2011). The future of hotel revenue management. Journal of Revenue and Pricing Management, 10, 62–72.

Kang, K. H., Stein, L., Heo, C. Y., & Lee, S. (2012). Consumers’ willingness to pay for green initiatives of the hotel industry. International Journal of Hospitality Management, 31(2), 564–572.

Lee, M. (2022). Evolution of hospitality and tourism technology research from Journal of Hospitality and Tourism Technology: A computer-assisted qualitative data analysis. Journal of Hospitality and Tourism Technology, 13(1), 62–84.

Lin, Y. (2023). Research on the influencing factors of cancellation of hotel reservations. Highlights in Science, Engineering and Technology, 61, 107–117.

Rahmawati, E., Nurohim, G. S., Agustina, C., Irawan, D., & Muttaqin, Z. (2024). Development of machine learning model to predict hotel room reservation cancellations. Jurnal Teknologi Informasi dan Terapan (J-TIT, 11(2), 58–64. https://doi.org/10.25047/jtit.v11i2.5440

Zafitri, Z., & Jambak, M. I. (2023). Karakteristik pembatalan reservasi kamar hotel pada online travel agent menggunakan algoritma C4.5. The Indonesian Journal of Computer Science, 12(4).

Zheng, A., & Casari, A. (2018). Feature engineering for machine learning: Principles and techniques for data scientists. O'Reilly Media.