Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review

Henrique Henriques; Luis Nobre Pereirsa

doi:10.18089/tms.20240304

Authors

Henrique Henriques School of Management, Hospitality and Tourism of the University of Algarve
Luis Nobre Pereirsa Universidade do Algarve, ESGHT/CinTurs https://orcid.org/0000-0003-0917-7163

DOI:

https://doi.org/10.18089/tms.20240304

Keywords:

Artificial Intelligence, Hotel demand Forecast, Revenue Management, Machine Learning, Artificial Neural Networks, Digital Transformation

Abstract

This systematic literature review (SLR) explores current state-of-the-art artificial intelligence (AI) methods for forecasting hotel demand. Since revenue management (RM) is crucial for business success in the hotel industry, this study aims to identify state-of-the-art effective AI-based solutions for hotel demand forecasting, including machine learning (ML), deep learning (DP), and artificial neural networks (ANNs). The study conducted an SLR using the PRISMA model and identified 20 papers indexed in Scopus and the Web of Science. It addresses the gaps in the literature on AI-based demand forecasting, highlighting the need for clarity in model specification, understanding the impact of AI on pricing accuracy and financial performance, and the challenges of available data quality and computational expertise. The review concludes that AI technology can significantly improve forecasting accuracy and empower data-driven decisions in hotel management. Additionally, this study discusses the limitations of AI-based demand forecasting, such as the need for high-quality data. It also suggests future research directions for further enhancing AI forecasting techniques in the hospitality industry.

Author Biographies

Henrique Henriques, School of Management, Hospitality and Tourism of the University of Algarve

Henrique Henriques specialises in Revenue Management and Hotel Management. Over the last decade he has worked as a training coordinator, hotel director, commercial director, trainer and university lecturer. He has extensive experience in consulting and managing hotel units, having worked with dozens of units nationwide. He enjoys constant learning and is focussed on results. He enjoys new challenges that allow him to develop both personally and professionally. He has worked in several hotel chains, organised events and seminars related to tourism and hospitality. He is entrepreneurial, likes innovation and sharing knowledge. He has taught more than 5,000 hours in the areas of Revenue Management and Digital Marketing for tourism and hospitality, collaborating with various training organisations. He is a guest assistant professor at the University of the Algarve on the Master's Degree in Hotel Management and Direction, teaching Revenue Management. He works in the area(s) of Social Sciences with an emphasis on Economics and Management, predominantly in the Organisation and Management of Tourism and Hotel Companies. His research areas: Hotel Management; Revenue Management; Tourism Business Management.
Luis Nobre Pereirsa, Universidade do Algarve, ESGHT/CinTurs

Luis Nobre Pereira is a passionate researcher in the field of Tourism & Hospitality Management through the application of advanced data science techniques. He is an integrated member of the Research Centre for Tourism, Sustainability and Well-being (CinTurs) since 2010 and Professor of Market Research and Data Analysis at the School of Management, Hospitality and Tourism of the University of Algarve. Luis Nobre Pereira was President of the Technical-Scientific Council of the School of Management, Hospitality and Tourism between 2019 and 2023 and Deputy Director of the School between 2016 and 2019. He holds an undergraduate degree in Mathematics Applied to Economics and Management from the ISEG - University of Lisbon (2000), and a Masters degree in Statistics and Information Management from the NOVA IMS - New University of Lisbon (2005). He completed his PhD in Quantitative Methods Applied to Economics and Management at the University of Algarve in 2009. Finally, he got his "Agregação" in Information Management - Survey Sampling and Marketing Research - from New University of Lisbon in 2017. Luis Nobre Pereira's current research interests include application of Data Science Techniques in Social Sciences (in the scientific area of Economics and Management). His research focuses on Tourism Management (sustainable tourism development, tourism demand modelling and forecasting, segmentation, measuring consumer behaviour, decision support system) and Hospitality Management (hotel demand forecasting, revenue management, segmentation, pricing, Data Science for revenue management). In addition, he also conducts research in Marketing Research (measuring consumer behavior, estimation of willingness to pay, market segmentation, quantitative models to solve marketing problems), and Health Economics (measurement of health related quality of life, modelling stated preference data). He is author or co-author of about 55 scientific papers published in academic journals with scientific refereeing. He has acted as Principal Investigator (PI) and as a team member in a number of international (EU funded) as well as national projects in the fields of Tourism & Hospitality Management and Marketing Research. Professor Luis is reviewer of many international journals, including the prestigious Tourism Management, International Journal of Hospitality Management, Current Issues in Tourism and Journal of Hospitality & Tourism Research. He has also taught short courses in the field of Survey Sampling for the Portuguese Statistical Office, Bank of Portugal, EUROSTAT and other producers of Official Statistics.

References

Ampountolas, A. (2021). Modeling and forecasting daily hotel demand: a comparison based on SARIMAX, neural networks, and GARCH models. Forecasting, 3(3), 580–595. doi.org/10.3390/forecast3030037

Ampountolas, A., & Legg, M. P. (2021). A segmented machine learning modeling approach of social media for predicting occupancy. International Journal of Contemporary Hospitality Management, 33(6), 2001–2021. doi.org/10.1108/IJCHM-06-2020-0611

Antonio, N., de Almeida, A., & Nunes, L. (2019). An automated machine learning based decision support system to predict hotel booking cancellations. Data Science Journal, 18(1). doi.org/10.5334/dsj-2019-032

Antonio, N., de Almeida, A., & Nunes, L. (2019). Big data in hotel revenue management: exploring cancellation drivers to gain ınsights ınto booking cancellation behavior. Cornell Hospitality Quarterly, 60(4), 298–319. doi.org/10.1177/1938965519851466

Bhushan, S. (2021). The impact of artificial intelligence and machine learning on the global economy and its implications for the hospitality sector in India. Worldwide Hospitality and Tourism Themes, 13(2), 252–259. doi.org/10.1108/WHATT-09-2020-0116

Bulchand-Gidumal, J. (2020). Impact of artificial ıntelligence in travel, tourism, and hospitality. In Handbook of e-Tourism (pp. 1–20). Springer International Publishing. doi.org/10.1007/978-3-030-05324-6_110-1

Caicedo-Torres, W., & Payares, F. (2016). A machine learning model for occupancy rates and demand forecasting in the hospitality ındustry. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 10022 LNAI (pp. 201–211). Springer Verlag. doi.org/10.1007/978-3-319-47955-2_17

Chen, S., Ngai, E. W. T., Ku, Y., Xu, Z., Gou, X., & Zhang, C. (2023). Prediction of hotel booking cancellations: Integration of machine learning and probability model based on interpretable feature interaction. Decision Support Systems, 170, 113959. doi.org/10.1016/j.dss.2023.113959

Claveria, O., Monte, E., & Torra, S. (2015). A new forecasting approach for the hospitality industry. International Journal of Contemporary Hospitality Management, 27(7), 1520–1538. doi.org/10.1108/IJCHM-06-2014-0286

Das, S., Dey, A., Pal, A., & Roy, N. (2015). Applications of artificial ıntelligence in machine learning: review and prospect. International Journal of Computer Applications, 115(9), 31–41. doi.org/10.5120/20182-2402

Doborjeh, Z., Hemmington, N., Doborjeh, M., & Kasabov, N. (2022). Artificial intelligence: a systematic review of methods and applications in hospitality and tourism. International Journal of Contemporary Hospitality Management, 34(3), 1154–1176. doi.org/10.1108/IJCHM-06-2021-0767

Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020 : An R package and shiny app for producing PRISMA 2020‐compliant flow diagrams, with interactivity for optimised digital transparency and open synthesis. Campbell Systematic Reviews, 18(2). doi.org/10.1002/cl2.1230

Huang, L., & Zheng, W. (2021). Novel deep learning approach for forecasting daily hotel demand with agglomeration effect. International Journal of Hospitality Management, 98. doi.org/10.1016/j.ijhm.2021.103038

Huang, L., & Zheng, W. (2023). Hotel demand forecasting: a comprehensive literature review. Tourism Review, 78(1), 218–244. doi.org/10.1108/TR-07-2022-0367

Kaya, K., Yılmaz, Y., Yaslan, Y., Öğüdücü, Ş. G., & Çıngı, F. (2022). Demand forecasting model using hotel clustering findings for hospitality industry. Information Processing & Management, 59(1), 102816. doi.org/10.1016/j.ipm.2021.102816

Kaynak, O. (2021). The golden age of Artificial Intelligence. Discover Artificial Intelligence, 1(1), 1. doi.org/10.1007/s44163-021-00009-x

Kimes, S. E. (1989a). The basics of yield management. Cornell Hotel and Restaurant Administration Quarterly, 30(3), 14–19. doi.org/doi.org/10.1177/0010880489030003

Kimes, S. E. (1989b). Yield management: A tool for capacity‐considered service firms. Journal of Operations Management, 8(4), 348–363. doi.org/10.1016/0272-6963(89)90035-1

Kimes, S. E., & Chase, R. B. (1998). The strategic levers of yield management. Journal of Service Research, 1(2), 156–166. doi.org/10.1177/109467059800100205

Kimes, S. E., & Wirtz, J. (2015). Revenue management: advanced strategies and tools to enhance firm profitability. Foundations and Trends® in Marketing, 8(1), 1–68. doi.org/10.1561/1700000037

Klein, R., Koch, S., Steinhardt, C., & Strauss, A. K. (2020). A review of revenue management: Recent generalisations and advances in industry applications. European Journal of Operational Research, 284(2), 397–412. doi.org/10.1016/j.ejor.2019.06.034

Koupriouchina, L., Van der Rest, J. P., & Schwartz, Z. (2014). On revenue management and the use of occupancy forecasting error measures. International Journal of Hospitality Management, 41, 104–114. doi.org/10.1016/j.ijhm.2014.05.002

Krajcik, V., Novotny, O., Civelek, M. & Semradova Zvolankova, S. (2023). Digital literacy and digital transformation activities of service and manufacturing SMEs. Journal of Tourism and Services, 26(14), 242-262. doi:10.29036/jots.v14i26.551

Lieberman, W. H. (2003). Getting the most from revenue management. Journal of Revenue and Pricing Management, 2(2), 103–115. doi.org/10.1057/palgrave.rpm.5170055

Lincényi, M.& Bulanda, I. (2022). Use of marketing communication tools in tourism in accommodation facilities during the COVID-19 pandemic. Journal of Tourism and Services, 26(14), 25-44. doi:10.29036/jots.v14i26.440

McGill, J. I., & van Ryzin, G. J. (1999). Revenue management: research overview and prospects. Transportation Science, 33(2), 233–256. doi.org/10.1287/trsc.33.2.233

Nam, K., Dutt, C. S., Chathoth, P., Daghfous, A., & Sajid Khan, & M. (2020). The adoption of artificial intelligence and robotics in the hotel industry: prospects and challenges. Electronic Markets, 31, 553–574. doi.org/10.1007/s12525-020-00442-3/Published

Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan: a web and mobile app for systematic reviews. Systematic Reviews, 5(1), 210. doi.org/10.1186/s13643-016-0384-4

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. doi.org/10.1080/13683500.2021.1999397

Phumchusri, N., & Ungtrakul, P. (2020). Hotel daily demand forecasting for high-frequency and complex seasonality data: a case study in Thailand. Journal of Revenue and Pricing Management, 19(1), 8–25. doi.org/10.1057/s41272-019-00221-6

Rajopadhye, M., Ben Ghalia, M., Wang, P. P., Baker, T., & Eister, C. V. (2001). Forecasting uncertain hotel room demand. Information Sciences, 132(1–4), 1–11. doi.org/10.1016/S0020-0255(00)00082-7

Rakesh, M. V., Kumar, S. P., Yogitha, & Aishwarya., R. (2022). Hotel booking cancelation prediction using ML algorithms. Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 466–471. doi.org/10.1109/ICAIS53314.2022.9742843

Salamanis, A., Xanthopoulou, G., Kehagias, D., & Tzovaras, D. (2022). LSTM-based deep learning models for long-term tourism demand forecasting. Electronics, 11(22), 3681. doi.org/10.3390/electronics11223681

Sánchez, E. C., Sánchez-Medina, A. J., & Pellejero, M. (2020). Identifying critical hotel cancellations using artificial intelligence. Tourism Management Perspectives, 35, 100718. doi.org/10.1016/j.tmp.2020.100718

Sánchez-Medina, A. J., & C-Sánchez, E. (2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management, 89, 102546. doi.org/10.1016/j.ijhm.2020.102546

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. doi.org/10.1177/1354816619884800

Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial ıntelligence: definition and background (pp. 15–41). Springer, Cham. doi.org/10.1007/978-3-031-21448-6_2

Ülkü, A. (2023). Artificial intelligence-based large language models and integrity of exams and assignments in higher education: the case of tourism courses. Tourism & Management Studies, 19(4), 21-34. https://doi.org/10.18089/tms.2023.1904

Viverit, L., Heo, C. Y., Pereira, L. N., & Tiana, G. (2023a). Application of machine learning to cluster hotel booking curves for hotel demand forecasting. International Journal of Hospitality Management, 111, 103455. doi.org/10.1016/j.ijhm.2023.103455

Wang, J., & Duggasani, A. (2020). Forecasting hotel reservations with long short-term memory-based recurrent neural networks. International Journal of Data Science and Analytics, 9(1), 77–94. doi.org/10.1007/s41060-018-0162-6

Webb, T., Schwartz, Z., Xiang, Z., & Singal, M. (2020). Revenue management forecasting: The resiliency of advanced booking methods given dynamic booking windows. International Journal of Hospitality Management, 89, 102590. doi.org/10.1016/j.ijhm.2020.102590

Wirtz, J., Kimes, S. E., Theng, J. H. P., & Patterson, P. (2003). Revenue management: resolving potential customer conflicts. Journal of Revenue and Pricing Management, 2(3), 216–226. doi.org/10.1057/palgrave.rpm.5170068

Wu, D.C., Song, H. and Shen, S. (2017). New developments in tourism and hotel demand modeling and forecasting. International Journal of Contemporary Hospitality Management, Vol. 29 No. 1, pp. 507-529. doi.org/10.1108/IJCHM-05-2015-0249

Wu, D. C., Zhong, S., Qiu, R. T. R., & Wu, J. (2022). Are customer reviews just reviews? Hotel forecasting using sentiment analysis. Tourism Economics, 28(3), 795–816. doi.org/10.1177/13548166211049865

Zhang, Q., Qiu, L., Wu, H., Wang, J., & Luo, H. (2019). Deep learning based dynamic pricing model for hotel revenue management. 2019 International Conference on Data Mining Workshops (ICDMW), 2019-November, 370–375. doi.org/10.1109/ICDMW.2019.00061

Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review

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