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Data Science in Supporting Hotel Management: Application of Predictive Models to Booking.com Guest Evaluations

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Advances in Tourism, Technology and Systems (ICOTTS 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 384))

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Abstract

Data science is a multidisciplinary area that gathers several branches, such as statistics, databases, and computer science and whose importance has become more substantial over the last few years. Using several techniques and algorithms from machine learning allows us to understand how certain variables are related, as well as to visualize data and make predictions. This paper aims to use data science as a strategic instrument for the hospitality industry by proposing a model that can help to predict which characteristics will be more valued by guests. By better understanding which features guests value most when evaluating a stay at a hotel, it will be easier for hotel managers to make informed decisions about which service operations management strategies should be used. It can also be helpful in terms of investment decisions, as it can indicate which aspects will be most important to value in a hotel. In this research, it was possible to conclude that guests’ ratings are related primarily to the commodities available at the hotels, followed by cleanliness, staff, location, price-quality relation, and comfort.

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Acknowledgements

This work was supported by REMIT and the FCT—Fundação para a Ciência e a Tecnologia, I.P. [Project UIDB/05105/2020]. Second author was supported by CIDMA and is funded by the Fundação para a Ciência e a Tecnologia, I.P. (FCT, Funder ID = 50110000187) under Grants https://doi.org/10.54499/UIDB/04106/2020 and https://doi.org/10.54499/UIDP/04106/2020.

Author information

Authors and Affiliations

  1. Department of Social, Political and Territorial Sciences, University of Aveiro, 3810-193, Aveiro, Portugal

    Ana Filipa Martins

  2. CIDMA - Center for Research and Development in Mathematics and Applications, Department of Mathematics, University of Aveiro, 3810-193, Aveiro, Portugal

    Luís M. Silva

  3. REMIT - Research on Economics, Management and Information Technologies, Department of Tourism, Heritage and Culture, University Portucalense, 4200-072, Porto, Portugal

    Jorge Marques

  4. CEGOT - Centre of Studies in Geography and Spatial Planning, University of Coimbra, 3004-530, Coimbra, Portugal

    Jorge Marques

Authors
  1. Ana Filipa Martins
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  2. Luís M. Silva
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  3. Jorge Marques
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Corresponding author

Correspondence to Jorge Marques .

Editor information

Editors and Affiliations

  1. Inst of Accounting and Admin of Porto, Polytechnic of Porto, São Mamede de Infesta, Portugal

    João Vidal Carvalho

  2. ISCAP Porto, S. Mamede de Infesta, Portugal

    António Abreu

  3. School of Hospitality and Tourism, Polytechnic of Porto, Vila do Conde, Portugal

    Dália Liberato

  4. Faculty of Tourism and Gastronomy, Universidad Anáhuac, Cancún Quintana Roo. C.P., Mexico

    José Angel Díaz Rebolledo

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Martins, A.F., Silva, L.M., Marques, J. (2024). Data Science in Supporting Hotel Management: Application of Predictive Models to Booking.com Guest Evaluations. In: Carvalho, J.V., Abreu, A., Liberato, D., Rebolledo, J.A.D. (eds) Advances in Tourism, Technology and Systems. ICOTTS 2023. Smart Innovation, Systems and Technologies, vol 384. Springer, Singapore. https://doi.org/10.1007/978-981-99-9758-9_5

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