Profiling satisfied and dissatisfied hotel visitors using publicly available data from a booking platform |
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| Affiliation: | 1. School of Economics, The University of Queensland, Brisbane, Queensland 4072, Australia;2. Strategic Planning in Research, National Arts Council, 90 Goodman Road, Goodman Arts Centre, Blk A #01-01, 439053, Singapore;1. Faculty of Management and Economics, Dalian University of Technology, Liaoning, Dalian 116024, China;2. College of Tourism, Dalian University, Liaoning, Dalian 116622, China;3. School of Control Science and Engineering, Dalian University of Technology, Liaoning Dalian 116024, China;1. Department of Hospitality and Tourism Management, College of Humanities and Legal Studies, University of Cape Coast, Ghana;2. College of Humanities and Social Sciences, UAE University, United Arab Emirates;1. UQ Business School, University of Queensland, Brisbane, QLD, 4072, Australia;2. School of Business and Law, Edith Cowan University, Joondalup, WA, 6027, Australia;1. Rosen College of Hospitality Management, University of Central Florida, 9907 Universal Blvd., Orlando, FL 32819, United States;2. Hospitality Business Management, Alfred Lerner College of Business & Economics, University of Delaware, 14 W. Main Street, Raub Hall, Newark, DE 19716, United States;3. Dept. of Hospitality & Tourism Management, Isenberg School of Management, University of Massachusetts, Flint Lab - 90 Campus Center Way, Amherst, MA 01003, United States;1. Department of Information and Service Economy, Aalto University School of Business, Helsinki, Finland;2. Chair of Marketing and Innovation, University of Hamburg, Hamburg, Germany;3. Turku School of Economics, University of Turku, Turku, Finland |
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| Abstract: | We develop a set of models for predicting hotel visitor satisfaction and the probability of complaints about various service aspects. Our empirical analysis is based on 3630 reviews from one of the Dubai hotels. We identify profiles of visitors who are likely to be dissatisfied with the hotel service and need special attention, as well as of visitors, who are likely to be satisfied with the service and, therefore, do not require extra attention. The predictions are based on observable characteristics of visitors, thus making it possible for hotel managers to apply the models in their everyday work. Using content analysis we also reveal specific problems that different groups of visitors encountered and infer which of the problems has the highest impact on the overall satisfaction with the hotel. |
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| Keywords: | Hotel Online reviews Online ratings Satisfaction Predictive analytics |
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