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Hotel customer segmentation using the integrated entropy-CRITIC method and the 2T-RFMB model

dc.conference.date13 de diciembre de 2022
dc.conference.placeSantiago de Compostela
dc.conference.titleMarketing and Smart Technologies
dc.contributor.authorShu, Ziwei
dc.contributor.authorCarrasco González, Ramón Alberto
dc.contributor.authorPortela García-Miguel, Javier
dc.contributor.authorSánchez Montañés, Manuel
dc.contributor.editorReis, José Luís
dc.contributor.editorPeter, Marc K.
dc.contributor.editorVarela González, José Antonio
dc.date.accessioned2023-12-15T11:05:37Z
dc.date.available2023-12-15T11:05:37Z
dc.date.issued2023
dc.description.abstractCustomer segmentation helps the company better understand its target audience, which is vital to optimizing marketing strategies and maximizing the customer value for the company. This paper improves the original RFM model by including the potential loss to the hotel from a customer canceling their reservation in the indicator “Monetary” and adding a new indicator “Bonding” to indicate the degree of customer bonding with the hotel. The proposed model also includes the 2-tuple linguistic model to give hotel managers or decision-makers more easily understandable customer segmentation results. The aggregation of the four indicators (recency, frequency, monetary, and bonding) into a unique value is a Multi-Criteria Decision-Making (MCDM) problem. To generate the weights that can consider the relationship between various indicators and the level of data diversification contained in each indicator, the Entropy method and the CRiteria Importance Through Intercriteria Correlation (CRITIC) method have been integrated. Customer overall values are generated based on the 2T-RFMB model and the integrated Entropy-CRITIC method. Finally, various customer segments are obtained with K-means clustering. This proposal has been evaluated by a real dataset from a hotel in Lisbon. The results show that the proposed model can increase the linguistic interpretability of clustering results. It also demonstrates that the proposed model can provide hotel managers with more realistic customer values to assist them in allocating their Customer Relationship Management (CRM) resources efficiently.en
dc.description.departmentDepto. de Estadística y Ciencia de los Datos
dc.description.facultyFac. de Estudios Estadísticos
dc.description.refereedTRUE
dc.description.statuspub
dc.identifier.citationShu, Z., Carrasco González, R.A., García-Miguel, J.P., Sánchez-Montañés, M. (2023). Hotel Customer Segmentation Using the Integrated Entropy-CRITIC Method and the 2T-RFMB Model. In: Reis, J.L., Peter, M.K., Varela González, J.A., Bogdanović, Z. (eds) Marketing and Smart Technologies. Smart Innovation, Systems and Technologies, vol 337. Springer, Singapore.
dc.identifier.doi10.1007/978-981-19-9099-1_5
dc.identifier.essn2190-3026
dc.identifier.isbn978-981-19-9098-4
dc.identifier.issn2190-3018
dc.identifier.officialurlhttps://doi.org/10.1007/978-981-19-9099-1_5
dc.identifier.urihttps://hdl.handle.net/20.500.14352/91318
dc.language.isoeng
dc.page.final72
dc.page.initial55
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsmetadata only access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.cdu004.6
dc.subject.cdu519.816.1
dc.subject.ucmEstadística
dc.subject.unesco1209.03 Análisis de Datos
dc.titleHotel customer segmentation using the integrated entropy-CRITIC method and the 2T-RFMB modelen
dc.typeconference paper
dspace.entity.typePublication
relation.isAuthorOfPublication658b3e73-df89-4013-b006-45ea9db05e25
relation.isAuthorOfPublication44f935e8-9acf-4613-ab4d-e007edda7540
relation.isAuthorOfPublication.latestForDiscovery658b3e73-df89-4013-b006-45ea9db05e25

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