Person:
Portela García-Miguel, Javier

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First Name
Javier
Last Name
Portela García-Miguel
URI
https://hdl.handle.net/20.500.14352/77436
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Estudios estadísticos
Department
Estadística y Ciencia de los Datos
Area
Estadística e Investigación Operativa
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Item Type: Publication × Subject: 004.6 ×

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    Hotel customer segmentation using the integrated entropy-CRITIC method and the 2T-RFMB model
    (2023) Shu, Ziwei; Carrasco González, Ramón Alberto; Portela García-Miguel, Javier; Sánchez-Montañés, Manuel; Reis, José Luís; Peter, Marc K.; Varela González, José Antonio
    Customer 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.
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    Clustering using ordered weighted averaging operator and 2-tuple linguistic model for hotel segmentation: the case of TripAdvisor
    (Expert Systems with Applications, 2023) Shu, Ziwei; Carrasco González, Ramón Alberto; Portela García-Miguel, Javier; Sanchez-Montañés, Manuel; Lin, Binshan
    With the growth of online tourism, it is important to analyze the reviews left by numerous customers on social networks to improve the hotel’s online reputation. Hotel segmentation based on online reviews has attracted an increasing interest from many academics. The problem is that many hotel segmentation models overlook the fact that some customers focus on positive reviews when choosing a hotel, while others focus on negative ones. To address this shortcoming, this paper develops a novel approach to classify hotels using the ordered weighted averaging (OWA) operator, the 2-tuple linguistic model, and K-means clustering. The proposed approach has been evaluated with a real dataset from TripAdvisor, which contains more than 50 million customer online reviews on eight aspects of the hotel. The results show that the proposed model can produce denser and more separated clusters than the model without linguistic quantifiers. From a business point of view, this model enables hotels to distinguish customers’ perceptions (from the less demanding to the most demanding) about their eight aspects, allowing them to specify which of them need to be improved and develop strategies more quickly. At the same time, it introduces a new way of ranking hotels online, allowing customers to create personalized rankings of hotels based on their degree of demand for various hotel aspects (better location, cleaner rooms, etc.) rather than the average ratings, so that they can select the most suitable hotels more quickly.