Abstract
In the tourism industry it is common that the information obtained from customers can be varied, dispersed, and with high volumes of data. In this context, the automatic analysis of information has been proposed through electronic customer relationship management, which refers to marketing activities, tools and techniques, delivered with the use of electronic channels for the specific purpose of locating, building and improving long- term relationships with customers, to enhance their individual potential. In this paper, we refer to the analysis of information in three aspects: customer satisfaction, the study of customer behavior and the forecast of tourist demand. Specifically, we have created a novel dataset comprising the non-verbal preference assessment of tourists who are clients of the Sol Cayo Guillermo hotel belonging to the Melia hotel chain, in Jardines del Rey, Cuba. Then, by applying Computational Intelligence algorithms to this dataset, we achieve segment customers according to their non-verbal preferences, in order to increase their satisfaction, and therefore the client profitability. In order to achieve a good performance in the realization of this task, we have proposed two modifications of the Naïve Associative Classifier, whose results are compared with the most relevant computational algorithms of the state of the art. The experimentally obtained values of balanced accuracy and averaged F1 measure show that, by clearly improving the results of the state-of-the-art algorithms, our proposal is adequate to successfully use electronic customer relationship management in the tourist services provided by hotel chains.
Highlights
With regard to tourism companies, it is a fact that this type of business increasingly makes use of Customer Relationship Management (CRM) in activities aimed at attracting new customers to the different services offered in hotel facilities and in tourist sites around the world
This hotel is considered as a sample of the research; it is a representative sample of the population of four-star hotels in Cuba, because in all these hotels there are similar difficulties in terms of the communicative relationship with the clients, given that the professional and academic training, as well as the training of all personnel working in tourism in Cuba is homogeneous, and does not include non-verbal communication elements
The balance accuracy and F1 measure values shown in Table 3 illustrate the superiority exhibited by the Naïve Associative Classifier with respect to eight of the most relevant computer algorithms of the state of the art, when performing the task of intelligent classification of tourist service clients, according to their non-verbal preferences
Summary
Tourism entrepreneurs continually invest resources to offer clients innovations in services In this context, CRM plays a fundamental role, because the use of cutting-edge technologies that are associated with CRM makes it possible to improve the quality of tourist services in the eyes of customers [4]. The main implication of this study for tourism is that it allows the client’s profitability to be increased, understanding this as the client continuing to use the services of that institution, and positively promoting said institution. This is achieved through a differentiated communicative attention that allows improving the perception of the services provided to the client
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