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.
Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence
