From Tandem to Simultaneous Dimensionality Reduction and Clustering of Tourism Data

The study of tourist demand is a critical component of a successful destination management strategy. In order to define tourist segments, many factors play an important role in the decision-making process. Tourism motivations are often used as segmentation bases of tourism market since they can affect the choices about travel destination, type of holiday and consumer behaviour. A tourist destination offers many experiences and products, which appeal different market segments. This paper aims to identify a posteriori segments of tourism demand by means of multidimensional approach employing a simultaneous factorial dimensionality reduction and clustering method. On the basis of results, tourists are classified in two clusters in order to understand the relationship between motivations and consumer behaviour. In particular, the two observed clusters represent the very satisfied tourists and the tourists unsatisfied at different level, respectively. Moreover, in terms of cost of the holiday, the first group has a per capita expenditure bigger than second group.