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The methodological proposal presented in this paper enables a comprehensive multivariate exploratory analysis of time series taken in different geographical contexts. More specifically, it is a four-stage protocol for the identification of different dynamic patterns and the subsequent formation of geographic clusters. The clustering strategy compares the shapes of the trajectories using a natural, unsupervised approach based on learning from data. The exploratory techniques used in this method, which are readily applicable in any area of study, begin by reducing the dimensionality of a three-way data array. The potential of the strategy design is illustrated through a study of tourism dynamics in 27 European countries over the period 2007-2019. The profiles found for the trend and seasonal components reveal five distinct tourism patterns with some singularities. The choice of this sector for empirical research is not accidental. Tourism is connected to areas related to both economic and social activity. That is why their correct measurement and analysis are so interesting and necessary.

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For a higher education public institution, young in relative terms, featuring local competition with another private and both long-established and reputed one, it is of great importance to become a reference university institution to be better known and felt with identification in the society it belongs to and ultimately to reach a good position within the European Higher Education Area. These considerations have made the university governors setting up the objective of achieving an adequate management of the university institutional brand focused on its logo and on image promotion, leading to the establishment of a university shop as it is considered a highly adequate instrument for such promotion. In this context, an on-line survey is launched on three different kinds of members of the institution, resulting in a large data sample. Different kinds of variables are analysed through appropriate exploratory multivariate techniques (symmetrical methods) and regression-related techniques (non-symmetrical methods). An advocacy for such combination is given as a conclusion. The application of statistical techniques of data and text mining provides us with empirical insights about the institution members' perceptions and helps us to extract some facts valuable to establish policies that would improve the corporate identity and the success of the corporate shop.

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