## 1 Introduction
Big Data and Data Warehouse technologies often provide us with command panels with this kind of information, but to our best knowledge, a limited number of instruments have been proposed to give principled visualization of interacting categorical features, which are the most usual type in CRMs [12; 16]. Therefore, our objective was to identify the profiles of clients from an international hotel chain using the overall data in its CRM system, in terms of its categorical features and their interactions. We aimed to build a method combining statistical robustness with the best interpretability for the managers. Our method is built on the basis of three processing blocks, previously existing in the literature, but harmoniously adapted to summarize large amounts of client records. On a first stage, Multiple Correspondence Analysis (MCA) is a well-known technique that provides us with statistical descriptions of the interactions among categorical features, hence it is the basic analysis unit for scrutinizing a set of fields in the forms and turning them into client profiles [17; 18]. However, the interpretation of projected factors on MCA requires expert knowledge on the technique, so that, for making it more readily interpretable, we performed bootstrap resampling techniques to yield a cloud of resamples for each category [19; 20]. As these represent a hard-to-plot entity, we built domain descriptors to characterize volumes including those examples with a confidence level, thus providing confidence volumes that can be interpreted as conventional confidence intervals. These confidence volumes allow scrutinizing the existing relationships as present (based on probabilistic proximity in the projected spaces) or strongly present (when the confidence volumes of several categories overlap). For this last step, we benchmarked two different techniques, namely Parzen windows [21; 22] and Support Vector Domain Description (SVDD) [23; 24], in order to analyze the impact of the technique used in this processing block on the quality of the representations. To guide hoteliers decision making in such a complex environment, we used the proposed approach to identify the profiles of clients from an international hotel chain usingthe overall data in its CRM system. For this purpose, we scrutinized the relevant groups of variables and we analyzed their practical meaning by using Big Data analytics from a twofold approach: by identifying the nationality general profile and paying attention to the repeater feature and its relationship to the other features under MCA description. The paper is organized as follows. Section 2 compiles a background on CRM and its elements in the context of its use by managers in hospitality, as well as an overview of the existing approaches to the field from Big Data or Data Science premises. Section 3 provides the basic equations of the three processing blocks (MCA, bootstrap resampling on the projected features and domain description for confidence volumes). Section 4 presents the experiments and results on the international chain database for providing the client profile and in particular the repeater profile. Finally, Section 5 provides the discussion and the conclusions of the work.