## 4 Experiments and Results
### Profile Analysis on Four Groups  
We further analyzed in four separate groups the CRM recordings in a client view. Given that the three most present nationalities were Spain, Germany and United Kingdom, we created a fourth group with all the other nationalities (group _Others_) which was roughly balanced in the dataset. Figures 2-5, show the six eigenvectors (bottom) with their confidence intervals for each client group. Repeater and non-repeater categories are at the end of the vector, and in general they were significant most of these preponderant factors (though not in all of them) for any nationality. The eigenvalue profiles (not shown) were in general similar to the one shown in Figure 1 for Spanish nationality, which means that about 3-4 first directions were predominant, followed by a smooth decay in the following ones, ending with about 3-6 faster-decaying categories. Despite this similarity in the eigenvalue profiles, the eigenvectors exhibited in general different profiles across nationalities. Eigenvectors for UK exhibited more variability than the other groups, whereas eigenvectors for Others also exhibited narrow confidence intervals, allowing us to analyze them and extract some set of conclusions as a fourth group with acceptable entity in terms of management. The eigenvector profiles can be used as factors to analyze the groups and relationships in our categorical data. However, their interactions are not always easy to interpret, as in this case. An alternative and visual tool, which holds the statistical support of MCA principles, is commonly used by projecting each separate category to a coordinate system given by the first eigenvectors. Whereas these feature projections are usually addressed in pairs of eigenvectors, we decided here to present the projection onto the three first eigenvectors, as far as they are the most relevant ones in all cases. This allows us to extract client profiles in terms of their closeness in the projected space, and said closeness has the sense of probability of joint occurrence. We roughly distinguish among related (close projected features in the representation) and strongly related (confidence volumes overlapping in the representation). Although the projections onto Eigenvectors 4-6 are often discarded in MCA, they could be also used to extract further relationships and client profiles. Finally, we can distinguish between interpretation in terms of client profiles, given by groups of features which are close among them in the map, and repeater client profiles, which can be scrutinized by focusing on the projection of repeater categories and their closer features. Figure 2 shows the projected categories for Spain clients. Categories of unknown types for different variables are closely related (overlapping confidence volumes), which can be due just to not being filled at the registration. The average profile is given by volumes overlapping the origin, in this case, male with partner (family) and distinguishing young (senior) clients associated with medium (very high) expenditure. Other groups with relevant associations are young with medium expenditure, and middle age with family holidays but not traveling with partner. The repeater client was close to high expenditure, and also similarly close to family holidays and to single status, whereas the first-timer client was moderately close to not family holidays and to age senior, as well as to unknown fields. Figure 3 shows the same representation for Germany clients. It can be said that in general the features are more distributed through the projected space, pointing towards diversity of the profiles. If an average profile has to be chosen, it can be young with low and medium expenditure together with unknown age and unknown expenditure. Some other relevant profiles are middle-aged clients in family holidays, as well as senior-aged clients in non-family holidays.