## 5 Discussion and Conclusions  
Previous studies have shown that simple Big Data tools such as Bootstrap resampling techniques for proportion and mean tests can be very effective to determine the client profile [12]. However, these statistical description were not able to provide the managers with information about the cross-relationships among features and especially among categories. This study carried out a more in-depth analysis to provide a better understanding of the cross-relationships among the business-relevant variables, through the multivariate joint description provided by MCA, and we aimed at extending it through bootstrap resampling and two possibilities of their statistical grouping similarly to confidence intervals, called confidence volumes. These tools allow us to extend the usefulness of MCA and to give the managers an interpretable and statistically solid tool. In the current study, we used it to establish the analysis of average groups of clients in terms of nationality, as well as to  
Figure 5: Confidence volumes (**top**) and six main eigenvectors (**bottom**) with their confidence intervals for clients from other locations. scrutinize the specific features of repeater and first-timer clients, which is a key point in hospitality. Nevertheless, one should keep in mind that the use of different approaches to identify relevant features can often provide us with different results, even with the same data input [53; 54], as they tend to rely on different optimization criteria. This issue represents a challenge for future works aiming to give stability to the proposal of sets of features in practical applications. Apart from that, other end points can be analyzed through this approach. The importance of an in depth hotel client-profile knowledge in circumstances such as COVID-19 crisis has already been highlighted. The reorientation towards domestic demand, service customization and guest loyalty and retention increase (reducing guest acquisition costs) become key recovery drivers [12; 55]. In this setting, understanding the differences in repeater profiles according to their nationality is crucial for the required reorientation of the business strategy. The results on the specific analyzed dataset show that average German and English clients are often quite similar and differ from the average Spanish client as in [12]. Thus, it can be said that young male Spanish clients traveling with partner are often associated with medium expenditure, whereas senior clients travel with family and their expenditure is often high. The average German client stands out for being young and low-medium expenditure but traveling with family when middle-aged. Finally, the average English client is low-middle expenditure traveling with partner. Therefore, expenditure and age seem to be linked throughout nationalities, al though [PERSON] and [PERSON] [46] stated that the size and the reference categories vary from one study to another and do not allow generalizations as to whether a certain age class is significantly related to tourism expenditure. The profiles exhibit differences that can be scrutinized with the proposed method. From a first-timers-vs.-repeaters point of view, repeater profiles are more similar to each other in the UK and Germany clients, whereas they are more differentiated from Spanish repeaters. For instance, gender appears as a differentiating variable since repeaters among German and UK clients are men. Young women are more likely to be first timers especially in UK clients. The literature supports the different preferences in men and women regarding to hotel characteristics [56; 57; 58]. Age is also a relevant socio-demographic variable in the client profile [47; 48] that influences motivation in purchasing patterns and client behavior. First timers are usually young (in German and UK clients), aligned with [PERSON] [49] who pointed out that older consumers have stronger brand loyalty than younger ones and [PERSON] and [PERSON] [50] who observed greater youthfulness among repeaters.