Examinando por Autor "Muñoz-Romero, Sergio"

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A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description
(MDPI, 2020-12-29) González-Serrano, Lydia; Talón-Ballestero, Pilar; Muñoz-Romero, Sergio; Soguero-Ruiz, Cristina; Rojo-Álvarez, José Luis
COVID-19 has hit the hotel sector in a hitherto unknown way. This situation is producing a fundamental change in client behavior that makes crucial an adequate knowledge of their profile to overcome an uncertain environment. Customer Relationship Management (CRM) can provide key strategies in hospitality industry by generating a great amount of valuable information about clients, whereas Big Data tools are providing with unprecedented facilities to conduct massive analysis and to focus the client-to-business relationship. However, few instruments have been proposed to handle categorical features, which are the most usual in CRMs, aiming to adapt the statistical robustness with the best interpretability for the managers. Therefore, our aim was to identify the profiles of clients from an international hotel chain using the overall data in its CRM system. An analysis method was created involving three elements: First, Multiple Correspondence Analysis provides us with a statistical description of the interactions among categories and features. Second, bootstrap resampling techniques give us information about the statistical variability of the feature maps. Third, kernel methods provide easy-to-visualize domain descriptions based on confidence areas in the maps. The proposed methodology can provide an operative and statistically principled way to scrutinize the CRM profiles in hospitality.
Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management
(MDPI, 2019) González-Serrano, Lydia; Talón-Ballestero, Pilar; Muñoz-Romero, Sergio; Soguero-Ruiz, Cristina; Rojo-Álvarez, José Luis
Customer Relationship Management (CRM) is a fundamental tool in the hospitality industry nowadays, which can be seen as a big-data scenario due to the large amount of recordings which are annually handled by managers. Data quality is crucial for the success of these systems, and one of the main issues to be solved by businesses in general and by hospitality businesses in particular in this setting is the identification of duplicated customers, which has not received much attention in recent literature, probably and partly because it is not an easy-to-state problem in statistical terms. In the present work, we address the problem statement of duplicated customer identification as a large-scale data analysis, and we propose and benchmark a general-purpose solution for it. Our system consists of four basic elements: (a) A generic feature representation for the customer fields in a simple table-shape database; (b) An efficient distance for comparison among feature values, in terms of the Wagner-Fischer algorithm to calculate the Levenshtein distance; (c) A big-data implementation using basic map-reduce techniques to readily support the comparison of strategies; (d) An X-from-M criterion to identify those possible neighbors to a duplicated-customer candidate. We analyze the mass density function of the distances in the CRM text-based fields and characterized their behavior and consistency in terms of the entropy and of the mutual information for these fields. Our experiments in a large CRM from a multinational hospitality chain show that the distance distributions are statistically consistent for each feature, and that neighbourhood thresholds are automatically adjusted by the system at a first step and they can be subsequently more-finely tuned according to the manager experience. The entropy distributions for the different variables, as well as the mutual information between pairs, are characterized by multimodal profiles, where a wide gap between close and far fields is often present. This motivates the proposal of the so-called X-from-M strategy, which is shown to be computationally affordable, and can provide the expert with a reduced number of duplicated candidates to supervise, with low X values being enough to warrant the sensitivity required at the automatic detection stage. The proposed system again encourages and supports the benefits of big-data technologies in CRM scenarios for hotel chains, and rather than the use of ad-hoc heuristic rules, it promotes the research and development of theoretically principled approaches.
Manifold analysis of the P-wave changes induced by pulmonary vein isolation during cryoballoon procedure
(Elsevier, 2023) Martinez-Mateu, Laura; Melgarejo-Meseguer, Francisco M.; Muñoz-Romero, Sergio; Gimeno-Blanes, Francisco Javier; García-Alberola, Arcadi; Rocher-Ventura, Sara; Saiz, Javier; Rojo-Álvarez, José Luis
Background/Aim: In atrial fibrillation (AF) ablation procedures, it is desirable to know whether a proper disconnection of the pulmonary veins (PVs) was achieved. We hypothesize that information about their isolation could be provided by analyzing changes in P-wave after ablation. Thus, we present a method to detect PV disconnection using P-wave signal analysis. Methods: Conventional P-wave feature extraction was compared to an automatic feature extraction procedure based on creating low-dimensional latent spaces for cardiac signals with the Uniform Manifold Approximation and Projection (UMAP) method. A database of patients (19 controls and 16 AF individuals who underwent a PV ablation procedure) was collected. Standard 12-lead ECG was recorded, and P-waves were segmented and averaged to extract conventional features (duration, amplitude, and area) and their manifold representations provided by UMAP on a 3-dimensional latent space. A virtual patient was used to validate these results further and study the spatial distribution of the extracted characteristics over the whole torso surface. Results: Both methods showed differences between P-wave before and after ablation. Conventional methods were more prone to noise, P-wave delineation errors, and inter-patient variability. P-wave differences were observed in the standard leads recordings. However, higher differences appeared in the torso region over the precordial leads. Recordings near the left scapula also yielded noticeable differences. Conclusions: P-wave analysis based on UMAP parameters detects PV disconnection after ablation in AF patients and is more robust than heuristic parameterization. Moreover, additional leads different from the standard 12-lead ECG should be used to detect PV isolation and possible future reconnections better.
Multivariate feature selection and autoencoder embeddings of ovarian cancer clinical and genetic data
(Elsevier, 2022) Bote-Curiel, Luis; Ruiz-Llorente, Sergio; Muñoz-Romero, Sergio; Yagüe-Fernández, Mónica; Barquín, Arantzazu; García-Donas, Jesús; Rojo-Álvarez, José Luis
Although certain genetic alterations have been defined as predictive and prognostic biomarkers in the context of ovarian cancer (OC), data science methods represent alternative approaches to identify novel correlations and define relevant markers in these gynecological tumors. Considering this potential, our work focused both on clinical and genomic data information collected from patients with OC to identify relationships between clinical and genetic factors and disease progression-related variables. For this aim, we proposed two analyses: (1) a nonlinear exploration of an OC dataset using autoencoders, a type of neural network that can be used as a feature extraction tool to represent a dataset in 3-dimensional latent space, so that we could assess whether there are intrinsic or natural nonlinear separability patterns between disease progression groups (in our case, platinum-sensitive and platinum-resistant groups); and (2) the identification of relevant variable relationships by means of an adaptation of the informative variable identifier (IVI), a feature selection method that labels each input feature as informative or noisy with respect to the task at hand, identifies the relationships among features, and builds a ranking of features, allowing us to study which input features and relationships may be most informative for the OC disease progression classification to define new biomarkers involved in disease progression. Our interest has been in clinical and genetic factors and in the combination of clinical features and genetic profile. Results with autoencoders suggest a pattern of separability between disease progression groups in the clinical part and for the combination of genes and clinical features of the OC dataset, that is increased via supervised fine tuning. In the genetic part, this pattern of separability is not observed, but it is more defined when a supervised fine tuning is performed. Results of the IVI-mediated feature selection method show significance for relevant clinical variables (such as type of surgery and neoadjuvant chemotherapy), some mutation genes, and low-risk genetic features. These results highlight the efficacy of the considered approaches to better understand the clinical course of OC.
Using big data from Customer Relationship Management information systems to determine the client profile in the hotel sector
(Elsevier Ltd., 2018) Talón Ballestero, Pilar; González-Serrano, Lydia; Soguero-Ruiz, Cristina; Muñoz-Romero, Sergio; Rojo-Álvarez, José Luis
Client knowledge remains a key strategic point in hospitality management. However, the role that can be played by large amounts of available information in the Customer Relationship Management (CRM) systems, when addressed by using emerging Big Data techniques for efficient client profiling, is still in its early stages. In this work, we addressed the client profile of the data in a CRM system of an international hotel chain, by using Big Data technology and Bootstrap resampling techniques for Proportion Tests. Strong consistency was found on the most representative feature of repeaters being traveling without children. Profiles were more similar for British and German clients, and their main differences with Spanish clients were in the stay duration and in age. For a vacation chain, these results suggest further analysis on the target orientation towards new market segments. Big Data technologies can be extremely useful for analyzing indoor data available in CRM information systems from hospitality industry.