A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description
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 hospit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/1/256 |
| _version_ | 1797543170987261952 |
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| author | Lydia González-Serrano Pilar Talón-Ballestero Sergio Muñoz-Romero Cristina Soguero-Ruiz José Luis Rojo-Álvarez |
| author_facet | Lydia González-Serrano Pilar Talón-Ballestero Sergio Muñoz-Romero Cristina Soguero-Ruiz José Luis Rojo-Álvarez |
| author_sort | Lydia González-Serrano |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:41:55Z |
| format | Article |
| id | doaj.art-a9331361afb4468c86ffad18c23ec719 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T13:41:55Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a9331361afb4468c86ffad18c23ec7192023-11-21T03:00:09ZengMDPI AGApplied Sciences2076-34172020-12-0111125610.3390/app11010256A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain DescriptionLydia González-Serrano0Pilar Talón-Ballestero1Sergio Muñoz-Romero2Cristina Soguero-Ruiz3José Luis Rojo-Álvarez4Department of Business and Management, Rey Juan Carlos University, 28943 Fuenlabrada, Madrid, SpainDepartment of Business and Management, Rey Juan Carlos University, 28943 Fuenlabrada, Madrid, SpainCenter for Computational Simulation, Universidad Politécnica de Madrid, 28223 Boadilla, Madrid, SpainDepartment of Signal Theory and Communications and Telematic Systems and Computation, Rey Juan Carlos University, 28943 Fuenlabrada, Madrid, SpainCenter for Computational Simulation, Universidad Politécnica de Madrid, 28223 Boadilla, Madrid, SpainCOVID-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.https://www.mdpi.com/2076-3417/11/1/256customer relationship managementmultiple correspondence analysisdomain descriptionhospitalitystrategykernel methods |
| spellingShingle | Lydia González-Serrano Pilar Talón-Ballestero Sergio Muñoz-Romero Cristina Soguero-Ruiz José Luis Rojo-Álvarez A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description Applied Sciences customer relationship management multiple correspondence analysis domain description hospitality strategy kernel methods |
| title | A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description |
| title_full | A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description |
| title_fullStr | A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description |
| title_full_unstemmed | A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description |
| title_short | A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description |
| title_sort | big data approach to customer relationship management strategy in hospitality using multiple correspondence domain description |
| topic | customer relationship management multiple correspondence analysis domain description hospitality strategy kernel methods |
| url | https://www.mdpi.com/2076-3417/11/1/256 |
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