Using big data from Customer Relationship Management information systems to determine the client profile in the hotel sector

Ballestero, Pilar Talón; Serrano, Lydia González; Soguero-Ruíz, Cristina; Muñoz-Romero, Sergio; Rojo-Álvarez, José Luis

doi:10.1016/j.tourman.2018.03.017

Cited by 116 publications

(89 citation statements)

References 72 publications

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“…Several studies [ 62 , 63 , 64 ] have underlined the relevance of managing customer data at a high quality level.Experian revealed that 66 % of companies lack a coherent, centralized approach to data quality [ 33 ]. Loose quality data such as duplicates or missing data, inaccurate or outdated data, could lead managers and analysts to develop an incorrect image of customers and their purchase preferences [ 65 ]. This is specially more patent in multinational companies that operate through more channels than in those who operate in a single country.…”

Section: Discussionmentioning

confidence: 99%

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management

Serrano

Ballestero

Muñoz-Romero

et al. 2019

Entropy

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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.

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Section: Discussionmentioning

confidence: 99%

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management

Serrano

Ballestero

Muñoz-Romero

et al. 2019

Entropy

Self Cite

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“…Based on the aforementioned literature review matched with a further review of articles published in the years 2017-2019, we draw some reflections on extant research. First, scholars seem increasingly interested in adopting large volumes of data to better understand complex tourism and hospitality phenomena as witnessed by an increase in the number of articles explicitly adopting BD and BD analytics (e.g., Liu et al, 2018;Mariani and Borghi, 2018;Talón-Ballestero et al, 2018). However this body of research is a marginal share of the studies produced in the T&H field.…”

Section: Past Perspective 75 Years Of Developments 1946-2020mentioning

confidence: 99%

Big Data and analytics in tourism and hospitality: a perspective article

Mariani

2019

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A perspective articlePurpose: This study aims at discussing the evolution of Big Data and Analytics in the tourism and hospitality field. It analyses the important role that Big Data (BD) has played so far in tourism and hospitality research and delineates how it might evolve in the future.Design/methodology/approach: In line with the Platinum Jubilee Special Issue of Tourism Review, this work consists of a critical and conceptual analysis including a mini literature review of recent work in the area at the intersection of BD and tourism and hospitality research.Findings: Findings suggest that tourism and hospitality scholars are increasingly aware of and adopting BD approaches to retrieve, collect, analyse, report and visualize their data. However, a number of avenues for improvement in the use and interpretation of BD and BD analytics as both sets of methods and technology need to be developed. Moreover, BD analytics promise to enhance a number of digital technologies in tourism and hospitality such as AI and IoT that heavily rely on data. As such we envision that a new digital entrepreneurship field might be shaped within the tourism and hospitality literature. Research pathways for future inquiry at the intersection of big data and tourism and hospitality are outlined.Originality/value: While thinking retrospectively about research revolving around BD and its role in the tourism and hospitality research field so far, this study also addresses the challenges pertaining to how BD research will be conducted in the next seven decades within tourism and hospitality.

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“…Academic researchers and different tourism stakeholders should understand the tourists' behavior and know their socio-demographic and geographical features. Also, client knowledge is a strategic fundamental point for the hospitality management, helping to improve the tourism offer, to create customized and adapted services and travel packages, to have good and fruitful relationships with customers and, also, to have suitable and advantageous management and marketing strategies (Adomavicius & Tuzhilin, 2001;Min et al, 2002;Talón-Ballestero et al, 2018). For the sustainable development of tourism, it is essential that the tourist services and packages be realized according to the need of the tourist demand.…”

Section: Fig 2 Number Of Papers Per Yearmentioning

confidence: 99%

Theoretical Approach to the Profile of Tourists Visiting Urban Travel Destinations

Talpoș¹,

Bolog²

2020

Studia UBB Geographia

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Theoretical Approach to the Profile of Tourists Visiting Urban Travel Destinations. The tourist profile is a key element in tourism, being addressed both in the academic and professional area. Possessing information regarding the tourist's features leads to market segmentation, creating adequate marketing and management strategies, providing an appropriate tourism offer, choosing the most advantageous promotion channels, obtaining tourists' loyalty or their recommendation to others. Also, urban tourism is a form of tourism practiced by an important number of tourists. Thus, the main purpose of this study is to provide a theoretical approach to the profile of tourists visiting cities and the secondary objectives are to identify research conducted recently on this topic, the approach of this concept by stakeholders and the variables of the profile of these tourists. This research consists of a review of studies and content analysis of the papers written on this topic was realized. Research on the tourist profile has been conducted all over the world, including Romania, approaching the characteristics of tourists with various preferences regarding, for example, the chosen types/forms of tourism, travel services, motivation, satisfaction or loyalty. We identified and analyzed the socio-demographic, geographical and behavioral variables and approached the importance of data regarding tourist features for various stakeholders.

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Using big data from Customer Relationship Management information systems to determine the client profile in the hotel sector

Cited by 116 publications

References 72 publications

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management

Big Data and analytics in tourism and hospitality: a perspective article

Theoretical Approach to the Profile of Tourists Visiting Urban Travel Destinations

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