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

Abstract

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.

Analysis on the Influence Mechanism of Employees’ Active Behavior Based on Data Mining and Mobile Edge Computing Technology

“…It has three typical characteristics: self-starting, overcoming obstacles, and initiative. Literature [21] suggests that active behavior includes goals and efforts to bring about environmental and/or selfchange in order to realize a different future. It is expected, self-initiated, and a change-oriented behavior.…”

Hotel Performance in the UK: The Role of Information Entropy in a Novel Slack-Based Data Envelopment Analysis

“…Third, a number of research articles have applied the following method in estimating hotel efficiency including (7) Network Data Envelopment Analysis model [43,44]; the network Data Envelopment Analysis supplements the previous slacks-based model by dividing the production into stages and being able to identify the source of (in)efficiencies for each of the stages [45], however, under the assumption of variable returns to scale, the network DEA does not provide information on divisional efficiency but only provide information on projection frontier [46]; (8) Stochastic Data Envelopment Analysis model [47,48]; as argued by [47], the stochastic Data Envelopment Analysis model improves the conventional Data Envelopment Analysis model by providing a higher ability to measure efficiency in the environment with uncertainty. On the other hand, the drawback of this method lies to the fact that stochastic variables are imposed limitations (i.e., the stochastic inputs are normally distributed) [49].…”

“…The tourism and hotel management literature has long praised the benefits of hospitality and leisure issues on customer satisfaction levels [1] and the resulting impact on performance [2][3][4][5] but most research has been limitedly based on individual hotel performance. Hence, there is a lack of studies looking at industry performance that can reflect learning process and cash flow generation from operating scale that could not be measured or captured by analyzing each firm alone [6,7]. Despite this call for further investigation on hotel performance, and the recognition of learning processes as a key theme for improving cash flow generation [8], we identify a research gap concerning whether information entropy related-variables can lead to different performance results at the hotel level within the industry ambit.…”

A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description

“…The synergistic relationship between Big Data and CRM is relatively new and so far little studied [16,30]. With regard to the hospitality industry, the scarcity of studies is even greater, and there are few works that have brought the two concepts together [31,32]. However, the CRM is a strategy that has been implemented for over thirty years in the hotel sector [27], although its use and importance has increased in recent years [7].…”

“…For this reason, the application of Big data tools and technology in the context of hotel CRM strategies is now indispensable and it is therefore pertinent to deepen the knowledge and usefulness of these tools. Thus, big data technology facilitates segmentation and better knowledge of the client [12] with the continuous updating of profiles, giving the hotel the possibility of interacting in real time with the client, as well as providing knowledge of the client's value and life cycle [31], adjusting the services to their needs, enabling the offer of personalized services and products and carrying out marketing campaigns aimed at each client segment, predicting client reaction to marketing messages and product offers or providing a higher quality of information [32], among many others. Data mining facilities are being increasingly implemented in the hotel CRM to help managers find hidden client information from available data, thus aiming to serve towards more effective and personalized CRM strategies [16].…”