A Study of the Commercial Application of Big Data of the International Hotel Group in China: Based on the Case Study of Marriott International

Abstract

The commercial application of big data has been gradually under progress in various industries. As one of the significant elements in tourism industry, it concerns whether the hoteliers can secure a competitive advantage. The mining of the data on customers is conducive to the analysis of the behaviors and consuming preferences of customers, which can improve the living experience and satisfaction degree of customers effectively, in an effort to realize the profit of the hotel. J.W. Marriott and The Ritz-Carlton, representatives of the subordinate grand-level hotels in China of Marriott International, are selected as the research targets. This study adopts an qualitative approach. We will have an in-depth interview with managers of the five main sections in each of the hotels, with the purpose of analyzing the tendency of the data use related to customers and its application level. In the light of the obtainment channel and actual application of big data of Marriott in China, it can judged that the hotel mainly appliesthe big data mainly to such aspects as sales of product, social media and online behavior of customers, offline data. In the end, on the basis of the case study, we can reach a conclusion that the commercial application of data in the hospitality industry will provide great assistance for hotel managers in formulating correct strategies of management and marketing.

Big Data in Hotel Revenue Management: Exploring Cancellation Drivers to Gain Insights Into Booking Cancellation Behavior

“…This is aimed to answer the challenges mentioned by Antonio et al (2017a) and Pan and Yang (2017b) regarding possible performance improvement in demand forecasting, more specifically in the prediction of booking cancellation probability based on the use of big data. In addition, we will confirm the benefits of employing big data in hospitality research forecasting (McGuire, 2017;Pan and Yang, 2017b;Talluri & Van Ryzin, 2005;Wang et al, 2015;Zhang, Shu, Ji, & Wang, 2015). Finally, rather than targeting only forecast accuracy as many big data forecasting studies have done (Hassani & Silva, 2015), we also wish to use the algorithms' interpretability features to explore other advantages of using big data and advanced prediction algorithms to understand whether the variables' predictive power holds for all hotels and to identify the drivers behind the cancellation of bookings, an area that is in need of further research (Falk & Vieru, 2018;Morales & Wang, 2010).…”

“…This contributes to the existence of differences among the features' importance rankings at different hotels. Fifth, despite the suggested potential benefits of big data application in hotel revenue management forecasting (McGuire, 2017;Pan & Yang, 2017b;Talluri & Van Ryzin, 2005;Wang et al, 2015;Zhang et al, 2015), no evidence of such benefits was found for booking cancellation prediction. The models' performance did not improve substantially with the inclusion of features from other sources, and none of the features from non-PMS data sources showed predictive importance.…”

Smart hospitality—Interconnectivity and interoperability towards an ecosystem

“…However, even for international chain hotels, the usage of technology for data mining and analysing is still at an early stage and the application level of big data in the hospitality industry still low (Y. Zhang et al, 2015).…”

“…Big data collected from both internal and external services enable hospitality practitioners to make use of historical databases to forecast and predict business trends such as occupancy, rates and yield, labour costs and investment decisions (Y. Zhang, Shu, Ji, & Wang, 2015). However, current big data is still spread around the Internet without a standardized format.…”

Optimization of real-time wireless sensor based big data with deep autoencodernetwork: a tourism sector application with distributed computing

“…Information sharing over social media and commenting sites is found more reliable by customers [15]. So, online commenting sites have great importance for tourism entrepreneurs [16].…”