Purpose – This research aims to uncover the weaknesses of traditional hotel revenue management metrics (RM) and evaluate the potential application of two new metrics, specifically net revenue per available room and revenue per available customer. Design/Methodology/Approach – Initially, a focus group roundtable discussion was conducted with 15 participants who held managerial positions in various hotel chains. The objective was to identify critical hotel revenue metrics to be included in the subsequent online questionnaire. An online questionnaire was then distributed to HSMAI members in Asia, the Americas and Europe, as well as through personal contacts. In addition to quantitative analyzes, the data were also content analyzed to reveal the weaknesses of the existing RM performance assessment tools based on the technology-organization-environment framework. Findings – Considering the positive results, the application of the new metrics would be well accepted by RM. However, the weaknesses of the traditional RM metrics in terms of data quality and robustness, completeness of measurements, comparability with industry, and organizational support should be considered when designing the new RM metrics. Originality of the research – This study is the first to offer insights into the potential of designing new RM measures. It also provides guidance on what to consider when developing new RM metrics.
Data analytics is currently the buzzword for the hospitality industry to stay ahead of their competitors. Service providers use data analytics to ensure their brand remains relevant for customers. Using data analytics in customer relationship management is a relatively novel initiative for the hospitality industry to enhance the efforts of customer relationship management. Obtaining customers’ data (i.e. customers’ hotel stay and preferences) provides both opportunity and challenges for the hospitality industry. Data analytics helps the hospitality industry to quickly, effectively, and efficiently pursue data-driven decision-making. At the same time, acquiring relevant customers’ data is a challenge, for example, data privacy and confidentiality. This case study is based on Alpen Hotel (pseudonym), a luxury hotel in Singapore with a good standing in the hospitality industry. This case is focused on the issues they experienced in implementing data analytics as part of the hotel’s customer relationship management efforts. This case study aims to highlight data analytics dilemma at the hotel and may create an opportunity for hospitality educators to work interdisciplinary with faculties from an information systems or technology discipline. Finally, the case study may enhance knowledge and minimise the practice gap between industry and academia.
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