Zvi Schwartz

Hotel daily occupancy forecasting with competitive sets: a recursive algorithm

Purpose – This paper aims to improve the accuracy of hotel daily occupancy forecasts – an essential element in the revenue management cycle – by proposing and testing a novel approach. The authors add the hotel competitive-set’s predicted occupancy as an input of the individual property forecast and, using a recursive approach, demonstrate that there is a potential for significant reduction in the forecasting error. Design/methodology/approach – The paper outlines the theoretical justification and the mechanism for this new approach. It applies a simulation for exploring the potential to improve the accuracy of the hotel’s daily occupancy forecasts, as well as analysis of data from a field study of two hotel clusters’ daily forecasts to provide empirical support to the procedure’s viability. Findings – The results provide strong support to the notion that the accuracy could be enhanced. Incorporating the competitive set prediction by using either a genetic algorithm or the simple linear regression model i...

What Types of Hotels Make Their Guests (Un)Happy? Text Analytics of Customer Experiences in Online Reviews

A hotel is a complex, experience-based product and thus, finding out what leads to guest satisfaction is a practically important question. In this study, we explored the usefulness of applying guest experience dimensions previously identified based upon authentic online customer reviews to understand what types of hotels make their guests (un)happy. Hotels were grouped by experience dimensions and satisfaction ratings using cluster analysis. Then, these hotel clusters were examined in relation to words in customer reviews with correspondence analysis. The findings show that there were different types of hotels with unique, salient traits that satisfied their customers, while those who failed to do so mostly had issues related to cleanliness and maintenance-related factors. This study demonstrates that consumer generated content such as customer reviews can be useful data sources to generate new insights into the nature of the hotel product. It also points to a promising direction to employ authentic consumer experience data to support perceptual mapping and market segmentation for the hospitality and tourism industry.

Revenue Management and Length-of-Stay-Based Room Pricing

From a theoretical perspective, it is unclear whether hotels should offer a discount to customers who stay for longer periods, while it is rather clear that customers feel entitled to such length-of-stay (LOS)-based discounts. Accordingly, the major theoretical contribution of this study is to provide empirical evidence of hotels’ LOS-based pricing practices. The findings revealed that on average, hotels charge more per night when the guests stay longer. The finding that hotels increase their room rates as LOS increases, suggests that hotel revenue managers may want to address the customers’ misinformed expectations of getting lower prices for longer LOS and/or devise pricing or communication strategies to deal with it.

Revenue Management Outsourcing: A Hybrid Model of Transaction Cost Economics and Organizational Capability

Hotel revenue management is an activity domain that is uniquely technical, complex, and dynamic, and as such, it is often outsourced. This study explores the motivation of hotels to outsource their revenue management function to an outside entity. The major theoretical contribution is in being first to suggest, and empirically test, the notion that in complex environments, such as revenue management, the firm’s capabilities are moderating the impact of assets specificity on managers’ inclination to outsource. From a practical perspective, this study is the first to empirically explore the contextual determinants of the decision to outsource in the hotel-specific domain of revenue management.

Evaluating hotel guest technologies: Does home matter?

Purpose With the advent of the smart home, where connectivity is facilitated by the Internet of Things (IOTs), the provision of guest technologies in hotel service delivery has acquired greater significance. This ubiquity of technology implies that hotels need to view their technological offerings as facilitating guest’s broader lifestyles, and not just services in isolated spaces. This study examines the role of “home” as a socio-technological unit, and how customers’ ownership of technologies at home impacts evaluations of guest technologies at hotels. Design/methodology/approach Data was collected from a sample of US lodging consumers using Amazon’s Mechanical Turk. Partial Least Squares, which is a component based structural equation modeling technique with SmartPLS 3.2 was used to test the hypotheses and meet the study’s objectives. Findings Findings show that hotel guest technologies should be of a higher standard than home, for guests to be satisfied with them. This relationship was robust across a...

Revenue management analysis with competitive sets: Vulnerability and a challenge to strategic co-opetition among hotels

Growing concerns about the integrity of the revenue per available room index—the lodging industry de facto performance measure—stem from previously published empirical evidence concerning the competitive set’s venerability and from management’s financial incentives to outperform the hotel’s competitive set. This study demonstrates the potential of the decision to include or exclude a hotel in, or from, competitive set averages to bias its performance index. The analysis shows that the bias potential is nonlinear and asymmetric in relation to the subject hotel’s revenues, and uses this revealed asymmetry to prove that when future relative performance levels are uncertain, exclusion tactics is preferred. Finally, the study demonstrates how the relative nature of the performance indicator, coupled with management potential opportunistic inclusion/exclusion competitive set tactics, are detrimental to the perceived desirability of co-opetition strategies.

Market Intelligence: Social Media Analytics and Hotel Online Reviews

In order to demonstrate the usefulness of social media analytics, this chapter applies text analytics to classify hotel properties based upon dimensions of guest experiences in association with satisfaction ratings in hotel online reviews. The findings of the study reveal the market structure of the industry; that is, hotel properties can be distinguished based upon words consumers use to describe their experience in combination with the level of satisfaction and to a better understanding of the competitive landscape of the hotel industry. This study contributes to the development of new approaches to identify competitive sets for hotels which, in turn, can help hotels better position themselves based upon eWOM.