Dynamic pricing with demand disaggregation for hotel revenue management

Bandalouski, A. M.; Kovalyov, Mikhail Y.; Pesch, Erwin; Tarim, Ş. Armağan

doi:10.1007/s10732-021-09480-2

Cited by 6 publications

(9 citation statements)

References 29 publications

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“…In the hotel demand forecasting literature, we can find models based on historical transaction data and on advanced booking data (Pereira, 2016). For example, Bandalouski et al (2021) used historical data to forecast disaggregated hotel demand, for short-and long-term horizons, using timeseries models. Then, they used these forecasts of hotel demand in each category to feed dynamic pricing models.…”

Section: Hotel Demand Forecastingmentioning

confidence: 99%

“…In the context of hotel demand forecasting, there is evidence that disaggregated forecasts of hotel demand, generated using traditional forecasting models, are more accurate than aggregated forecasts (Weatherford et al, 2001). Although this line of research has been unexplored, recently Bandalouski et al (2021) disaggregated hotel demand into several categories (e.g., time of the booking, time and length of the stay, room type) in order to improve forecasts accuracy. Indeed, disaggregation of hotel demand is an attempt to use subsets of data with the same behavior for forecasting purposes, for which the cluster analysis might have a key role.…”

Section: Hotel Demand Forecastingmentioning

confidence: 99%

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Application of machine learning to cluster hotel booking curves for hotel demand forecasting

Viverit¹,

Heo

Pereira

et al. 2023

International Journal of Hospitality Management

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Section: Hotel Demand Forecastingmentioning

confidence: 99%

Section: Hotel Demand Forecastingmentioning

confidence: 99%

Application of machine learning to cluster hotel booking curves for hotel demand forecasting

Viverit¹,

Heo

Pereira

et al. 2023

International Journal of Hospitality Management

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“…As mentioned, there are few studies focussing on RM measures applied in hospitality during COVID-19 (Bandalouski et al ., 2021; Guillet and Chu, 2021; Hao et al ., 2020; Meatchi et al ., 2021; Nair, 2021; Piga et al. , 2022; Qiu, 2021; Smart et al ., 2021; Talón-Ballestero et al.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…RM measures have proven to be effective in previous crises (Caudillo-Fuentes and Li, 2009; Gehrels and Blanar, 2013; Kimes, 2010; Kimes and Anderson, 2011; Mainzer, 2009) although there is scarce literature that analyses the measures applied during the COVID-19 crisis (Bandalouski et al ., 2021; Guillet and Chu, 2021; Hao et al ., 2020; Meatchi et al ., 2021; Nair, 2021; Piga et al. , 2022; Qiu, 2021; Smart et al ., 2021; Talón-Ballestero et al.…”

Section: Introductionmentioning

confidence: 99%

A longitudinal analysis of revenue management strategies and measures implemented in the hospitality industry during the COVID-19 crisis

Ballestero

Serrano

Flecha-Barrio

et al. 2023

IMR

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PurposeThis research aims to answer two major research questions related to the COVID-19 crisis from a longitudinal approach: What is the revenue management (RM) role during the different periods subject to analysis? What are the RM strategies and measures implemented during this crisis in contrast with a non-crisis context? It also aims to propose an RM implementation model that provides a contingency plan to face future crises.Design/methodology/approachThis qualitative study, following a longitudinal approach, analyses three round-table discussions with 11 internationally renowned experts during three key scenarios of the COVID-19 crisis: the lockdown period (from March to June 2020) and the following two summer seasons (the post-lockdown period): Post-lockdown I (the summer campaign, 2020) and Post-lockdown II (the summer campaign, 2021). Based on a deductive approach, thematic analysis is conducted using NVivo.FindingsFurther professionalisation of revenue managers, which has enabled the correct application of strategies and measures, highlighting the importance of not lowering prices, the flexibility of booking conditions, the development of other sources of income and the increase in the value of services, amongst others, are key factors in managing this crisis. The longitudinal analysis carried out in three different periods of this crisis shows how these measures have evolved and the contrast with RM application in a non-crisis context. The revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RM systems (RMSs) by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.Research limitations/implicationsThis study has various limitations. First, the three round-table discussions were held online due to the health crisis, and the chosen webinar format may have biased the participants' answers due to its public nature. Second, the survey was carried out in Spanish. Despite the strong international profiles of the participants, cultural distortion may appear, suggesting that the research should possibly be extended to other cultural contexts in the future. Third, some of the participants were unable to attend all the round-table discussions due to their professional duties, so people with similar profiles were invited to the rest of the sessions.Practical implicationsThe revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RMSs by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.Originality/valueThis work contributes to the literature by providing a model that considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context. The novelty of this research is mainly found in the conducting of a deductive and longitudinal study considering previous research focussed on RM strategies applied during the COVID-19 crisis and supplementing it with new measures by applying qualitative techniques.

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“…Alternatively, revenue management is examined from a purely technological perspective, with data subjected to various simulation models, such as in the study by Bandalouski et al (2021). Some research on revenue management and its impact on hotel performance focuses on some specific issues related to the hotel industry, such as the study by Lentz et al (2021), Uncovering the relationship between revenue management and hotel loyalty programs.…”

Section: Revenue Managementmentioning

confidence: 99%

The Influence of the Level of Corporate Culture on the Ability to Implement Revenue Management Processes: Review

Kocourek¹,

Poulová²

2022

Proceedings of the International Scientific Conference Hradec Economic Days 2022

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The aim of this review article is to examine the existing research on the relationship between a company's ability to apply revenue management processes in the hotel industry and the level of the corporate culture. Revenue management as a tool to help achieve desired occupancy levels at average room rates is widely reported in the literature. Similarly, the issue of corporate culture and corporate processes has been the subject of such research.However, the issue of the possible interdependence of these two concepts, i.e. the ability to apply revenue management on the one hand and the level of corporate culture in the hotel industry on the other, has not been sufficiently described in the literature so far. This review article aims to summarize the existing research on this issue, i.e. a possible influence of the level of corporate culture on the level of implementation of the revenue management tool.Given the results of the research, the authors present in the 3rd part the idea of the possible benefits of mutual integration of Revenue management processes and processes associated with the development of corporate culture.

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Dynamic pricing with demand disaggregation for hotel revenue management

Cited by 6 publications

References 29 publications

Application of machine learning to cluster hotel booking curves for hotel demand forecasting

Application of machine learning to cluster hotel booking curves for hotel demand forecasting

A longitudinal analysis of revenue management strategies and measures implemented in the hospitality industry during the COVID-19 crisis

The Influence of the Level of Corporate Culture on the Ability to Implement Revenue Management Processes: Review

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