Hotel Revenue Management: Strategies for the Future

Top industry experts weigh up the role of data science in hotel revenue management, 2020.

By Cindy Heo - Assistant Professor of Reveneu Management at Ecole hôtelière de Lausanne and Isabella Blengini - Assistant Professor in Economics at EHL

17 March 2020
  • Hotel Revenue Management: Strategies for the Future

Cindy  Heo
Cindy Heo
Isabella  Blengini
Isabella Blengini

Last December, over a 100 industry experts and scholars attended the 3rd Revenue Management & Pricing in Services Conference (2019 RevME Europe), hosted by the Ecole hôtelière de Lausanne in collaboration with The University of Applied Sciences and Arts of Western Switzerland (HES-SO), STR, Hotel Performance and Room Price Genie. The aims of the conference were to create a unique platform for addressing issues related to Revenue Management and Pricing in the services, the role of Artificial Intelligence and Data Analytics, and lastly new approaches in revenue management education and training. What follows is a summary of the key takeaways proposed by the experts in the field.

The importance of data science in Revenue Management

With experienced speakers representing Google.com/travel, Expedia and IDeaS, all agreed on the important potential associated with using big data and artificial intelligence in revenue management, and emphasized the need to adapt the revenue management discipline to new technologies that already exist in order to become competitive on the market and maximize returns. The role of AI, machine learning and data science cannot be underestimated if the savvy traveler is to make speedy decisions and if the smart hotelier wants to forecast market occupancy, send price optimization alerts and gain insights into clientele trends. Admittedly, certain human roles are affected: with tools such as Rev+, the post of a Revenue Manager is now changing into more of a Revenue Strategist, since machines cannot be outperformed but someone is still needed to look at the bigger picture and identify goals. The rise of AI cannot replace the need for human intelligence.

Examples of how Data Science can impact Revenue Management

  • Mr. Kevin Hof, Data Scientist at RoomPriceGenie highlighted the big potential for revenue management practices in the Swiss hospitality industry, especially for small, independent hotels. He shared a case study where nine hotels experienced a 22% increase, on average, in revenue and a 4% jump, on average, in terms of ADR after adopting a pricing optimization tool.
  • Mr. Emanuele Mansueti, Consultant at HotelPerformance, demonstrated how tools such as Smart Pricer and Dynamitick, just two of the many pricing platforms that enable businesses in a wide range of sectors (sports, theatre, cinema and concerts), can help increase revenue by adapting prices to fluctuating demand. He also discussed the emergence of dynamic pricing on secondary markets and its implications.
  • Mr. Daniel Krisch, Senior Director at Oracle Hospitality, focused on how artificial intelligence can help to provide a better hotel guest experience. AI applied to hotel property management system (PMS) can enhance auto-room assignment, which helps to minimize operational costs while meeting guests' expectations. He also explained how the system can identify a guest's persona based on profile and transaction data mining, and predict a guest's interests.
  • Dr. Luciano Viverit, CEO of Hotelnet, showed how cloud machine learning tools such as Microsoft Azure or IBM Watson can be utilized for predictive analytics for hotels. While business intelligence is a 'descriptive' diagnostic, advanced analytics using machine learning tools are predictive and prescriptive so that decisions can be automated.

The future of Revenue Management Education and Training

Regarding issue of how to improve revenue management education and training, panelists agreed that there is a growing demand for well-qualified revenue managers but that the skill sets of the job are changing, hence RM education has to evolve accordingly. While good analytical skills are important, good communication skills (for sharing valuable information based on data analysis among colleagues in the organization) are even more critical. To this effect, data visualization tools can be used productively in revenue management education.

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Ecole hôtelière de Lausanne (EHL)

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Lausanne, 1000
Switzerland
Phone: 41 21 785 1111
www.ehl.edu

Cindy Heo

Dr. Cindy Y. Heo joined EHL as an Assistant Professor in 2014. Before joining EHL, she has taught at the Hong Kong Polytechnic University as an assistant professor since 2010 and at Temple University in the U.S. for two years. She also taught revenue management course at University of Angers in France as a visiting professor. She has been an editorial board member in Journal of Tourism Studies and International Journal of Tourism and Hospitality Research and is an ad-hoc reviewer for several international journals including International Journal of Hospitality Management and Cornell Hospitality Quarterly. Before joining academia, Dr. Heo worked in strategic marketing department of Samsung Everland in Korea for four years and has extensive experience within the tourism and hospitality industries including hotels, restaurants, and travel agencies.

Isabella Blengini

Dr Isabella Blengini, is an Assistant Professor in Economics. Her research interests include exchange rate movements, expectations, international capital flows, financial crises optimal monetary policy and portfolio choices.Isabella teaches in the bachelor program the courses of microeconomics and hospitality economics. After taking her Ph.D. at Boston College, she did a post-doc at the University of Lausanne. She has been visiting fellow in the economics department at MIT and summer intern at the Boston Fed.

Ecole hôtelière de Lausanne

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