Hotel Data Analytics
In an increasingly digital world, data generated by users and businesses have dramatically increased in scale, scope and quality. Business decisions are changing and become increasingly data-driven. Revenue management, electronic distribution and demand management are fields that can greatly benefit from sound data analytics.
This course aims at introducing core principles of business data analytics as applied to hotel demand management. This course is based on real-life hotel cases and will use existing users and businesses generated data, including user reviews and hotel performance (e.g. ReviewPro reputation scores, STR data, etc.). The course will require the use of laptops equipped with Excel (English version).
Price : €2,300 (excl. tax)
|Duration:||2 days and a half|
|Date:||March 17, 2016|
|Location:||CNIT Paris La Défense|
ESSEC Hospitality Executive Education - Participants Testimonials - Online Distribution
Nicolas Graf is on the faculty of ESSEC business school in Paris, where he teaches strategy and real estate finance. He received his Ph.D. from Virginia Polytechnic Institute and State University, with a concentration in corporate finance and strategic management and his MBA and bachelor’s degree from the Ecole hôtelière de Lausanne. Prior to his teaching and research, Dr. Graf spent several years in operations, managing individual restaurants in Switzerland and working in full-service hotels in the US. As a scholar, his research focuses on competitive strategy, product development and franchise systems in service industries. He has published in the International Journal of Hospitality Management, the Real Estate Finance Journal and the Journal of Retail and Leisure Property, as well as a number of applied articles in industry journals and newsletters. Dr. Graf has delivered executive education courses for hotel and restaurant companies in Asia, North and South America and Europe, and conducted consulting projects in the fields of asset management, strategic p...Topics include
- Developing demand analysis frameworks.
- Data selection and preparation.
- Data analysis and modeling.
- Demand and channels analysis.
- Online reputation analysis.
- Benchmark analysis.
- Spreadsheet modeling.
- Demand forecasting.
Participants will learn to appreciate the value of data analytics for decision making. They will also be capable of planning and performing various statistical analyses to better evaluate strategic and tactical alternatives to better sell their properties.