Hotel Revenue
Revenue is a very important part of hotels industry. A hotel price can change several times per day in competitive destinations. To maximize revenue it is important to have as less as possible empty rooms and to charge for them as much as possible.
Introduction
Hotels can have 30 to 50% of reservations cancellated (in Covid-19 time can reach 100%). If the hotel stops selling rooms when is full, at the end the hotel will have many empty rooms because of cacelled ones. That is the reason that hotels sells more rooms than available, but have to be cautious to do not do overbooking. So machine learning can give a solution for this using historical data of the hotel, wheater reports and hotel demand from touroperators. Also choosing the right price is important, so with the same data data also we can have sellings forecast.
Research
The Challenge here is to mix very diferent data sources to give reliable predictions on cancellations and selling forecast. Weather forecast is also a challenge because only is reliable on the next 7 days.
Final Result
The proyect is still on development phase, our expectations is that it can be a great tool for hotels to be more competitive. As covid 19 is affecting very hard to the industry, the launch of the project will be delayed until next year.