Hotel revenue management is the practice of selling the hotel room at the right price, to the right customer, at the right time, through the right channel to maximize profitability for the hotel.
In today’s world, most hotel customers book hotel rooms through online channels such as Booking.com, Expedia, Hotels.com, etc, without prior planning, very close to the days of travel. With high competition and oversupply of hotel rooms, hotels now need to react to these changing demand patterns in very little time and adjust prices 24×7 to maximize business. Hence there is a higher need to use automation and AI for revenue management to maximize revenues, ADRs and occupancy for a hotel.
Unfortunately, most hotels still use old inefficient methods for pricing & revenue management, done by revenue managers who adjust rates manually few times a day. Most existing Revenue Management Systems make rate recommendations based on past data and do not take into account current & future dynamics, thus requiring Revenue Managers to evaluate these recommendations and make pricing decisions manually. This often results in higher costs and lower profits for the hotels, because the process is inefficient and there is enough money left on the table due to slow reaction times.
Hence, there is strong need for an Automated & AI based Revenue Management system that uses dynamic pricing to make pricing decisions that can help hotel maximize revenues, ADRs, and occupancy.
Revenue management has traditionally been an activity that has been done in hotels either manually using spreadsheets or by using age old-age RMS systems. Past data is typically used to make assumptions on demand and prices are adjusted few times a day based on simplistic parameters. Even though larger hotel chains use RMS systems, these systems make pricing recommendations that are only indicative, and hence there is manual intervention required by Revenue Managers to override these recommendations based on contextual parameters.
In recent times, due to changing market scenario, the demand for hotel rooms has become very volatile and keep changing by the hour. Most customers now increasingly book their hotel stays within the last 48 hours of travel. This is due to easy access and internet connectivity through OTAs like booking.com & expedia on handheld devices. This makes the prediction of room demand very uncertain and hence requires a paradigm shift in the field of hotel revenue management.
Revenue Management systems now need to account for past, present, and future parameters that keep changing 24×7 based on current context, demand, & competition. There is a new need for a real-time dynamic pricing system that uses automation, artificial intelligence and machine learning to make pricing decisions that change 24×7. Moreover, hotel systems (PMS, RMS, Channel Manager, Booking Engines) now need to integrate with each other seamlessly because rates now need to change hundreds of times day, and it is not possible to do this efficiently across multiple systems.
Hotel rooms are a perishable commodity; hence pricing and revenue management strategy is very important to maximize business for hotels. Below are factors to be taken into consideration for effective revenue management:
Occupancy: Room prices can increase as the occupancy increases to get the maximum yield per room. Leisure hotels have a large spectrum of rate variation whereas business hotels have a smaller spectrum of occupancy-based rate variation.
Lead time: Room prices can be adjusted when booked early depending on number of days to stay date, typically called the ‘booking window’. Leisure hotels have a much higher ‘booking window’, and city destinations have a shorter window of bookings.
Competition: Room prices can be set inline with competition to ensure there they are keeping up with any changing dynamics of competing hotels.
Market demand: Several market demand factors like incoming flights, number of search queries, weather etc can be used as indicators to determine market demand to any destination that can influence hotel prices.
Time of day: A huge percentage of bookings have started happening within the last 24 or 48 hours of stay, the time of day has become an important factor in determining the optimum prices.
Seasons: Several markets have seasonal factors that affect demand, which can range from specific months or specific dates (long weekends/holidays) that can be used as important criteria for revenue management.
Past Data & Trends: Each hotel has unique demand pattern which can be understood based on past data and customer behavior, which needs to be used effectively to determine best prices.
Price Ranges: Most prices need to fluctuate within an acceptable range of minimum and maximum ranges, which are relevant for each hotel in each market.
Additionally, you can alter prices based on the type of packages, length of the stay, add-on services and cancellation polices, etc.
Revenue Management in today’s world, is a highly complex activity and needs to be overhauled with the use of smart technology, automation & AI.