Real-Time Data and Hotel Pricing Strategies
Dynamic Pricing Automation requires real-time, accurate data from a variety of sources, which refers to local and global economic factors, events and weather reports as well as key metrics, such as average daily rate, cancellation and occupancy, revenue per available room, reservation behavior, average occupancy rate, gross operating profit per available room. By integrating and analyzing all this information, Hotel industry is able to predict customer behaviour, understand how its properties are performing compared to their competitors in the same area targeting similar profiles and eventually adjust its pricing strategy accordingly and proactively.
Tracking real-time data is directly profitable for the hotel industry.
Analyzing booking patterns shows the demand trends which you can use to implement dynamic pricing.
Hotels can also use real-time data to create tailor-made packages and offer them to the right customers at the right time.
Adopting a pricing strategy based on real-time data analytics is essential for hotel revenue management
Benefits for the company
Booking the rooms at the right price is not just for the customer but also for hotelier is an important task. The price must not only include the costs to maintain and run but also should also take into account the ability of a customer to pay for that while keeping in mind competitor prices as well in order to drive profits.
Type of expertise/ AI domain
Bayesian Approach, Price Elasticity, Market basket Penetration and Reinforcement Learning Model
Internal data required
Pricing History, Bookings, Cancellations, Occupancies, etc.
External data possible
Local Global Economic factors, Events and Weather Reports