Machine learning transforming the hospitality industry

Machine learning transforming the hospitality industry

Machine learning transforming the hospitality industry
The hospitality industry has not always been at the forefront of high-tech innovation or implementation.
Until recently, most of the bookings, transactions and administrative tasks at a hotel were handled manually.
Revenue management – the process by which a revenue manager determines the best room rate at a given time, in order to maximise bookings and revenue – was a particularly difficult task.

Revenue managers had to manually collect, review and analyse numerous data sets each time the rate needed to be updated, and then calculate the ideal room rate based on those variables.

Even before the Internet, this was a very time-consuming task, which meant that revenue managers could not update rates as often as necessary (to ensure a property’s continued financial success).
With the creation of online travel agencies (also known as OTAs) unparalleled quantities of data became available and the task of manually executing pricing decisions became impossible.

Machine learning in hospitality

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With the recent emergence of cloud and cluster computing, and by using scale-out methodology, hotels now have access to revenue management systems (RMS) that leverage machine learning and artificial intelligence (AI) technology to automatically collect and compute large amounts of complex and disparate data, convert it into a more manageable size and easily understood format, and determine the best possible room rate in real-time, as the market changes.

Using machine learning-based RMS, revenue managers no longer need to be involved in the manual implementation of revenue management tasks.
These sophisticated RMS can effectively sift through the signals detected from market variables, discover patterns and anomalies, make predictions for guest arrivals and calculate optimum prices in real-time, as the market changes.

As new data sets become available, the RMS can effectively gauge whether the information is important, and if so, integrate it into current data parameters without the involvement of the revenue manager.

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As additional pertinent data is integrated into the existing parameters, the signals will change, making the pricing suggestions generated by the solution even more accurate.
Without a machine learning-based RMS revenue managers would receive too much unfiltered data, making it nearly impossible to process all of the data effectively and determine an accurate price.

Machine learning-based RMS also allows the implementation of dynamic rates based on specific variables chosen by the revenue manager.
For example, a hotel could increase prices based on market demand signals obtained from analysing vacation property demand, often an early indicator of future demand.
Unlike prior solutions, which had a limited view of market demand and thereby missed market demand signals that support a revenue improvement opportunity.

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