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
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.
>See also: How machine learning will transform hospitality
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.
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. When combined with other market data collected, dynamic pricing ensures that hotels are not ceding revenue unnecessarily, making a the technology an important component of a proactive revenue management strategy.
There are many other possible applications for machine learning in the hospitality industry.
Algorithms can be designed to analyse sentiments in online guest reviews and suggest operational improvements that can be made to improve guests’ experiences.
Over time, these sentiments can also be used as another basis for dynamic pricing; when guests are happy with their stay, the RMS will increase the room rate and if that changes, the room rates may decrease.
>See also: 3 ways the hospitality industry can personalise the customer experience
Machine learning-based RMS can use a k-nearest neighbours algorithm to dynamically identify similar properties within the hotel’s competitive set (also known as a comp set).
Unlike many RMS, a sophisticated machine learning-based solution can also factor vacation rental properties into a property’s comp set, which is important given how many leisure travellers compare pricing at vacation rental to hotels within a destination before booking.
Having a dynamically accurate comp set is important because a significant part of an RMS’ pricing calculation is based on the comparison of pricing, booking pace and supply data collected from similar properties in the destination.
For example, if there are more rooms than normal available at other properties, low historical demand and if inclement weather is anticipated, an RMS using machine learning technology will automatically lower the room rate to ensure that every potential guest is presented the optimal rate to suit his/her buying behaviour and the market’s specific price-elasticity.
Machine learning technology can also be used to compute dynamic clusters of guests to create fluid segmentation in real-time.
As consumer buying habits and/or booking patterns evolve, fluid segmentation ensures that the hotel continues to reach the right guests, at the right time and price, through the right channels.
There are 2 machine learning trends that we anticipate becoming especially prevalent in the hotel industry this year: first, the use of deep neural networks and image classifiers to analyse and parse images, which enables hotel marketers to monitor the images that provide the highest booking conversion rate through each channel and secondly, voice-oriented contextual conversations and notifications, which use natural language processing to provide context-based information – a feature which was not available in an RMS prior to 2016.
Throughout 2016, there is an anticipation of adopting machine learning technology to really take hold in all industries worldwide.
Due to the breadth of data available, only machine learning–based RMS can effectively leverage this information to dynamically maximise hotels’ booking conversion rates and revenues.
Sourced by Ravneet Bhandari, CEO of LodgIQ
