Any hotelier will say it is their people that make the difference, hoteliers take pride in their ability to offer personalised experiences that rely heavily on human interaction; From making a restaurant recommendation when overhearing a guest conversation at check-in to allocating a guest room with a sunrise view for an early-riser, human contact has long held an important role in the hotel sector.
This may, in part, explain apprehension around the emerging role of machine learning in hotel technology today.
While some hoteliers may be reluctant to defer to automated systems over their own staff, machine learning in an age of big data presents hoteliers with considerable advantages. Machine learning should be considered another opportunity to utilise the advanced power of analytics to better analyse all available data â€" something that manual or spreadsheet-based environments simply cannot achieve.
Automated decision making was first introduced into the world of hotel revenue management nearly 30 years ago. At the time, many hoteliers had concerns about letting revenue management software implement their rate and inventory decisions automatically within their various booking systems.
Over time, however, these same hoteliers quickly recognised the significant benefits this provided, in addition to the value of better consistency, reliability and profitable revenue optimisation. After all, why not use the power of analytics to take care of the tactical and time-consuming routines around forecasting, optimisation and decision making?
By doing so, it allowed hoteliers and their teams to focus more of their time on strategic initiatives like creative marketing campaigns and implementing processes that enhance guest experiences.
Machine learning uses algorithms that iteratively learn from data, allowing technology to glean more actionable insights from the available data. Examples of machine learning outside of the hotel sector include credit scoring and the targeting of marketing advertisements.
In hotel revenue technology, machine learning is often used in conjunction with statistical methods to produce cutting-edge forecasting and decision optimisation. High-performance technology can use machine-learning processes to better understand the relationship between price and demand, and generate room rates that adapt and anticipate market fluctuations.
In the age of big data, machine learning systems are critical. Any revenue manager working without the support of an analytical revenue management solution will find themselves overwhelmed by the sheer volume and complexity of data. Forward-looking predictive analytics, embedded in today's advanced revenue management systems and supported by machine learning, help hoteliers uncover emerging trends and identify more revenue opportunities.
While machine-learning technology can aide in the organisation and analysis of vast volumes of data, and sophisticated revenue systems automatically deploy business decisions based on this type of analysis; there is still a need for human interaction. Hoteliers will still need to validate actions and alert the technology to things it cannot anticipate.
Today, thanks to big data, machines now have a lot more to learn from. However, the question remains: Are these systems truly learning and helping automate and streamline a hotelâ€™s operations, or, are they causing hotels to work harder to decipher all the extra data?
Letâ€™s consider machine learning in relation to common hospitality industry market intelligence tools, which help inform a hotelâ€™s revenue strategy. Hotels can look at data generated by industry market intelligence tools by arrival date, channel, source, segment and more. Several revenue strategy tools bring this data into their systems, but few actually leverage the data as part of their optimisation process to ensure strategy is also optimised against price and inventory in the market.
A high-performance revenue management solution supported by machine learning that analytically determines decisions, like pricing and inventory controls, should be able to generate a price that adapts to fluctuations in the market and anticipates them in advance. It should understand the impacts of a particular price in the market and if you raise or lower that price by $10, what the change in actualised revenue will be as a result.
If demand can influence price, and price can influence demand, it should stand to reason a machine-learning tool will also understand that relationship and better optimise pricing to secure the optimal mix of business from the demand. After all, when demand is only forecasted based on the price you set, you never truly understand what the optimal outcome is or what impact that may have on another rate derived from the price you set (e.g. an advance purchase rate).
Machine learning solutions built into advanced revenue management systems present hoteliers with enormous benefits. The systems can effectively collate and analyse data to discover patterns, detect market variables, and calculate optimum prices as the market changes.
Without a machine learning-based revenue management system, hoteliers are unable to effectively make sense of the vast amounts of market and property data available today; making it near impossible to process information accurately and efficiently and determine an optimal price.
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Blake Madril is a Revenue Technology Strategist for IDeaS Revenue Solutions, where he helps to deliver innovative solutions that drive optimal profitability for hotel executives.