How AI and Deep Machine Learning Are Powering the Next Generation of Revenue Management Systems
By Murphy Mathew
Tuesday, 19th September 2023

Exclusive feature: Today, hoteliers are taking a much more holistic, collaborative, and strategic approach to how they run their businesses based on advances in AI-powered revenue technology.

Hotels are using revenue management technology to understand demand more accurately, enabling them to operate more intelligently. They utilise their revenue management system (RMS) not just to optimise pricing and assess the viability of group business, but also to plan staffing, support, and services they offer.

A hotel makes up to 5 million pricing decisions each year, often derived from disparate and siloed data. When you factor in merchandising programs, additional revenue streams, and unforeseen events, the complexities of hotel pricing increase exponentially.

Put simply, in the age of big data, AI and machine-learning systems are not just a ‘nice to have’ for hotel revenue managers; they are business-critical systems.

“We expect that advances in machine learning will improve hotels’ ability to optimise pricing through more accurate analyses and predictions based on market demand signals, local room availability, and a deep understanding of the individual customer’s willingness to pay,” McKinsey said in a report.

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 AI, help hoteliers uncover emerging trends and better identify revenue opportunities.

How AI can help revenue managers today

With all the hype surrounding AI today, it’s important to differentiate between Generative AI and the Deep Machine Learning models driving revenue management. While Generative AI excels at emulating human input and, more significantly, output, it often isn't adept at mathematical tasks. Essential elements for revenue management, such as hybrid forecasting models, data movement and cleansing, automated configuration based on best use case scenarios, and exception-based management stand distinct from Generative AI's current capabilities.

AI, when integrated with data analytics, streamlines numerous daily revenue management tasks. The production of various daily reports, for instance, has become more time-consuming due to the surge in data collected by hotels and the growing demand for in-depth analysis.

As the time devoted to these ongoing reports increases, it can detract from crucial pricing decision tasks. AI’s data analysis techniques are evolving to be increasingly sophisticated and more accurate, enabling it to handle repetitive tasks and produce analysis that forms the foundation for key pricing decisions.

As AI-powered systems process and discern patterns, revenue managers can leverage toolsets to enhance their responsiveness and efficiency in addressing diverse pricing scenarios. However, to achieve this revenue managers must establish processes in areas like scheduling, resource allocation, and planning. All of these areas significantly influence a hotel's bottom line – and all are possible with an advanced RMS.

Improving pricing efficiency and competitor monitoring

Modern RMS, powered by deep machine learning and AI can greatly enhance pricing efficiency for hotels. For example, in some hotel groups, the RMS integrates different strategies and data sources to determine the optimal rate for each room class on any given date. The algorithms driving this dynamic pricing engine consider a multitude of factors: customer profiles, room types and prices, as well as external data, such as competitor prices, reputation score data and even booking patterns captured on other websites.

With the latest advances in RMS technologies, these systems can now evaluate factors like the demand level of the closest competing hotels, competitor pricing, destination special events, room type, and so on. These demand forecasts offer valuable information for making pricing decisions for each market segment or room class.

This in turn supports revenue managers in choosing the right distribution strategies, understanding customer preferences, and gauging their price sensitivity. This also supports collaboration between revenue and sales / marketing teams to design successful campaigns.

Operational effectiveness is now a key focus for hotel owners and investors

In a competitive labour market, technologies that boost operational efficiencies are key. Given its proven effectiveness at scale, the use of revenue management technology is expanding beyond daily room pricing. It’s being adopted in other areas of strategic growth areas such as meetings and events spaces, food and beverage, and other parts of ancillary spending within the hotel.

When applied to its fullest potential, AI-powered revenue management and technology can positively impact efficiency and improve operational performance across an entire property. Advanced forecasting tools provide powerful insights into business demand, which assists with project planning and staffing.

For example, if a hotel can anticipate accurate levels of guest occupancy, it can determine the ideal staffing levels, preventing both understaffing and overstaffing.

An intelligent way to operate

Revenue management was the first major hotel business function to deploy advanced analytics at scale, making practices like dynamic pricing an industry standard.

As artificial intelligence and machine learning increasingly powers these predictions, pricing and revenue management strategies are set to further evolve and present new opportunities for hoteliers across different departments to improve the performance and revenue outcomes for their properties.

Murphy Mathew, APAC Solutions Engineer, IDeaS

For more information on how your hotel can benefit from AI and Deep Machine Learning powered revenue management technologies, please visit: www.ideas.com

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