Driving Profitability with Precision: AI's Pivotal Role in Revenue Management - By Andrew Rubinacci

Revenue management systems play a critical role in the hospitality industry. Traditionally, these systems attempt to predict future demand from past trends to optimize pricing. That’s a reasonable approach. But as the pandemic taught us, relying on the past to predict the future doesn’t always work - the unexpected still occurs.

Now, what if I told you it was possible to predict future demand based on current trends? How would that impact your pricing strategies? We’ve come a long way since the first RMSs hit the scene. And the recent advancements in artificial intelligence have made them more powerful (and relevant) than ever.

Now, we’re not talking about simply augmenting a traditional RMS with an AI functionality added to a legacy system. What we’re looking at is a new type of RMS built with AI from the beginning. For AI to properly do its thing, it needs to process massive amounts of data and create links between the billions of data points. A traditional RMS - simply cannot handle the load.

According to McKinsey, “We expect that advances in machine learning will improve hotels’ ability to optimize 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. With AI-driven systems identifying trends, revenue managers can harness these tools to bolster their agility and effectiveness in tackling a plethora of pricing situations.

Nothing Beats “The Now”

Paradigms change with time. The core assumption of revenue management used to be that past trends are the best way to predict future performance. But we’re no longer there. Technological advances continually redefine the breadth of the possible. And we’re now at a point where we can not only look to the past to inform the future, but we can also - and primarily - look to the present for future insights.

You want to know what is happening in “the now” to infer what will happen tomorrow, next week, and next year. That doesn’t mean disregarding the past. But it does mean relying on the “here and now” to ensure that you’re offering the right price to the right customer at the right time.

And that leads us to our next point.

Real-Time Agility is Key

Why do you want real-time data to inform your pricing strategies? It enables pricing strategies to be deployed in real-time - perfectly optimized for this moment in time - the now.

AI’s ability to analyze data in real-time enables businesses to stay on top of the latest trends and allows for up-to-the-minute pricing. This not only helps businesses to remain competitive but also maximizes their revenue potential. It’s just better business intelligence and correlates your pricing strategies to your current circumstances - whatever they may be.

This also relates to price elasticity models. You want your price elasticity models to be dynamic rather than static. AI-driven RMS’s machine learning algorithms can continuously monitor and detect any changes in price elasticity. This provides insights into demand patterns relative to internal and external price fluctuations. And because it’s a dynamic process, you can adjust your pricing strategies based on accurate and current data.

Don’t Settle for a Data Silo

Research indicates that a hotel might make up to 5 million pricing choices each year, often relying on scattered and compartmentalized data. Factor in marketing initiatives, emerging revenue sources, unforeseen events, and hotel pricing complexities escalate significantly.

A primary hurdle for many hotels are the data silos created when specific departments or groups withhold information and expertise from one another. This creates barriers that have multiple consequences—from producing a weak culture to negatively impacting productivity and the bottom line. As a result, opportunities are missed, and problems don’t get solved.

Since AI and ML pull from and adapt to diverse data streams, they can effectively dismantle those silos by centralizing data and making it integrated and available, automatically identifying meaningful insights, trends, and recommendations. This opens revenue managers to focus on the tasks that truly matter: gaining insights from their data and making decisions based on those insights.

AI-driven RMSs can analyze external demand indicators and use this data to augment their forecasting algorithms. This provides your pricing strategies with a level of accuracy and precision that simply cannot be achieved with traditional revenue management systems.

Complex Doesn’t Mean Better

AI is complicated tech under the hood. But your RMS doesn’t have to be. A powerful and sophisticated AI-driven RMS can be simple to use and understand. The onboarding process should also be straightforward and streamlined. Your revenue managers aren’t data scientists, but they need to be comfortable using your hotel RMS to get the full benefit.

Another critical point is going to be customization. You want your RMS to fit into your current pipeline and support your processes - not the other way around. Tying into ease-of-use, as an organization, you want to be able to tailor your RMS to your needs so that it outputs valuable and meaningful data that is actionable.

You can have the most powerful solution on the market, but if no one can use it, it won’t be of much use.

Revenue Management in the Era of AI and Big Data

Revenue Management is one of those areas that is set to reap the benefits of AI integration. Since the key to being successful in revenue management is following data trends closely and using these insights to set the right pricing for the right guests, AI is perfectly suited to supporting this hotel function. However, effective decision-making depends on understanding the context, which means recognizing cause and effect.

Unlike traditional systems, FLYR’s cloud-based RMS brings together elements from AI, machine learning, people, and systems to deliver Decision Intelligence. It is the foundation of self-driving revenue management, which makes real-time recommendations and learns from every decision made. FLYR RMS can quickly analyze vast amounts of data, uncover hidden connections, and come up with predictions faster and more accurately than anyone ever imagined.

Gartner predicts that "in the next two years, one-third of large companies will use Decision Intelligence, including decision modeling, to enhance their competitive advantage."By embracing this revolutionary technology, hoteliers will have unlimited opportunities to enhance guest interactions, boost operational efficiency, and optimize pricing and commercial decisions to maintain a lasting competitive edge—even in the midst of rapid change and global disruption.

About the Author

As the Chief Advisory Officer of FLYR for Hospitality, the hospitality line of business at FLYR, Andrew leads a global team whose mission is to amplify the commercial performance of FLYR customers. He also serves a critical role in helping FLYR grow the hospitality business and lead the industry with modern approaches to commercial strategy.

With over 25 years of commercial leadership experience, Andrew is one of the foremost revenue management and global distribution experts in the hospitality industry. He has held various executive positions, including Chief Commercial Officer and CIO for Omni Hotels & Resorts, and Senior Vice President of Distribution & Revenue Management for InterContinental Hotels Group (IHG). Most recently, Andrew served as Executive Vice President, Commercial & Revenue Strategy at Aimbridge Hospitality Corporate Office.

Andrew holds bachelor's degrees in business administration, majoring in both marketing and hospitality administration at Florida State University. He has served on the boards of HEDNA, Worldres, HSMAI, Open Travel, Roomkey, and the Dedman College of Hospitality at Florida State University.