By Andrew Rubinacci
Artificial intelligence is everywhere. It’s making inroads into practically every field of human activity. It’s integrated into search engines, word processors, and various apps likely installed on your smartphone in your pocket. And there’s a reason for the tech’s proliferation: it is incredibly adept at analyzing data much faster and at a much larger scale.
In the hospitality realm, most people consider revenue management to be something that only humans can do effectively. Now, with the advent of AI, that’s no longer the case. As technology continues to advance, there is a growing trend toward enhancing, automating, and optimizing decision-making processes, even for complex and collaborative scenarios. This shift reflects the ongoing integration of AI technology to make decisions more efficient, accurate, and streamlined.
Indeed, a growing number of hotels and hotel chains have been looking to reap the rewards of an AI-driven revenue management system (RMS) to optimize their pricing strategies. However, just because a system bears an AI moniker in its marketing doesn’t suddenly turn it into a revenue management silver bullet. The truth is that legacy solutions have algorithms that were created before AI made its appearance, so they can’t be AI-first. These days, that’s a costly mistake.
Hoteliers should recognize the distinctions between a revenue management system just using buzzwords like AI and what a truly modern AI-first system will do when it’s well designed. FLYR’s solution is also designed to be user-friendly and intuitive. It presents complex data in a simple, easy-to-understand format, enabling hoteliers to make informed decisions quickly and efficiently.
We’ll look at why FLYR’s AI-first solution leads the RMS pack with its decision intelligence capabilities in a little more detail, but first, let’s take a deeper look at what decision intelligence is.
What is Decision Intelligence?
Decision intelligence (DI) is a multidisciplinary field that uses advanced techniques from data science, machine learning, and artificial intelligence to guide decision-making processes. In the context of hotel revenue management systems, DI can bring about a transformative change by enabling more accurate, data-driven decisions.
In the realm of dynamic pricing, DI utilizes comprehensive analyses of booking patterns, market trends, and competitor pricing to optimize rates strategically for maximum revenue. Its impact extends to enhancing demand forecasting and providing unparalleled precision for inventory control and staffing decisions.
DI refines guest segmentation by delving into data, allowing personalized services that elevate satisfaction and boost revenue. Furthermore, it guides strategic choices in distribution channel management, optimizing focus for revenue maximization. In addressing the perennial challenge of overbooking, DI’s predictive capabilities empower informed decision-making, minimizing the risks of guest disappointment.
Moving beyond conventional revenue management systems that offer decision support solely based on supply and demand cues, decision intelligence algorithms and models meticulously examine data to grasp price sensitivity. They can autonomously modify rates for specific rooms, nights, and lead times, providing a more dynamic and responsive approach.
Unlike other systems, FLYR algorithms are totally prescriptive, providing insights that would have otherwise flown under the radar – and delivering recommendations for sound decision-making to respond to data-based signals rapidly.
Historical vs. Real-time
A lot of customers of other “AI” RMS providers we talk to describe the challenges around the importance those systems place in historical data. These systems rely on historical data as the basis for their pricing and inventory recommendations, limiting the scope of those recommendations to past patterns and trends. This is less than ideal in dynamic markets prone to rapid shifts – historical data can lose its relevance rather quickly.
The algorithms used in decision intelligence continually analyze your hotel’s stream of data. They can make micro-targeted adjustments on room nights, providing an edge to revenue managers who can quickly respond to shifts in market conditions, which translates to enhanced profitability. That’s no small benefit when demand patterns fluctuate rapidly, such as in the hospitality industry.
In many of the older RMS systems, they typically have to run for a minimum of three months in order to generate useful recommendations. You really don’t get optimized results until you’ve been running the system for a year. In a next-generation system like FLYR, you can start getting optimized pricing and forecasting results as soon as a week after the system is connected. This is what differentiates the FLYR technology and is a true indication that it just works in a new and better way.
Automation & Scalability
With a fully automated pipeline, decision intelligence-capable systems are quite simply more efficient and less error-prone than legacy systems. The latter typically rely on rule-based approaches and manual analysis to spawn their inventory and pricing recommendations. Again, this slows down the process, and the resulting insights may well be out of date. And we all know that human intervention can introduce errors in the data, resulting in errors in the recommendations.
Case in point: a McKinsey study found that an AI-driven RMS could reduce errors by 20% to 50% and reduce lost sales by up to 65% – not a fringe benefit by any measure.
You may have noticed my use of the word “recommendation” when discussing other revenue management systems. That’s because these solutions typically provide recommendations rather than actionable insights. They make suggestions, but the final decision-making will hinge on human judgment, leading to the potential introduction of errors and biases.
They also struggle to handle the sheer volume and complexity of data available today. In contrast, FLYR’s solution automates these processes and uses AI to analyze data more accurately and efficiently. It is also scalable enough to grow your business without skipping a beat. The bigger your business, the more data it generates. So you’re going to want a system that won’t balk under pressure.
Other systems tend to be developed for specific hotel sizes or types and cannot easily accommodate changes in business needs or market conditions. FLYR’s solution is more adaptable and responsive to changes in the market. It continuously learns and adjusts its predictions and recommendations based on new data, ensuring that hotels are always equipped with the most up-to-date and accurate information. This is a significant advantage in the fast-paced and unpredictable hospitality industry.
Integrated vs. Siloed
As mentioned above, decision intelligence operates holistically. That means it integrates, among other metrics, booking patterns, market demand, competitor pricing, and guest preferences. So, it will require access to existing systems for optimal performance and to yield the maximum benefits. FLYR’s decision intelligence-enabled RMS is designed for easy, seamless integration with a plethora of existing systems. This enables hotels to leverage data from various sources in real-time, leading to more informed revenue and guest relationship management decisions.
With limited integration capabilities, legacy systems can’t consider many complex and interrelated factors that significantly impact revenue management. They cannot factor in external data points, like market demand, competitor pricing, and customer segmentation, limiting the insights they can produce.
Leveraging Decision Intelligence
FLYR brings together AI and intuitive workflows to deliver the first-of-its-kind AI-augmented decision intelligence platform. This is a paradigm shift for the hospitality industry, providing massive accuracy, efficiency, and profitability gains over legacy RMS systems.
Offering a trifecta of advantages, decision intelligence enhances predictive insights, optimizes pricing strategies, and refines guest segmentation. Recognizing that the effectiveness of any algorithm hinges on the quality of the input data, as the principle “garbage in, garbage out” aptly underscores, FLYR places a paramount emphasis on obtaining accurate and high-quality data, aiming for a standard of precision that surpasses previous benchmarks.
Beyond revenue optimization, DI serves as a risk management tool, predicting the impact of external factors on hotel revenues allowing proactive measures to mitigate potential losses. By automating routine tasks and facilitating highly advanced real-time data analysis, FLYR’s decision intelligence capabilities enhance operational efficiency, enabling hotel revenue managers to focus on strategic, profitable decision-making and elevating the overall guest experience. This not only yields cost savings but also fosters a positive impact on the hotel’s reputation, ultimately contributing to sustained revenue growth.
Ready for a personalized tour of the cutting-edge RMS platform that will revolutionize how you optimize decisions and create memorable guest experiences? Contact me directly for a walk-through of this amazing system today.