Automation has become the driving force in the evolution of revenue management.
Leveraging advances in artificial intelligence and machine learning, the best of today’s solutions make pricing decisions and rate updates automatically. This allows revenue managers to focus their time on tactics and strategy rather than spending it crunching data and punching numbers into spreadsheets. The speed and complexity of the pricing decisions, and financial outcomes they generally produce, are unmatched by the most seasoned revenue manager using the most advanced solutions on the market only a few years ago. Such has been the blindingly rapid pace of technology innovation.
The ability to integrate new sources of data has also played a key role in driving smarter pricing decisions. Advanced revenue management solutions leverage not only the repository of historic data that resides in a hotel’s property management system, but also, in many cases, a vast array of market intelligence and other data, from competitor rates data to booking trends data. This makes it possible to more accurately forecast demand, and, as a result, increase hotel revenue and profitability in unprecedented ways.
That being the case, it’s no surprise that next-generation, AI-powered revenue management has taken the industry by storm. Some of the leading AI-powered solutions, often replacing legacy solutions that use a hands-on, rules-based approach for generating pricing decisions, now automatically generate in excess of a 100 million decisions across tens of thousands of properties each day. The results are impressive, with major hotel brands seeing their revenue numbers increase by millions of dollars a year. Smaller properties, too, are seeing substantial gains, in some cases driving incremental sales lift by more than 15 percent.
Increase revenue
Increase profitability
Optimize staffing
Increase ancillary revenue
Identify new revenue
opportunities
Reduce time to generate and
execute pricing decisions
Reduce time associated
with traditional pricing tactics
Improve marketing and
sales efficiency
Generate smarter pricing
decisions
Improve predictive abilities
(occupancy, arrivals, etc.)
Gain market and guest insights
Gain sales and marketing insights
Improve performance reporting
Interestingly, AI-powered solutions sometimes produce pricing decisions that revenue managers may view as overly aggressive, irrational, or just plain wrong. Therein lies the power of big data and machine learning compared to the data processing and analytical capabilities of mere mortals. Even the most experienced revenue managers report that they have sold rates recommended by AI-enabled solutions that they would not have published in the past.
AI-powered revenue management is all about smart pricing. It’s about using demand forecasts, competitor rates, and price sensitivities — while taking into account any number of other inputs, including demand drivers like seasonality, special event dates, and day-of-week differences —to maximize room occupancy at the best possible price. Smart pricing also means considering other factors, such as the type of room, the length of stay, and the extent to which a discounted price promotion could potentially dilute revenue and profits in the long run. The combinatorial complexities involved in smart pricing are nothing to sneeze at.
Smart pricing is channel agnostic. Rather than thinking in terms of “OTA booking versus direct booking,” for example, smart pricing considers the relative value of all distribution channels, weighing how much each channel drives guest room demand and will help achieve the overriding objective, which is to maximize the profitability of hotel inventory. Smart pricing calculates demand from all sources, including OTAs. In an ideal world, algorithms then automatically apply the right tactics and strategy to funnel business through the most profitable channels.
Total revenue management, as this bigger-picture approach to revenue optimization is often called, takes into account a guest’s potential spend on recreational facilities, restaurants, spas, and various other ancillary revenue streams when making pricing decisions. For hotels with casino operations, even the “theoretical loss” (the amount of money a specific category of player can be expected to lose during their stay) should ideally factor into guest room and group sales pricing decisions.
Empowering a hotel with the ability to make smart pricing decisions in an automated fashion makes the business case for investing in an AI-powered revenue management solution compelling. It is compelling in terms of driving increased profitability. It is also compelling in terms of averting potential revenue loss that can result when a hotel fails to maximize occupancy or, worse, experiences a loss in occupancy. Consider: A mere $2 reduction in the ADR for a 500-room hotel with a 75 percent occupancy rate would cost it more than a quarter million dollars in lost profit in a single year.
Other benefits abound. The business intelligence gleaned from the reporting capabilities, for example, can help improve sales effectiveness, generate competitive intelligence, and provide valuable insights into occupancy trends, guest demographics, market positioning, and channel profitability. A marketing department can use the forecasts as a guide for determining when to increase promotional spend to spur demand. An operations team can know when to increase (or decrease) staffing based on projected occupancy. In short, the benefits tend to go well beyond the department known as “revenue management,” ultimately transcending all parts of the organization.
