Every year, revenue management experts opine on the future direction of their discipline. You would think that a science based so heavily on forecasting, would have no problem predicting the next generation of developments in its own field. But things don’t always work that way. This is especially true beyond a one-year time horizon. Previous predictions often looked like simple extensions of the current trends. Perhaps the next thing around the corner might be a surprise.
Human resource issues have created many predictions for revenue management trends lately. For example, over the past few years, many experts have accurately stated that revenue management practitioners will be expanding their knowledge base in areas like leadership skills and social media. Before that, it was all about training revenue managers to reduce their focus on room revenue and expand it to include optimizing all types of revenue and profit centers on the property. For a few years, the predictions focused on ancillary revenue streams, like cabana rentals, resort fees and room upgrades. Other predictions included the need for revenue managers to hone their skills in areas like leadership, team building and gaining consensus from the various departments in the hotel so that the revenue plan was implemented cohesively.
One definition of big data is any compilation of data that is too big for traditional database technologies. It has only come into practical existence in the last decade as the cost of storing data has plummeted. A recent whitepaper estimated that it cost $17.68 to store 1 gigabyte of data in an accessible database in 2003; today that figure is 7 cents. Any analysis that was too expensive by 2003 standards is now possibly a downright bargain. But it is really impossible to define big data without referring to the way it is used to solve business decisions. Therefore another development that was required for “big data” was an exponential increase in live, real time, computational power. This is the ability to crunch these huge volumes of data in real time for the instantaneous decision making required by today’s faster business pace.
The first major example of big data is the search engines like Google and Yahoo. If you are like me, you were simply blown away the first time you typed in an obscure term in a search engine only to see millions of results in less than 1 second. The search engines provided the business case for developing this big data technology, but the actual applications are endless now that the technology is here. This means that all business processes have access to a whole new world of possibilities. We can now change our whole focus from the hotel room availability to the guest and his willingness and ability to pay.
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Today, revenue management systems look at the rooms available against the forecasted demand. Then, as bookings build for a given date, the systems analyze whether the hotel is on pace to fill before the arrival date or not. If it will fill before arrival, it recommends raising the rates according to business rules in the systems. If not, it recommends lowering the rate to increase the booking pace. Notice that the focus is on the hotel and the number of rooms left to sell. This system evolved because the data was readily available and the algorithms to analyze it were technologically feasible to discover. But what if there were no bounds to the data and instantaneous decisions on whether to take or deny a booking based on live information about the guest, the hotel and the city and millions of other data points were available. In the big data world there may be correlations that are meaningful to maximizing revenues that are not immediately apparent. However, they come to light as the technology pours through the data and high-level analytics draws inferences from the data and tests it against known results.
Envision a future revenue management system that knows things like the weather forecasts, the recent online activity of the guest, the guest profile and persona related to the time of travel, time of booking, the mode of travel and the fare paid. It may also have in mind all the previous stays for this guest, such as when and how they were booked and how much was spent on room and even ancillary revenues. All of this will be compared to millions of other potential future, guest reservation requests to deliver a unique room and rate for a targeted guest experience. This offer will take into consideration the room availability, but will have more weight on the guest value and willingness to pay. This sounds like a distant dream, but the technology to do it exists today.
