To successfully compete, revenue management systems (RMSs) are an essential tool for effective and efficient room pricing. It’s no longer an issue of whether one is needed, but in which one will you invest. Yet, not all RMSs are created equal, especially in terms of forecasting.
That’s because many products on the market are rooted in solely leveraging historical property management system (PMS) data and regression modeling to provide pricing recommendations. It’s an outmoded approach because those systems only provide a partial picture, limiting the hotelier’s accuracy for room revenue forecasting.
Confusing, right? Don’t worry, we’ll explain.
Here’s what happens. Traditional RMSs use historical PMS data from a single property, such as last year’s occupancy and the room price on that day when creating this year’s pricing recommendation. They might combines that with pace and on the books data.
Unfortunately, that information is the equivalent of a crescent moon; it looks pretty but you’re only seeing a sliver. It’s the full moon that provides the complete picture. So, to get the full moon view, all valuable information must be revealed. Hoteliers must also understand the context in which this information is used. For example, perhaps an economic recession has recently lifted, or a major weather event is expected this year. Both factors greatly change how a hotelier would set a price today for the same room. Not just looking into the past, but more importantly the future.
This data gap occurs because traditional RMSs use regression modeling as a second process for calculating today’s room rates.
Basically, regression modeling uses past data to analyze how one specific factor (say, the economic recession) affects future pricing. However, all other variables stay consistent. That’s the crescent moon, a partial view.
For example, an RMS using regression modeling calculates the future rate under the assumption the recession is still affecting pricing power, lowering its forecast. That creates an erroneous conclusion demand will be down, and you’ll wind up selling rooms at a rate much lower than the typical consumer would be willing to pay. Ouch!
The Full Moon Forecasting Approach
The most effective RMSs today consider many variables the average revenue manager didn’t realize could or should be considered. For example, the LodgIQ RM platform collects and analyzes myriad market variables including the current supply and demand of hotels (and vacation rental properties) in the destination, room rates of direct and indirect competitors, historical room rates, flight patterns, meteorological patterns, local events, and more.
So rather than analyze a sliver of data, it’s all scooped up and analyzed by the RMS. Each data set is weighted for importance and relevance before a more accurate pricing recommendation is determined. This results in more rooms sold at an optimized price. The name for this process is machine learning, a fascinating science you can learn all about in a podcast with LodgIQ’s CTO Somnath Banerjee. Listen to that here.
A machine learning based RMS provides smarter pricing recommendations using highly sophisticated data analysis processes. And it gets more effective over time because as more data is collected it’s incorporated into a pricing forecast.
So, while the crescent moon may be nice to look at, it’s the full moon that exposes you to the most moonlight.
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