Which came first: the forecast or the price?
This is right up there with the philosophical riddle, “which came first, the chicken or the egg”. As a metaphor it provides lively conversation and debate, even when the answer may not be as straight-forward or absolute as one would hope.
NB: This is an article by Blake Madril, Revenue Technology Strategist at IDeaS
The technology hotels can use to optimize their revenue strategy grows more aware, adaptive and sophisticated each day, so its approach to pricing and forecasting is more pertinent—and available—than ever before. Understanding how pricing and forecasting impact not only one another, but your hotel’s profit potential, is imperative for savvy hoteliers who seek quick time to value.
As hotels draw a close to budgeting season, I’m reminded of a much more rudimentary way of building a revenue strategy. For those of you once responsible for a hotel budget, did you ever receive marching orders like this: “We need to hit X revenue with X occupancy at X ADR. Now, go and figure out how to achieve that for each month, day, segment and so on.”
Budgets often defined strategy and the budgets themselves were not derived from analytics so the potential for optimal revenue was never known. Hotels may create a monthly occupancy forecast as they get into the 90 day window, but that forecast is now constrained by the budget that helped define the initial strategy. Unfortunately, there are many hotels where this practice may still be in place because of the “if it ain’t broke don’t fix it” mentality, or the lack of time or resources to investigate alternatives.
For those of us not content with the status quo, we seek more innovative ways to help guide our strategy. As a result, we rely on an unconstrained demand forecast. This is also known as the ability to forecast the demand for a hotel uninhibited by the capacity of inventory.
The reason it’s so important to “unconstrain” the demand is quite straightforward. Simply put, the strategy for a 100 room hotel with 300 units of demand is very different than the strategy for the same 100 room hotel with 100 units of demand. Your actuals or constrained forecast may look similar on a given day but they don’t represent the whole picture.
How can you determine the optimal mix of business without knowing what the potential business is? Optimizing your hotel’s available capacity is what revenue management is all about, and without a foundational unconstrained demand forecast, you will never solve the chicken-egg conundrum. That’s why forecasting is so critical but this leads us to the other side of the same coin: price.
When hotels set prices they are effectively constraining their demand. They are making a strategic business decision to accept the business at that price, or perhaps at a known discount of that price. This means price does, in fact, have an impact on forecast but how does one know if it is a positive impact or more important, the optimal impact?
It’s true, with machine-learning and sophisticated algorithms, that a solution can be trained to learn what those impacts are. But when thousands of consumers come across your product every day with dozens of rate plans across dozens of room types for any number of days into the future, it seems like a lot of decisions to first get wrong in order to get them right.
If you first set a price and create a forecast off that price, you commit the same crime as when you create a forecast which then dictates price to achieve revenue goals. Neither approach gets you to revenue nirvana. So, what’s the answer to the chicken-egg quandary?
The answer is they arrived together or at least with today’s adaptive machine-learning technology they should. Both are strongly tied to one another and neither should be deterministically selected with rules or gut-instinct.
Revenue technology that truly understands this relationship can holistically price guest rooms while simultaneously adapting the forecast to account for that price. This means that intelligent revenue management solutions know the impact a pricing change may have on a forecast and conversely, the impact a change in the forecast should have on price.
When you approach these key components of revenue management with a holistic automated business optimization solution—one that leverages clean historical data and relevant forward-looking demand intelligence—the proof will be in greater stay lengths, stronger shoulder and quiet nights, higher average daily rates, more sellout nights and overall revenue growth. Go beyond your gut, your spreadsheet and your manual rules. Tap into the technology that delivers value to your bottom line each and every day.