Revenue Management Must-Haves: Demand Forecasting

Effective demand forecasting can feel almost magical in the way it’s able to examine past behaviors in order to predict future ones. It’s not magic, though… It’s data.

Understanding how demand forecasting works — and how it can positively impact your revenue management strategy — is essential for any revenue manager interested in driving increased revenue and profitability.

How does a revenue management system help define customer segments?

Different customer segments have different booking and staying patterns. For example:

  • Corporate customers typically stay on weekdays and generally have short booking windows.
  • Some people only seek out extended stays or serviced apartments due to their lifestyle.
  • Leisure transient guests have different patterns and often opt for weekend or week-long travel.
  • Groups and tours will have their own attributes, and their individual preferences and behavioral patterns should be considered.

Here’s the crux: those preferences can change. Your customers are unique, dynamic people — over time, their behaviors evolve. When it comes to keeping up with these changes, there’s one tool all hoteliers can rely on: the RMS.

What difference does demand forecasting make in revenue management?

An advanced RMS uses complex analytics to crunch disparate data sets to accurately forecast customer demand patterns while noting evolving trends for the future. The problem with most legacy revenue management solutions is they try to fit one demand forecasting approach to all the different customer segments and channels.

At IDeaS, complex analytics are used to automatically review the distinct patterns in each property, segment and channel – determining which forecasting model will best fit these patterns from of a wide variety of models. Within a given property, different segments may have different patterns – requiring different forecasting methodologies.

To keep up with evolving customer behaviors, the system regularly reviews properties and segments to see if any patterns are changing and refreshes the forecasting model as appropriate. The result is a more accurate and dependable forecasting process rather than the inherent limitations of a single forecasting approach.

Does your RMS function in a silo or does it have the ability to capture broad and nuanced customer demand patterns? If you long for more, it’s time for an upgrade.

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