NB: This is an article from IDeaS
In this post, demand forecasts take center stage. If your revenue management system (RMS) uses a single forecasting approach for all of your customers, you’re missing out on a huge opportunity.
Think about being a parent for a moment. There are things your kids have in common, but they likely have their own unique preferences and personalities. From food to clothes to bedtime routines, what works for one probably varies from another. Embracing these differences and finding solutions that work for the entire family is key to a happy, productive home.
Now apply that thinking to hotel guest segments.
Different customer segments have different booking and staying patterns. For example, corporate customers typically stay on weekdays and generally have short booking windows. Leisure transient guests have different patterns and often opt for weekend or week-long travel. Groups and tours will have their own attributes, and just like those darn kids, their individual preferences and behavioral patterns should be considered.
And here’s the crux: those preferences can change. The methods you used for your toddler won’t work with your feisty teenager. Just like kids change, so do customers – over time, their behaviors evolve. However, unlike a frustrated parent trying to decipher their kids’ new codes, hoteliers have a tool that does it for them: the RMS.
An advanced RMS uses complex analytics to crunch disparate data sets and accurately forecasts customer demand patterns while noting evolving trends for the future. The problem with most legacy RMS solutions is they try to fit one demand forecasting approach to all the different customer segments and channels. This single forecasting approach is extremely limiting and not an accurate way to predict the unique behaviors of many different segments.
At SAS and 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?
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