There has never been a more complex time to practise revenue management in Asia: Changing guest behaviours, the evolving and increasingly intricate nature of distribution channels and an abundance of data to make sense of, all add challenges to a revenue manager’s role.

Additionally, deciding on the right rate structures, forecasting demand, managing groups and lengths-of-stay are introducing even more difficulties for revenue managers, especially in the presence of intensifying competition.

Fortunately there are some significant positives to come from the apparent information overload that revenue managers seem to face these days.

In the past, revenue management systems (RMS) were the biggest data owners within a hotel, with two or more years of detailed reservations data consumed by the system, across a variety of room types, customer segments, length of stays and more.

With this data, RMS analytics generated billions of forecasts used for further optimisation, subsequently producing billions of pricing, availability and overbooking decisions. However, new methods for collecting and analysing data today have resulted in completely new data sets that can also assist with making optimal pricing decisions: competitor price data, industry data, web data, and social media data.

Big Data is here to stay but how can it help a hotel improve its revenue performance? Big Data’s value comes both in informing revenue managers as they set overall pricing strategies, as well as driving automated solutions and analytics. But when it comes to taking advantage of this information, new analytical approaches are necessary.

Not all data is the right data

In many cases, much of the Big Data begging to be incorporated in RMS is demand-related data; that is, data that is assumed to improve forecast accuracy. Some examples of Big Data having an impact on forecast accuracy are by improving price-elasticity estimations, recommending better competitive pricing decisions, changing the objective (profitability vs. revenue) used by optimisation algorithms, and adding the user-centric information that guests actually use in selecting hotels.

RMS technology must incorporate Big Data into analytics not just because the data is available, but because the addition of more data is statistically significant in the RMS process. RMS providers have to be extremely careful when continuing to add more and more data into the RMS forecasting algorithms as this may not always be good thing and adding data for the sake of adding data could dilute a forecast’s effectiveness.

Look at data that enhances the optimisation process

More data is better only when the RMS analytics improve price-demand estimates, provide controls for your particular business mix and pricing strategy, and enhance the optimisation process. A good example of this is the use of rate shopping data for competitive pricing.

Revenue managers have long known that incorporating all of competitors’ prices rather than just primary competitors’ in their market place is not always the wisest pricing strategy. An analytical approach is necessary to determine which competitive properties are actually relevant to a customer’s willingness to pay and to the type of demand, in contrast to using all competitors rate information equally.

Read full article at: