Exclusive Feature: 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.

A key evolution-in-the-making in revenue management technology in an age of Big Data is the optimisation of profitability rather than revenue. Profitability optimisation can be undertaken by obtaining ancillary revenue and cost data to generate profit contributions by various customer segments. Ancillary revenues range the spectrum of food and beverage revenue streams as well as golf, spa, events and more.

Cost or margin data is required across each customer segment when the RMS maximises total profitability, since certain customer segments, while contributing extra revenue, can also incur additional variable costs. This data can be shared to other departments within a hotel like marketing and operations to help identify those people and activities which help maximise profits.

Reputation matters in an age of Big Data

Recent innovations in RMS technology have also shown that reputation-related Big Data is growing in importance within hospitality. This growth stems from research which indicates that online reputation and price are two of the most important considerations for guests to make their booking decisions.

Reputation-related data has become more available to hotels from reputation vendors, and today there are many RMS providers that display a property’s reputation and rate in relation to their competitive set for decision support. In the case of online reputation data, the key is incorporating it into demand modeling and optimisation processes, rather than merely reporting it or utilising it as post-decision support.

The insurmountable amount of structured and unstructured (such as sentiments) reputation data makes it a very complex and unsustainable process for revenue managers to use it as an ongoing post-decision support mechanism. Thus in the case of incorporating customer-centric data in pricing decisions, revenue managers must consider demand as a function of price, where the demand is also a function of the specific customer-centric data type to be added to the mix.

When even the weather matters

As an input to hospitality demand models, weather data may improve the short term demand fit if, and only if, its immediate impact can be assigned to a particular market or property. Imagine a tsunami hitting Hong Kong and closing down all highways and airports. Is it good or bad for the hotels in the area?

The answer in an age of Big Data: it depends. If all departing guests have nowhere to go and extend their stays while the number of expected arrivals are low â€" it might be good for some of the hotels as the expected occupancy has increased. However, if all departing guests have left and arrivals have stalled it would be really bad for most area hotels. Not only does the impact of such weather events depend on the specific circumstances, but also it will vary greatly for an airport property versus a property far away from the airport.

Incorporating weather patterns into a hotels short term forecasts and demand models would have been unthinkable in years past. These days operating in an era of Big Data and with new technologies available, weather data can actually improve the short term demand fit for a hotel if its immediate impact can be assigned to a particular market or property. However, all of this is to say weather data trends may be impactful to travel patterns at large but their relationship to business or leisure bookings at a particular location is still loosely coupled.

In this day of easy access to data and progressively lower costs to store and process this data, it is easy to believe and act upon the idea that RMS should incorporate the newest available data immediately. But, like any investment decision, it is a decision that should be considered carefully, ensuring that the hotel’s pricing strategy and decisions are improved because of these additions. 

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Paul van Meerendonk is manager of hospitality consulting at IDeaS, a SAS company. He is an industry expert with multi-year revenue management, pricing and distribution experience across several continents. 

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