Revenue Management in the Age of Big Data:
Is More Data Equal to Better Data?
By Paul van Meerendonk Director of Advisory Services, IDeaS Revenue Solutions | March 01, 2015
For some time, the biggest buzz in business has been around the influx of Big Data and its application to hospitality – and specifically to revenue management systems. Historically, revenue management systems (RMS) were already the biggest data owners within the hospitality enterprise, 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 optimization, subsequently producing billions of pricing, availability and overbooking decisions. That is to say, Big Data existed in revenue management systems before it was even known as "Big Data."
But today's Big Data story tends to focus on the increased variety of data sources available for revenue management, including data from social media, reputation management engines, web traffic sources, weather, and information related to your competitors. This data is increasingly easy to access or purchase, and arguments can be persuasive that all of these new data sources should be incorporated into the revenue management process. "More is better," according to some in the industry. And that is true in a universe where every new data element is statistically relevant – where new data improves forecast accuracy and increases property profitability by improving RMS pricing decisions. "More data? It can't hurt!" some loudly proclaim. But, in fact – from a statistical perspective – sometimes it can.
Relevance Is Key in Big Data: Consider the Case of Rate Shopping Data
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 optimization 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 their competitors' prices rather than their 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 competitor rate information equally. In the absence of statistical relevance, inclusion of more rate shopping data can significantly impact your competitive and brand positions, and diminish your pricing strategy.
Use of Customer-Centric Data in Hospitality
Recent innovations in RMS technology have also shown that reputation-related Big Data is growing in importance within hospitality. This growth stems from the various research (including studies by Kelly McGuire and Breffni Noone, and by Chris Anderson ) which indicated that online reputation and price are two of the most important considerations for guests to make their booking decisions. Access to 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 optimization processes, rather than merely reporting it or utilizing 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.
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