For some time, the biggest buzz in business has been around the advent of big data and its application to hospitality and specifically, Revenue Management Systems (RMS).

Historically, RMS were already the biggest data owners within hospitality, with two or more years of detailed reservations data consumed by the RMS, across a variety of room types, customer segments, length of stays, etc.

With this data, RMS analytics generated billions of forecasts used for further processing within RMS optimization, subsequently producing billions of pricing, availability and overbooking decisions. That is to say, Big Data existed in RMS before it was even known as “Big Data.”

The Case for Relevance of Big Data: Think of Your Competitive Set

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. 

Revenue Managers have long known that incorporating all of their competitors, rather than their primary competitors, in their market place is not always the wisest pricing strategy.

RMS should incorporate analytics to determine which competitive properties are actually relevant to customer’s willingness to pay, in contrast to using all competitor rate information equally.

Use of Customer-Centric Data in Hospitality

As we recently covered how user generated content is re-shaping hotels’ revenue management strategies, reputation-related big data is growing in importance within hospitality.

Access to online reputation data has become easier for hotels and today there are many RMS providers that display a property’s reputation and rate, in relation to their competitive set, for decision support. It is even better when this data is incorporated into demand modeling and optimization processes, rather than utilized as post-decision support.

This is a great example of how to use customer-centric data for forecasting demand as a function of price, when demand is also a function of your online reputation performance.

The Case of Regrets and Denials in Incorporating Big Data: Simply Unreliable

The case for using regrets and denials for revenue optimization has recently caused significant hype.

As we discussed in great detail in our recent white paper about regrets and denials there is an important distinction between “denials” that are due to unavailability and “regrets” that are due to price or other factors, and many reservation or booking systems are unable to automatically capture the difference between regrets and denials.

There are RMS providers who claim to use brand.com data to partially capture data but, given that brand.com makes of up only 27% of the reservations for transient nights, this data becomes unreliable for demand forecasting. This partial data captured on brand.com uses only the unqualified transient demand without sufficient regard for demand for wholesale, group, corporate negotiated, and unqualified business etc.

Add to that the realities around how high the look-to-book ratio is, it is not at all reliable to forecast without the cross-usage of additional websites or data from multiple visits per buyer. That is primarily why leading data scientists refer to regrets and denials as “dirty data.”

In Part 2 of our series we’ll take another look at the use of Big Data in Revenue Management, and specifically discuss profit vs. revenue maximization, use of weather and airline data, and the three questions every revenue manager should ask when presented with a claim about use of Big Data in Revenue Management analytics.

Download our white paper on this topic.

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