Ms. Noone

Revenue Management

New Data for Revenue Management: Adding Value or Adding Distraction?

By Breffni Noone, Associate Professor, School of Hospitality, The Pennsylvania State University

We have seen a major shift in the role of revenue management in the lodging industry from a very tactical orientation to one that is much more strategic in focus. What this means for revenue managers is that their job is transitioning from one where they are responsible for opening and closing predefined room rates such that the best combination of occupancy and rate is achieved for any given night, to one that is now much broader in scope. Revenue managers are often expected to contribute to, and provide analyses to support, decisions that are much more strategic in nature such as the development of market share, competitive positioning, new product and service development, and distribution channel management strategy.

At the same time, in this era of "big data", revenue managers have access to more data than ever before. The decreasing cost of data storage, coupled with increased processing power, also makes it feasible for revenue management systems to handle more data inputs and complexity. At face value, this access to, and the ability to handle, more data sounds very appealing. More data means more informed decisions, right?

We all know that many revenue managers already feel like they are experiencing data overload so, before adding any new data source into the equation, you've got to be pretty confident that it will sufficiently enhance decision making over and above the insights that current data inputs provide. On a related note, you've also got to carefully consider the level at which a potential data source can be most useful. Think for a moment about the differences between the "job" of a revenue manager and the job of a revenue management system. A good revenue management system supports tactical pricing decisions. It doesn't think strategically. It is optimized for tactical day-to-day revenue generation. A good revenue manager, on the other hand, monitors the work of the revenue management system, and then takes the short-term optimized outputs of the system, and uses them along with other data sources to support long-term strategy development. So when thinking about new data, you've got to think about where they have the potential to add the most value. Can the data be applied at a tactical level, feeding into a revenue management system to influence tactical pricing decisions, or are they best leveraged by the revenue manager in strategic planning?

Recently, there has been discussion among hoteliers and systems vendors around the introduction of a number of new data sources to support revenue management decisions, from weather forecasts to forward-looking demand data. Let's take a look at some examples of these potential data sources and consider their potential to support the revenue management function and the level at which they might best be applied.

Weather Forecasts

We all know that weather can impact demand. Consider how we explicitly account for severe weather events (hurricanes, snowstorms, etc.) in a typical revenue management system. We flag those events as exceptional or special events such that, if a similar weather event is expected to occur in the near future, we can use historical special event data to estimate the effect of that event on demand. But should weather forecast data be routinely included as an input to a revenue management system? Think about the consumer for a moment. Unless the booking window on a hotel reservation is very short (i.e., a couple of days), the customer will only know, at best, what the average temperature tends to be at their destination of choice during their planned time of travel.

For example, I might decide to travel to Tuscany next July because I know that the weather tends to be good there at that time of the year. When I go to book a hotel room in, say, March, I will have no idea whether or not a thunderstorm is going to roll through while I am there. It is merely knowledge of average temperatures during July in Tuscany that I must rely on to guide my decision. Equally, for a hotel dealing with longer booking windows, chances are that weather predictions are not going to be adequately robust to include as an input to demand forecasting. In this instance, the best predictor of future weather is typical weather patterns in the past. And, arguably, these typical weather patterns are likely already captured by your revenue management system in the form of historical demand patterns. If, on the other hand, you are a hotel dealing with extremely short booking windows, where small day-to-day variations in temperature, rainfall or humidity, impact consumer choice, and you have access to weather forecast data that can be validated for accuracy, it may make sense to routinely include weather forecast data as an input to your revenue management system.

Data From Related Sectors

Changes in supply in other sectors can have a significant effect on hotel room demand. Non-routine adjustments in capacity in other sectors such as an airline dropping or adding flights to a given destination will likely impact overall hotel room demand, and disrupt market share for individual hotels, at that destination. This type of capacity adjustment information is arguably most useful at a strategic level, providing revenue managers an input to long-term demand and resource planning. Routine changes in capacity in related sectors (e.g., seasonal airline lift), on the other hand, are most likely already reflected in historical seasonal demand data, negating the need to explicitly incorporate these data as separate inputs to a revenue management system.

Forward-Looking Demand Data

Clearly, any insights into future demand can provide a valuable input to your revenue management system to support tactical pricing decisions. However, the feasibility of incorporating forward-looking demand data into your revenue management system depends specifically on whether or not you can access this forward-looking data for your competitive set. Unless this data can be accessed at the level of your competitive set, it cannot be directly mapped to existing competitive set data in your revenue management system. However, if you cannot access this information at the competitive set level, any available market and/or segment level data can be leveraged at a strategic level by revenue managers as an input to long-term demand forecasting, and customer segment development decisions.

Web Shopping Data

In order to establish optimal price points, a hotel must forecast unconstrained demand. Unconstrained demand is the level of room nights that guests would reserve in the absence of any constraints, whether they are imposed by the hotel (e.g., minimum length of stay, rate controls) or inventory limitations (e.g., room type sold out ). A simple calculation of unconstrained demand is the addition of realized demand (i.e., bookings) and latent demand (i.e., denials where reservation requests turned down by the hotel because the room type desired or rate are not available). Sounds easy to calculate, right? Think again. Consider regrets where the guest's request can be accommodated but he or she declines to book. Regrets are not part of latent demand. Yet, it is not always a straightforward task to distinguish between denials and regrets. In his classic article, Eric Orkin provides powerful examples of how difficult it can be to distinguish between the two (see: Wishful thinking and rocket science: the essential matter of calculating unconstrained demand for revenue management, Cornell Hotel and Restaurant Administration Quarterly, 1998).

Despite the challenges of accurately capturing denials and regret data, many hotels have used central reservations data to track these data. More recently, given the significant extent to which consumers look and search for hotels online, the web has provided hotels the opportunity to capture volumes of consumer shopping data. Hotels can now track the number of customers that search for a hotel room but do not complete an online purchase. While this data is arguably an indicator of demand (indeed, people have to look before they book!), does this lost business data provide a good indicator of latent demand? Maybe, but only if you can be sure that you are accurately able to distinguish denials from regrets. Assuming that you can, this online denials data may only be a good indicator of latent demand if the majority of your bookings are made online. Different distribution channels can have very different denials distributions so inferring denials from web shopping data, particularly where a significant amount of a hotel's bookings come from other distribution channels, may lead to a very inaccurate estimation of latent demand. A more robust approach might be to use a statistical approach to deriving unconstrained demand as advocated by Orkin.

So, let's say that you decide that you won't use web shopping data as an input to your revenue management system to derive unconstrained demand, does that mean this data is not useful? Certainly not. Rather, a revenue manager can leverage that data to support his or her assessment of conversion rates and the development of reservation recovery strategies.

Reputation Data

Research that I have conducted with my colleague Kelly McGuire of SAS (see the article that we co-authored in this issue) demonstrates the strong influence that a hotel's reputation can have on consumers' hotel choice behavior. If consumers use reputation-related data with price to choose among hotels, there is arguably great value in having your revenue management system use your reputation data and that of your competitive set, along with price, to derive price recommendations. Since price is influenced by reputation, addition of reputation data to short-term pricings decision may provide the opportunity to garner a price premium over the competition. In addition to influencing tactical pricing decisions, a visualization of market position in relation to competitors' rate and reputation performance can provide a valuable input to long-term pricing and competitive positioning strategy development.

So, what can we learn from these examples? Don't assume that "more" is always better. Every market and hotel is different. Critically evaluate all potential data sources in the context of your specific property or properties to determine if they can truly add value - or whether they will just add noise - to revenue management decisions. Also ask yourself where the data can provide value. Is that at a tactical level as an input to your revenue management system or can the data be leveraged as an input to strategy development?

Regardless of the level at which you apply a data source, you need to think about how you are going to validate its accuracy. You also need to seek quantitative evidence, when considering the incorporation of a new data source into your revenue management system, that its addition improves, and not diminishes, system performance. Finally, while data storage and processing costs have gone down, the acquisition of any new data source will come at a cost, both in terms of technology and human capital. Weigh the added value against the costs before taking the plunge.

Breffni M. Noone is an Associate Professor at the Pennsylvania State University School of Hospitality Management. She teaches courses in revenue management and service operations management. Before joining the Pennsylvania State University, she was a visiting professor in the School of Hotel Administration at Cornell University, and was on faculty at the Dublin Institute of Technology, Ireland. Dr. Noone earned her doctorate from Cornell University. She also holds an M.B.S. from Dublin City University, Ireland and a B.Sc. (Mgmt.) from Dublin University, Trinity College, Ireland. Dr. Noone has been honored with many teaching, research, and advising excellence awards. Ms. Noone can be contacted at 814-865-7128 or bmn2@psu.edu Please visit http://www.psu.edu for more information. Extended Bio...

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