William wishes to thank Stuart Mullins, senior data warehousing architect at CSI, for his contribution to this month’s column.


Inside the hotel, there is, or could be, business intelligence (BI) all around you. The market has shifted from one focused solely on providing the traveler with a clean room and the necessary amenities to one focused on capturing repeat corporate business through discounts and loyalty programs. With occupancy rates remaining fairly high amid a supply shortage, hotel managers are tasked with driving more revenue, or revenue per available room (REVPAR). The wave of acquisition and consolidation has created new opportunities and demand for BI products and services across the industry.


Many functions, such as revenue management, involve highly manual processes resulting in replication of data across desktops and nonstandardized techniques for creating forecast models in spreadsheets. Such issues lead to higher costs and less agile systems. Many of the challenges faced by hotel managers, analysts and executives could be enormously improved by the advancement of BI technologies.


Rate optimization is perhaps the single most important function in the hotel organization. Given limited space within any particular hotel, managers are tasked with determining the optimum price points that will maximize revenue and occupancy. Often, it is more profitable to increase the rate five dollars than to sell additional rooms at a discounted price. Hotel managers must understand this demand curve.


Data mining techniques can be used to develop models that will reveal the factors affecting price elasticity. We can feed booking data through the models to forecast bookings and make the appropriate rate adjustments. For example, the model may indicate that a hotel historically has filled by the Tuesday before Memorial Day weekend. Knowing this ahead of time might allow them to adjust their rates earlier and increase their REVPAR for the weekend. Traditionally, these types of adjustments have been analyzed and made manually in the reservation systems. With operational BI, we use the analysis to automatically feed the rate adjustments back into the operational systems. This can happen automatically each day based on the availability of data. Data mining and operational BI can save time and effort spent by analysts to determine price points as well as provide real-time response to changes in demand.


Deriving customer value is now about understanding different segments and the profitability that comes from those segments. The products (rooms) within each segment have been commoditized. Guest loyalty is often driven by the program which pays out the highest rewards. An example would be business customers versus leisure customers. We want to understand the demand curves for each segment and how they are affected by changes in rates. These segments can be extended across various demographic factors as well as the types or source of business.


A master data management (MDM) program led by a customer data integration (CDI) initiative is the first step in tackling these complex issues. Customers have been traditionally defined in hotel systems by individual stays. Those that are loyalty-program members are easier to identify and track, but often there are important details missing from the information, such as the customer’s history prior to joining the loyalty program. In addition, merger and acquisition activity has created the need to view customer activity across brands and across segments (i.e., limited service versus full service).


By developing a single repository for customer details and sharing that data across platforms, the hotel organization can take a giant leap in the organizational knowledge of customers and their behavior. This impacts the ability to market and price certain customer segments as well as run targeted marketing programs.


Once the customer integration is in place, various data mining activities and analytics can be performed to track the opportunity and effectiveness of marketing and loyalty programs. As loyalty programs are very costly to administer, CDI can provide the basis for an analysis of the program and its profitability. This type of analysis would also allow us to understand the profitability of third-party bookings, such as those provided by travel Web sites and travel agents. Perhaps the individual booking occurs at a loss, but the lifetime value of the customer gained is profitable.


Master data initiatives should be developed around other data assets of the hospitality organizations, such as hotel properties, geography, sales hierarchy, competitors, travel agents, airlines and other third-party marketing programs, suppliers, contractors, franchisees, etc. The MDM hub will facilitate data transmission through the organization and subsequently, the sharing of knowledge.


The hospitality industry has historically been rather sensitive to changes in the economy as a whole. With the growth that comes through mergers and acquisitions as well as franchising, globalization and development, the players in this industry need to be attuned to the performance of their hotels and the needs of their customers. Investment in information management initiatives, data warehousing, BI and MDM are the keys to securing the organizational knowledge needed to navigate these waters. How many keys do you need?


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