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Data mining can provide a window into customers' behavior-if it's handled correctly.
In the hotel industry, knowing your guests-where they are from, how much they spend, and when and on what they spend it-can help you formulate marketing strategies and maximize profits. Fueled by the proliferation of centralized reservation and property-management systems, hotel corporations accumulate large amounts of consumer data. This information can be organized and integrated in databases that can then be tapped to guide marketing decisions. However, identifying important variables and relationships located in these consumer-information systems can be a daunting task. The relatively new process known as data mining can be instrumental in overcoming such obstacles.1 From stores of information, data-mining technology extracts meaningful patterns and builds predictive customer-behavior models that aid in decision making.2
Data mining is a largely automated process that uses statistical analyses to sift through massive data sets to detect useful, non-obvious, and previously unknown patterns or data trends.3 The emphasis is on the computer-based exploration of previously uncharted relationships (i.e., using "machine learning" methods that typically require only limited human involvement).4 Without data mining, valuable marketing insights about customers' characteristics and purchase patterns may remain largely untapped.5 By uncovering such previously unknown relationships, managers have the potential to develop a winning marketing strategy that increases their hotel's bottom line.
Hotel managers understand the importance of adapting to the changing business environment not only to remain competitive, but merely to survive. As a result, technology has become a large and growing expense for many hotel corporations. Under such a technology framework, data mining is a valuable competitive tool being adopted by hotel corporations in an effort to create customer value. However, given the importance and complexity of data mining, senior hotel managers report a low level of understanding about data mining's capabilities, how it works, and what value this technology contributes.6 The purpose of this paper is to educate hotel managers about the benefits and application of data mining on the properties they oversee.
Data Mining vs. Statistical Modeling
Data mining differs from traditional statistical modeling in a variety of ways. Data mining focuses on machine-driven model building, while statistical modeling stresses theory-driven hypothesis testing. Data-mining techniques build models, whereas classical statistical tools are supervised by...