Firms collect consumer responses from telephone, mail, and online surveys. They scan data from retail sales. They record business transactions and log text from focus groups, online bulletin boards, and user groups. Spurred on by lower costs of data acquisition, storage, retrieval, and analysis, business databases grow larger each day. Business managers work in a world in which data are plentiful and well-formulated theories rare. This is a world well suited to data and text mining. Data and text mining represent flexible approaches to information management, research, and analysis. They are data-driven rather than theorydriven. They rely upon powerful computers and efficient algorithms. Relatively new and little understood by business and marketing managers, data and text mining are important enough to require an adequate introduction. That is the reason for this book. This book advocates a disciplined approach to data and text analysis. It is through the development of meaningful models that data and text mining contribute to information management, research, and analysis. Models should fit the data, yielding small errors of prediction and classification. Models should be as simple as possible because simple, parsimonious models are easy to understand and use. Model selection in data and text mining is a matter of striking the proper balance between fit and parsimony. When analysts strike the proper balance, they develop models with explanatory power. To serve as a business introduction to data and text mining, a book cannot rely upon statistics and computer algorithms alone. A business book must give students a feeling for the work of data and text mining and how it serves business needs. This book focuses upon business applications, including customer relationship management, database marketing, consumer choice modeling, market segmentation, market response modeling, sales forecasting, and the analysis of corporate databases. It reviews traditional and data-adaptive methods and shows how the results of data and text mining can be used to guide business decision making. The book provides an introduction to data and text mining methods and applications. It shows how to use tools for data manipulation and integration, statistical graphics, traditional statistics, and data-adaptive methods. It shows output from data and text mining programs and reviews the literature, citing relevant books and articles in business, marketing research, statistics, computer science, and information management. The book draws upon a rich set of business cases and data sets described at length in Appendix A. Cases promote experiential learning; students learn about data and text mining by doing data and text mining. Case documentation and data sets have been placed in the public domain, available on the Web site for the book. Additional cases and discussion are provided in Miller (2004). Data and text mining offer great promise as technologies for learning about customers, competitors, and markets. But having the ability to organize and analyze large quantities of data does not excuse us from our obligation to conduct research in a responsible manner. Appendix B reviews the important topic of privacy in business research. Recognizing that business and research professionals have strong feelings about computing software and systems, our coverage of data and text mining topics is sufficiently broad to accommodate users of many systems. The Web site for the book provides data, documentation, and examples for use with various software systems. Examples in the book were prepared using S-PLUS, Insightful Miner, R, and Perl. Many leading researchers in statistics use S-PLUS and R, providing a substantial body of public-domain code for data mining applications. The Perl user community provides an extensive set of utilities for text processing. By relying upon public-domain systems and code, we can do more work for less cost, and we can write programs that run on many computer platforms. Both R and Perl, for example, have Apple Macintosh OS X, Microsoft Windows, Linux, and Unix implementations. The book can serve as a textbook in business, marketing research, statistics, management information systems, computer science, information science, quantitative methods, decision science, and operations research. It may be used as a standalone introduction to data and text mining or as a technical reference for practitioners. Written in a non-technical, nonmathematical style, the book is accessible to many readers. I have many people to thank for making this book possible. Wendy Craven of Prentice Hall was a key proponent of the book throughout its development, always willing to listen to ideas for making the book relevant to a wide range of business disciplines. Rebecca Cummings and John Roberts of Prentice Hall assisted in the final stages of production. Special recognition is due to Dana H. James for copyediting and indexing and to Amy Hendrickson, 'Ij3Xnology, Inc., for her assistance in the development of IfEX class and style files. Data entry, proofreading, graphics, and electronic typesetting services were provided by Teresa Cheng, Kristin Gill, and Krista Sorenson. Kim Kok, Giovanni Marchisio, Jeff Scott, and Michael Sannella of Insightful Corporation provided advice and technical assistance in the area of text mining. Hung T. Nguyen helped in writing the supplement for instructors. Reviewers and colleagues provided many helpful suggestions. For their feedback and encouragement in the reviewing process, I thank Lynd Bacon, Jerry L. Oglesby of SAS Institute Inc., David M. Smith of Insightful Corporation, and Michel Wedel. Most of all, my wife Chris and son Daniel stood by me in good times and bad, tolerating my unusual writer's lifestyle. Thomas W. Miller Madison, Wisconsin