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"This is a great book, and it will be in my stack of four or five essential resources for my professional work." -Ralph Kimball, Author of The Data Warehouse Lifecycle Toolkit
Mastering Data Mining
In this follow-up to their successful first book, Data Mining Techniques, Michael J. A. Berry and Gordon S. Linoff offer a case study-based guide to best practices in commercial data mining. Their first book acquainted you with the new generation of data mining tools and techniques and showed you how to use them to make better business decisions. Mastering Data Mining shifts the focus from understanding data mining techniques to achieving business results, placing particular emphasis on customer relationship management.
In this book, you'll learn how to apply data mining techniques to solve practical business problems. After providing the fundamental principles of data mining and customer relationship management, Berry and Linoff share the lessons they have learned through a series of warts-and-all case studies drawn from their experience in a variety of industries, including e-commerce, banking, cataloging, retailing, and telecommunications.
Through the cases, you will learn how to formulate the business problem, analyze the data, evaluate the results, and utilize this information for similar business problems in different industries.
Berry and Linoff show you how to use data mining to:Retain customer loyaltyTarget the right prospectsIdentify new markets for products and servicesRecognize cross-selling opportunities on and off the Web
The companion Web site at http://www.data-miners.com features:Updated information on data mining products and service providersInformation on data mining conferences, courses, and other sources of informationFull-color versions of the illustrations used in the book.
In their critically acclaimed book, Data Mining Techniques, Michael Berry and Gordon Linoff showed readers how to use data mining techniques to improve marketing and sales. In their new, book they take readers to the next level with a series of step-by-step lessons, built around 20 real-world cases illustrating how they used their techniques to solve problems.
Data Mining in Context.
Why Master the Art?
Data Mining Methodology: The Virtuous Cycle Revisited.
Customers and Their Lifecycles.
THE THREE PILLARS OF DATA MINING.
Data Mining Techniques and Algorithms.
Data, Data Everywhere...
Building Effective Predictive Models.
Taking Control: Setting Up a Data Mining Environment.
Who Needs Bag Balm and Pants Stretchers.
Who Gets What? Building a Best Next Offer Model for an Online Bank.
Please Don't Go! Churn Modeling in Wireless Communication.
Converging on the Customer: Understanding Customer Behavior in the Telecommunications Industry.
Who Is Buying What? Getting to Know Supermarket Shoppers.
Waste Not, Want Not: Improving Manufacturing Processes.
The Societal Context: Data Mining and Privacy.
Since writing Data Mining Techniques, much as changed. We have now founded our own company, Data Miners, so we can focus exclusively on data mining. Data Miners is dedicated to a vision of data mining that puts as much emphasis on understanding as it does on model results, and as much emphasis on process as it does on technology.
For those readers who have not had the experience, leaving the regularity of a paycheck to work independently is, shall I say, a fascinating and sometimes traumatic experience. It has given us the opportunity to learn first-hand about business and the business problems facing our clients; about different approaches to allocating budgets and choosing vendors; and so on. It has also given us the opportunity to partner with the leading data mining vendors, and to work with some of the top people in the field.
As our families and friends have asked us on more than one occasion, why would we take time out to work on a second book? The answer is simply that Mastering Data Mining needed to be written. The field of data mining has been changing rapidly over the past few years, and we want to address the needs of practitioners, on both the business and the technical sides.
To see how quickly the data mining market is evolving, we have only to look to the business pages. During the time it took to write thisbook, we witnessed a number of mergers and acquisitions that spoke eloquently of the burgeoning role for data mining in customer relationship management and e-commerce:
The signs are easy to read-whatever the problem, data mining is becoming part of the solution. Done well, data mining can indeed solve many problems, but doing it well requires understanding the entire process. This understanding comes from both successes and failures of earlier projects. Each project is a learning experience. In this book, we take you through many of our own projects in order to share the lessons we have learned.
Mastering Data Mining addresses data mining in context. Part One looks at the business context. These four chapters answer questions such as: Why is data mining important? What is a successful approach to data mining? The last chapter in this set looks at customers and customer relationship management. Although data mining is applied through a myriad of fields, it is in the area of customer relationship management that it garners the most press.
Part Two of the book looks at data mining from a technical perspective. One chapter reviews data mining techniques (covered in much more detail in our earlier book). Another looks at data, and a third on good modeling practices. This chapter, in particular, is very important-it embodies many of the lessons that we have learned through the years.
Part Three is the most important-and the longest-part of the book. These are case studies in data mining. Although all the case studies are in business, they range over many facets, from the exploration of hundreds of gigabytes of data, to predicting the next banner ad to display to a Web banking customer, to improving the printing process. All the case studies discuss the relevant business problems, as well as showing the technical approach, the data used, the results (where possible), and lessons learned.
The final chapter looks at data mining in the broadest context-that of society. Data mining has underscored some of our fears of "big brother" and other threats to privacy. Michael and I tend to look at the benefits that data mining offers. However, it is important to understand the issue of privacy, particularly in the context of analyzing data.
Throughout the book, we have stressed practical applications of data mining. We hope that this book helps you master the art.