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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, by Michael J. A. Berry, Gordon S. Linoff
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- Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems
- Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support
- The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining
- More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining
- Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis
- Sales Rank: #1299541 in Books
- Published on: 2004-04-09
- Original language: English
- Number of items: 1
- Dimensions: 9.28" h x 1.38" w x 7.40" l, 2.05 pounds
- Binding: Paperback
- 672 pages
From the Back Cover
The unparalleled author team of Berry and Linoff are back with an invaluable revised edition to their groundbreaking text
The world of data mining has changed tremendously since the publication of the first edition of Data Mining Techniques in 1997. For the most part, the underlying algorithms have remained the same, but the software in which the algorithms are imbedded, the databases to which they are applied, and the business problems they are used to solve have all grown and evolved. With that in mind, Michael Berry and Gordon Linoff–the leading authorities on the use of data mining techniques for business applications–have written a new edition to show you how to harness fundamental data mining methods and techniques to solve common types of business problems.
Berry and Linoff’s years of hands-on data mining experience is reflected in every chapter of this extensively updated and revised edition. They discuss core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis. In addition, they provide an overview of data mining best practices. Each chapter covers a new data mining technique and then immediately explains how to apply the technique for improved marketing, sales, and customer support. The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining for both business professionals and students.
With more than forty percent new and updated material, this second edition of Data Mining Techniques shows you how to:
- Create stable and accurate predictive models
- Prepare data for analysis
- Create the necessary infrastructure for data mining at your company
The companion Web site provides exercises for each chapter, plus data that can be used to test out the various data mining techniques in the book.
About the Author
MICHAEL J. A. BERRY and GORDON S. LINOFF are the founders of Data Miners, Inc., a consultancy specializing in data mining. They have jointly authored some of the leading data mining titles in the field, Data Mining Techniques, Mastering Data Mining, and Mining the Web (all from Wiley). They each have more than a decade of experience applying data mining techniques to business problems in marketing and customer relationship management.
Most helpful customer reviews
7 of 7 people found the following review helpful.
Data Mining book you should read first
By Keith McCormick
Be careful, the first edition is MUCH older. Make sure you get the current 2004 edition.
There are most recent books, but this one is still worth reading first. This is especially true is you are an analyst. Managers of analysts might enjoy Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart or Competing on Analytics: The New Science of Winning, but analysts will need much more detail.
This the best single volume on Data Mining you can buy. As one who mostly teaches methodologies, I like that all the major topics are here: neural nets, market basket, cluster, and trees. But there are also techniques that SPSS and Clementine (the software packages I use) can not do like "link analysis". Also, unlike Larose Discovering Knowledge in Data: An Introduction to Data Mining, the data preparation reads like preparing data for data mining, not a carbon copy of preparing data for statistics. Regarding this issue see the excellent Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems). I have pretty much concluded that a data mining book that does not make clear that data mining and OLAP are not the same is not a great book. This book has an extended section on just that. It is highly readable and comprehensive.
11 of 13 people found the following review helpful.
Everyone Should Do This
By John Matlock
Data mining is such a simple thing that you wonder why more companies don't do a better job of mining their own data sitting on their own hard disks.
If a customer buys the first in a series of mystery novels, who better to send a note telling him that the second book is now available. That's the essence of data mining. This would allow you to get a much higher return on your mailing, saving money and increasing return on your marketing.
This is one of those books that you need to read every few months. Each time you go through it you will find some idea that will enable you to get more out of your data. It isn't a book heavy on programming, but on the concepts that have worked for others.
Highly recommended.
6 of 7 people found the following review helpful.
A must-have book for your technical library
By James Taylor
Anyone interested in automating and improving decisions should have this book. It is one of the classic works on data mining and well worth the read.
I really liked the book both because it is well written and because, although it drilled into a fair amount of detail about some of the techniques, it started each new section off at a high level. This allows someone without a statistical background, such as me, to read as far as I can in each section and then skip ahead to the next technique. This is a nice change from books that simply get more and more detailed as page follows page, preventing you from gaining an overview of the subject.
The book introduces data mining and a methodology for applying it, talks about some of the applications in "Marketing, Sales, and Customer Relationship Management" (as the subtitle puts it), walks through some statistical techniques and then spends the bulk of the book on various data mining techniques. It wraps up with a nice summary of how data mining plays with other technologies and with some practical advice on getting started.
One of the best summaries of where data mining fits is given early in the book where an enterprise is encouraged to:
- Notice what its customers are doing
- Remember what it and its customers have done over time
- Learn from what it has remembered
- Act on what if has learned to make customers more profitable
The authors point out that Data Mining is focused on the "Learn" stage or, as they put it data mining suggests but businesses decide.
The methodology section, and the subsequent notes that relate to applying these techniques in real life, talked about the feedback loops between steps in data mining - there is not a linear "waterfall" sequence of steps but constant iteration and learning. They also emphasized the importance of finding the right business problem at the beginning - start as someone once said, with the end in mind. This was reiterated when they quote Voltaire who said "Le mieux est l'ennemi du bien" ("The best is the enemy of good"). In other words, don't get hung up on trying to find the perfect algorithm, perfect answer. Instead build something that is good, that works, and learn and improve over time.
The authors made a big point out of the value of data mining for "mass intimacy", where you want to treat customers differently and there is a business reason to do so but where customers are too numerous to be assigned to staff. One of the issues they pointed out was that staff must be trained in customer interaction skills while also using all the data you have. The value of data mining in building a customer-centric organization cannot be overestimated.
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