Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition
- 16h 22m
- Gordon S. Linoff, Michael J.A. Berry
- John Wiley & Sons (US)
- 2011
The leading introductory book on data mining, fully updated and revised!
When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition--more than 50% new and revised--is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company.
- Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems
- Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately
- Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more
- Provides best practices for performing data mining using simple tools such as Excel
Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.
About the Authors
Gordon S. Linoff and Michael J. A. Berry are well known in the data mining field. They are the founders of Data Miners, Inc., a boutique data mining consultancy, and they have jointly authored several influential and widely read books in the field. The first of their jointly authored books was the first edition of Data Mining Techniques, which appeared in 1997. Since that time, they have been actively mining data in a wide variety of industries. Their continuing hands©\on analytical work allows the authors to keep abreast of developments in the rapidly evolving fields of data mining, forecasting, and predictive analytics. Gordon and Michael are scrupulously vendor©\neutral. Through their consulting work, the authors have been exposed to data analysis software from all of the major software vendors (and quite a few minor ones as well). They are convinced that good results are not determined by whether the software employed is proprietary or open©\source, command©\line or point©\and©\click; good results come from creative thinking and sound methodology.
Gordon and Michael specialize in applications of data mining in marketing and customer relationship management ¡ª applications such as improving recommendations for cross©\sell and up©\sell, forecasting future subscriber levels, modeling lifetime customer value, segmenting customers according to their behavior, choosing optimal landing pages for customers arriving at a website, identifying good candidates for inclusion in marketing campaigns, and predicting which customers are at risk of discontinuing use of a software package, service, or drug regimen. Gordon and Michael are dedicated to sharing their knowledge, skills, and enthusiasm for the subject. When not mining data themselves, they enjoy teaching others through courses, lectures, articles, on©\site classes, and of course, the book you are about to read. They can frequently be found speaking at conferences and teaching classes. The authors also maintain a data mining blog at blog.data©\miners.com.
Gordon lives in Manhattan. His most recent book before this one is Data Analysis Using SQL and Excel, which was published by Wiley in 2008.
Michael lives in Cambridge, Massachusetts. In addition to his consulting work with Data Miners, he teaches Marketing Analytics at the Carroll School of Management at Boston College.
In this Book
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What Is Data Mining and Why Do It?
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Data Mining Applications in Marketing and Customer Relationship Management
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The Data Mining Process
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Statistics 101: What You Should Know About Data
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Descriptions and Prediction: Profiling and Predictive Modeling
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Data Mining Using Classic Statistical Techniques
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Decision Trees
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Artificial Neural Networks
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Nearest Neighbor Approaches: Memory‐Based Reasoning and Collaborative Filtering
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Knowing When to Worry: Using Survival Analysis to Understand Customers
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Genetic Algorithms and Swarm Intelligence
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Tell Me Something New: Pattern Discovery and Data Mining
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Finding Islands of Similarity: Automatic Cluster Detection
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Alternative Approaches to Cluster Detection
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Market Basket Analysis and Association Rules
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Link Analysis
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Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining
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Building Customer Signatures
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Derived Variables: Making the Data Mean More
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Too Much of a Good Thing? Techniques for Reducing the Number of Variables
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Listen Carefully to What Your Customers Say: Text Mining
