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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management Paperback – April 12, 2011

19 customer reviews

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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management + Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die + Data Science for Business: What you need to know about data mining and data-analytic thinking
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Editorial Reviews

From the Back Cover

The newest edition of the leading introductory book on data mining, fully updated and revised

Who will remain a loyal customer and who won't? Which messages are most effective with which segments? How can customer value be maximized? This book supplies powerful tools for extracting the answers to these and other crucial business questions from the corporate databases where they lie buried. In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business. In this latest edition, Linoff and Berry have made extensive updates and revisions to every chapter and added several new ones. The book retains the focus of earlier editions—showing marketing analysts, business managers, and data mining specialists how to harness data mining methods and techniques to solve important business problems. While never sacrificing accuracy for the sake of simplicity, Linoff and Berry present even complex topics in clear, concise English with minimal use of technical jargon or mathematical formulas. Technical topics are illustrated with case studies and practical real-world examples drawn from the authors' experiences, and every chapter contains valuable tips for practitioners. Among the techniques newly covered, or covered in greater depth, are linear and logistic regression models, incremental response (uplift) modeling, naïve Bayesian models, table lookup models, similarity models, radial basis function networks, expectation maximization (EM) clustering, and swarm intelligence. New chapters are devoted to data preparation, derived variables, principal components and other variable reduction techniques, and text mining.

After establishing the business context with an overview of data mining applications, and introducing aspects of data mining methodology common to all data mining projects, the book covers each important data mining technique in detail.

This third edition of Data Mining Techniques covers such topics as:

  • How to create stable, long-lasting predictive models

  • Data preparation and variable selection

  • Modeling specific targets with directed techniques such as regression, decision trees, neural networks, and memory based reasoning

  • Finding patterns with undirected techniques such as clustering, association rules, and link analysis

  • Modeling business time-to-event problems such as time to next purchase and expected remaining lifetime

  • Mining unstructured text

The companion website provides data that can be used to test out the various data mining techniques in the book.

About the Author

GORDON S. LINOFF and MICHAEL J. A. BERRY are the founders of Data Miners, Inc., a consultancy specializing in data mining. They have jointly authored two of the leading data mining titles in the field, Data Mining Techniques and Mastering Data Mining (both from Wiley). They each have decades of experience applying data mining techniques to business problems in marketing and customer relationship management.

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Product Details

  • Paperback: 888 pages
  • Publisher: Wiley; 3 edition (April 12, 2011)
  • Language: English
  • ISBN-10: 0470650931
  • ISBN-13: 978-0470650936
  • Product Dimensions: 7.4 x 1.9 x 9.3 inches
  • Shipping Weight: 2.9 pounds (View shipping rates and policies)
  • Average Customer Review: 3.9 out of 5 stars  See all reviews (19 customer reviews)
  • Amazon Best Sellers Rank: #110,257 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

5 of 5 people found the following review helpful By cool_einstein on March 26, 2014
Format: Paperback
This book has useful nuggets but one needs to be patient to weed through ill-structured content.

Problem1: Examples and content repeats quite a bit across chapters, but unfortunately never discusses things properly at one place. In every edition authors have added chapters but seemed to have forgotten what they have already discussed in earlier chapters.

Problem2: Many suggestions, scenarios have been incompletely discussed. Without enough information one has to assume quite a bit about the scenario, problem, solution and the value of it. It is okay if it had happened once in a while, but this sprinkling of anecdotes without fully discussing is rampant in this book.

Problem3: It is quite verbose.

Problem4: Keeps on changing the depth of the discussion. The discussion is overall at high-level, however at times authors would go really deep to discuss details around some random topic eg calculation of silhouette scores. The primary focus seemed to be business people and not statistics students. Going deep "selectively" is also a big problem in this book.

This book has the potential to become a really good book, but it needs major restructuring.
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3 of 3 people found the following review helpful By Zain Khandwala on February 23, 2015
Format: Paperback Verified Purchase
In a field evolving as dynamically as data science, 2011 seems a long time ago, and I've since bought a number of the newer titles out there. Still, however, I often find myself reverting to Linoff and Barry's text for a lucid explanation of, or interesting take on a particular data mining subject area.

The book is thorough (at 800+ pages this should be the expectation) and technical, but isn't really a how-to manual in that it stops short of containing actual code or instructions. That's not an issue, however, as such instructional information is available elsewhere if needed.

My only complaint about the work is that it is a little redundant and otherwise verbose at times. I hope a fourth edition is forthcoming, and that it is a little more tightly edited.

---
Z. Khandwala
Institute for Advanced Analytics
Bellarmine University - Louisville, KY
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2 of 2 people found the following review helpful By Geoffrey M. Lucas on July 2, 2014
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I got this book for a class on Data-Mining and I found it to be a very good book. It has good visuals to help the reader understand the concepts in the book and maintains a good sense of humor throughout so reading it doesn't seem as dense as some of my typical statistics books. My only criticism of the book would be that it never discusses common software platforms for performing these tasks. While I understand that he probably didn't want to favor a particular platform over another, it seems that introducing the major ones could be helpful for people that may be very used to using just one.
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2 of 2 people found the following review helpful By M. Collins on June 10, 2013
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I haven't made it through the entire book, but this serves as a solid reference for different topics in data mining. I used it in a graduate level course I took this spring and it was easy to read and understand.
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1 of 1 people found the following review helpful By Christopher Reilly on October 15, 2013
Format: Paperback Verified Purchase
Very good introduction to newer data miners, but also comprehensive enough to use as perhaps the only guidebook you will need to understand, choose, and implement analysis techniques.
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2 of 3 people found the following review helpful By fjurado on May 29, 2012
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I was looking for a reference book that could bring me a clear picture of all data mining techniques for marketing and CRM, and there it was. But it goes beyond that, it brings many examples and tips that makes the life eaiser to the data miner. Highly recommended.
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2 of 3 people found the following review helpful By LafIng on July 11, 2014
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This is not a book for a beginner. It really should be used for somebody who already uses a lot of data and is comfortable working with various programs. It's good to advance your knowledge but not begin it.
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Format: Kindle Edition Verified Purchase
The book provides a very general overview of data mining concepts. It is primarily for non-technical audience. Its chapters on statistical methods are weak. The best chapters are on neural nets and clustering. Text mining is incredibly superficial.
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