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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management [Kindle Edition]

Gordon S. Linoff , Michael J. A. Berry

Print List Price: 33.99
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Product Description

Product Description

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.

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.

Product details

  • Format: Kindle Edition
  • File Size: 14091 KB
  • Print Length: 891 pages
  • Page Numbers Source ISBN: 0470650931
  • Publisher: Wiley; 3 edition (23 Mar 2011)
  • Sold by: Amazon Media EU S. r.l.
  • Language: English
  • ASIN: B004UB2KE4
  • Text-to-Speech: Enabled
  • X-Ray:
  • Amazon Bestsellers Rank: #189,378 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most Helpful Customer Reviews on (beta) 3.8 out of 5 stars  11 reviews
1 of 1 people found the following review helpful
5.0 out of 5 stars Excellent 15 Oct 2013
By Christopher Reilly - Published on
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.
1 of 1 people found the following review helpful
3.0 out of 5 stars Very verbose 15 Jun 2013
By Dan Bowker - Published on
Format:Paperback|Verified Purchase
The author(s) has a way with words, he/they say in 10,000 words what could be said in 1000. I found myself skimming more than reading and eventually gave up. I think I made it through the first few chapters but that's all.
3.0 out of 5 stars Needs major restructuring 26 Mar 2014
By cool_einstein - Published on
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.
5.0 out of 5 stars Great for new learners 20 Feb 2014
By Tasha - Published on
Format:Kindle Edition|Verified Purchase
This book is the best in the industry for people new to data mining and those who are working in the industry. I would recommend this to cohort professionals.
4.0 out of 5 stars Solid introduction to data mining 10 Jun 2013
By M. Collins - Published on
Format:Paperback|Verified Purchase
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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Popular Highlights

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Merely finding patterns is not enough. You must respond to the patterns and act on them, ultimately turning data into information, information into action, and action into value. &quote;
Highlighted by 23 Kindle users
Data mining is a business process for exploring large amounts of data to discover meaningful patterns and rules. &quote;
Highlighted by 22 Kindle users
The goal should be reaching prospects who are more likely to make purchases because of having been contacted. &quote;
Highlighted by 17 Kindle users

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