Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data [Hardcover]

Bruce Ratner (Author)

Book Description

Publication Date: May 28, 2003

Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses.
Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data delivers a collection of successful database marketing methodologies for big data. This compendium solves common database marketing problems by applying new hybrid modeling techniques that combine traditional statistical and new machine learning methods. The book delivers a thorough analysis of these cutting-edge techniques, which include non-statistical machine learning and genetic intelligent hybrid models.
By following the step-by-step procedures detailed in the text, database marketing professionals can learn how to apply the proper statistical techniques to any database marketing challenge. The practical case studies and examples provided involve real problems and real data, and are taken from a variety of industries, including banking, insurance, finance, retail, and telecommunications.

Product Details

• Hardcover: 384 pages
• Publisher: Chapman and Hall/CRC; 1 edition (May 28, 2003)
• Language: English
• ISBN-10: 1574443445
• ISBN-13: 978-1574443448
• Product Dimensions: 9.7 x 6.2 x 1 inches
• Shipping Weight: 1.5 pounds
• Average Customer Review: 4.5 out of 5 stars  See all reviews (4 customer reviews)
• Amazon Best Sellers Rank: #308,419 in Books (See Top 100 in Books)
  • #90 in Books > Business & Investing > Marketing & Sales > Marketing > Multilevel

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Customer Reviews

Most Helpful Customer Reviews

18 of 20 people found the following review helpful:

5.0 out of 5 stars Data Mining for Database marketing, June 10, 2003

By 

Bilal M Karriem (Bronxville, NY United States) - See all my reviews

This review is from: Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data (Hardcover)

I predict that Dr. Ratner's Statistical Modeling and Analysis for Database Marketers: Effective Techniques for Mining Big Data will be on every database marketer's bookshelf. Dr Ratner has put together an assembly of chapters that provide an indispensable resource for the daily problems facing data analysts and model builders in the database/direct marketing community. In each of the seveenteen chatpers Dr. Ratner addresses a typical problem and discusses the common solution. He points out unknown working assumptions or weaknesses of the latter, and then offers better solutions, which require basic knowledge of EDA/data mining. Dr. Ratner's writing style is unique as he makes familar concepts new, and new concepts familar. Thus, the book is easy and enjoyable reading. I specially like chapter that blends statistics with the machine learning, such as the introduction of the GenIQ Model.

8 of 8 people found the following review helpful:

5.0 out of 5 stars An essential book for statistical analysts building predictive models for database marketing, January 5, 2006

By 

Jude C. Ryan (Springfield, NJ United States) - See all my reviews
(REAL NAME)   

This review is from: Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data (Hardcover)

This is a must have introductory book for the practitioner using data mining to build predictive models in industry. While it does have a few snippets of SAS code, it is a conceptual book that explains the "why" and the "how" of practical model building. (If you want SAS code buy "The Data Mining Cookbook" by Olivia Parr Rud.) It dispenses of with the antiquated notion of the "true" model of classical statistics and econometrics, and shows how to arrive at an acceptable model that yeilds good predictions. As practitioner's, this is what we care about most. Among other things, it gives good explanations of: (1) the EDA paradigm versus classical statistics (2) Tukey's bulging rule for transforming variables (3) variable selection, though there is no mention of clustering to eliminate redundant variables. It discusses some of the weaknesses of automatic variable selection methods (4) smoothed scatterplots and logit plots (5) decile analysis and using bootstrapping to derive confidence intervals for cum lift.

The book shows you how to use logistic regression, OLS, and CHAID to build predictive models. For those interested in Genetic modeling, it has a clearly written chapter on the subject that explains how genetic modeling can be used to create new variables that can have more information than either of the original variables.

While this book does not cover everything, and is definitely not the last word on the subject, it is a solid first word. In particular, the book does not cover splines, shrinkage techniques such as model averaging, ridge regression, ..etc. For treatments of these and similar advanced topics see Frank Harrell's "Regression Modeling Strategies" and Hastie, Tibsharani and Friedman's "Elements of Statistical Learning".

15 of 18 people found the following review helpful:

5.0 out of 5 stars "EDA III" for Database Marketing, June 10, 2003

By 

"astralflash" (Brooklyn, NY United States) - See all my reviews

This review is from: Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data (Hardcover)

I consider myself fortunate to be the first to review this book. The title aptly indicates what the book is about: Statistical Modeling and Analysis for Database Marketers: Effective Techniques for Mining Big Data. The author provides in a Tukey-esque manner a collection of solutions to common problems facing database analysts, model builders, and marketers. The book can uniquely serve as a textbook, a how-to guide, and a reference source depending on the reader's statistical training and database marketing experience. Moreover, the author actually goes where other authors provide lip service: he creates the marriage of the "old" statistical methodologies with the new machine learning influence by introducing machine learning methods specifically tailored to database assessment of optimal model performance. The book's illustrations involve real problems, real data, and better solutions. This book is a keeper!