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Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management 1st Edition

4.2 out of 5 stars 14 customer reviews
ISBN-13: 978-0471385646
ISBN-10: 0471385646
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“…the descriptions are clear, concise, unambiguous…she has clearly succeeded…” (The Institute of Direct Marketing -theidm.com

From the Inside Flap

CD-ROM InstructionsInsert the CD-ROM and launch the readme.htm file in a web browser, or navigate using Windows® Explorer to browse the contents of the CD. The model programs and output are in text format that can be opened in any editing software (including SAS) that reads ASCII files. Spreadsheets are in Microsoft® Excel 97/2000 or 5.0/95. Launch the application (SAS 6.12 or higher) and open the file directly from the CD-ROM. If you wish to make changes, you can rename the files and save them to your local hard drive.Customer Note: Please read the following before launching the CD-ROMThis software contains files to help you utilize the models and code described in the accompanying book, sold separately. By opening the package, you are agreeing to be bound by the following agreement:This software product is protected by copyright and all rights are reserved by the author, John Wiley & Sons, Inc., or their licensors. You are licensed to use this software as described in the software and the accompanying book. Copying the software for any other purpose may be a violation of the U.S. Copyright Law.This software product is sold as is without warranty of any kind, either express or implied, including but not limited to the implied warranty of merchantability and fitness for a particular purpose. Neither Wiley nor its dealers or distributors assumes any liability for any alleged or actual damages arising from the use of or the inability to use this software. (Some states do not allow the exclusion of implied warranties, so the exclusion may not apply to you.)©2000 John Wiley & Sons, Inc. --This text refers to an out of print or unavailable edition of this title.

Product Details

  • Series: Datawarehousing
  • Paperback: 367 pages
  • Publisher: Wiley; 1 edition (November 3, 2000)
  • Language: English
  • ISBN-10: 0471385646
  • ISBN-13: 978-0471385646
  • Product Dimensions: 7.4 x 0.9 x 9.2 inches
  • Shipping Weight: 1.2 pounds
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (14 customer reviews)
  • Amazon Best Sellers Rank: #1,493,672 in Books (See Top 100 in Books)

Important Information

Example Ingredients

Example Directions

More About the Author

Olivia is an internationally known expert in Business Intelligence and Organizational Alignment. Her passion for finding successful solutions for her clients and partners has inspired her research in systems thinking and integrated business practices. She is considered a thought leader in the integration of analytic tools and holistic organization practices that deliver success through the optimal leverage of quantitative and qualitative practices and methodologies.

Her many years of research and study in the areas of Data Mining and Business Intelligence led to the writing of her first book, Data Mining Cookbook, Modeling for Acquisition, Risk and Customer Relationship Management (Wiley 2001). Seeing a larger need in the blending of analytics with softer skills, she was inspired to conduct research in the areas the integration of more qualitative skills building. This led to her research into the link between Organizational Development and Complexity Science and the writing of her second book, Her book, Business Intelligence Success Factors, Aligning for Success in a Global Economy (Wiley/SAS, 2009).

She has been working with clients for several years in areas of communication, change management, team building and leadership development. Olivia is a certified Holacracy Practitioner. Holacracy is a set of practice designed to tap into the wisdom of the organization.

Her clients include Cisco, Citizen's Bank, Clorox, HP, IBM, Xerox, Providian Insurance, Ameriquest, State Farm, Nationwide, and SAS. Olivia has a BA in Mathematics from Gettysburg College and an MS in Decision and Information Systems, with a concentration in Statistics, from Arizona State University.

Please visit www.oliviagroup.com for more information.

Customer Reviews

Top Customer Reviews

By A Customer on April 16, 2002
Format: Software
Book is OK, but DON'T BUY THE CD-ROM! I dropped [a large amount] for what I thought would be a worth-while "self-learning" course on Data Mining programming in SAS. To my great disappointment, I found that while Olivia had included the code (which you can type in yourself), there was NO DATA PROVIDED, making the code all but useless (can't run the models with no data!). I e-mailed her asking for some kind of a sample data set. She agreed, but after months of begging she provided nothing whatsoever. Don't make the same mistake I did - STAY AWAY FROM THE CD-ROM!!!
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Format: Paperback
By reading this book you should learn how to cook data mining applications...but if you have very little or no appreciation of data mining and customer relationship management (CRM), and you have never used SAS software, you'll probably end up burning your first few cakes or even worst your fingers !!
As the author gives a very brief introduction to data mining, make sure before you even start reading this book that you have a grasp of statistical modelling and data mining in a CRM context, otherwise you will find the material presented in this book too much to take in at once, and worst, you may probably end up being put off building your own data mining applications.
The author clearly has a solid statistical (read SAS) background, making this book a strong contender as one of the best books on data mining around, providing the reader with a number of useful recipes, practical examples and pragmatic data mining approaches which should be studied and understood in detail. Being a cookbook, the author's (or should I say the chef's) particular style may not suite your palate. In other words, you may not like the author's bias towards using logistic regression as the main data mining technique. As a result, you will not learn how to cook exotic dishes using ingredients such as neural networks. However, the choice to use logistic regression as the main statistical techniques pays off, as this allows the reader to start learning to cook robust/reliable meals (models), before cooking with the more exotic ingredients (techniques).
The topics and interventions provided by the well-experienced contributors are in context with the author's material, strengthening the practical context in which data mining applications are presented.
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1 Comment 18 of 18 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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Format: Paperback
Yes, its not for beginners or executives. That's great, because so many other books are aimed at glossing over the details for novices. This book gets down and dirty with exactly how one goes about analyzing and modeling data in applied marketing. It assumes some knowledge of data and basic analysis, but it also reviews assumptions along the way and points out "gotchas" for the less experienced. Yes, there is an overemphasis on Logistic Regression and a paucity of info on other techniques, but the Logistic Regression work is well done. Yes, it is very SAS based, but the code is not hard to translate to other systems. She doesn't spend as much time as I would prefer on explaining all the output that she presents, though its an excellent start. But she does provide specific details on ALL the steps, from getting and transforming data to how to present your results and use your model, things that are ignored in many other books. Sure, there are easy quibbles and minor errors throughout, but what tech book today is error free? So, if you are looking for the basic guidebook on just how one goes about "modeling", then this is the one. Its got a permanent place on my bookshelf, and is one of the standards I recommend along with Kimball, Pyle, and other "ya gotta have" books.
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Format: Paperback Verified Purchase
I was disappointed after reading 3 chapters of this book. Leafing forward, the book is saturated with SAS examples that I not only cannot understand but do not care about. It seems the whole book was written just to promote the (sold separately)...CD ROM with source SAS code. If you are going to write a SAS book, label it as such.
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Format: Paperback
Generally speaking, it is a good book. Contains a lot of real world examples. and it gets into a lot of details on modeling which you don't normally see in a data mining book.
however, some parts of the book were pretty crude. It contains some mistakes. for example, in one chapter the author tries to compare a few repricing scenerios. she compared the account after rate increase with the account before rate increase. and before rate increase, the attrition rate is zero. and it is just not the right way to evaluate a strategy. normally, you would have to compare an account which got a rate raise with the same account as if it didn't receive the increase. and even without the rate increase, the attrition rate down the road can't be zero. normally, you have to use test and control group on this kind of situation. besides, the author made some calculation mistakes in the comparison table. the numbers simply don't add up.
Anyway, overall the book is still a nice one if you can absorb all the nice information in it.
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Format: Paperback
Logistic Regression From A - Z! This book has it all.
The author lays out clear, concise methodologies to build robust predictive models using SAS. The nice thing is this book lays out the process step by step with SAS code examples. You do not have to be a statistics major to understand how to use the built in SAS functionality.
The modeling methods are unbelievably detailed including topics like defining the objective function, testing variables for predictability using chi squared, fitting continuous variables using the most linear variable transformation format (squared, cubed, cubed root, log, exponent, tangent, sine, cosine, etc... 19 total formats), changing categorical variables to continuous indicator variables for logistic regression use, using stepwise, backward, and score regression methods to further eliminate less predictive variables, defining deciles, and model testing methods like bootstrapping, jackknifing and gains tables to validate the model.
I do not fully understand the mathematical concepts involved throughout the entire process nor do I want to. The book provides a consistent repeatable programming methodology to follow that is broken down into very quantifiable steps.
I would recommend this book for anyone with limited statistical knowledge and a need to understand predictive modeling programming methodologies. Knowledge of the SAS programming language is essential to make full use of this material. The book uses real life examples from the banking, insurance, and marketing industries and contains additional valuable information related to these fields.
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