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Book CRM Segmentation and Clustering Using SAS Enterprise Miner (Sas Press Series)

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CRM Segmentation and Clustering Using SAS Enterprise Miner (Sas Press Series)

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    Available in PDF - DJVU Format | CRM Segmentation and Clustering Using SAS Enterprise Miner (Sas Press Series).pdf | Language: English

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Using nontechnical terms, this book skillfully guides the user through the powerful SAS Enterprise Miner software to answer critical questions such as: What are the characteristics of my best customers?, How can I optimize their value?, and How can I use this knowledge to grow my business?. The ease and precision with which these techniques are explained will be appreciated by marketers, managers, analysts, and anyone else whose role it is to turn customer data into actionable knowledge. For those who seek practical techniques and rich examples for applying statistical concepts to common marketing and CRM challenges, this book is destined to become an essential reference. --C. Olivia Parr Rud, M.S., Data Mining, Strategist/Author/Facilitator, OLIVIAGroupThis book addresses an important and still often-misunderstood topic of utilizing advanced analytics to better understand consumer behavior. It has an application, how-to focus rather than a detailed description of algorithmsand the inclusion of data at the end of the book is a tremendous value-add to professors. --Stephan Kudyba, Ph.D., Founder of Null Sigma, Inc.The application of SAS Enterprise Miner and data mining analytics continues to broaden to new domains, from medical epidemiology to advanced business practices. Randy Collica provides us with an intuitive, hands-on guide to implementing the science behind comprehensively and accurately understanding one's customers. The efficiency of these CRM techniques has been maximized through the described segmentation and clustering methods and through the mining of underutilized data types, such as free text. These techniques are presented in a detailed, yet appealing and sensible manner; one that is a welcomed contribution to the state of the art in data mining analytics. --Daniel C. Payne, Ph.D., M.S.P.H, Epidemiologist

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Book details

  • PDF | 320 pages
  • SAS Publishing (June 4, 2007)
  • English
  • 1
  • Science & Math

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Review Text

  • By Guest on February 2, 2012

    Many typos. Not as well illustrated as Predictive Analytics Using EM by Sarma. At least some theoretical foundations need to be added. Frequently asking reader to refer to the EM manual.CRM Segmentation and Clustering Using SAS Enterprise Miner (Sas Press Series)

  • By J. Xin on February 7, 2008

    A very well written book on the clustering subject. Clean. Basic facts on techniques are correct, although not perfect, avoided controversies anyway.The book's framework is very well structured, from skin, through nerve, to the bones. Sub-topics get deeper and deeper. Tactical intentions at each turn relay very well.This subject has a lot of details that have been in the 'dust' for decades. This book smells no dust at all. The content is in strong 'mint' conditions.If you just want to get started, this book is enough.The book is still an earlier stage. The weak tradition in presenting this subject, for business or academy, to use small data sets to get the points, remains with this book. Really, for a solider like user, more cases based upon larger data sets (>1 mm) are more relevant. Efficiency, technology and software (setting up EM 5.2 on Windows is no longer as simple as loading Office. And loading it on Linux requires a $60k/year system engineer).One big hurdle that caps promises from using clustering is how data are collected. People take data as given and seldom think about how to collect data optimal for CRM. Even when people do, they mostly think about survey data. This book clearly retains that condition.With all this said, is it fair to blame this auther and book for the shortcomings? Hardly. Because this is a fair reflection of the maturity of our collective usage of clustering in CRM in general, as of 2007. I expect a book published in 2012 to be more....Thank you.


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