- Tapa blanda: 312 páginas
- Editor: SAS Publishing; Edición: Pap/Cdr (4 de junio de 2007)
- Colección: Sas Press Series
- Idioma: Inglés
- ISBN-10: 1590475089
- ISBN-13: 978-1590475089
- Valoración media de los clientes: Sé el primero en opinar sobre este producto
CRM Segmentation and Clustering Using SAS Enterprise Miner (Sas Press Series) (Inglés) Tapa blanda – 4 jun 2007
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Understanding the customer is critical to your company's success. In this instructive guide, Randy Collica employs SAS Enterprise Miner and the most commonly available techniques for customer relationship management (CRM). You will learn how to segment customers more intelligently and to achieve, or at least get closer to, the one-to-one customer relationship that today's businesses want. Step-by-step examples and exercises clearly illustrate the concepts of segmentation and clustering in the context of CRM. The book, with a foreword by Michael J. A. Berry, is sectioned into three parts. Part 1 reviews the basics of segmentation and clustering at an introductory level, providing examples from a variety of industries. Part 2 offers an in-depth treatment of segmentation with practical topics such as when and how to update your models and clustering with many attributes. Part 3 presents an introduction to newer, more advanced techniques, such as product affinity clustering, missing data imputation, and text mining segmentation. This straight-forward guide will appeal to anyone who seeks to better understand customers or prospective customers. Additionally, professors and students will find the book well suited for a business data mining analytics course in an MBA program or related course of study. You should understand basic statistics, but no prior knowledge of data mining or SAS Enterprise Miner is required. A foreword by Michael J. A. Berry is featured. Included on your bonus CD-ROM are the following: example SAS code, data sets, macros, and Enterprise Miner templates.
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CRM Segmentation and Clustering Using SAS Enterprise Miner (Sas Press Series)
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....