Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition

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9781607648109: Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition
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Understanding the customer is critical to your company's success. In Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition, 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 reviews the basics of segmentation and clustering at an introductory level, providing examples from a variety of industries; offers an in-depth treatment of segmentation with practical topics such as when and how to update your models and clustering with many attributes; and then goes beyond traditional segmentation practices to introduce recommended strategies for clustering product affinities, handling missing data, and incorporating textual records into your predictive model with SAS Text Miner software.

Updates to the second edition include new chapters that introduce some new and advanced analytic techniques that can be valuable in many customer segmentation applications. In addition, the book contains a new section on using the Imputation node in SAS Enterprise Miner to accomplish missing data imputation. Also included are business insights and motivations for selection settings and analytical decisions on many of the examples included in this second edition.

This straightforward 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.

Über den Autor:

Mr. Collica received a B.S. in electronic engineering from Northern Arizona University in 1982. He has 16 years' experience in the semiconductor manufacturing industry working on yield and product and quality engineering. Since 1998 he has been with Compaq and Hewlett-Packard as a Senior Business Analyst using data mining techniques for customer analytics in the Corp Customer Intelligence department. He is currently a Senior Solutions Architect for SAS Institute, supporting the communications, entertainment/media, and high-tech manufacturing industries. His current interests are in clustering and ensemble models, knowledge and data engineering, missing data and imputation, and text mining techniques for use in business and customer intelligence. He has authored and co-authored 11 articles and 2 books. Mr. Collica has been a full member of the IEEE since 1982.

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