Book description
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 is divided into four 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 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. Part 4 takes segmentation to a new level with advanced techniques such as clustering of product associations, developing segmentation scoring models from customer survey data, combining segmentations using ensemble segmentation, and segmentation of customer transactions. Updates to the second edition include four new chapters in Part 4, Chapters 13-16, that introduce new and advanced analytic techniques that can be valuable in many customer segmentation applications. In addition, Chapter 9 has a new section on using the Imputation node in SAS Enterprise Miner to accomplish missing data imputation, compared to PROC MI used in earlier sections of Chapter 9. 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. This book is part of the SAS Press program.Table of contents
- Cover Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- Foreword to the Second Edition
- Foreword to the First Edition
- About this Book
- Acknowledgments
-
Part 1 - The Basics
- Chapter 1 - Introduction
- Chapter 2 - Why Segment? The Motivation for Segment-Based Descriptive Models
- Chapter 3 - Distance: The Basic Measures of Similarity and Association
-
Part 2 - Segmentation Galore
- Chapter 4 - Segmentation Using a Cell-Based Approach
- Chapter 5 - Segmentation of Several Attributes with Clustering
-
Chapter 6 - Clustering of Many Attributes
- 6.1 - Closer to Reality of Customer Segmentation
- 6.2 - Representing Many Attributes in Multi-dimensions
- 6.3 - How Can i Better Understand My Customers of Many Attributes?
- 6.4 - Data Assay and Profiling
- 6.5 - Understanding What the Cluster Segmentation Found
- 6.6 - Planning for Customer Attentiveness with Each Segment
- 6.7 - Creating Cluster Segments on Very Large Data Sets
- 6.8 - Additional Exercise
- 6.9 - References
- Chapter 7 - When and How to Update Cluster Segments
- Chapter 8 - Using Segments in Predictive Models
-
Part 3 - Beyond Traditional Segmentation
-
Chapter 9 - Clustering and the issue of Missing Data
- 9.1 - Missing Data and How it Can Affect Clustering
- 9.2 - Analysis of Missing Data Patterns
- 9.3 - Effects of Missing Data on Clustering.
- 9.4 - Methods of Missing Data Imputation
- 9.5 - Obtaining Confidence Interval Estimates on Imputed Values
- 9.6 - Using the SAS Enterprise Miner Imputation Node
- 9.7 - References
-
Chapter 10 - Product Affinity and Clustering of Product Affinities
- 10.1 - Motivation of Estimating Product Affinity by Segment
- 10.2 - Estimating Product Affinity Using Purchase Quantities
- 10.3 - Combining Product Affinities by Cluster Segments
- 10.4 - Pros and Cons of Segment Affinity Scores.
- 10.5 - issues with Clustering Non-normal Quantities
- 10.6 - Approximating a Graph-Theoretic Approach Using a Decision Tree
- 10.7 - Using the Product Affinities for Cross-Sell Programs
- 10.8 - Additional Exercises
- 10.9 - References
-
Chapter 11 - Computing Segments Using SOM/Kohonen for Clustering
- 11.1 - When Ordinary Clustering Does not Produce Desired Results
- 11.2 - What is a Self-Organizing Map?
- 11.3 - Computing and Applying SOM Network Cluster Segments
- 11.4 - Comparing Clustering with SOM Segmentation
- 11.5 - Customer Distinction Analysis Example
- 11.6 - Additional Exercises
- 11.7 - References
- Chapter 12 - Segmentation of Textual Data
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Chapter 9 - Clustering and the issue of Missing Data
-
Part 4 - Advanced Segmentation Applications
- Chapter 13 - Clustering of Product Associations
-
Chapter 14 - Predicting Attitudinal Segments from Survey Responses
- 14.1 - Typical Market Research Surveys.
- 14.2 - Match-back of Survey Responses
- 14.3 - Analysis of Survey Responses: An Overview
- 14.4 - Developing a Predictive Segmentation Model from a Survey Analysis
- 14.5 - Issues with Scoring a Predictive Segmentation on Customer or Prospect Data
- 14.6 - Assessing the Confidence of Predicted Segments
- 14.7 - Business Implications for Using Attitudinal Segmentation.
- 14.8 - References
- Chapter 15 - Combining Attitudinal and Behavioral Segments
- Chapter 16 - Segmentation of Customer Transactions
- Index
- Accelerate Your SAS Knowledge with SAS Books
Product information
- Title: Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition, 2nd Edition
- Author(s):
- Release date: November 2011
- Publisher(s): SAS Institute
- ISBN: 9781612900926
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