Understanding your customers is the key to your companyâ€™s success!
Segmentation is one of the first and most basic machine learning methods. It can be used by companies to understand their customers better, boost relevance of marketing messaging, and increase efficacy of predictive models. In Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition, Randy Collica explains, in step-by-step fashion, the most commonly available techniques for segmentation using the powerful data mining software SAS Enterprise Miner.
A working guide that uses real-world data, this new edition will show you how to segment customers more intelligently and achieve the one-to-one customer relationship that your business needs. Step-by-step examples and exercises, using a number of machine learning and data mining techniques, clearly illustrate the concepts of segmentation and clustering in the context of customer relationship management.
The book includes four parts, each of which increases in complexity. 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. 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. Finally, 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.
New to the third edition is a chapter that focuses on predictive models within microsegments and combined segments, and a new parallel process technique is introduced using SAS Factory Miner. In addition, all examples have been updated to the latest version of SAS Enterprise Miner.
Table of Contents
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
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
Chapter 10 Product Affinity and Clustering of Product Affinities
Chapter 11 Computing Segments Using SOM/Kohonen for Clustering
Chapter 12 Segmentation of Textual Data
Part 4 Advanced Segmentation Applications
Chapter 13 Clustering of Product Associations
Chapter 14 Predicting Attitudinal Segments from Survey Responses
Chapter 15 Combining Attitudinal and Behavioral Segments: Ensemble Segmentation
Chapter 16 Segmentation of Customer Transactions
Chapter 17 Micro-Segmentation: Using SAS Factory Miner for Predictive Models in Segments