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Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition, 2nd Edition

Name: Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition, 2nd Edition
Author: Randy Collica
ISBN: 9781612900926

by Randy Collica

November 2011

Beginner to intermediate

378 pages

10h 48m

English

SAS Institute

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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
1.1 - What is Segmentation in the Context of CRM?1.2 - Types of Segmentation and Methods1.2.1 - Customer Profiling1.2.2 - Customer Likeness Clustering1.2.3 - RFM Cell Classification Grouping1.2.4 - Purchase Affinity Clustering1.3 - Typical Uses of Segmentation in Industry1.4 - Segmentation as a CRM Tool1.5 - References
Chapter 2 - Why Segment? The Motivation for Segment-Based Descriptive Models
2.1 - Mass Customization Instead of Mass Marketing2.2 - Specialized Promotions or Communications by Segment Groups2.3 - Profiling of Customers and Prospects2.3.1 - Example 2.1: The Data Assay Project2.3.2 - Example 2.2: Customer Profiling of the Buytest Data Set2.3.3 - Additional Exercise2.4 - References
Chapter 3 - Distance: The Basic Measures of Similarity and Association
3.1 - What is Similar and What is Not3.2 - Distance Metrics as a Measure of Similarity and Association3.3 - What is Clustering? The k-Means Algorithm and Variations.3.3.1 - Variations of the k-Means Algorithm3.3.2 - The Agglomerative Algorithm3.4 - References
Part 2 - Segmentation Galore
Chapter 4 - Segmentation Using a Cell-Based Approach
4.1 - Introduction to Cell-Based Segmentation4.2 - Segmentation Using Cell Groups—RFM4.2.1 - Other Cell Types for Segmentation4.3 - Example Development of RFM Cells4.4 - Tree-Based Segmentation Using RFM4.5 - Using RFM and CRM—Customer Distinction4.6 - Additional Exercise4.7 - References4.8 - Additional Reading
Chapter 5 - Segmentation of Several Attributes with Clustering
5.1 - Motivation for Clustering of Customer Attributes: Beginning CRM5.2 - How Can i Better Understand My Customer Base of Over 100,000?5.3 - Using a Decision Tree to Create Cluster Segments5.4 - References5.5 - Additional Reading
Chapter 6 - Clustering of Many Attributes
6.1 - Closer to Reality of Customer Segmentation6.2 - Representing Many Attributes in Multi-dimensions6.3 - How Can i Better Understand My Customers of Many Attributes?6.4 - Data Assay and Profiling6.5 - Understanding What the Cluster Segmentation Found6.6 - Planning for Customer Attentiveness with Each Segment6.7 - Creating Cluster Segments on Very Large Data Sets6.8 - Additional Exercise6.9 - References
Chapter 7 - When and How to Update Cluster Segments
7.1 - What is the Shelf Life of a Model, and How Can it Affect Your Results?7.2 - How to Detect When your Clustering Model Should be Updated7.3 - Testing New Observations and Score Results7.4 - Other Practical Considerations7.5 - Additional Reading
Chapter 8 - Using Segments in Predictive Models
8.1 - The Basis of Breaking up the Data Space8.2 - Predicting a Segment Level8.3 - Using the Segment Level Predictions for Customer Scoring8.4 - Creating Customer Value Segments8.5 - References8.6 - Additional Exercises
Part 3 - Beyond Traditional Segmentation
Chapter 9 - Clustering and the issue of Missing Data
9.1 - Missing Data and How it Can Affect Clustering9.2 - Analysis of Missing Data Patterns9.3 - Effects of Missing Data on Clustering.9.4 - Methods of Missing Data Imputation9.5 - Obtaining Confidence Interval Estimates on Imputed Values9.6 - Using the SAS Enterprise Miner Imputation Node9.7 - References
Chapter 10 - Product Affinity and Clustering of Product Affinities
10.1 - Motivation of Estimating Product Affinity by Segment10.2 - Estimating Product Affinity Using Purchase Quantities10.3 - Combining Product Affinities by Cluster Segments10.4 - Pros and Cons of Segment Affinity Scores.10.5 - issues with Clustering Non-normal Quantities10.6 - Approximating a Graph-Theoretic Approach Using a Decision Tree10.7 - Using the Product Affinities for Cross-Sell Programs10.8 - Additional Exercises10.9 - References
Chapter 11 - Computing Segments Using SOM/Kohonen for Clustering
11.1 - When Ordinary Clustering Does not Produce Desired Results11.2 - What is a Self-Organizing Map?11.3 - Computing and Applying SOM Network Cluster Segments11.4 - Comparing Clustering with SOM Segmentation11.5 - Customer Distinction Analysis Example11.6 - Additional Exercises11.7 - References
Chapter 12 - Segmentation of Textual Data
12.1 - Background of Textual Data in the Context of CRM12.2 - Notes on Text Mining versus Natural Language Processing12.3 - Simple Text Mining Example12.4 - Text Document Clustering12.5 - Using Text Mining in CRM Applications12.6 - References
Part 4 - Advanced Segmentation Applications
Chapter 13 - Clustering of Product Associations
13.1 - What is Association Analysis and its Uses in Business?13.2 - Market Basket Association Analysis13.3 - Revisiting Product Affinity Using Clustered Associations13.4 - The Business and Technical Side of Clustering Associations.13.5 - References
Chapter 14 - Predicting Attitudinal Segments from Survey Responses
14.1 - Typical Market Research Surveys.14.2 - Match-back of Survey Responses14.3 - Analysis of Survey Responses: An Overview14.4 - Developing a Predictive Segmentation Model from a Survey Analysis14.5 - Issues with Scoring a Predictive Segmentation on Customer or Prospect Data14.6 - Assessing the Confidence of Predicted Segments14.7 - Business Implications for Using Attitudinal Segmentation.14.8 - References
Chapter 15 - Combining Attitudinal and Behavioral Segments
15.1 - Survey of Methods of Ensemble Segmentations15.2 - Two Methods for Combining Attitudinal and Behavioral Segments15.3 - Presenting the Business Case Simply from a Complex Analysis15.4 - References15.5 - Additional Exercise
Chapter 16 - Segmentation of Customer Transactions
16.1 - Measuring Transactions as a Time Series.16.2 - References16.3 - Additional Reading16.4 - Additional Exercise
Index
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Content preview from Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition, 2nd Edition

Foreword to the First Edition

Artists call it white shock—the paralyzing effect brought on by a blank canvas. Where should the first brush stroke be applied? Every student who has ever taken a class in some technical topic and then gone back to the office to try out the newly learned techniques is familiar with the feeling. You thought you understood everything the teacher was saying, but now you are staring at a blank pad of paper, or the open program editor window on your computer screen, with no idea how to get started.

If the project you are embarking on involves customer segmentation or clustering, then Randy Collica has written the cure for your white shock. This book is not so much a discussion of segmentation and clustering as it is ...

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Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition, 2nd Edition

by Randy Collica

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.