Customer Segmentation and Clustering Using SAS Enterprise Miner,Third Edition , livre ebook

Customer Segmentation and Clustering Using SAS Enterprise Miner
Third Edition
Randall S. Collica
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The correct bibliographic citation for this manual is as follows: Collica, Randall S. 2017. Customer Segmentation and Clustering Using SAS Enterprise Miner , Third Edition. Cary, NC: SAS Institute Inc.
Customer Segmentation and Clustering Using SAS Enterprise Miner , Third Edition
Copyright 2017, SAS Institute Inc., Cary, NC, USA
ISBN 978-1-62960-106-9 (Hard copy) ISBN 978-1-62960-527-2 (EPUB) ISBN 978-1-62960-528-9 (MOBI) ISBN 978-1-62960-529-6 (PDF)
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This book is dedicated to my lovely wife, Nanci, and our children, Janelle and her husband Mike Carter (and their children Hudson and Henley), Brian, Danae, Jamie, and Carmella .
Contents
Foreword to the Second Edition
Foreword to the First Edition
About This Book
About The Author
Acknowledgments
Part 1 The Basics
Chapter 1: Introduction
1.1 What Is Segmentation in the Context of CRM?
1.2 Types of Segmentation and Methods
1.2.1 Customer Profiling
1.2.2 Customer Likeness Clustering
1.2.3 RFM Cell Classification Grouping
1.2.4 Purchase Affinity Clustering
1.3 Typical Uses of Segmentation in Industry
1.4 Segmentation as a CRM Tool
1.5 References
Chapter 2: Why Segment? The Motivation for Segment-Based Descriptive Models
2.1 Mass Customization Instead of Mass Marketing
2.2 Specialized Promotions or Communications by Segment Groups
2.3 Profiling of Customers and Prospects
Process Flow Table: Data Assay Project
2.3.1 Example 2.1: The Data Assay Project
2.3.2 Example 2.2: Customer Profiling of the BUYTEST Data Set
2.3.3 Additional Exercise
2.4 References
Chapter 3: Distance: The Basic Measures of Similarity and Association
3.1 What Is Similar and What Is Not
3.2 Distance Metrics As a Measure of Similarity and Association
3.3 What Is Clustering? The k-Means Algorithm and Variations
3.3.1 Variations of the k-Means Algorithm
3.3.2 The Agglomerative Algorithm
3.4 References
Part 2 Segmentation Galore
Chapter 4: Segmentation Using a Cell-Based Approach
4.1 Introduction to Cell-Based Segmentation
4.2 Segmentation Using Cell Groups-RFM
Recency
Frequency
Monetary Value
4.2.1 Other Cell Types for Segmentation
4.3 Example Development of RFM Cells
Process Flow Table: RFM Cell Development
4.4 Tree-Based Segmentation Using RFM
4.5 Using RFM and CRM-Customer Distinction
4.6 Additional Exercise
4.7 References
4.8 Additional Reading
Chapter 5: Segmentation of Several Attributes with Clustering
5.1 Motivation for Clustering of Customer Attributes: Beginning CRM
5.2 How Can I Better Understand My Customer Base of Over 100,000?
5.3 Using a Decision Tree to Create Cluster Segments
Process Flow Table 2: Decision Tree Clustering
5.4 Reference
5.5 Additional Reading
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?
Process Flow Table: NY Towns Clustering
6.4 Data Assay and Profiling
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
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 Updated
Process Flow Table: Distance Metrics
7.3 Testing New Observations and Score Results
7.4 Other Practical Considerations
7.5 Additional Reading
Chapter 8: Using Segments in Predictive Models
8.1 The Basis of Breaking Up the Data Space
8.2 Predicting a Segment Level
Process Flow Table 1: Predicting Segments Project
8.3 Using the Segment Level Predictions for Customer Scoring
8.4 Creating Customer Value Segments
Process Flow Table 2: Most Valuable Customers (MVCs)
8.5 Additional Exercises
8.6 References
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
Process Flow Table 1: Clustering with Missing Data
9.3 Effects of Missing Data on Clustering
Process Flow Table 2: Clustering with Missing Data
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
Process Flow Table 1: Binary Product Affinity
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
Process Flow Table 2: Graph-Theory Approach
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
Process Flow Table 1: SOM Segmentation
11.4 Comparing Clustering with SOM Segmentation
11.5 Customer Distinction Analysis Example
Process Flow Table 2: SOM Segmentation
11.6 Additional Exercises
11.7 References
Chapter 12: Segmentation of Textual Data
12.1 Background of Textual Data in the Context of CRM
12.2 Notes on Text Mining versus Natural Language Processing
12.3 Simple Text Mining Example
Process Flow Table 1: Text Segmentation-News Stories
12.4 Text Document Clustering
Process Flow Table 2: Text Segmentation-Text Clustering
12.5 Using Text Mining in CRM Applications
12.6 References
Part 4 Advanced Segmentation Applications
Chapter 13: Clustering of Product Associations
13.1 What Is Association Analysis and Its Uses in Business?
Process Flow Table 1: Association Analysis Process Flow
13.2 Market Basket Association Analysis
Process Flow Table 2: Market Basket Analysis Process Flow
13.3 Revisiting Product Affinity Using Clustered Associations
Process Flow Table 3: Clustering Association Rules
13.4 The Business and Technical Side of Clustering Associations
13.5 Extra Analysis
13.6 References
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 Mod