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之前的论坛中也有类似的附件，好像申请加精了。此处上传的是完整版本，也申请加精。
https://bbs.pinggu.org/thread-1528282-1-1.html。
以下是目录：
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
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
4.2.1 Other Cell Types for Segmentation
4.3 Example Development of RFM Cells
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
5.4 References
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?
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
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
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
8.3 Using the Segment Level Predictions for Customer Scoring
8.4 Creating Customer Value Segments
8.5 References
8.6 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 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
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
12.4 Text Document 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?
13.2 Market Basket Association Analysis
13.3 Revisiting Product Affinity Using Clustered Associations
13.4 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 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
15.1 Survey of Methods of Ensemble Segmentations
15.2 Two Methods for Combining Attitudinal and Behavioral Segments
15.3 Presenting the Business Case Simply from a Complex Analysis
15.4 References
15.5 Additional Exercise
Chapter 16 Segmentation of Customer Transactions
16.1 Measuring Transactions as a Time Series.
16.2 References
16.3 Additional Reading
16.4 Additional Exercise
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