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

Book Description

Understanding the customer is critical to your company's success. In this book, 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.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. Contents
  6. Foreword to the Second Edition
  7. Foreword to the First Edition
  8. About this Book
  9. Acknowledgments
  10. Part 1 - The Basics
    1. Chapter 1 - Introduction
      1. 1.1 - What is Segmentation in the Context of CRM?
      2. 1.2 - Types of Segmentation and Methods
        1. 1.2.1 - Customer Profiling
        2. 1.2.2 - Customer Likeness Clustering
        3. 1.2.3 - RFM Cell Classification Grouping
        4. 1.2.4 - Purchase Affinity Clustering
      3. 1.3 - Typical Uses of Segmentation in Industry
      4. 1.4 - Segmentation as a CRM Tool
      5. 1.5 - References
    2. Chapter 2 - Why Segment? The Motivation for Segment-Based Descriptive Models
      1. 2.1 - Mass Customization Instead of Mass Marketing
      2. 2.2 - Specialized Promotions or Communications by Segment Groups
      3. 2.3 - Profiling of Customers and Prospects
        1. 2.3.1 - Example 2.1: The Data Assay Project
        2. 2.3.2 - Example 2.2: Customer Profiling of the Buytest Data Set
        3. 2.3.3 - Additional Exercise
      4. 2.4 - References
    3. Chapter 3 - Distance: The Basic Measures of Similarity and Association
      1. 3.1 - What is Similar and What is Not
      2. 3.2 - Distance Metrics as a Measure of Similarity and Association
      3. 3.3 - What is Clustering? The k-Means Algorithm and Variations.
        1. 3.3.1 - Variations of the k-Means Algorithm
        2. 3.3.2 - The Agglomerative Algorithm
      4. 3.4 - References
  11. Part 2 - Segmentation Galore
    1. Chapter 4 - Segmentation Using a Cell-Based Approach
      1. 4.1 - Introduction to Cell-Based Segmentation
      2. 4.2 - Segmentation Using Cell Groups—RFM
        1. 4.2.1 - Other Cell Types for Segmentation
      3. 4.3 - Example Development of RFM Cells
      4. 4.4 - Tree-Based Segmentation Using RFM
      5. 4.5 - Using RFM and CRM—Customer Distinction
      6. 4.6 - Additional Exercise
      7. 4.7 - References
      8. 4.8 - Additional Reading
    2. Chapter 5 - Segmentation of Several Attributes with Clustering
      1. 5.1 - Motivation for Clustering of Customer Attributes: Beginning CRM
      2. 5.2 - How Can i Better Understand My Customer Base of Over 100,000?
      3. 5.3 - Using a Decision Tree to Create Cluster Segments
      4. 5.4 - References
      5. 5.5 - Additional Reading
    3. Chapter 6 - Clustering of Many Attributes
      1. 6.1 - Closer to Reality of Customer Segmentation
      2. 6.2 - Representing Many Attributes in Multi-dimensions
      3. 6.3 - How Can i Better Understand My Customers of Many Attributes?
      4. 6.4 - Data Assay and Profiling
      5. 6.5 - Understanding What the Cluster Segmentation Found
      6. 6.6 - Planning for Customer Attentiveness with Each Segment
      7. 6.7 - Creating Cluster Segments on Very Large Data Sets
      8. 6.8 - Additional Exercise
      9. 6.9 - References
    4. Chapter 7 - When and How to Update Cluster Segments
      1. 7.1 - What is the Shelf Life of a Model, and How Can it Affect Your Results?
      2. 7.2 - How to Detect When your Clustering Model Should be Updated
      3. 7.3 - Testing New Observations and Score Results
      4. 7.4 - Other Practical Considerations
      5. 7.5 - Additional Reading
    5. Chapter 8 - Using Segments in Predictive Models
      1. 8.1 - The Basis of Breaking up the Data Space
      2. 8.2 - Predicting a Segment Level
      3. 8.3 - Using the Segment Level Predictions for Customer Scoring
      4. 8.4 - Creating Customer Value Segments
      5. 8.5 - References
      6. 8.6 - Additional Exercises
  12. Part 3 - Beyond Traditional Segmentation
    1. Chapter 9 - Clustering and the issue of Missing Data
      1. 9.1 - Missing Data and How it Can Affect Clustering
      2. 9.2 - Analysis of Missing Data Patterns
      3. 9.3 - Effects of Missing Data on Clustering.
      4. 9.4 - Methods of Missing Data Imputation
      5. 9.5 - Obtaining Confidence Interval Estimates on Imputed Values
      6. 9.6 - Using the SAS Enterprise Miner Imputation Node
      7. 9.7 - References
    2. Chapter 10 - Product Affinity and Clustering of Product Affinities
      1. 10.1 - Motivation of Estimating Product Affinity by Segment
      2. 10.2 - Estimating Product Affinity Using Purchase Quantities
      3. 10.3 - Combining Product Affinities by Cluster Segments
      4. 10.4 - Pros and Cons of Segment Affinity Scores.
      5. 10.5 - issues with Clustering Non-normal Quantities
      6. 10.6 - Approximating a Graph-Theoretic Approach Using a Decision Tree
      7. 10.7 - Using the Product Affinities for Cross-Sell Programs
      8. 10.8 - Additional Exercises
      9. 10.9 - References
    3. Chapter 11 - Computing Segments Using SOM/Kohonen for Clustering
      1. 11.1 - When Ordinary Clustering Does not Produce Desired Results
      2. 11.2 - What is a Self-Organizing Map?
      3. 11.3 - Computing and Applying SOM Network Cluster Segments
      4. 11.4 - Comparing Clustering with SOM Segmentation
      5. 11.5 - Customer Distinction Analysis Example
      6. 11.6 - Additional Exercises
      7. 11.7 - References
    4. Chapter 12 - Segmentation of Textual Data
      1. 12.1 - Background of Textual Data in the Context of CRM
      2. 12.2 - Notes on Text Mining versus Natural Language Processing
      3. 12.3 - Simple Text Mining Example
      4. 12.4 - Text Document Clustering
      5. 12.5 - Using Text Mining in CRM Applications
      6. 12.6 - References
  13. Part 4 - Advanced Segmentation Applications
    1. Chapter 13 - Clustering of Product Associations
      1. 13.1 - What is Association Analysis and its Uses in Business?
      2. 13.2 - Market Basket Association Analysis
      3. 13.3 - Revisiting Product Affinity Using Clustered Associations
      4. 13.4 - The Business and Technical Side of Clustering Associations.
      5. 13.5 - References
    2. Chapter 14 - Predicting Attitudinal Segments from Survey Responses
      1. 14.1 - Typical Market Research Surveys.
      2. 14.2 - Match-back of Survey Responses
      3. 14.3 - Analysis of Survey Responses: An Overview
      4. 14.4 - Developing a Predictive Segmentation Model from a Survey Analysis
      5. 14.5 - Issues with Scoring a Predictive Segmentation on Customer or Prospect Data
      6. 14.6 - Assessing the Confidence of Predicted Segments
      7. 14.7 - Business Implications for Using Attitudinal Segmentation.
      8. 14.8 - References
    3. Chapter 15 - Combining Attitudinal and Behavioral Segments
      1. 15.1 - Survey of Methods of Ensemble Segmentations
      2. 15.2 - Two Methods for Combining Attitudinal and Behavioral Segments
      3. 15.3 - Presenting the Business Case Simply from a Complex Analysis
      4. 15.4 - References
      5. 15.5 - Additional Exercise
    4. Chapter 16 - Segmentation of Customer Transactions
      1. 16.1 - Measuring Transactions as a Time Series.
      2. 16.2 - References
      3. 16.3 - Additional Reading
      4. 16.4 - Additional Exercise
  14. Index
  15. Accelerate Your SAS Knowledge with SAS Books