Customer Segmentation and Clustering Using SAS Enterprise Miner,Third Edition
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284 pages
English

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Understanding your customers is the key to your company’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.

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Publié par
Date de parution 23 mars 2017
Nombre de lectures 0
EAN13 9781629605272
Langue English
Poids de l'ouvrage 19 Mo

Informations légales : prix de location à la page 0,0122€. Cette information est donnée uniquement à titre indicatif conformément à la législation en vigueur.

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Customer Segmentation and Clustering Using SAS Enterprise Miner
Third Edition
Randall S. Collica
support.sas.com/bookstore
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)
All Rights Reserved. Produced in the United States of America.
For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc.
For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication.
The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others rights is appreciated.
U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 227.7202-4, and, to the extent required under U.S. federal law, the minimum restricted rights as set out in FAR 52.227-19 (DEC 2007). If FAR 52.227-19 is applicable, this provision serves as notice under clause (c) thereof and no other notice is required to be affixed to the Software or documentation. The Government s rights in Software and documentation shall be only those set forth in this Agreement.
SAS Institute Inc., SAS Campus Drive, Cary, NC 27513-2414
March 2017
SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration.
Other brand and product names are trademarks of their respective companies.
SAS software may be provided with certain third-party software, including but not limited to open-source software, which is licensed under its applicable third-party software license agreement. For license information about third-party software distributed with SAS software, refer to http://support.sas.com/thirdpartylicenses .
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

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