SAS training course notes: Effective Web Mining:Attracting and Keeping Valued Cy-经管之家官网！

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Effective Web Mining:Attracting and Keeping Valued Cyber Consumers (632 pages)
Course Notes of CustomerRelationship Management Training
Using Data Mining for Customer Relationship Management Using SAS's award winning statistical, data mining and information delivery software you will learn how to build models that business users and campaign managers can utilize. For people new to CRM or business decision makers, the Customer Relationship Management Through Data Mining seminar provides a solid CRM background and high-level overview of how data mining techniques can be applied to acquire and retain customers. The curriculum includes courses detailing how to prepare, create models on, and analyze customer and web data.
Course Description
This course introduces data mining methodology for solving business problems arising from commercial use of the World Wide Web. Enterprise Miner™ software provides a foundation for Web mining projects.
Additional tools from other SAS products, including SAS/STAT® and SAS/ETS® software, are used to drive applications relevant to e-commerce. The course emphasizes business problems in e-commerce and illustrates the solution of these problems using predictive modeling techniques applied to Web log,transactional, marketing, and operational data.
Table of Contents
Course Description....................................................................................................................vii
Prerequisites ..............................................................................................................................viii
General Conventions ................................................................................................................... ix
Chapter 1 Introduction ........................................................................................... 1-1
1.1 Web Sites and Web Solutions ..........................................................................................1-3
1.2 A Selection of Business Pains........................................................................................1-22
1.3 A Collection of Data Mining Tools................................................................................1-31
1.4 Introduction to Predictive Modeling..............................................................................1-38
1.5 The Apache Web Server (Optional) ...............................................................................1-66
1.6 References.....................................................................................................................1-73
Chapter 2 Data ........................................................................................................ 2-1
2.1 Types of Data ...................................................................................................................2-3
2.2 Web Log Data ................................................................................................................2-44
2.3 Cookies and Other Data Collection Tools......................................................................2-71
2.4 Proactive Web Data Gathering: Bots and Intelligent Agents .........................................2-81
2.5 Data Preparation for Predictive Modeling .....................................................................2-91
2.6 Exercises ........................................................................................................................2-99
2.7 References....................................................................................................................2-100
Chapter 3 Knowing Your Customers .................................................................... 3-1
3.1 Web Site Statistics for Evaluating Visitors ......................................................................3-3
3.2 Introduction to Clustering and Segmentation ................................................................3-53
3.3 Customer Profiling.........................................................................................................3-79
3.4 Exercises ......................................................................................................................3-119
3.5 References....................................................................................................................3-120
Chapter 4 Attracting Cyber Consumers ............................................................... 4-1
4.1 Introduction to Web Site Marketing.................................................................................4-3
4.2 Evaluating Visitor Behavior...........................................................................................4-51
4.3 Evaluating Web Page Design .........................................................................................4-69
4.4 Comparing Your Web Site to Competitors...................................................................4-106
4.5 Exercises ......................................................................................................................4-113
4.6 References....................................................................................................................4-114
Chapter 5 Evaluating Cyber Consumers .............................................................. 5-1
5.1 Descriptive Techniques for Evaluating Buyer Behavior..................................................5-3
5.2 Estimating the Propensity to Buy ..................................................................................5-21
5.3 Estimating the Propensity to Abandon the Site..............................................................5-33
5.4 Model-Based Selection of Banner Ads ..........................................................................5-54
5.5 Exercises ........................................................................................................................5-75
5.6 References.....................................................................................................................5-76
Chapter 6 Keeping Cyber Consumers .................................................................. 6-1
6.1 Data Driven Service for Shopping Comparison Sites......................................................6-3
6.2 Introduction to Recommender Systems .........................................................................6-19
6.3 Recommender System Applications ..............................................................................6-27
6.4 Exercises ........................................................................................................................6-55
6.5 References.....................................................................................................................6-56
Appendix A Data....................................................................................................... A-1
A.1 Ad Campaign Data..........................................................................................................A-3
A.2 Banner Ad Data...............................................................................................................A-4
A.3 Buy Data and Abandon Data...........................................................................................A-6
A.4 Customers Data...............................................................................................................A-9
A.5 Direct Mail Data ........................................................................................................... A-11
A.6 Financial Services Data.................................................................................................A-13
A.7 Movie Data ...................................................................................................................A-15
A.8 Path Analysis Data ........................................................................................................A-18
A.9 Profile Data ...................................................................................................................A-19
A.10 Stochastic Process Data ................................................................................................A-21
A.11 Web Logs ......................................................................................................................A-22
A.12 Web Time Series Data...................................................................................................A-23
Appendix B SAS Programs ..................................................................................... B-1
B.1 The SAS System ............................................................................................................. B-3
B.2 Reading Web Log Files................................................................................................... B-9
B.3 A SAS Robot................................................................................................................. B-14
B.4 The Output Delivery System and HTML ..................................................................... B-21
B.5 Web Stats....................................................................................................................... B-23
B.6 Time Series Methods .................................................................................................... B-27
B.7 Analysis of Data from Designed Experiments.............................................................. B-30
B.8 Transition Probabilities for Stochastic Processes.......................................................... B-31
B.9 Logistic Regression....................................................................................................... B-32
B.10 Data Driven Web Services ............................................................................................ B-34
B.11 Enterprise Miner Macro Variables and Score Code...................................................... B-40
Appendix C SEMMA Methodology.......................................................................... C-1
C.1 Enterprise Miner SEMMA Methodology ....................................................................... C-3