Course Description.....................................................................................................................vii

Prerequisites ..............................................................................................................................viii

General Conventions ................................................................................................................... ix

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

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

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

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

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

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

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

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