| Preface | p. i |
| Laying the Groundwork for Data Mining Technology | |
| An Introduction to Information Technology and Business Intelligence | p. 2 |
| A Driving Source of Productivity (IT, Economic Theory and Business Strategy) | p. 2 |
| An Introduction to Business Intelligence | p. 5 |
| The BI Spectrum: Data Extraction and Report Writing, OLAP, Internets, Extranets and the Internet | p. 5 |
| Business Intelligence Extended (An Introduction to Data Mining) | p. 7 |
| An Introduction to Data Mining Methodologies | p. 8 |
| Visualization Tools for Reporting and Monitoring: The Humble Chart | p. 12 |
| The Business Intelligence Cycle | p. 17 |
| Using Mining to Extend OLAP | p. 18 |
| Closing Thoughts on Business Intelligence and Productivity | p. 19 |
| Reducing Uncertainty by Minimizing the Variance | p. 20 |
| Data Mining Defined | p. 23 |
| The Roots of Data Mining | p. 23 |
| A Closer Look at the Mining Process: (The Traditional Method) | p. 24 |
| Segmentation and Classification Revisited | p. 28 |
| Neural Network and Regression Mining (The Robust Approach) | p. 32 |
| A Brief History of Neural Networks | p. 32 |
| Back to Regression and Neural Nets | p. 34 |
| How Mining Differs from the Traditional Approach (A Focus on Neural Nets and Regression) | p. 36 |
| Summing up the Definition of Data Mining | p. 37 |
| A Broad Overview of Data Mining Technologies | p. 38 |
| Steps To Success for the Mining Process | p. 43 |
| Mining the Right Data (Garbage In, Garbage Out) | p. 43 |
| How Much Is My Existing Data Worth? | p. 44 |
| Think First, Mine Later (Nine Easy Steps for Success) | p. 45 |
| Pitfalls: Torturing the Data to Get the Answer You Want to Hear | p. 53 |
| Pitfall: The Pursuit of Statistical Perfection | p. 54 |
| Bridging the Steps to Success to 6 Sigma Applications (Productivity Enhancing Strategies) | p. 56 |
| Essential Mining Approaches to Problem Solving | p. 58 |
| Forecasting Tools | p. 58 |
| Forecasting: Univariate and Multivariate | p. 58 |
| From Analysis Over Time to Analysis of a Snapshot in Time | p. 64 |
| Target Measures and Probability Mining | p. 67 |
| Prevalent Applications in the World of Commerce | |
| A Closer Look at Marketing/Advertising, Promotions and Pricing Policies Using Econometric Based Modeling | p. 70 |
| Regression/Neural Networks for Marketing Analysis | p. 71 |
| Measuring the Immediate Impact of Advertising | p. 75 |
| Measuring the Impact of Price and Promotions | p. 79 |
| Measuring the Longer Term Impact of Advertising | p. 81 |
| Other Techniques to Measure Longer Term Impacts of Advertising on Target Measures | p. 84 |
| A Closer Look at Pricing Strategies | p. 85 |
| Price Optimization Using Cross-Sections: Data Mining for Pricing | p. 85 |
| Price Elasticity | p. 86 |
| Finding the Optimum and Break-Even Prices | p. 87 |
| A Brief Mention of Other Regression and Neural Network Applications | p. 90 |
| Market Research Analysis | p. 90 |
| Personnel Performance and Retention (HR) | p. 90 |
| Personnel Selection | p. 91 |
| Retail Outlet Location Analysis | p. 91 |
| Business Unit Analysis | p. 92 |
| Closing Thoughts | p. 92 |
| Turning Your Brick and Mortar into a Click and Mortar by Engage Inc. | p. 94 |
| The Establishment Goes Wired | p. 94 |
| Online Profiling is the Key | p. 96 |
| The 360 Degree Customer View | p. 98 |
| Modify and Convert | p. 100 |
| A Word on Privacy | p. 102 |
| To Infinity...And Beyond | p. 103 |
| Did You Ever Think Your Data Would Be This Valuable? | p. 105 |
| Improving the Web Experience through Real-Time Analysis (A Market Basket Approach) by Macromedia | p. 106 |
| An Introduction to Internet Personalization | p. 107 |
| Basic Personalization | p. 108 |
| Collaborative Filtering Brings New Dimension of Pattern Recognition to Personalization | p. 109 |
| Item Affinity: An Extension to Traditional Market Basket Analysis | p. 110 |
| One Advantage of Market Basket Analysis Over Item Affinity | p. 110 |
| Item Affinity: Taking the Best of Market Basket Theory to the Internet | p. 111 |
| How an Implementation of Item Affinity Works in Comparison to Market Basket Analysis | p. 111 |
| A Recap of Why Item Affinity is Superior to Market Basket Analysis for Online Marketing | p. 112 |
| Feature Comparisons of Market Basket Analysis and an Implementation of Real-Time Item Affinity | p. 113 |
| A More Detailed Description of Item Affinity | p. 114 |
| Counting Basics--How Item Affinity Goes About Its Tasks | p. 114 |
| Multiple Baskets: Telling Item Affinity How to Count By Compressing Time | p. 114 |
| Multiple Events: Telling Item Affinity What to Count by Expanding Events | p. 116 |
| Item Affinity in the Real World | p. 116 |
| Conclusion | p. 118 |
| Bringing It All Together (Data Mining on an Enterprise Level) | p. 119 |
| The Gap Problems (Communication and Knowledge) | p. 120 |
| Steps to Achieving a Total Solution with Mining | p. 121 |
| One Model Rarely Captures an Entire Business Solution: A Human Resource Application | p. 121 |
| Model Optimization and an Introduction to Variance | p. 124 |
| CRM Revisited | p. 124 |
| Other Factors that Promote a Dynamic Business Environment | p. 125 |
| The Changing Structure of the Economy and Macro Model Optimization | p. 125 |
| Feedback from Functional Areas | p. 127 |
| The Data Mining Solution within the BI Spectrum and Integration with Other IT Components | p. 132 |
| Using IT to Survive in the Information Age | p. 135 |
| What the Future Holds for Data Mining | p. 137 |
| A Quick Word on Innovations in Algorithms | p. 138 |
| The Evolution of E-Business and New Data Mining | p. 138 |
| User Friendly Mining Reports | p. 139 |
| Regression and Neural Network Reports | p. 141 |
| CRM One More Time | p. 142 |
| Continued Streamlining or Simplification of the Data Access and Extraction Process for Mining | p. 143 |
| Enhancements in Data Storage Techniques (Warehouses and Marts) | p. 144 |
| Closing the Gap (The Evolution of the Workforce and Data Mining Technology) | p. 145 |
| Conclusion | p. 147 |
| Appendices | p. 149 |
| About the Authors | p. 164 |
| Index | p. 165 |
| Table of Contents provided by Syndetics. All Rights Reserved. |
