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| List of Figures | |
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| List of Tables | |
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| Preface | |
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| Purpose and Process | |
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| Database Marketing and Data Mining | |
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| Database Marketing | |
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| Common Database Marketing Applications | |
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| Obstacles to Implementing a Database Marketing Program | |
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| Who Stands to Benefit the Most from the Use of Database Marketing? | |
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| Data Mining | |
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| Two Definitions of Data Mining | |
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| Classes of Data Mining Methods | |
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| Grouping Methods | |
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| Predictive Modeling Methods | |
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| Linking Methods to Marketing Applications | |
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| A Process Model for Data Mining-CRISP-DM | |
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| History and Background | |
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| The Basic Structure of CRISP-DM | |
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| CRISP-DM Phases | |
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| The Process Model within a Phase | |
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| The CRISP-DM Phases in More Detail | |
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| Business Understanding | |
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| Data Understanding | |
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| Data Preparation | |
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| Modeling | |
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| Evaluation | |
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| Deployment | |
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| The Typical Allocation of Effort across Project Phases | |
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| Predictive Modeling Tools | |
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| Basic Tools for Understanding Data | |
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| Measurement Scales | |
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| Software Tools | |
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| Getting R | |
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| Installing R on Windows | |
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| Installing R on OS X | |
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| Installing the RcmdrPlugin.BCA Package and Its Dependencies | |
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| Reading Data into R Tutorial | |
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| Creating Simple Summary Statistics Tutorial | |
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| Frequency Distributions and Histograms Tutorial | |
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| Contingency Tables Tutorial | |
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| Multiple Linear Regression | |
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| Jargon Clarification | |
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| Graphical and Algebraic Representation of the Single Predictor Problem | |
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| The Probability of a Relationship between the Variables | |
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| Outliers | |
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| Multiple Regression | |
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| Categorical Predictors | |
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| Nonlinear Relationships and Variable Transformations | |
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| Too Many Predictor Variables: Overfitting and Adjusted R<sup>2</sup> | |
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| Summary | |
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| Data Visualization and Linear Regression Tutorial | |
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| Logistic Regression | |
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| A Graphical Illustration of the Problem | |
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| The Generalized Linear Model | |
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| Logistic Regression Details | |
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| Logistic Regression Tutorial | |
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| Highly Targeted Database Marketing | |
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| Oversampling | |
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| Overfitting and Model Validation | |
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| Lift Charts | |
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| Constructing Lift Charts | |
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| Predict, Sort, and Compare to Actual Behavior | |
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| Correcting Lift Charts for Oversampling | |
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| Using Lift Charts | |
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| Lift Chart Tutorial | |
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| Tree Models | |
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| The Tree Algorithm | |
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| Calibrating the Tree on an Estimation Sample | |
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| Stopping Rules and Controlling Overfitting | |
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| Trees Models Tutorial | |
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| Neural Network Models | |
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| The Biological Inspiration for Artificial Neural Networks | |
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| Artificial Neural Networks as Predictive Models | |
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| Neural Network Models Tutorial | |
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| Putting It All Together | |
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| Stepwise Variable Selection | |
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| The Rapid Model Development Framework | |
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| Up-Selling Using the Wesbrook Database | |
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| Think about the Behavior That You Are Trying to Predict | |
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| Carefully Examine the Variables Contained in the Data Set | |
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| Use Decision Trees and Regression to Find the Important Predictor Variables | |
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| Use a Neural Network to Examine Whether Nonlinear Relationships Are Present | |
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| If There Are Nonlinear Relationships, Use Visualization to Find and Understand Them | |
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| Applying the Rapid Development Framework Tutorial | |
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| Grouping Methods | |
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| Ward's Method of Cluster Analysis and Principal Components | |
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| Summarizing Data Sets | |
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| Ward's Method of Cluster Analysis | |
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| A Single Variable Example | |
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| Extension to Two or More Variables | |
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| Principal Components | |
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| Ward's Method Tutorial | |
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| K-Centroids Partitioning Cluster Analysis | |
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| How K-Centroid Clustering Works | |
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| The Basic Algorithm to Find K-Centroids Clusters | |
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| Specific K-Centroid Clustering Algorithms | |
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| Cluster Types and the Nature of Customer Segments | |
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| Methods to Assess Cluster Structure | |
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| The Adjusted Rand Index to Assess Cluster Structure Reproducibility | |
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| The Calinski-Harabasz Index to Assess within Cluster Homogeneity and between Cluster Separation | |
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| K-Centroids Clustering Tutorial | |
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| Bibliography | |
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| Index | |