Customer and Business Analytic

Transcription

1 Customer and Business Analytic Applied Data Mining for Business Decision Making Using R Daniel S. Putler Robert E. Krider CRC Press Taylor &. Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group an informa business A CHAPMAN & HALL BOOK

2 Contents List of Figures List of Tables Preface xiii xxi xxiii I Purpose and Process 1 1 Database Marketing and Data Mining Database Marketing Common Database Marketing Applications Obstacles to Implementing a Database Marketing Program Who Stands to Benefit the Most from the Use of Database Marketing? Data Mining ^ Two Definitions of Data Mining Classes of Data Mining Methods Grouping Methods Predictive Modeling Methods Linking Methods to Marketing Applications 14 2 A Process Model for Data Mining CRISP-DM History and Background The Basic Structure of CRISP-DM 19 vii

3 viii Contents CRISP-DM Phases The Process Model within a Phase The CRISP-DM Phases in More Detail Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment The Typical Allocation of Effort across Project Phases 28 II Predictive Modeling Tools 31 3 Basic Tools for Understanding Data Measurement Scales Software Tools Getting R Installing R on Windows Installing R on OS X Installing the RcmdrPlugin.BCA Package and Its Dependencies Reading Data into R Tutorial Creating Simple Summary Statistics Tutorial Frequency Distributions and Histograms Tutorial Contingency Tables Tutorial 73 4 Multiple Linear Regression Jargon Clarification Graphical and Algebraic Representation of the Single Predictor Problem 83

4 Contents ix The Probability of a Relationship between the Variables Outliers Multiple Regression Categorical Predictors Nonlinear Relationships and Variable Transformations Too Many Predictor Variables: Overfitting and Adjusted R Summary Data Visualization and Linear Regression Tutorial 99 5 Logistic Regression A Graphical Illustration of the Problem The Generalized Linear Model Logistic Regression Details Logistic Regression Tutorial Highly Targeted Database Marketing Oversampling Overfitting and Model Validation Lift Charts Constructing Lift Charts Predict, Sort, and Compare to Actual Behavior Correcting Lift Charts for Oversampling Using Lift, Charts Lift Chart Tutorial Tree Models The Tree Algorithm Calibrating the Tree on an Estimation Sample Stopping Rules and Controlling Overfitting Trees Models Tutorial 172

5 x Contents 8 Neural Network Models The Biological Inspiration for Artificial Neural Networks Artificial Neural Networks as Predictive Models Neural Network Models Tutorial Putting It All Together Stepwise Variable Selection The Rapid Model Development Framework Up-Selling Using the Wesbrook Database Think about the Behavior That You Are Trying to Predict Carefully Examine the Variables Contained in the Data Set Use Decision Trees and Regression to Find the Important Predictor Variables Use a Neural Network to Examine Whether Nonlinear Relationships Are Present If There Are Nonlinear Relationships, Use Visualization to Find and Understand Them Applying the Rapid Development Framework Tutorial III Grouping Methods Ward's Method of Cluster Analysis and Principal Components Summarizing Data Sets Ward's Method of Cluster Analysis A Single Variable Example Extension to Two or More Variables Principal Components Ward's Method Tutorial 248

6 Contents xi 11 K-Centroids Partitioning Cluster Analysis How K-Centroid Clustering Works The Basic Algorithm to Find K-Centroids Clusters Specific K-Centroid Clustering Algorithms Cluster Types and the Nature of Customer Segments Methods to Assess Cluster Structure The Adjusted Rand Index to Assess Cluster Structure Reproducibility The Calinski-Harabasz Index to Assess within Cluster Homogeneity and between Cluster Separation K-Centroids Clustering Tutorial 275 Bibliography 283 Index 287