book

Customer and Business Analytics

by Daniel S. Putler, Robert E. Krider

May 2012

Beginner to intermediate

315 pages

8h 5m

English

Chapman and Hall/CRC

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Preliminaries
Series
Dedication
Preface
Part I Purpose and Process
Chapter 1 Database Marketing and Data Mining
1.1 Database Marketing1.1.1 Common Database Marketing Applications1.1.2 Obstacles to Implementing a Database Marketing Program1.1.3 Who Stands to Benefit the Most from the Use of Database Marketing?1.2 Data Mining1.2.1 Two Definitions of Data Mining1.2.2 Classes of Data Mining Methods1.2.2.1 Grouping Methods1.2.2.2 Predictive Modeling Methods1.3 Linking Methods to Marketing ApplicationsFigure 1.1Figure 1.2Table 1.1
Chapter 2 A Process Model for Data Mining—CRISP-DM
2.1 History and Background2.2 The Basic Structure of CRISP-DM2.2.1 CRISP-DM Phases2.2.2 The Process Model within a Phase2.2.3 The CRISP-DM Phases in More Detail2.2.3.1 Business Understanding2.2.3.2 Data Understanding2.2.3.3 Data Preparation2.2.3.4 Modeling2.2.3.5 Evaluation2.2.3.6 Deployment2.2.4 The Typical Allocation of Effort across Project PhasesFigure 2.1
Part II Predictive Modeling Tools
Chapter 3 Basic Tools for Understanding Data
3.1 Measurement Scales3.2 Software Tools3.2.1 Getting R3.2.2 Installing R on Windows3.2.3 Installing R on OS X3.2.4 Installing the RcmdrPlugin.BCA Package and Its Dependencies3.3 Reading Data into R Tutorial3.4 Creating Simple Summary Statistics Tutorial3.5 Frequency Distributions and Histograms Tutorial3.6 Contingency Tables TutorialFigure 3.1Figure 3.2Figure 3.3Figure 3.4Figure 3.5Figure 3.6Figure 3.7Figure 3.8Figure 3.9Figure 3.10Figure 3.11Figure 3.12Figure 3.13Figure 3.14Figure 3.15Figure 3.16Figure 3.17Figure 3.18Figure 3.19Figure 3.20Figure 3.21Figure 3.22Figure 3.23Figure 3.24Figure 3.25Figure 3.26Figure 3.27Figure 3.28Figure 3.29Figure 3.30Figure 3.31Figure 3.32Figure 3.33Figure 3.34Figure 3.35Figure 3.36Figure 3.37Figure 3.38Figure 3.39Figure 3.40Figure 3.41Figure 3.42Figure 3.43Figure 3.44Figure 3.45Figure 3.46Figure 3.47Figure 3.48Figure 3.49Figure 3.50Figure 3.51Figure 3.52Table 3.1
Chapter 4 Multiple Linear Regression
4.1 Jargon Clarification4.2 Graphical and Algebraic Representation of the Single Predictor Problem4.2.1 The Probability of a Relationship between the Variables4.2.2 Outliers4.3 Multiple Regression4.3.1 Categorical Predictors4.3.2 Nonlinear Relationships and Variable Transformations4.3.3 Too Many Predictor Variables: Overfitting and Adjusted R24.4 Summary4.5 Data Visualization and Linear Regression TutorialFigure 4.1Figure 4.2Figure 4.3Figure 4.4Figure 4.5Figure 4.6Figure 4.7Figure 4.8Figure 4.9Figure 4.10Figure 4.11Figure 4.12Figure 4.13Figure 4.14Figure 4.15Figure 4.16Figure 4.17Figure 4.18Figure 4.19Figure 4.20Figure 4.21Figure 4.22Figure 4.23Table 4.1Table 4.3Table 4.5

Chapter 5 Logistic Regression
5.1 A Graphical Illustration of the Problem5.2 The Generalized Linear Model5.3 Logistic Regression Details5.4 Logistic Regression Tutorial5.4.1 Highly Targeted Database Marketing5.4.2 Oversampling5.4.3 Overfitting and Model ValidationFigure 5.1Figure 5.2Figure 5.3Figure 5.4Figure 5.5Figure 5.6Figure 5.7Figure 5.8Figure 5.9Figure 5.10Figure 5.11Figure 5.12Figure 5.13Figure 5.14Figure 5.15Figure 5.16Figure 5.17Figure 5.18Figure 5.19Figure 5.20Figure 5.21Figure 5.22Table 5.1
Chapter 6 Lift Charts
6.1 Constructing Lift Charts6.1.1 Predict, Sort, and Compare to Actual Behavior6.1.2 Correcting Lift Charts for Oversampling6.2 Using Lift Charts6.3 Lift Chart TutorialFigure 6.1Figure 6.2Figure 6.3Figure 6.4Figure 6.5Figure 6.6Figure 6.7Figure 6.8Figure 6.9Figure 6.10Figure 6.11Figure 6.12Table 6.1Table 6.2Table 6.3
Chapter 7 Tree Models
7.1 The Tree Algorithm7.1.1 Calibrating the Tree on an Estimation Sample7.1.2 Stopping Rules and Controlling Overfitting7.2 Trees Models TutorialFigure 7.1Figure 7.2Figure 7.3Figure 7.4Figure 7.5Figure 7.6Figure 7.7Figure 7.8Figure 7.9Figure 7.10Figure 7.11Figure 7.12Figure 7.13Figure 7.14Figure 7.15Figure 7.16Table 7.1
Chapter 8 Neural Network Models
8.1 The Biological Inspiration for Artificial Neural Networks8.2 Artificial Neural Networks as Predictive Models8.3 Neural Network Models TutorialFigure 8.1Figure 8.2Figure 8.3Figure 8.4Figure 8.5Figure 8.6Figure 8.7Figure 8.8Figure 8.9Figure 8.10
Chapter 9 Putting It All Together
9.1 Stepwise Variable Selection9.2 The Rapid Model Development Framework9.2.1 Up-Selling Using the Wesbrook Database9.2.2 Think about the Behavior That You Are Trying to Predict9.2.3 Carefully Examine the Variables Contained in the Data Set9.2.4 Use Decision Trees and Regression to Find the Important Predictor Variables9.2.5 Use a Neural Network to Examine Whether Nonlinear Relationships Are Present9.2.6 If There Are Nonlinear Relationships, Use Visualization to Find and Understand Them9.3 Applying the Rapid Development Framework TutorialFigure 9.1Figure 9.2Figure 9.3Figure 9.4Figure 9.5Figure 9.6Figure 9.7Figure 9.8Figure 9.9Figure 9.10Figure 9.11Figure 9.12Figure 9.13Table 9.1Table 9.2Table 9.3Table 9.4Table 9.5Table 9.6
Part III Grouping Methods
Chapter 10 Ward's Method of Cluster Analysis and Principal Components
10.1 Summarizing Data Sets10.2 Ward’s Method of Cluster Analysis10.2.1 A Single Variable Example10.2.2 Extension to Two or More Variables10.3 Principal Components10.4 Ward’s Method TutorialFigure 10.1Figure 10.2Figure 10.3Figure 10.4Figure 10.5Figure 10.6Figure 10.7Figure 10.8Figure 10.9Figure 10.10Figure 10.11Figure 10.12Figure 10.13Figure 10.14Figure 10.15Figure 10.16Table 10.1
Chapter 11 K-Centroids Partitioning Cluster Analysis
11.1 How K-Centroid Clustering Works11.1.1 The Basic Algorithm to Find K-Centroids Clusters11.1.2 Specific K-Centroid Clustering Algorithms11.2 Cluster Types and the Nature of Customer Segments11.3 Methods to Assess Cluster Structure11.3.1 The Adjusted Rand Index to Assess Cluster Structure Reproducibility11.3.2 The Calinski-Harabasz Index to Assess within Cluster Homogeneity and between Cluster Separation11.4 K-Centroids Clustering TutorialFigure 11.1Figure 11.2Figure 11.3Figure 11.4Figure 11.5Figure 11.6Figure 11.7Figure 11.8Figure 11.9Figure 11.10Figure 11.11Table 11.1Table 11.2
Bibliography

Content preview from Customer and Business Analytics

Chapter 10

Ward’s Method of Cluster Analysis and Principal Components

10.1 Summarizing Data Sets

Data mining, or any form of statistical analysis for that matter, can be viewed as a set of methods to summarize large amounts of data so that we can usefully interpret the data. Collapsing the data by simply grouping it is common and useful. Cluster analysis and principal components are two broad classes of methods for grouping data. Since we will be using both in the tutorials that follow, a brief explanation of the difference is warranted.

Consider a typical data table that has one row for each individual (a row is also called a “case” or “record”). Across the top, we have the names of the variables (or “fields”) which describe the individuals, ...

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Customer and Business Analytics

by Daniel S. Putler, Robert E. Krider

Ward’s Method of Cluster Analysis and Principal Components

10.1 Summarizing Data Sets

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