Statistical Modeling and Analysis for Database Marketing_ Effective Techniques f

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Statistical Modeling and Analysis for Database Marketing_ Effective Techniques for Mining Big Data
1 Introduction
1.1 The Personal Computer and Statistics
1.2 Statistics and Data Analysis
1.3 EDA
1.4 The EDA Paradigm
1.5 EDA Weaknesses
1.6 Small and Big Data
1.6.1 Data Size Characteristics
1.6.2 Data Size: Personal Observation of One
1.7 Data Mining Paradigm
1.8 Statistics and Machine Learning
1.9 Statistical Learning
2 Two Simple Data Mining Methods for Variable
Assessment
2.1 Correlation Coefficient
2.2 Scatterplots
2.3 Data Mining
2.3.1 Example #1
2.3.2 Example #2
2.4 Smoothed Scatterplot
2.5 General Association Test
2.6 Summary
3 Logistic Regression: The Workhorse of Database Response
Modeling
3.1 Logistic Regression Model
3.1.1 Illustration
3.1.2 Scoring a LRM
3.2 Case Study
3.2.1 Candidate Predictor and Dependent Variables
3.3 Logits and Logit Plots
3.3.1 Logits for Case Study
3.4 The Importance of Straight Data
3.5 Re-expressing for Straight Data
3.5.1 Ladder of Powers
© 2003 by CRC Press LLC
3.5.2 Bulging Rule
3.5.3 Measuring Straight Data
3.6 Straight Data for Case Study
3.6.1 Re-expressing FD2_OPEN
3.6.2 Re-expressing INVESTMENT
3.7 Techniques When Bulging Rule Does Not Apply
3.7.1 Fitted Logit Plot
3.7.2 Smooth Predicted vs. Actual Plot
3.8 Re-expressing MOS_OPEN
3.8.1 Smooth Predicted vs. Actual Plot for MOS_OPEN
3.9 Assessing the Importance of Variables
3.9.1 Computing the G Statistic
3.9.2 Importance of a Single Variable
3.9.3 Importance of a Subset of Variables
3.9.4 Comparing the Importance of Different Subsets
of Variables
3.10 Important Variables for Case Study
3.10.1 Importance of the Predictor Variables
3.11 Relative Importance of the Variables
3.11.1 Selecting the Best Subset
3.12 Best Subset of Variables for Case Study
3.13 Visual Indicators of Goodness of Model Predictions
3.13.1 Smooth Residual by Score Groups Plot
3.13.1.1 Smooth Residual by Score Groups Plot
for Case Study
3.13.2 Smooth Actual vs. Predicted by Decile Groups
Plot
3.13.2.1 Smooth Actual vs. Predicted by Decile Groups
Plot for Case Study
3.13.3 Smooth Actual vs. Predicted by Score Groups
Plot
3.13.3.1 Smooth Actual vs. Predicted by Score
Groups Plot for Case Study
3.14 Evaluating the Data Mining Work
3.14.1 Comparison of Smooth Residual by Score Groups
Plots: EDA vs. NonEDA Models
3.14.2 Comparison of Smooth Actual vs. Predicted by Decile
Groups Plots: EDA vs. NonEDA Models
3.14.3 Comparison of Smooth Actual vs. Predicted by Score
Groups Plots: EDA vs. NonEDA Models
3.14.4 Summary of the Data Mining Work
3.15 Smoothing a Categorical Variable
3.15.1 Smoothing FD_TYPE with CHAID
3.15.2 Importance of CH_FTY_1 and CH_FTY_2
3.16 Additional Data Mining Work for Case Study
© 2003 by CRC Press LLC
3.16.1 Comparison of Smooth Residual by Score Group Plots:
4var- vs. 3var-EDA Models
3.16.2 Comparison of Smooth Actual vs. Predicted by Decile
Groups Plots: 4var- vs. 3var-EDA Models
3.16.3 Comparison of Smooth Actual vs. Predicted by Score
Groups Plots: 4var- vs. 3var-EDA Models
3.16.4 Final Summary of the Additional Data Mining
Work
3.17 Summary
4 Ordinary Regression: The Workhorse of Database Profit
Modeling
4.1 Ordinary Regression Model
4.1.1 Illustration
4.1.2 Scoring A OLS Profit Model
4.2 Mini Case Study
4.2.1 Straight Data for Mini Case Study
4.2.1.1 Re-expressing INCOME
4.2.1.2 Re-expressing AGE
4.2.2 Smooth Predicted vs. Actual Plot
4.2.3 Assessing the Importance of Variables
4.2.3.1 Defining the F Statistic and R-squared
4.2.3.2 Importance of a Single Variable
4.2.3.3 Importance of a Subset of Variables
4.2.3.4 Comparing the Importance of Different
Subsets of Variables
4.3 Important Variables for Mini Case Study
4.3.1 Relative Importance of the Variables
4.3.2 Selecting the Best Subset
4.4 Best Subset of Variable for Case Study
4.4.1 PROFIT Model with gINCOME and AGE
4.4.2 Best PROFIT Model
4.5 Suppressor Variable AGE
4.6 Summary
5 CHAID for Interpreting a Logistic Regression Model
5.1 Logistic Regression Model
5.2 Database Marketing Response Model Case Study
5.2.1 Odds Ratio
5.3 CHAID
5.3.1 Proposed CHAID-Based Method
5.4 Multivariable CHAID Trees
5.5 CHAID Market Segmentation
5.6 CHAID Tree Graphs
5.7 Summary
© 2003 by CRC Press LLC
6 The Importance of the Regression Coefficient
6.1 The Ordinary Regression Model
6.2 Four Questions
6.3 Important Predictor Variables
6.4 P-Values and Big Data
6.5 Returning to Question #1
6.6 Predictor Variable’s Effect on Prediction
6.7 The Caveat
6.8 Returning to Question #2
6.9 Ranking Predictor Variables by Effect On Prediction
6.10 Returning to Question #3
6.11 Returning to Question #4
6.12 Summary
7 The Predictive Contribution Coefficient: A Measure
of Predictive Importance
7.1 Background
7.2 Illustration of Decision Rule
7.3 Predictive Contribution Coefficient
7.4 Calculation of Predictive Contribution Coefficient
7.5 Extra Illustration of Predictive Contribution Coefficient
7.6 Summary
8 CHAID for Specifying a Model with Interaction
Variables
8.1 Interaction Variables
8.2 Strategy for Modeling with Interaction Variables
8.3 Strategy Based on the Notion of a Special Point
8.4 Example of a Response Model with an Interaction
Variable
8.5 CHAID for Uncovering Relationships
8.6 Illustration of CHAID for Specifying a Model
8.7 An Exploratory Look
8.8 Database Implication
8.9 Summary
9 Market Segment Classification Modeling with Logistic
Regression
9.1 Binary Logistic Regression
9.1.1 Necessary Notation
9.2 Polychotomous Logistic Regression Model
9.3 Model Building with PLR
9.4 Market Segmentation Classification Model
9.4.1 Survey of Cellular Phone Users
9.4.2 CHAID Analysis
© 2003 by CRC Press LLC
9.4.3 CHAID Tree Graphs
9.4.4 Market Segment Classification Model
9.5 Summary
10 CHAID as a Method for Filling in Missing Values
10.1 Introduction to the Problem of Missing Data
10.2 Missing-Data Assumption
10.3 CHAID Imputation
10.4 Illustration
10.4.1 CHAID Mean-Value Imputation for a Continuous
Variable
10.4.2 Many Mean-Value CHAID Imputations for a Continuous
Variable
10.4.3 Regression-Tree Imputation for LIF_DOL
10.5 CHAID Most-Likely Category Imputation for a Categorical
Variable
10.5.1 CHAID Most-Likely Category Imputation for
GENDER
10.5.2 Classification Tree Imputation for GENDER
10.6 Summary
11 Identifying Your Best Customers: Descriptive, Predictive and
Look-Alike Profiling
11.1 Some Definitions
11.2 Illustration of a Flawed Targeting Effort
11.3 Well-Defined Targeting Effort
11.4 Predictive Profiles
11.5 Continuous Trees
11.6 Look-Alike Profiling
11.7 Look-Alike Tree Characteristics
11.8 Summary
12 Assessment of Database Marketing Models
12.1 Accuracy for Response Model
12.2 Accuracy for Profit Model
12.3 Decile Analysis and Cum Lift for Response Model
12.4 Decile Analysis and Cum Lift for Profit Model
12.5 Precision for Response Model
12.6 Precision for Profit Model
12.6.1 Construction of SWMAD
12.7 Separability for Response and Profit Models
12.8 Guidelines for Using Cum Lift, HL/SWMAD and CV
12.9 Summary
© 2003 by CRC Press LLC
13 Bootstrapping in Database Marketing: A New Approach for
Validating Models
13.1 Traditional Model Validation
13.2 Illustration
13.3 Three Questions
13.4 The Bootstrap
13.4.1 Traditional Construction of Confidence Intervals
13.5 How to Bootstrap
13.5.1 Simple Illustration
13.6 Bootstrap Decile Analysis Validation
13.7 Another Question
13.8 Bootstrap Assessment of Model Implementation
Performance
13.8.1 Illustration
13.9 Bootstrap Assessment of Model Efficiency
13.10 Summary
14 Visualization of Database Models
14.1 Brief History of the Graph
14.2 Star Graph Basics
14.2.1 Illustration
14.3 Star Graphs for Single Variables
14.4 Star Graphs for Many Variables Considered Jointly
14.5 Profile Curves Method
14.5.1 Profile Curves Basics
14.5.2 Profile Analysis
14.6 Illustration
14.6.1 Profile Curves for RESPONSE Model
14.6.2 Decile-Group Profile Curves
14.7 Summary
14.8 SAS Code for Star Graphs for Each Demographic Variable
about the Deciles
14.9 SAS Code for Star Graphs for Each Decile about the
Demographic Variables
14.10 SAS Code for Profile Curves: All Deciles
15 Genetic Modeling in Database Marketing: The GenIQ
Model
15.1 What Is Optimization?
15.2 What Is Genetic Modeling?
15.3 Genetic Modeling: An Illustration
15.3.1 Reproduction
15.3.2 Crossover
15.3.3 Mutation
15.4 Parameters for Controlling a Genetic Model Run
© 2003 by CRC Press LLC
15.5 Genetic Modeling: Strengths and Limitations
15.6 Goals of Modeling in Database Marketing
15.7 The GenIQ Response Model
15.8 The GenIQ Profit Model
15.9 Case Study — Response Model
15.10 Case Study — Profit Model
15.11 Summary
16 Finding the Best Variables for Database Marketing
Models
16.1 Background
16.2 Weakness in the Variable Selection Methods
16.3 Goals of Modeling in Database Marketing
16.4 Variable Selection with GenIQ
16.4.1 GenIQ Modeling
16.4.2 GenIQ-Structure Identification
16.4.3 GenIQ Variable Selection
16.5 Nonlinear Alternative to Logistic Regression Model
16.6 Summary
17 Interpretation of Coefficient-Free Models
17.1 The Linear Regression Coefficient
17.1.1 Illustration for the Simple Ordinary Regression
Model
17.1.2 Illustration for the Simple Logistic Regression
Model
17.2 The Quasi-Regression Coefficient for Simple Regression
Models
17.2.1 Illustration of Quasi-RC for the Simple Ordinary
Regression Model
17.2.2 Illustration of Quasi-RC for the Simple Logistic
Regression Model
17.2.3 Illustration of Quasi-RC for Nonlinear
Predictions
17.3 Partial Quasi-RC for the Everymodel
17.3.1 Calculating the Partial Quasi-RC for the
Everymodel
17.3.2 Illustration for the Multiple Logistic Regression
Model
17.4 Quasi-RC for a Coefficient-Free Model
17.4.1 Illustration of Quasi-RC for a Coefficient-Free
Model

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