Contents

About This Book

About the Author

Chapter 1: Defining the Business Objective

Introduction

Setting Goals

Descriptive Analyses

Customer Profile

Customer Loyalty

Market Penetration or Wallet Share

Predictive Analyses

Marketing Models

Risk and Approval Models

Predictive Modeling Opportunities by Industry

Notes from the Field

Chapter 2: Data Types, Categories, and Sources

Introduction

The Evolution of Data

Types of Data

Nominal Data

Ordinal Data

Continuous Data

Categories of Data

Demographic or Firmographic Data

Behavioral Data

Psychographic Data

Data Category Comparison

Sources of Data

Internal Sources

Storage of Data

External Sources

Notes from the Field

Chapter 3: Overview of Descriptive and Predictive Analyses

Introduction

Descriptive Analyses

Frequency Distributions

Cluster

Decision Tree

Predictive Analyses

Linear Regression

Logistic Regression

Neural Networks

Modeling Process

Define the Objective

Develop the Model

Implement the Model

Maintain the Model

Notes from the Field

Chapter 4: Data Construction for Analysis

Introduction

Data for Descriptive Analysis

Data for Predictive Analysis

Prospect Models

Customer Models

Risk Models

External Sources of Data

Notes from the Field

Chapter 5: Descriptive Analysis Using SAS Enterprise Guide

Introduction

Project Overview

Project Initiation

Exploratory Analysis

Importing the Data

Viewing the Data

Exploring the Data

Segmentation and Profile Analysis

Correlation Analysis

Notes from the Field

Chapter 6: Market Analysis Using SAS Enterprise Guide

Introduction

Project Overview

Market Analysis

Project Initiation

Data Preparation

Penetration and Share of Wallet

Results

Notes from the Field

Chapter 7: Cluster Analysis Using SAS Enterprise Miner

Introduction

Project Overview

Cluster Analysis

Initiate the Project

Input the Data Source and Assign Variable Roles

Transform Variables

Filter Data

Build Clusters

Build Segment Profiles

Analyze Clusters and Recommend Marketing or Product Development Actions

Notes from the Field

Chapter 8: Tree Analysis Using SAS Enterprise Miner

Introduction

Project Overview

Decision Tree Analysis

Initiate the Project

Input the Data Source

Create Target Variable

Partition the Data

Build the Decision Tree

View the Decision Tree Output

Interpret the Findings

Alternate Uses for Tree Analysis

Notes from the Field

Chapter 9: Predictive Analysis Using SAS Enterprise Miner

Introduction

Select

Initiate the Project

Select the Data

Explore

StatExplore

MultiPlot

Modify

Replace Missing Values via Imputation

Partition Data into Subsamples

Manage Outliers

Transform the Variables

Model

Decision Tree

Neural Network

Regression

Assess

Notes from the Field

References