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Customer and Business Analytics Applied Data Mining for Business Decision Making Using R

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ISBN-10: 1466503963

ISBN-13: 9781466503960

Edition: 2012

Authors: Robert E. Krider, Daniel S. Putler

List price: $50.82
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A practical hands-on guide, this book covers database marketing, surveys, and other data sources for businesses. Each chapter introduces a method or model and ends with a detailed tutorial that guides students through an analysis of a real case. The book takes a data mining approach yet readers are encouraged to consider the substantive business problem rather than taking a purely exploratory approach to customer analytics. The text is suitable for advanced undergraduate or Master's courses on marketing studying customer analytics, data mining for marketing, database marketing, marketing analytics, or business intelligence.
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Book details

List price: $50.82
Copyright year: 2012
Publisher: Taylor & Francis Group
Publication date: 5/7/2012
Binding: Paperback
Pages: 315
Size: 7.20" wide x 10.31" long x 0.94" tall
Weight: 1.826
Language: English

List of Figures
List of Tables
Purpose and Process
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
A Process Model for Data Mining-CRISP-DM
History and Background
The Basic Structure of CRISP-DM
The Process Model within a Phase
The CRISP-DM Phases in More Detail
Business Understanding
Data Understanding
Data Preparation
The Typical Allocation of Effort across Project Phases
Predictive Modeling Tools
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
Multiple Linear Regression
Jargon Clarification
Graphical and Algebraic Representation of the Single Predictor Problem
The Probability of a Relationship between the Variables
Multiple Regression
Categorical Predictors
Nonlinear Relationships and Variable Transformations
Too Many Predictor Variables: Overfitting and Adjusted R<sup>2</sup>
Data Visualization and Linear Regression Tutorial
Logistic Regression
A Graphical Illustration of the Problem
The Generalized Linear Model
Logistic Regression Details
Logistic Regression Tutorial
Highly Targeted Database Marketing
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
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
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
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