Customer and Business Analytics

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

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Features

  • Enables an understanding of the types of business problems that advanced analytical tools can address
  • Explores the benefits and challenges of using data mining tools in business applications
  • Provides online access to a powerful, GUI-enhanced customized R package, allowing easy experimentation with data mining techniques
  • Includes example data sets on the book’s website

Summary

Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations.

The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects.

Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.

Table of Contents

I Purpose and Process
Database Marketing and Data Mining
Database Marketing
Data Mining
Linking Methods to Marketing Applications

A Process Model for Data Mining—CRISP-DM
History and Background
The Basic Structure of CRISP-DM

II Predictive Modeling Tools
Basic Tools for Understanding Data
Measurement Scales
Software Tools
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
Multiple Regression
Summary
Data Visualization and Linear Regression Tutorial

Logistic Regression
A Graphical Illustration of the Problem
The Generalized Linear Model
Logistic Regression Details
Logistic Regression Tutorial

Lift Charts
Constructing Lift Charts
Using Lift Charts
Lift Chart Tutorial

Tree Models
The Tree Algorithm
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
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
Principal Components
Ward’s Method Tutorial

K-Centroids Partitioning Cluster Analysis
How K-Centroid Clustering Works
Cluster Types and the Nature of Customer Segments
Methods to Assess Cluster Structure
K-Centroids Clustering Tutorial

Bibliography

Index

Author Bio(s)

Dr. Daniel S. Putler is a Data Artisan in Residence at Alteryx, a business intelligence/analytics software company.

Dr. Robert E. Krider is a professor of marketing in the Beedie School of Business at Simon Fraser University. He has also taught in Hong Kong, Shanghai, Portugal, and Germany. His research tackles questions of customer and competitor behavior in retailing and media industries.

Editorial Reviews

"This book is derived from a lecture course in data mining for MBA students. … assumes very little in the way of mathematical or statistical background. The writing style is generally good, and the book should prove useful to its target audience."
—David Scott, International Statistical Review (2013), 81, 2

 
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