Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R, 1st Edition (Paperback) book cover

Customer and Business Analytics

Applied Data Mining for Business Decision Making Using R, 1st Edition

By Daniel S. Putler, Robert E. Krider

Chapman and Hall/CRC

315 pages | 178 B/W Illus.

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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.


"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

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


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



About the Authors

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.

About the Series

Chapman & Hall/CRC The R Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
BUSINESS & ECONOMICS / Management Science
COMPUTERS / Programming / Games
COMPUTERS / Database Management / Data Mining
MATHEMATICS / Probability & Statistics / General