1st Edition

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

By Daniel S. Putler, Robert E. Krider Copyright 2012
    316 Pages 178 B/W Illustrations
    by Chapman & Hall

    316 Pages
    by Chapman & Hall

    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.

    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




    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.

    "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