Putler / Krider Customer and Business Analytics

Applied Data Mining for Business Decision Making Using R
1. Auflage 2012
ISBN: 978-1-4665-0398-4
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 315 Seiten

Reihe: Chapman & Hall/CRC The R Series

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.

Zielgruppe

Advanced undergraduate and Master's students in business and marketing.

Autoren/Hrsg.

Putler, Daniel S.

Krider, Robert E.

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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

Über Autor(innen)

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.