Customer and Business Analytics

Applied Data Mining for Business Decision Making Using R
1. Auflage 2012
ISBN: 978-1-4665-0396-0
Verlag: CRC Press

Buch, Englisch, 316 Seiten, Format (B × H): 179 mm x 261 mm, Gewicht: 722 g

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

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

Inhaltsverzeichnis

I Purpose and Process: Database Marketing and Data Mining. A Process Model for Data Mining-CRISP-DM. II Predictive Modeling Tools: Basic Tools for Understanding Data. Multiple Linear Regression. Logistic Regression. Lift Charts. Tree Models. Neural Network Models. Putting It All Together. III Grouping Methods: Ward's Method of Cluster Analysis and Principal Components. K-Centroids Partitioning Cluster Analysis. 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.