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Customer and Business Analytics

Daniel S. Putler | Robert E. Krider
€248.00
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A01=Daniel S. Putler
A01=Robert E. Krider
ANOVA Table
ARI
Author_Daniel S. Putler
Author_Robert E. Krider
Binary Target Variable
business intelligence analytics
Category=KJM
Category=UN
cluster analysis techniques
CRISP-DM framework
CSV File
Customer Relationship Management Systems
Data Mining
Data Mining Methods
Data Mining Project
Data Set
Database Marketing and Data Mining
Dialog Box
DSL Service
eq_bestseller
eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Existing Customers
Grouping Methods
Hierarchical Agglomerative Methods
Holdout Sample
Inverse Link Function
Lift Charts
McFadden R2
Minimum AIC
Missing Values
Multiple Linear Regression
NA NA
NA NA NA
Neural Network Models
practical data mining in R
Predictive Modeling Tools
principal components analysis
regression modelling
RI
Single Hidden Layer Neural Networks
Stepwise Variable Selection
supervised learning methods

Product details

  • ISBN 9781138416512
  • Weight: 453g
  • Dimensions: 174 x 246mm
  • Publication Date: 27 Jul 2017
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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.
About Daniel S. Putler | Robert E. Krider
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.

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