Customer and business analytics : applied data mining for business decision making using R / Daniel S. Putler, Robert E. Krider.

Includes bibliographical references (p. 283-285) and index.

I Purpose and Process Database Marketing and Data Mining Database MarketingData MiningLinking Methods to Marketing ApplicationsA Process Model for Data Mining-CRISP-DM History and Background The Basic Structure of CRISP-DMII Predictive Modeling Tools Basic Tools for Understanding Data Measurement Scales Software ToolsReading Data into R Tutorial Creating Simple Summary Statistics Tutorial Frequency Distributions and Histograms Tutorial Contingency Tables TutorialMultiple Linear Regression Jargon Clarification Graphical and Algebraic Representation of the Single Predictor ProblemMultiple RegressionSummary Data Visualization and Linear Regression TutorialLogistic RegressionA Graphical Illustration of the Problem The Generalized Linear Model Logistic Regression Details Logistic Regression TutorialLift Charts Constructing Lift Charts Using Lift Charts Lift Chart TutorialTree Models The Tree Algorithm Trees Models TutorialNeural Network Models The Biological Inspiration for Artificial Neural Networks Artificial Neural Networks as Predictive Models Neural Network Models TutorialPutting It All Together Stepwise Variable Selection The Rapid Model Development FrameworkApplying the Rapid Development Framework TutorialIII 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 TutorialK-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 TutorialBibliography Index

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.