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 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
