Effective CRM using predictive analytics

Intro -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 An overview of data mining: The applications, the methodology, the algorithms, and the data -- 1.1 The applications -- 1.2 The methodology -- 1.3 The algorithms -- 1.3.1 Supervised models -- 1.3.1.1 Classification models -- 1.3.1.2 Estimation (regression) models -- 1.3.1.3 Feature selection (field screening) -- 1.3.2 Unsupervised models -- 1.3.2.1 Cluster models -- 1.3.2.2 Association (affinity) and sequence models -- 1.3.2.3 Dimensionality reduction models -- 1.3.2.4 Record screening models -- 1.4 The data -- 1.4.1 The mining datamart -- 1.4.2 The required data per industry -- 1.4.3 The customer "signature": from the mining datamart to the enriched, marketing reference table -- 1.5 Summary -- Part I The Methodology -- Chapter 2 Classification modeling methodology -- 2.1 An overview of the methodology for classification modeling -- 2.2 Business understanding and design of the process -- 2.2.1 Definition of the business objective -- 2.2.2 Definition of the mining approach and of the data model -- 2.2.3 Design of the modeling process -- 2.2.3.1 Defining the modeling population -- 2.2.3.2 Determining the modeling (analysis) level -- 2.2.3.3 Definition of the target event and population -- 2.2.3.4 Deciding on time frames -- 2.3 Data understanding, preparation, and enrichment -- 2.3.1 Investigation of data sources -- 2.3.2 Selecting the data sources to be used -- 2.3.3 Data integration and aggregation -- 2.3.4 Data exploration, validation, and cleaning -- 2.3.5 Data transformations and enrichment -- 2.3.6 Applying a validation technique -- 2.3.6.1 Split or Holdout validation -- 2.3.6.2 Cross or n-fold validation -- 2.3.6.3 Bootstrap validation -- 2.3.7 Dealing with imbalanced and rare outcomes -- 2.3.7.1 Balancing -- 2.3.7.2 Applying class weights.