Effective CRM using predictive analytics / / Antonios Chorianopoulos

• Autore: Chorianopoulos Antonios
• Pubblicazione: West Sussex, England : , : Wiley, , 2016; ©2016
• Descrizione fisica: 1 online resource (390 p.)
• Disciplina: 658.8/12
• Soggetto topico: Customer relations - Management - Data processing; Data mining
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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 ; 2.4 Classification modeling 2.4.1 Trying different models and parameter settings; 2.4.2 Combining models; 2.4.2.1 Bagging; 2.4.2.2 Boosting; 2.4.2.3 Random Forests; 2.5 Model evaluation; 2.5.1 Thorough evaluation of the model accuracy; 2.5.1.1 Accuracy measures and confusion matrices; 2.5.1.2 Gains, Response, and Lift charts; 2.5.1.3 ROC curve; 2.5.1.4 Profit/ROI charts; 2.5.2 Evaluating a deployed model with test-control groups; 2.6 Model deployment; 2.6.1 Scoring customers to roll the marketing campaign; 2.6.1.1 Building propensity segments ; 2.6.2 Designing a deployment procedure and disseminating the results 2.7 Using classification models in direct marketing campaigns; 2.8 Acquisition modeling; 2.8.1.1 Pilot campaign; 2.8.1.2 Profiling of high-value customers; 2.9 Cross-selling modeling; 2.9.1.1 Pilot campaign; 2.9.1.2 Product uptake; 2.9.1.3 Profiling of owners; 2.10 Offer optimization with next best product campaigns; 2.11 Deep-selling modeling; 2.11.1.1 Pilot campaign; 2.11.1.2 Usage increase; 2.11.1.3 Profiling of customers with heavy product usage; 2.12 Up-selling modeling; 2.12.1.1 Pilot campaign; 2.12.1.2 Product upgrade ; 2.12.1.3 Profiling of "premium" product owners
• Titolo autorizzato: Effective CRM using predictive analytics
• ISBN: 1-119-01157-4; 1-119-01158-2; 1-119-01156-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910814030903321
• Lo trovi qui: Univ. Federico II
• Serie: THEi Wiley ebooks.