LEADER 04956nam 2200709 450 001 9910814030903321 005 20231222060855.0 010 $a1-119-01157-4 010 $a1-119-01158-2 010 $a1-119-01156-6 035 $a(CKB)3710000000498433 035 $a(EBL)4183006 035 $a(SSID)ssj0001570174 035 $a(PQKBManifestationID)16221931 035 $a(PQKBTitleCode)TC0001570174 035 $a(PQKBWorkID)13229196 035 $a(PQKB)10754459 035 $a(PQKBManifestationID)14920304 035 $a(PQKBWorkID)12796373 035 $a(PQKB)23204944 035 $a(DLC) 2015022946 035 $a(Au-PeEL)EBL4183006 035 $a(CaPaEBR)ebr11125576 035 $a(OCoLC)910936405 035 $a(CaSebORM)9781119011552 035 $a(MiAaPQ)EBC4183006 035 $a(MiAaPQ)EBC4183016 035 $a(EXLCZ)993710000000498433 100 $a20160105h20162016 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEffective CRM using predictive analytics /$fAntonios Chorianopoulos 210 1$aWest Sussex, England :$cWiley,$d2016. 210 4$dİ2016 215 $a1 online resource (390 p.) 225 1 $aTHEi Wiley ebooks 300 $aDescription based upon print version of record. 311 $a1-119-01155-8 320 $aIncludes bibliographical references and index. 327 $aTitle 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 327 $a1.4.2 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 327 $a2.2.3.3 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 327 $a2.4 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 327 $a2.6.2 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 327 $a2.12.1.3 Profiling of "premium" product owners 410 0$aTHEi Wiley ebooks. 606 $aCustomer relations$xManagement$xData processing 606 $aData mining 615 0$aCustomer relations$xManagement$xData processing. 615 0$aData mining. 676 $a658.8/12 700 $aChorianopoulos$b Antonios$0522177 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910814030903321 996 $aEffective CRM using predictive analytics$93995121 997 $aUNINA