Data Mining Techniques in CRM: Inside Customer Segmentation

365 Pages·2009·71.166 MB·English

by  Konstantinos Tsiptsis, Antonios Chorianopoulos(auth.)

Data Mining Techniques
in CRM
Data Mining Techniques in CRM: Inside Customer Segmentation Konstantinos Tsiptsis and Antonios Chorianopoulos
© 2009 John Wiley & Sons, Ltd. ISBN: 978-0-470-74397-3
Data Mining Techniques
in CRM
Inside Customer Segmentation
Konstantinos Tsiptsis
CRM&CustomerIntelligenceExpert,Athens,Greece
Antonios Chorianopoulos
DataMiningExpert,Athens,Greece
A John Wiley and Sons, Ltd., Publication
Thiseditionfirstpublished2009
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TomydaughterEugeniaandmywifeVirginia,fortheirsupportandunderstanding.
Andtomyparents.
–Antonios
Inmemoryofmyfather.
DedicatedtomydaughtersMarcellaandChristina,mywifeMaria,mysisterMarinaand
mynieceJuliaandofcourse,tomymotherMariawhotaughtmetosetmygoalsinlife.
–Konstantinos
CONTENTS
ACKNOWLEDGEMENTS xiii
1 DATAMININGINCRM 1
TheCRMStrategy 1
WhatCanDataMiningDo? 2
Supervised/PredictiveModels 3
UnsupervisedModels 3
DataMiningintheCRMFramework 4
CustomerSegmentation 4
DirectMarketingCampaigns 5
MarketBasketandSequenceAnalysis 7
TheNextBestActivityStrategyand‘‘Individualized’’CustomerManagement 8
TheDataMiningMethodology 10
DataMiningandBusinessDomainExpertise 13
Summary 13
2 ANOVERVIEWOFDATAMININGTECHNIQUES 17
SupervisedModeling 17
PredictingEventswithClassificationModeling 19
EvaluationofClassificationModels 25
ScoringwithClassificationModels 32
MarketingApplicationsSupportedbyClassificationModeling 32
SettingUpaVoluntaryChurnModel 33
FindingUsefulPredictorswithSupervisedFieldScreeningModels 36
PredictingContinuousOutcomeswithEstimationModeling 37
UnsupervisedModelingTechniques 39
SegmentingCustomerswithClusteringTechniques 40
ReducingtheDimensionalityofDatawithDataReductionTechniques 47
Finding‘‘WhatGoeswithWhat’’withAssociationorAffinityModeling
Techniques 50
DiscoveringEventSequenceswithSequenceModelingTechniques 56
DetectingUnusualRecordswithRecordScreeningModelingTechniques 59
MachineLearning/ArtificialIntelligencevs.StatisticalTechniques 61
Summary 62
viii CONTENTS
3 DATAMININGTECHNIQUESFORSEGMENTATION 65
SegmentingCustomerswithDataMiningTechniques 65
PrincipalComponentsAnalysis 65
PCADataConsiderations 67
HowManyComponentsAretoBeExtracted? 67
WhatIstheMeaningofEachComponent? 75
DoestheSolutionAccountforAlltheOriginalFields? 78
ProceedingtotheNextStepswiththeComponentScores 79
RecommendedPCAOptions 80
ClusteringTechniques 82
DataConsiderationsforClusteringModels 83
ClusteringwithK-means 85
RecommendedK-meansOptions 87
ClusteringwiththeTwoStepAlgorithm 88
RecommendedTwoStepOptions 90
ClusteringwithKohonenNetwork/Self-organizingMap 91
RecommendedKohonenNetwork/SOMOptions 93
ExaminingandEvaluatingtheClusterSolution 96
TheNumberofClustersandtheSizeofEachCluster 96
CohesionoftheClusters 97
SeparationoftheClusters 99
UnderstandingtheClustersthroughProfiling 100
ProfilingtheClusterswithIBMSPSSModeler’sClusterViewer 102
AdditionalProfilingSuggestions 105
SelectingtheOptimalClusterSolution 108
ClusterProfilingandScoringwithSupervisedModels 110
AnIntroductiontoDecisionTreeModels 110
TheAdvantagesofUsingDecisionTreesforClassificationModeling 121
OneGoal,DifferentDecisionTreeAlgorithms:C&RT,C5.0,andCHAID 123
RecommendedCHAIDOptions 125
Summary 127
4 THEMININGDATAMART 133
DesigningtheMiningDataMart 133
TheTimeFrameCoveredbytheMiningDataMart 135
TheMiningDataMartforRetailBanking 137
CurrentInformation 138
CustomerInformation 138
ProductStatus 138
MonthlyInformation 140
SegmentandGroupMembership 141
ProductOwnershipandUtilization 141
BankTransactions 141
LookupInformation 143
ProductCodes 144
CONTENTS ix
TransactionChannels 145
TransactionTypes 145
TheCustomer‘‘Signature’’–fromtheMiningDataMarttotheMarketing
CustomerInformationFile 148
CreatingtheMCIFthroughDataProcessing 149
DerivedMeasuresUsedtoProvidean‘‘Enriched’’CustomerView 154
TheMCIFforRetailBanking 155
TheMiningDataMartforMobileTelephonyConsumer(Residential)Customers 160
MobileTelephonyDataandCDRs 162
TransformingCDRDataintoMarketingInformation 162
CurrentInformation 163
CustomerInformation 164
RatePlanHistory 165
MonthlyInformation 167
OutgoingUsage 167
IncomingUsage 169
OutgoingNetwork 170
IncomingNetwork 170
LookupInformation 170
RatePlans 171
ServiceTypes 171
Networks 172
TheMCIFforMobileTelephony 172
TheMiningDataMartforRetailers 177
TransactionRecords 179
CurrentInformation 179
CustomerInformation 179
MonthlyInformation 180
Transactions 180
PurchasesbyProductGroups 182
LookupInformation 183
TheProductHierarchy 183
TheMCIFforRetailers 184
Summary 187
5 CUSTOMERSEGMENTATION 189
AnIntroductiontoCustomerSegmentation 189
SegmentationinMarketing 190
SegmentationTasksandCriteria 191
SegmentationTypesinConsumerMarkets 191
Value-BasedSegmentation 193
BehavioralSegmentation 194
Propensity-BasedSegmentation 195
LoyaltySegmentation 196
Socio-demographicandLife-StageSegmentation 198
x CONTENTS
Needs/Attitudinal-BasedSegmentation 199
SegmentationinBusinessMarkets 200
AGuideforBehavioralSegmentation 203
BehavioralSegmentationMethodology 203
BusinessUnderstandingandDesignoftheSegmentationProcess 203
DataUnderstanding,Preparation,andEnrichment 205
IdentificationoftheSegmentswithClusterModeling 208
EvaluationandProfilingoftheRevealedSegments 208
DeploymentoftheSegmentationSolution,Design,andDeliveryof
DifferentiatedStrategies 211
TipsandTricks 211
SegmentationManagementStrategy 213
AGuideforValue-BasedSegmentation 216
Value-BasedSegmentationMethodology 216
BusinessUnderstandingandDesignoftheSegmentationProcess 216
DataUnderstandingandPreparation–CalculationoftheValueMeasure 218
GroupingCustomersAccordingtoTheirValue 218
ProfilingandEvaluationoftheValueSegments 219
DeploymentoftheSegmentationSolution 219
DesigningDifferentiatedStrategiesfortheValueSegments 220
Summary 223
6 SEGMENTATIONAPPLICATIONSINBANKING 225
SegmentationforCreditCardHolders 225
DesigningtheBehavioralSegmentationProject 226
BuildingtheMiningDataset 227
SelectingtheSegmentationPopulation 228
TheSegmentationFields 230
TheAnalyticalProcess 233
RevealingtheSegmentationDimensions 233
IdentificationandProfilingofSegments 237
UsingtheSegmentationResults 256
BehavioralSegmentationRevisited:SegmentationAccordingtoAllAspectsof
CardUsage 258
TheCreditCardCaseStudy:ASummary 263
SegmentationinRetailBanking 264
WhySegmentation? 264
SegmentingCustomersAccordingtoTheirValue:TheVitalFewCustomers 267
UsingBusinessRulestoDefinetheCoreSegments 268
SegmentationUsingBehavioralAttributes 271
SelectingtheSegmentationFields 271
TheAnalyticalProcess 274
IdentifyingtheSegmentationDimensionswithPCA/FactorAnalysis 274
Segmentingthe‘‘PureMass’’CustomerswithClusterAnalysis 276
ProfilingofSegments 276
CONTENTS xi
TheMarketingProcess 283
SettingtheBusinessObjectives 283
SegmentationinRetailBanking:ASummary 288
7 SEGMENTATIONAPPLICATIONSINTELECOMMUNICATIONS 291
MobileTelephony 291
MobileTelephonyCoreSegments–SelectingtheSegmentationPopulation 292
BehavioralandValue-BasedSegmentation–SettingUptheProject 294
SegmentationFields 295
Value-BasedSegmentation 300
Value-BasedSegments:ExplorationandMarketingUsage 304
PreparingDataforClustering–CombiningFieldsintoDataComponents 307
Identifying,Interpreting,andUsingSegments 313
SegmentationDeployment 326
TheFixedTelephonyCase 329
Summary 331
8 SEGMENTATIONFORRETAILERS 333
SegmentationintheRetailIndustry 333
TheRFMAnalysis 334
TheRFMSegmentationProcedure 338
RFM:Benefits,Usage,andLimitations 345
GroupingCustomersAccordingtotheProductsTheyBuy 346
Summary 348
FURTHERREADING 349
INDEX 351
ACKNOWLEDGEMENTS
We would like to thank Vlassis Papapanagis, Leonidas Georgiou and Ioanna
KoutrouvisofSPSS,Greece.Also,AndreasKokkinos,GeorgeKrassadakis,Kyriakos
KokkalasandLoukasMaragos.SpecialthankstoIoannisMataragasforthecreation
ofthelinedrawings.