基于數(shù)據(jù)挖掘的電信行業(yè)中客戶流失模型的分析與實現(xiàn)

基于數(shù)據(jù)挖掘的電信行業(yè)中客戶流失模型的分析與實現(xiàn)

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頁數(shù):71頁

時間:2019-01-30

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1、浙江理工大學(xué)碩士學(xué)位論文基于數(shù)據(jù)挖掘的電信行業(yè)中客戶流失模型的研究與實現(xiàn)AbstractInrecentyears,customerchumintelecombecomesserious.ForthethreeoperatorsincludingMobile,ChinaUnicornandTelcomwhoCallassuretheircustomersnottoabandonthedefaultgotoanotherone,atthesametimecangetthelostfromothers,wil

2、lbethefinalwinnerAbittercontesthaslaunchedamongoperatorswhodoalltheycouldtogainmorecustomers.However,operatorspaymuchmoreattentiontocustomerschurnedratherthanthosewhoarereducingtheirconsumption.Hardlyrealizethatthosepeoplearerunningoffgraduallyaspotential

3、lOSScustomers.Telecommunicationsindus仃),hasamassofdataanddiversitywhichmeansthateverycustomerhasalargenumberofattributeswhichcalledvariablesinthedataminingmodel,suchasARPU,chargeway,downtimesetc.Inordertobettermodel,thispaperdesignswidesheetfromthreeaspec

4、tsofoperatorsendsmessagestoremindcustomer,customerperceptivevalueandvaluebehaviorofcustomerbasedontheassumptionthatthereasonsforcustomerschum.Thendividesthesevariablesintodifferentgroupstodecidewhichvariablestoparticipateinmodelmodeling.Combinedthecustome

5、r’Sowncharacteristicsandthegroupingvariablesthensubdivideallofthecustomers.Customer-chummodelbecomesmoreandmoreintelecommunicationsindustry,inordertoimprovethehitrateofthemodel,thispaperproposesacombinedmodelphilosophy.Thecombinedmodelisbasedontheconstrai

6、ntmodel,predictionmodel,markmodel.Constraintmodelselectsvariableshaslargedistinctiontobeconstraintconditions,predictionmodelscreensvisiblelossandrelativelyobviousvariables,markmodelselectsimplicitcustomerchumvariablestomakeupsmallamountofsamplestoidentify

7、customermorecomprehensively.Eachmodelhasitsownspecialvariables,asaresult,thecombinationmodelproposedinthispaperplaysasignificantroleincustomer-chummodel.ThismodelusesIBMSPSSStatistics,putsforwardtheconceptofpheromonedifferenceinantcolonyalgorithm,usingthe

8、improvedantcolonyalgorithmtosubdividethecustomertoimprovethecustomerclusteringeffect.Canonicaltransformationeliminatetheimpactofthedimensionlesscoefficientstomakethemodelmoreregular.Usinglogisticregressionalgorithmi

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