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DescriptionBackground:A relatively young bank is growing rapidly in terms of overall customer acquisition.Majority of these are Liability customers with varying sizes of relationship with the bank.The customer base of Asset customers is quite small, and the bank WANTS to growthis base rapidly to bring in more loan business.Specifically, it wants to explore ways of converting its liability customers toPersonal Loan customers.A campaign the bank ran for liability customers last year showed a healthy conversionrate of over 9% successes. This has encouraged the Retail Marketing departmentto devise smarter campaigns with better target marketing.Analytics Objectives:1While designing a new campaign, can we model the previous campaign's customer behavior toanalyze what combination of parameters make a customer more likely toaccept a personal loan?2There are several special products / facilities the bank offers like CD and security accounts,online services, credit cards, etc. Can we spot any association among thesefor finding cross-selling opportunities?Data Description:IDCustomer IDAgeCustomer's age in completed yearsExperience#years of professional experienceIncomeAnnual income of the customer ($000)ZIPCodeHome Address ZIP code.FamilyFamily size of the customerCCAvgAvg. spending on credit cards per month ($000)EducationEducation Level. 1: Undergrad; 2: Graduate; 3: Advanced/ProfessionalMortgageValue of house mortgage if any. ($000)Personal LoanDid this customer accept the personal loan offered in the last campaign?Securities AccountDoes the customer have a securities account with the bank?CD AccountDoes the customer have a certificate of deposit (CD) account with the bank?OnlineDoes the customer use internet banking facilities?CreditCardDoes the customer use a credit card issued by UniversalBank?Note: Data are hypothetical© Cytel, Inc. 2005
DataUniversal Bank Customer ProfilesIDAgeExperienceIncomeZIP CodeFamilyCCAvgEducationMortgagePersonal LoanSecurities AccountCD AccountOnlineCreditCard1251499110741.60100100024519349008931.50100100033915119472011.00100000043591009411212.7020000005358459133041.00200000163713299212140.4021550001075327729171121.50200001085024229394310.30300000193510819008930.60210400010103491809302318.9030100001165391059471042.40300000012295459027730.1020000101348231149310623.803001000145932409492042.5020000101567411129174112.001001000166030229505411.5030000111738141309501044.70313410000184218819430542.4010000001946211939160428.103010000205528219472010.502001001215631259401540.90211100010225727639009532.00300001023295629027711.20126000010244418439132020.701163010002536111529552123.90115900001264319299430530.5019700010274016839506440.2030000002846201589006412.401000011295630489453912.2030000113038131199410413.302010111315935359310611.20312200010324016299411712.002000010335328419480120.6031930000034306189133030.90300000035315509403541.803000010364824819264730.7010000003759351219472012.901000001385125719581411.40319800000394218141941143.
DescriptionBackground:A relatively young bank is growing rapidly in terms of overall customer acquisition.Majority of these are Liability customers with varying sizes of relationship with the bank.The customer base of Asset customers is quite small, and the bank WANTS to growthis base rapidly to bring in more loan business.Specifically, it wants to explore ways of converting its liability customers toPersonal Loan customers.A campaign the bank ran for liability customers last year showed a healthy conversionrate of over 9% successes. This has encouraged the Retail Marketing departmentto devise smarter campaigns with better target marketing.Analytics Objectives:1While designing a new campaign, can we model the previous campaign's customer behavior toanalyze what combination of parameters make a customer more likely toaccept a personal loan?2There are several special products / facilities the bank offers like CD and security accounts,online services, credit cards, etc. Can we spot any association among thesefor finding cross-selling opportunities?Data Description:IDCustomer IDAgeCustomer's age in completed yearsExperience#years of professional experienceIncomeAnnual income of the customer ($000)ZIPCodeHome Address ZIP code.FamilyFamily size of the customerCCAvgAvg. spending on credit cards per month ($000)EducationEducation Level. 1: Undergrad; 2: Graduate; 3: Advanced/ProfessionalMortgageValue of house mortgage if any. ($000)Personal LoanDid this customer accept the personal loan offered in the last campaign?Securities AccountDoes the customer have a securities account with the bank?CD AccountDoes the customer have a certificate of deposit (CD) account with the bank?OnlineDoes the customer use internet banking facilities?CreditCardDoes the customer use a credit card issued by UniversalBank?Note: Data are hypothetical© Cytel, Inc. 2005
DataUniversal Bank Customer ProfilesIDAgeExperienceIncomeZIP CodeFamilyCCAvgEducationMortgagePersonal LoanSecurities AccountCD AccountOnlineCreditCard1251499110741.60100100024519349008931.50100100033915119472011.00100000043591009411212.7020000005358459133041.00200000163713299212140.4021550001075327729171121.50200001085024229394310.30300000193510819008930.60210400010103491809302318.9030100001165391059471042.40300000012295459027730.1020000101348231149310623.803001000145932409492042.5020000101567411129174112.001001000166030229505411.5030000111738141309501044.70313410000184218819430542.4010000001946211939160428.103010000205528219472010.502001001215631259401540.90211100010225727639009532.00300001023295629027711.20126000010244418439132020.701163010002536111529552123.90115900001264319299430530.5019700010274016839506440.2030000002846201589006412.401000011295630489453912.2030000113038131199410413.302010111315935359310611.20312200010324016299411712.002000010335328419480120.6031930000034306189133030.90300000035315509403541.803000010364824819264730.7010000003759351219472012.901000001385125719581411.40319800000394218141941143.
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