Altosoft: Financial Services Case Study

8/9/2019 Altosoft: Financial Services Case Study

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CASE STUDY:

Sales and Marketing

Optimization for

Financial Services

April, 2009


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ALTOSOFTCASESTUDYFINANCIALSERVICES

CASESTUDY:SALESANDMARKETINGOPTIMIZATIONFORINVESTMENTANDRETIREMENTSERVICES

TheinvestmentandretirementservicesdivisionofoneofthelargestcommercialbanksintheUnitedStatesfaced

seriouschallengesharnessingandanalyzingdatarelatedtosales,customerretention,andassetmanagement

activities.Withrelevantdatalocatedinmultipleterabytesizedoperationaldatastores,traditionaldata

warehousingtechniqueshadprovedinadequate.UsingitsInsightBIandMetricsMartproducts,Altosoftbuilta

solutionthatdeliveredtheanalyticsthebankrequiredinjustoneweek,withoutrequiringadditionalinvestmentin

ETLsoftwareordatawarehousedevelopment.

Copyright2009AltosoftCorporation.Allrightsreserved. 1


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CustomerChallengeTheinvestmentandretirementservicesdivisionofalargedomesticbankrequiredbetteranalyticsinorderto

supportitsprimarybusinessactivity,sellingandmarketingvariousinvestmentvehiclestoconsumers.Inorderto

optimizesalesandmarketingefforts,thebankneededtobeabletoanswerquestionssuchas:

Whichinvestmentvehiclesorretirementproductswerethemostprofitabletothebank? Whatwasthecorrelationbetweeninvestmentvehicleprofitabilityandcustomersatisfaction? Howwasinvestmentproductrevenuespreadacrossvariousregions,markets,typesofthecustomers,

products,etc.?

Whichsalesgroups(regional,functional,etc.)andindividualrepresentativesweremostsuccessfulsellingwhichinvestmentvehiclesandretainingexistingcustomers?

Thisdatawouldenablethebanktomakebetterdecisionsintermsofhowitinvestedresourcesinitssalesand

marketingprograms.Thecorporategoalwastomaximizetheimpactofsalesandmarketingresourcestodrivethe

highestoverallcombinationofprofitabilityandcustomersatisfaction.

Thedatarequiredtoanswerthesequestionsexistedinthreeseparateoperationaldatastores(oneinstanceof

TeradataandtwoinstancesofOracle)containingcustomerdata,productperformancedata,andsalesdata.Each

databasecontainedaboutaterabyteofdata.Withthebanksexistingsystemsandsemiautomatedprocesses,it

typicallytook45to60daystogenerateonlyalimitedamountofperformanceanalysisfromthedatainthese

systems.Inmanycases,theanalysiswasoutofdatebeforeitevenbecameavailabletodecisionmakers.Inmany

casestheanalysisonlyrepresentedasubsetoftheessentialmetricsrequiredtooptimallymanagethebusiness.

Thebankneededtofindawaytodeliveracompleteandcomprehensiveanalysisinasignificantlyreducedtime

frame.

Atraditional

approach

to

this

problem

would

start

with

the

construction

of

aseparate

data

warehouse.

Data

warehouseprojectstypicallytakemanymonthsandrequireinvestmentinsignificantamountsofhardwareand

storage.Inaddition,adatawarehouseprojectrequiresthatallthepotentialdimensionsofanalysisandpathsfor

analyticaldrilldownbedefinedinadvance.Forexample,inthiscase,thebankwouldneedtopredetermine

analysisby,say,region,andthenbysalesperson,investmentproducttype,etc.Ifadimensionwereneglectedor

neededtobeaddedlater,theentirecubewouldhavetoberedimensionedandredeployed.



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Typically,thedatawarehousewouldbeprogrammaticallyloadedusingETL(extract,transform,load)software.

Thiscomplexsoftwarewouldalsohavetobeimplemented,customized,andmaintained.

Inaddition

to

time

and

complexity,

certain

additional

factors

made

atraditional

data

warehouse

approach

impossibleforthebank.Specifically,thenatureofthisparticulardataanalysiswouldhaverequiredcomplexand

extremelyinefficientouterjoinsacrossthethreedifferentterabytesizeddatabases.Becausethebanksdatabases

areoptimizedforoperationalperformanceandarenotindexedforthistypeofquery,completionofthesejoins

wouldrequirerowbyrowanalysisacrossmillionsofrecords.Foradailyupdate,thebankestimatedthat

calculationofthiscubewouldtakeapproximately34hours.Initialcalculationofthewarehousecouldtakemany

days.Thiswasnotacceptabletothebanksincethethreedatastores,tiedtomissioncriticaloperationalsystems,

simplycouldnotbeburdenedbythatanalysisoneitheraninitialorongoingbasis.

Movingthedataintoanewwarehousewouldalsopresentasignificantsecurityconcernduetothesensitive

natureoftherecords.

AltosoftsApproachRatherthanreplicatedatafromoperationalsystemsintoarigid(butoptimized)datawarehouse,Altosofts

approachistoleavethedatainplaceinexistingoperationalsystems.Altosoftanalyzedthedatainthebanks

operationalsystems,usingitsownhighlyoptimized,inmemorycalculationenginetoperformtheanalysis.

Altosoftreliedontheoperationaldatabasestosimplydowhattheydobestretrievedata.Unliketraditional

warehouseapproaches,AltosoftdoesnotuseSQLtoperformoperationsforwhichitwasnotoptimallyintended.

SQLisrestrictedtorecordselectionandfetchingmechanisms(includingtheouterjoinsdiscussedabove,aswellas

additionalcalculations).

Asaresult,thedailyupdatethatwouldhaverequired34hoursofprocessingtimeforcalculationofadatacube

takesAltosoftapproximatelyeightminutes.Theloadplacedonthebanksoperationaldatastoresisreducedtoa

minimum.

Usingthisapproach,Altosoftwasabletodemonstrateacompletesolutiontothebankinlessthanoneweek.

Accordingtothebank,Altosoftwasabletoshowthemdatathat[theyhad]neverseenbefore.Dataandanalysis

weredelivereddirectlytorelevantbankemployeesusingAltosoftssecure,personalized,webbaseddashboard

system.Thisdashboardfullysupportsinteractivevisualizationanddimensionalanalysisofdata.Thesystemgives



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thebankadditionalflexibilityifitneedstochangeorexpandKPIs,itcandosowithouttimeconsumingand

burdensomerecalculationofdatacubes.

Altosoftdeliversanalyticsthrougheasytouse,interactivedashboards

Finally,usingAltosoftsintegratedalertingandincidenttrackingcapability,thebanknowhasrealtimevisibility

andmonitoringofitssalesactivity.Thismeansthatifaninvestmentvehicleisextremelyprofitableforthebank

andisalsohighlyappealingtoacertaincustomerdemographic,thebankcanimmediatelyalertitssalesforceto

prioritizemarketingofthatproduct.Previously,thisanalysistook4560days,atwhichpointmarketdynamicshad

oftenchanged.

ConclusionsUsingAltosoftInsight,thebanknowhastheabilitytooptimizesalesandmarketingofitsretirementproducts.

Withinamatterofdays,Altosoftwasabletodelivervaluableanalyticsthatthebankhadnotbeenabletoproduce

previously.ImplementationofAltosoftavoidedthecomplexityofatypicalETLimplementationanddata

warehousingproject,significantlyreducingtheamountofresourcesrequiredtosupporttheproject.Finally,the

Altosoftsystemplacesaminimalburdenonoperationaldatastores,sothebankscriticalsystemsarenot

impactedbytheproject.


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Copyright2009AltosoftCorporation.Allrightsreserved.Altosoft,theAltosoftlogo,andallotherAltosoft

productnamesorlogosaretrademarksorregisteredtrademarksofAltosoftintheUnitedStatesandinother

countries.Allothermarksarethepropertiesoftheirrespectiveowners.

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Altosoft: Financial Services Case Study