Predictive Analytics + Constrained Optimization = Efficient Marketing

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1 Predictive Analytics + Constrained Optimization = Efficient Marketing PaulMaiste,President,Lityx March18,2015 INFORMSNewYork

2 Abstract PredicGvemodelinghasbecomemoremainstreaminthelast5yearsas companiesaddmoreadvancedcapabiligestotheiranalygctoolsets.therehasalso beenmoretalkaboutincorporagngconceptsrelatedtoopgmizagon(constrainedor unconstrained)intobusinessdecisionmaking.thereisnoargumentthateachcan bequitepowerfulintheirownright. InthispresentaGon,thespeakerwilltakeittothenextlevel:combiningpredicGve models,predicgveopgmizagon,andconstrainedopgmizagontechniquestoprovide toolsthatallowmarketerstomakeopgmaldecisionsanddramagcallyimprove efficiency. 2

3 Agenda! Background! PredicGveOpGmizaGonModels! ConstrainedOpGmizaGon! CaseStudies ProspectOfferOpGmizaGon PrintMediaTargeGngOpGmizaGon RetailDirectMailTargeGngOpGmizaGon! WrapUp 3

4 Lityx Background! LityxisananalyGcsoluGonsand servicesfirmwithaproprietarycloud\ basedpredicgvemodelingand opgmizagonpla]ormlityxiq.! WeapplydeepexperGsein complexanalygcsolugonswitha focusonapplicagonsin markegnganalygcsand customerrelagonship management. 4

5 Analytic Progression HowCan GartnerframedanalyGcprogressionasthemoveforwardfromInformaGonto WeMakeIt OpGmizaGon Happen? High Complexity What Happened? Descriptive Analytics Why Did It Happen? Diagnostic Analytics What Will Happen? Predictive Analytics How Can We Make It Happen? Prescriptive Analytics Low Low Business Value High 5

6 Analytic Progression HowCan GartnerframedanalyGcprogressionasthemoveforwardfromInformaGonto WeMakeIt OpGmizaGon Happen? High Complexity What Happened? Descriptive Analytics Why Did It Happen? Diagnostic Analytics What Will Happen? Predictive Analytics How Can We Make It Happen? Prescriptive Analytics Low Low Business Value High 6

7 Generic Concept Constrained OpGmizaGon PredicGve (OpGmizaGon) Modeling Scoringand ImplementaGon! Separatetechniques worktogether! Wecan tforgetthe execugonalpieceof it(scoring/ implementagon)! Theglobalproblem (opgmalmarkegng) issolvedwiththe combinagon Op#mal'Marke#ng'Strategy 7

8 Optimization Components! WheredoesOpGmizaGonappearinthisapproach? PredicGvemodelsarenaturallyanopGmizaGonprocess (nothingnewhere). PredicGveOpGmizaGonModels incorporatemarkegng tacgcsintothepredicgvemodelinputs. o Offertype o Offervalue o Discounts o Messaging,sequencing o Channel ConstrainedOpGmizaGonincorporateshardconstraintsinto theoverallsolugon o BusinessormarkeGngconstraints o Contractualconstraints o Channelconstraints 8

9 AcoupleofpredicGveopGmizaGonmodelingexamples 9

10 Offer Optimization 2TNDB DC 0RP M DO OMEH AH H U - 3 0=5 3 =O LP B HML 4HP MOU 6MU U 1D MFO N HBP ModeledBehaviors NNDLCDC 1 OID HLF 2 D DL P MarkeGngLevers $ OID HLF 0MP P MCD DC /D SHMOP OID HLF 6DSDOP 2TNDB DC OMEH AH H U EEDO $ DPNMLPD D $ NDLC RD $ EEDO =UND $ EEDO RD OMEH A D OMNMPH HML EEDO ( EEDO ) Offer'Elas#city'Curves' LNOMEH A D OMNMPH HML ( ) + N H MEEDO HP EEDO ( CHPBMRL EEDO RD 1HPBMRL 10

11 Actual Offer Elasticity Curves 11

12 Cadence Optimization Cells'show'average' observed'response'rates' More Freshness Three'and'four'months'ago' Bestvalueshere Nottargeted3or4 monthsago Targeted4months ago Targeted3months ago Targetedboth3and 4monthsago Not targetedin lasttwo months More Freshness Last'Two'Months'Ac#vity' Targeted two monthsago Targeted lastmonth Targeted bothoflast two months Note:Gray=lowsamplesize Lowestvalues here We'can'incorporate'cadence'into'our'predic#ve'op#miza#on'model.'' 'Recency'and'frequency'can'be'categorized'in'a'number'of'different'ways' 12

13 Whataboutconstraints? 13

14 Business/Marketing Constraints! Constraintsmaketheoverallproblemharderto solve,and(evenworse)givealessthanglobally opgmalsolugon.! Butweneedtoaccountforthem.! Numberandcomplexityofconstraintsdetermine thesolugonapproach. Dimensions' Constraints' Solu#on' Example' 0 1(totalbudget) Rankorderall OverallmarkeGngbudget 1(channel) 1(totalbudget) Rankorderbydim MarkeGngbudgetbychannel 1(channel) 2(channelbudget) Rankorderbydim Channelmaxspendandminspend Harder! Needtools Channelmaxspendwithproductmin performance+more 14

15 Sub-optimality! Inouroffer opgmizagon example,ifthere wasapolicyto neveroffermore than$100,we wouldnotrealize globalopgmality. 15

16 Case Study 1 ProspectOfferOpGmizaGon 16

17 Offer Optimization Problem! Wewanttomaximize ExpectedprofitabilityOR ResponserateOR Expectedrevenue! WehavepredicGveopGmizaGonmodelsthatpredict Spendbyprospect Responseratebyprospect! Constraints Limitsontotalamountofoffers Limitsonfreebies(e.g.,hotelroomscapacityconstraints) ExperimentaltesGngandcross\decilesampling SegmentlevelmarkeGngplan 17

18 Data View 18

19 Outputs Number'to'Contact'by'Offer' Budget'by'Offer' 19

20 Implementation! Forthisindustry,prospectsareplacedintoanoffer matrixformarkegngexecugon. Predicted' Value'' <$75 Predicted' Value'' $75R$299' Predicted' Value'' $300R$749' Predicted' Value'' $750+ High'Offer'Value' Low'Offer'Value' Offer1 Offer2 Offer3 Offer1 Offer2 Offer

21 Case Study 2 PrintMediaTargeGngOpGmizaGon (Cadence) 21

22 Print Media Targeting Problem! MulGpleChannels Newspaperinserts Sharedmail(mulGpleprograms)! Manychoices 30,000+zipcodes 60,000+targeGngzones! Manyconstraints! PredicGveopGmizaGonmodelspredictzip\level responserategivenpriorhistory,cadence, demographics! Decision Onamonthlybasis,howdowebestallocateourfixed budget?(i.e.,whatzips/zonesshouldbetargeted) 22

23 It s a complex process Static Models (updated approx yearly) Dynamic Data (Processed Monthly) Zip/zone targeting history (cadence) Predictive Model of Newspaper Zone Response Rates Predictive Model of Shared Mail Zone Response Rates Scoring Final Optimization Result Ranked Zone List - Ordered by Decreasing Expected CPO Historical response rates Aggregation of program/zip level actual historical response rates Prediction of Response Rates at Zone Level Mathematical Optimization Model Census and Geodemographic data at Zip level Media Data Business Rules and Constraints Marketing constraints Zip/zone maps CPM Circulation Somewhat Static Data (yearly) Minimum insert restrictions Fixed setup costs Target/Do not target rules Cross-channel budget min/max percent Max papers Restricted papers 23 23

24 Constraints! Theconstraintlistcoversa widevarietyofmetrics, dimensions,andbusiness rules.! Withouttheseaccounted for,theproblemcouldnot besolvedtothesagsfacgon ofthebusiness. 24

25 Outputs 25

26 Output 26

27 Case Study 3 RetailDirectMailTargeGngOpGmizaGon 27

28 Retail Direct Mail Problem! Maximizeoverallresponserate! EachprospecthasapredicGvemodelscorebased on: Demographics(age,income,educaGon) LocaGon(distance,driveGmetocloseststore) Appendedbehaviors(creditcardusage,internetusage)! Constraints MeetstoreminimumquanGtyneeds Cross\deciletesGngandmetrics PriorGmestargeted o Cadenceis(currently)controlledinthiscasestudythrough constraints notincorporatedyetintoamodelingparadigm. 28

29 Outputs Mail'Qty'by'Decile' Mail'Qty'by'Prior'Times'Targeted' Mail'Qty'by' Store'Loca#on' 29

30 Results! ImplemenGngmodelingandconstraintshashelped generateincrementalsalesconsistentlysincethe programbegainmid\july

31 Future Objectives! IncorporatepredicGveopGmizaGonmodeling Cadence/priorcontacts Directmailpackage/message o WhatisopGmalwaytocommunicatewitheachprospect? 31

32 Summary! Purngitalltogetherraisesoverallcomplexity! Buttherewardcanbesignificant Improvekeybusinessmetrics EaseofmarkeGngexecuGon! Toolscanhelp 32

33 Questions PaulMaiste 33

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