WHITE PAPER

New Product Forecasting Using Structured Analogies

SAS White Paper

Table of Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

The Challenges of New Product Forecasting . . . . . . . . . . . . . . . . . . . . 1

The Structured Analogy Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

An Example of Forecasting New Product Sales . . . . . . . . . . . . . . . . . . 3

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Content for this white paper was provided by Michael Gilliland, Product Marketing Manager for

SAS Forecasting. It is based on a patent-pending method of new product forecasting developed by

Michael Leonard, Tom Dickey, Sam Guseman and Michele Trovero.

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New Product Forecasting Using Structured Analogies

IntroductionNew product forecasting (NPF) is a recurrent challenge for consumer goods manufacturers and retailers . There are many kinds of NPF situations these organizations can encounter:

• Entirelynewtypesofproducts.

• Newmarketsforexistingproducts(suchasexpandingaregionalbrandnationwideorglobally).

• Refinementsofexistingproducts(suchas“newandimproved”versionsorpackaging changes) .

TherearemanyNPFapproachesavailabletotry.Someofthecommononesinclude:

• Executive opinion–topmanagementprovidestheforecast.

• Sales roll-up–abottom-uppollofthesalesforce.

• Delphi method – a structured formal process for anonymously gathering forecasts andbuildingaconsensus.

• Prediction market – anonymous wagering used to gather group opinion .

• Analogy–expectinganewproducttobehavelikesimilarproductsfromthepast.

Allofthesemethodsusejudgmenttosomeextentandtherearegoodreasonswhy.Judgmentcompensatesforthelackofhistoricalinformationbecausewearedealingwith new products with no historical sales . Judgment also compensates for lack of futureinformation–itmaybetoodifficultorcostlytoconductmarketresearchtests,orto quantify such things as the direction of fads and fashion trends .

The Challenges of New Product ForecastingWhileuseofjudgmentisnecessaryinnewproductforecasting,ithasitsdisadvantagesaswell.Judgmentisfrequentlybiasedwithover-optimism,orallowingrecenteventstohaveunwarrantedimpact.Judgmentisalsocloudedbypersonalorpoliticalagendas,where the forecast is used to represent the forecasters’ wants or needs rather than what theyhonestlybelievewillhappen.

Asanexample,ifasalesrepknowshisforecastisgoingtobeusedtosetthesalesquota,thereisanaturaltendencytoforecastlow–tosetalowquotathatiseasiertobeat.Ifaproductmanagerwantstointroduceanewproduct,thereisanaturaltendency to forecast high – at least high enough to meet any hurdles for getting the new productapprovedfordevelopment.(Haveyoueverheardofanyoneforecastinganewproduct to fail in the marketplace?)

The Structured Analogy ApproachTheuseofanalogiesisacommonNPFpractice.Weseethisintherealestatemarket,whereanagentwillpreparealistof“comps”–similarhousesintheareathatareonthemarketorhaverecentlysold–andusethistosuggestasellingprice.

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SAShasanewpatent-pendingapproachtoNPFthatcombinestheuseofanalogieswithstructuredjudgment.This“structuredanalogy”approach:

• Includesguidedstatisticalanalysisthatincorporateshumanjudgment.

• Attemptstoremovejudgmentalbiasbyprovidingahistoricalcontextforeachdecision .

• Attemptstovalidateandtestthedecisions.

• Driveschoiceofanalogybyastatisticalprocess.

Thestructuredanalogyapproachrequirestwotypesofdata:productattributes(forpriorandnewproducts)andhistoricalsales(forpriorproducts).Productattributescanincludemanythings,suchas:

• Producttype(toy,music,clothing,shirts,etc.).

• Seasonofintroduction(summeritem,winteritem,etc.).

• Financial(ownprice,competitorprice,etc.).

• Targetmarketdemographic(gender,age,income,ethnicity,etc.).

• Physicalcharacteristics(style,color,size,etc.).

• Andmanyothers.

Historicalinformationonpastnewproductintroductionsisalsoneeded.Herearescaledthumbnailsofthefirsteightweeksofsalesfor100newDVDreleases:

Figure 1: Scaled thumbnails of the first eight weeks of sales for 100 new DVD releases.

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New Product Forecasting Using Structured Analogies

Thestructuredanalogyprocessfornewproductforecastinghassixmainsteps:

• Query step:Findasetofcandidateproductsthathavesimilarattributestothenew product .

• Filter step:Manuallyremoveinappropriateoroutlierproductsfromthesetofcandidate products .

• Cluster step:Clusterthecandidateproductsaccordingtotheirsalespattern,andmanuallyselectthemostappropriateclustertoserveasthesurrogateproducts.

• Model step: Select the most appropriate statistical model for the cluster of surrogateproducts,andextractthestatisticalmodelfeatures.

• Forecast step:Usetheextractedstatisticalmodelfeaturestoforecastthenewproduct .

• Override step: Make manual adjustments to the statistical model’s forecast .

An Example of Forecasting New Product SalesLet’sworkthroughanexampleofforecastingsalesofanewDVDmovierelease.TheQuerystepbeginswithselectingapubliclyavailabledatasetonhistoricalDVDsales,andthenspecifyingtheattributesofpriorDVDsthatmatchthenewDVD.Inthiscase,thenewDVDisanR-ratedhorrormovie,sowedecidetospecifytwoattributes:“Horror”fortheGenre,and“R”fortheMPAArating.

Figure 2: Specifying attributes for candidate products.

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Notethatweareusingjudgmenttodeterminewhichattributesaremostrelevant–thesystemisnotgoingtotellusthis.However,thesystemdoesautomatealltheworkofextractingtheR-ratedhorrormoviesfromthedatasetofallDVDs,andtheseformourpool of candidate products .

TheoutputfromtheQuerystepisaprofileoverlayingtheinitialsalesofallthecandidateproducts,alongwithalistofallthecandidates.

Figure 3: Overlay of candidate product sales over their first 20 weeks.

JudgmentagaincomesintoplayintheFilterstep,aswedecidethatDawn of the Dead isanoutlier,anduncheckittoremoveitfromthecandidatepool.

Figure 4: Remaining candidate products (after filtering).

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New Product Forecasting Using Structured Analogies

TheQuerystepletusexplorecandidateDVDswithsimilarattributes,andtheFilterstepallowedustomakeajudgmentoninappropriatecandidatestobefilteredout.Afterthefilteringisapplied,theClusterstepclusterstheremainingcandidatesaccordingtosimilarityoftheirsalespatterns.Judgmentagaincomesintoplay,astheuserselectsasurrogateclusterbasedontheanticipatedsalespatternforthenewproduct.InthiscasetheR-ratedhorrormoviesfellintothreeclusters,andthefirstwaschosen.Wearenow ready for the Model step .

Figure 5: User-selected cluster of surrogate products.

TheModelstepgeneratesastatisticalmodelthatfitsthesurrogatecluster.Theuserhasaccesstoavarietyofmodelstoutilize,includingdiffusion,mixed,smoothing,Bayesianandothers.Afterthemodelisselected,theForecaststepgeneratesaforecastforthenewproduct,whichisshownbelowinblueoverlaidonthesurrogateproducthistories.

Figure 6: Forecast model predictions overlayed on surrogate products.

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JudgmenthasbeenusedthroughoutthestructuredanalogyprocessandisusedoncemoreintheOverridestepwheremanualadjustmentscanbemadetothestatisticalmodel forecast .

Figure 7: Users can override model predictions.

Afteranymanualoverridesaremade,thenewDVDforecastscanbeexportedtodownstream planning systems .

SummaryThestructuredanalogyapproachcanbeusefulinmany,butcertainlynotall,newproductforecastingsituations.Itattemptstoimproveonhumanjudgmentalonebyautomatingthehistoricaldatahandlingandincorporatingstatisticalanalysis.Thesoftwaremakesitpossibletoquicklyextractcandidateproductsbasedontheuser-specifiedattributecriteria.Italigns,scalesandclustersthehistoricalpatternsautomatically,providinganeasytounderstandvisualizationofpastnewproductbehavior.Thisvisualizationhelpstheforecasterrealizetherisks,uncertaintiesandvariabilityinnewproductbehavior,sothattheorganizationcanmaketheappropriatedecisionsbasedontheseuncertainties.

Judgmentisalwaysgoingtobeabigpartofnewproductforecasting.AcomputerwillneverbeabletotelluswhetherlimegreenorDay-Gloorangeisgoingtobethehotfashioncolornextseason.Butjudgmentneedsassistancetokeepitontrackandasobjectiveaspossible.Theroleofstructuredanalogysoftwareistoautomatethedataextractionandprocessingwork,providevisualizationofthehistoricalcontextforjudgmentsandmaketheNPFprocessasefficientandobjectiveaspossible.

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