Model de Marketing exemplu

8/13/2019 Model de Marketing exemplu

1/16

A Prelaunch Diffusion Model for Evaluating Market Defense Strategies

Author(s): John H. Roberts, Charles J. Nelson and Pamela D. MorrisonSource: Marketing Science, Vol. 24, No. 1 (Winter, 2005), pp. 150-164Published by: INFORMSStable URL: http://www.jstor.org/stable/40056945.

Accessed: 05/12/2013 09:31

Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at.http://www.jstor.org/page/info/about/policies/terms.jsp

.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of

content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact [email protected].

.

INFORMSis collaborating with JSTOR to digitize, preserve and extend access toMarketing Science.

http://www.jstor.org
http://www.jstor.org/action/showPublisher?publisherCode=informshttp://www.jstor.org/stable/40056945?origin=JSTOR-pdfhttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/stable/40056945?origin=JSTOR-pdfhttp://www.jstor.org/action/showPublisher?publisherCode=informs


2/16

Prelaunch iffusion o d e l o r Evaluat ingM a r k e t e f e n s e StrategiesJohnH. RobertsAustralianGraduateSchool of Management,Universityof New SouthWales,Sydney,NSW2052,Australia,andLondon BusinessSchool,RegentsPark,LondonNW14SA,[email protected],[email protected])CharlesJ.NelsonForeseechange ty Limited,6 PatersonStreet,BrunswickVIC3056, Australia, [email protected] D. MorrisonSchoolof Marketing,Universityof New SouthWales,Sydney,NSW2052,Australia,[email protected]

Marketing ScienceVol.24, No. 1, Winter2005,pp. 150-164issn 0732-2399 issn 1526-548X 51 4011 150 doi 10.1287/mksc.l040.00862005INFORMS

paper describes the development and applicationof a marketingmodel to help set an incumbent'sdefensive marketingstrategy prior to a new competitor's aunch. The managementproblemaddressedisto assess the market share impact of a new entrant n the residentialAustralianlong distancetelephonecallmarketand determine the factorsthatwould influence its dynamicsand ultimate marketappeal.The paper uses probability low models to provide a framework o generateforecastsand assess the deter-minants of share loss. We develop models at two levels of complexity to give both simple, robust forecastsand more detaileddiagnostic analysisof the effect of marketingactions.The models are calibratedpriorto thenew entrant's aunch,enabling preemptive marketingstrategiesto be put in place by the defending company.The equilibrium evel of considerationof the new entrantwas driven by respondents'strengthof relationshipwith the defenderand inertia,while trial was more price-based.Continued use of the defender depends onboth service factors and price. The rate at which share loss eventuates is negatively related to the defender'sperceivedresponsiveness,saving money being the only reason to switch, and risk aversion.Prelaunchmodel forecasts,validated six months after launchusing both aggregatemonthly sales data anddetailedtrackingsurveys, are shown to closely follow the actual evolution of the market.The paperprovidesa closed-formmultistatemodel of the new entrant'sdiffusion,a methodologyfor the prelaunchcalibrationofdynamicmodels in practice,and insights into defensive strategiesfor existing companiesfacingnew entrants.Keywords defensive strategy;brandchoice; diffusion; orecastingHistory:This paperwas receivedAugust 30, 2001,and was with the authors18 months for 4 revisions;processed by CharlesWeinberg.

Management ProblemDeregulation of long-distance telephone calls (tollcalls) is occurringin many countries.Following thelead of the United States, Britain,Japan, Singapore,Australia,andmanyothergovernmentshaveexposedtheirmonopoly toll-call carriers o competition.Morecountriesare following, e.g., Thailand, China, LatinAmerica,and EasternEurope (Beardsleyet al. 2002).We describe the applicationof a marketingmodel toassist an existing company,Telstra, n defending itscompetitive position against Optus, the subsidiaryoftwo large multinationals,which was about to enterits market.1In their home markets, the parents ofthe new entrant,Bell South and Cable & Wireless,were known for excellent customer service.The newentrant also enjoyed cost advantages over Telstra,

at least in the short term, as a result of a regulatorydecision.Optuswas expectedto attackTelstraon twofronts:betterservice and lower prices.For Telstra,the defending company,the manage-ment problem was first to assess the likely impactof Optus on its share, and second to understandthe determinantsof customer switching so it coulddevelop and test defensive strategies.The researchobjectivesstemming from this managementproblemcalled for a dynamicmodel giving the new entrant'sultimate shareand its evolution.Theseforecastswererequired for network dimensioning, financialplan-ning, and monitoringand control,as well as to enablemanagement to test differentmarketing-mixstrate-gies and environmentalscenarios.Dynamic marketdefense is an importantproblem. A wide range ofpower, transport,and service monopolies once gov-erned by regulation are being exposed to competi-tion. Robertsonand Gatignon (1991) point out that

1At the time of this application, Telstra was called TelecomAustralia.150

This content downloaded from 86.55.176.96 on Thu, 5 Dec 2013 09:31:05 AMAll use subject to JSTOR Terms and Conditions
http://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsp


3/16

Roberts, Nelson, and Morrison: A PrelaunchDiffusionModelfor EvaluatingMarketDefenseStrategiesMarketing cience24(1),pp. 150-164,2005INFORMS 151

incumbentshave an advantageover new entrants,butfirms without a responsivedefense strategy may for-feit thatadvantage.We address the managementproblemof dynamicdefense by first considering available modelingframeworks. After choosing a probabilistic flowapproach,we derive two specific models to addressthe research objectives. The models are calibratedpriorto launch,and theirimplications orpreemptivemanagementaction described. Telstraalso used themodels for trackingpostlaunch,and we provide theresultsof a forecastvalidation of theprelaunchmodel.We concludewith a discussionof how this applicationmight be used to help othercompaniesset defensivestrategyover time.In the application,we show how multistate dy-namic models can be made to work in practice.The literaturecontainsfew real-worldapplicationsofdynamicmodels calibratedprelaunch eitherfrom thenew entrant'sor defender's perspective).There areeven fewer that are validated not just on postlaunchsales databut also on trackingthe evolution of deci-sion states. In developing our approach,we obtaina closed-form solution to a dynamic six-state deci-sion model as well as comparingrespondent-basedinformationon rate parametercalibration o the useof analogy.Substantively,n looking at a new prod-uct from the defender's perspective,we consider anumberof interestingmanagerial ssues. Unlikepost-launchtracking ools that have been criticizedbecause"after the fact, it's sort of like accident reports"(Dipasquale2002),we provide managerswith defen-sive tools when they are needed.Available Modeling ApproachesCompetitive strategy may be thought to consist ofthree components:understandingwhat competitorsdo, reviewing how incumbentscan and do react tothose actions, and calibratingmarketplaceresponseto both sets of behaviors. We focus on the thirdissue, prelaunchestimationof the pay-off matrix tothe defender's and new entrant'spossible marketingactions.This provides a criticalinput to any game-theoreticanalysisof optimalcompetitivestrategies,aswell as managementguidance.We startby reviewingexisting calibrationapproaches n defensive market-ing and the adoptionof new products.MarketDefenseThereis a growingbody of descriptiveresearch hatexamines the success of defensive strategies usinghistoricaland cross-sectionaldata (e.g., Ramaswamyet al. 1994, Gatignon et al. 1997). Such studies arevaluable in identifying useful tools for the incum-bent, but provide directionalrather than quantita-tive estimates of optimal response to new entrants'

actions. The normative stream of researchprovidesanalyticalguidanceto profit-maximizingtrategies ordefenders(e.g.,Hauserand Shugan1983,KumarandSudharsan1988,Hauser and Gaskin1984).However,this research ooks only at comparativestaticequilib-ria,not at the dynamicsof a new entrant'sattack.Models of New ProductAcceptanceIn the absence of specific response functions in themarketdefense literatureto address particularman-agement problems,we turnto the new productadop-tion literature or models of how much share a newentrantwill get, how it will evolve over time, and itsdeterminants.

Preference/ChoiceModels. Theprimaryparadigmfor determining the acceptance of a new productis utility theory, combined with discrete-choice he-ory (see Robertsand Lilien 1993,Table2.6 for appli-cations). Discrete-choicemodels show how productpositioning (in terms of perceivedattributes)may betranslated nto market share but rarelyaddress howthat share will change over time. Thus, they do nothelp with the market's evolution (see Roberts andLilien 1993 for the few exceptions).Forthat,we turnto diffusion models.

Diffusion Models. In their most simple form, dif-fusion models have two components;a pool of futureadopters, and a flow rate at which potential pur-chasers become adopters.Numerous extensions havebeen made to Bass' (1969) diffusion model, includ-ing the incorporationof marketing-mix lements andcompetition. Additionally,multistate diffusion mod-els provide diagnostic guidance to the manager byincluding rejection,awareness,and repeat purchase(see Mahajan t al. 2001 for a review).While diffusionmodels provide insight into the dynamicsof an inno-vation's sales, they do so from the perspectiveof thenew entrant,not the incumbent. Additionally, theydo not lend themselves easily to rigorous prelaunchcalibration.

ProbabilityFlow Models. A flexiblemodeling ap-proach that capturesboth the explanatory power ofdiscrete-choice models and the dynamics of diffu-sion models uses probability low models. The frame-work consists of behavioral states through whichconsumers flow (e.g., awareness, trial, repeat),withthe relative flow levels and rates from each stateto all others being estimated. A specific type offlow model, semi-Markovmodels, allows flow incontinuous rather than discrete time (Hauser andWisniewski 1982a,b). Typically,Markov models arewrittenin recursive orm forcomputationalpurposes.The closed-form equivalentbecomes complex whenmodel parametersvary by observationperiod, butsimplifies when, as in our detailed model, they are



4/16

Roberts, Nelson, and Morrison: A PrelaunchDiffusionModel or EvaluatingMarketDefenseStrategies152 Marketing cience24(1),pp. 150-164,2005INFORMS

stable.Indetermining he number of behavioralstatesto include, there is a trade-off between the greaterdiagnostic nformationof a richermodel and the costand difficultyof estimatinga more complex system.Theabilityto specifythebehavioralstatesappropriatefor a particularproblem, ogetherwith the flow levels,flow rates,and their determinants,makes the frame-workappealingfor the forecastingandmarketing-mixallocationproblemfacedby defendersagainsta newentrant.Anotheradvantage of these models is theirsuitability ormonitoringand controlpurposes, giventhe detaileddiagnosticsthey provide over time.We drawon the probability low approach or mod-eling consumer response to defensive strategy,themarketdefense literature to specify the appropriatemarketing-mixelements, the utility choice literatureto provide a rigorousway to examine how these fac-tors arelikely to affect the flow levels between states,and diffusionmodels to specify the flow rates.

Model DevelopmentThe dynamic defense model is developed at twolevels of complexity,following Urban and Karash's(1971)advocacyof evolutionarymodelbuilding.First,we form a base model. Then, we expand the num-ber of behavioralstates to provide a more detailed

view of the consumer. Both models are calibratedin three stages: specifying the behavioralstates,esti-mating the relative flow levels in terms of manage-ment decision variables,and calculatingthe rate atwhich these flows will occur. Base model benefitsinclude speed and ease of calibration(particularlyprelaunch,when there is little marketplace nforma-tion and consumers cannotprovidedetailedreactionsto unfamiliar stimuli), robustness of forecasts,andease of managerialinterpretation.The advantage ofthe detailedmodel is a richnessof diagnostic nforma-tion, indicating managerial leverage points to influ-ence consumerbehavior.Base Model SpecificationWe use only three states in the base model: the ini-tial state in which all customers belong to Telstra,and two captive states where customers either con-vert to Optus or remainwith Telstra.All customers,M, startwith Telstra.We assume that m will flow tothe new entrant and (M - m) will stay with Telstra(see Figure1).Specifying Flow Levels ( in Figure1)Hauserand Wisniewski(1982a,p. 465)show that thelogit model to estimate flow levels between states isa natural outcome of their assumptions. Using this

Figure Relationshipetween heStagesofConsumer ecisionMaking,Models, ndMeasuresBaseModel)



5/16

Roberts, Nelson, and Morrison: A PrelaunchDiffusionModelfor EvaluatingMarketDefenseStrategiesMarketing cience24(1),pp. 150-164,2005 INFORMS 153

formulation,Optus'share,MSO,afterdiffusion ism ( e^~l \ms=m={7^^)' (1)

where Uo is the utility of Optus, UT s the utility ofTelstra,and I is the inertia or search cost associatedwith changing company.2By expressing these utili-ties in terms of their constituentperceivedattributes,we can examine the effect of positioning and pricingon the equilibriumshare of the new entrant,Optus(m/M):I4= w**;*-Ac; (for; = 0,1), (2)

where Xjks the perceivedlevel of attributek of com-pany j, wk is the importance weight of attributek(for k = 1,2, ... ,K), A is the opportunity cost ofmoney,and c; is the price of company /'s service.In keepingwith Urbanand Hauser(1993,pp. 268-269), we allow for heterogeneityof perceptions,Xjk,but not tastes,wk.However,in the application,differ-ent flow levels were calculated for four usage seg-ments. The results are very similarwhen aggregatedand for the sake of parsimonyarenot reportedhere.

Specifying Flow Rate. Citing extensive empiricalevidence, Hauser and Wisniewski (1982a, p. 461)propose negative exponential or Erlang flow rates.We use the Bass model to specify the flow ratebecause it subsumes the negative exponential and,like the Erlang,allows maximum sales at a nonzeropoint in time. The flow rate parameters (p and qin Equation(3)) may be modeled in terms of theirdeterminants.

Summary of the Base Model Formulation.Thesolution to the differentialequationform of the Bassmodel aftersubstitutingm fromEquation(1) is^m\ i.g-(P+


6/16

Roberts, Nelson, and Morrison: A PrelaunchDiffusionModelfor EvaluatingMarketDefenseStrategies154 Marketing cience24(1),pp. 150-164,2005INFORMS

Figure RelationshipetweenheStagesof Consumer ecisionMaking,Models, ndMeasuresDetailedModel)

these flow levels. Algebraically,we represent he equi-libriumflow levels below:ProportionfPopulationConsidering ptus,ac (O inFigure2): ecu CoaC= eCUo+eU}=^> where CUq s the Optus considerationutility,and U?is the benchmarkutility againstwhich it will be com-pared. Roberts and Lattin (1991,Equation(6)) pro-vide an expressionfor the benchmark,Uj, in termsofTelstra's onsiderationutility and search costs.Proportion f ConsiderersTrying Optus,aT ( inFigure 2): eEU~l _ To

a?- e^ + e^T-_

cof K)where EUqis Optus' and EUT s Telstra'sevaluationutility, and / representssearch and inertia costs attrial.Proportion f TriersBecomingOptusLoyal,aL (> inFigure 2). Optus triers may continue to considerTelstraand alternatebetween the two companies,orbecomeOptusloyalists.Theproportionof Optus loy-als, aL,will be one minus the share of triers who stillconsider Telstra.This will be a function of the consid-erationutility of Telstra,CUT,and some benchmark

utility depending on inertia,searchcosts, and Optus'utility, LZ^:_*___&_ Lo ,..aL~ ecur+euo ecur+euo To' {)

Proportion f CallsThat SwitchersAllocate o Optus,as ( in Figure 2). For those who continue to useboth carriers,we can model their relativeusage as afunctionof the relativeperceivedutilityof OptusandTelstra: eusoas = euso_j_usT (7)where USOand USTare switchers'utilities of Optusand Telstra,respectively, or specific calls. All of theconsiderationutilities (CUq,U}, CUt, Ug), trialutili-ties {EUq,EUt), and call allocationutilities(USO,UST)may be expressed in terms of their multiattributecomponents(Equation 2)).

Specifying Flow Rates. Forthe detailedmodel,weassumenegativeexponentialdistributions orall flowrates. Fader et al. (2003)find that it fits and forecaststrial well for a variety of categories if a saturationconstraintis imposed. Although it is not as general



7/16


as the base Bass model, we use the simpler flow-rate model for parsimony.3 We also assume that flowrates to different destinations from the same sourceare equal. Under the negative exponential flow-rateassumption, we specify the rates at which customersleave the prelaunch incumbent state (the considera-tion decision), Equation (8); the consideration state(the trial decision), Equation (9); and the trial state(the Optus loyalty decision), Equation (10); as follows:

^l = acPc(M-Co(t)/ac) O in Figure 2, (8)^Ml = aTpT(Co(t)-To(t)/aT) in Figure 2, (9)

^P- = aLpL(To(t)-Lo(t)/aL) in Figure 2, (10)

where pc, pT, and pL, the flow-rate parameters, areconstants. We could model these rates in terms of con-sumer attitudes (as with the base model), but for thesake of parsimony and because of the difficulty in cal-ibrating such relationships prelaunch, we only look atrate determinants in the base model.Summary of the Detailed ModelWe can combine the flow levels represented by Equa-tions (4) to (6) with the flow rates in Equations (8)to (10) to get a closed-form representation of the num-ber of consumers in each state at any point in time.Combined with the call allocation of switchers (Equa-tion (7)), this gives the market share of Optus at anypoint in time. Because utility is modeled as a functionof constituent attributes and price, we can see howTelstra's and Optus' positioning will affect the diffu-sion process. The different determinants of flows atdifferent decision stages allow us to identify whereTelstra can best limit adoption of the new product.

Cumulative Level of Considerers at Time t, Co(t).To obtain the cumulative number of considerers,Co(t), we look at the proportion that has flowed fromthe Prelaunch Incumbent state in Figure 2 to Consid-eration. Equation (8) specifies how many people haveflowed out of the Prelaunch Incumbent state, whileEquation (4) determines how many of them flowedto Consider. Solving Equation (8) and setting initialconditions of Co(t) = 0 at t = 0, we obtain

Co(t) = acM(l-e-^). (11)Cumulative Level of Trial at Time t, To(t). Toobtain the cumulative number of triers, To(t), we sub-

stitute Co(t) from Equation (11) into Equation (9).Solving the resultant differential equation in t, andsetting initial conditions of To(t) = 0 at t = 0, givescumulative trial, To{t), in terms of time and the leveland rate parameters, ac, aT, pc, and pT:To(t)= acaTM(l Pi- g-?c*+ Pc e-rTt\ (12)V Pt-Pc Pt-Pc )

Cumulative Level of Optus Loyalty at Time t,Lo(t). Cumulative levels of Optus loyalty, Lo(t), canalso be estimated by substituting the expression forTo(t) from Equation (12) into Equation (10) and solv-ing for Lo(t), setting initial conditions of Lo(t) = 0 att = 0:Lo(t) = LacaTaLM(l-V

^ -e~^L V (Pl-Pc)(Pt-Pc)I PcPl c-PTt(Pl-Pt)(Pt-Pc)

PcPt e-pLt\\ (13)(Pl-Pc)(Pl-Pt) /]'

Membership of Other States at Time t. Because aproportion (1 - aL) of Optus triers become switchers(see Figure 2), the number of switchers at time t, So(t),is given by

So(t) = (l-aL)/aL*Lo(t). (14)The number of Telstra loyals at time t, LT(t), noncon-siderers plus nontriers, is

LT(0 = (1 - ac)/ac * Co(0 + (1 - aT)/aT * To(t). (15)Optus' share, MSo(t), at any point in time is the pro-portion of the population that is Optus Loyal (Lo(t))plus the proportion of switchers (So(t)) times the pro-portion of their calls that are going to Optus (as):

MSo(t) = Lo(t) + So(t)*as. (16)Thus, the model gives us a closed-form expression ofthe number of people in each state in terms of time t;flow levels ac, aT, and aL; and flow rates, pc, pT,and pL. Because we can express ac, aT, aL, and asin terms of their determinants in Equations (4) to (7),management also has a good idea as to how to influ-ence flow levels between states (and in principle theflow rates: pc, pT,and pL).In formulating the model we make a numberof trade-offs. Comparing our detailed model toASSESSOR (Silk and Urban 1978), we see firstthat the original ASSESSOR paper does not modelsales dynamics. It only examines equilibrium shares

3Onereason orincludingthe coefficientof internal nfluence,q,inthe Bass model is to allow it to trackS-shapeddiffusionpatterns.Theuse of a multistate low model achievesthe samepurpose.



8/16


(although some subsequent applications have esti-mated dynamics).ASSESSOR ncludes many of thesame stages (consideration, rial,purchaseallocation,etc.),but by using two differentmodels. To gain cor-respondencebetween its models, the authors makesome assumptionsmorelikely to hold with packagedgoods thanwith telecommunications(e.g., "consider-ation and trial are operationallysimilar").Also, theoriginalASSESSORpaperdoes not measurethe deter-minants of consideration, trial, and repeat that areimportantin this application.Within our multistateapproach, he use of discrete-choicemodels for flowlevels and diffusionmodels for flow rates subsumesexamplesof a largenumber of othermodels (hazardrate,conjointanalysis,etc.).The base model providessome insuranceagainstthe dangersof increasedcom-plexity associated with the detailed model.Applyingthe ModelWe develop a methodology to calibrate the modelpriorto launch.Calibrationhas five components:elic-itationof the drivers of utility (theattitudinal temsinEquation(2)), identificationof the targetmarket andsample selection, design of concept stimuli to repre-sent the Optus service offering and possible Telstraresponses to it, development of measures to cali-braterespondents'reactions to the new concept,andestimation.Identificationof Drivers of UtilityWithTelstra'ssenior management,we identified therange of strategic options available to Optus andpossible responses by Telstra, ncluding possible ser-vice features and different pricing levels and for-mats. Next, 17 customerfocus groups were held toexplore the decision-making process, including keyfactors that would influence switching: attitudes tothe incumbent,expectationsof the new entrant,andinertial and search cost factors. Focus groups wereconductedin threestate capitalsand two rural areasby a professionalmarketresearchfirm, with partici-pants screenedon the basis of call usage.Sample SelectionTelstraprovided a list of possible respondents, seg-mented by call usage, based on its internal com-pany records. We used systematic sampling on thissampling frame to select 1,200 respondentsyielding801 completed, usable interviews. Information wascollected in 45-minute face-to-faceinterviews con-ducted at the respondent'sresidence. Field researchwas conductedapproximately hree months prior tothe Optus launch and results from calibratingboththe base model and detailedmodel were made avail-able to Telstrasenior managementone month beforelaunch.

Design of ConceptStimuliTelstradevoted considerableresources o understand-ing the positioningthatOptuswould seek and how itwould be priced.Forexample, job vacancyadvertise-ments of Optus described the sort of companythat itwas going to be. Thepresscontainedarticles n whichOptus management discussed its objectives.Finally,a lot was known about how Bell South and Cable&Wirelessbehaved in theirhome and foreignmarkets.Based on this information,Telstracommissioned itsadvertising agency to develop a series of advertise-ments to simulate how Optus might communicate.Optus had also released some informationabout itsplans for pricing.Afterevaluatingthe likely position-ing and marketingmix of Optus, Telstradevelopedpossible pricing plans and service initiatives to bluntthe impactof the Optus campaign.Respondentswereexposedto two stimuliregardingOptus.The first wasa detaileddescriptionof its serviceofferingsandposi-tion, designed to simulate full informationevaluationof the service,after which considerationwas gauged.Service evels were describedby sound quality,avail-ability,billing format,chargingmethod,and customerservice and complainthandling. The second stimu-lus was a series of pricingscenarios,after which trialintent,call allocation,and continued Telstraconsider-ationwere measured.Theconjointprice analysiscon-sisted of a full factorialof six pricelevels (withOptusdiscountsfrom -5% to +20%of Telstra'sprice in 5%gradations)combined with four price-discountplanscenarios(Optus only,Telstraonly,both,neither).Dis-count plans were specified in terms of cost of plan,time of savings, level of savings, geographic cover-age of savings, phone numbers covered, and mini-mum thresholds.Respondentssaw fourpricing planseach.Thesetwo sets of materialsallowed stimuli thatreflected how the market would evolve from a sup-ply perspective. This application is consistent withthe criteria that Wittink and Bergestuen(2001) sug-gest as those under whichconjointanalysis s likelytoperformwell (incremental nnovation,weighting byusage, few attributes,etc.).MeasuresWe used standard validated measures to calibrateconsumers'reactionsto the new productstimuli. Thebase model in Equations 1)and (3)requiresestimatesof the flow level to Optus (tn/M) and the flow rates(p and q) to establish the dynamics of Optus' shareover time. Additionally,we need to determine howthe marketingactivitiesof Telstra andOptus)wouldaffectthe flow level and its rate(Equation 2)).Toesti-mate these equations, we requiredmeasures of theultimateprobabilityof adoption,rateparameters, hetwo competitors'pricing, and consumer attitudes totheir service levels.



9/16


Attitude items were measured afterexposureto theOptus offeringon a five-pointLikert scale. We used14 items for Telstra'sperformancerelativeto Optus'expected performance,and 9 items for respondents'attitudes to the difficultyand cost of changing.Prob-abilityof switchingwas gauged by the probabilityofOptus trial, times the call allocationto Optus, giventrial.Utility was not measureddirectly,but imputedfrom the logit model. Rate parametersfor the basemodelwere estimated n two ways. We askedrespon-dents how long they thought it would take them tomakea decisionand act on a possible changeof long-distanceprovider.To obtain some convergentvalid-ity, we used sales dynamics from a similar launchin an overseas market. U.S. marketshare data wereavailable on a quarterlybasis from the Federal Com-municationsCommission(1990),giving early marketshare gains of MCI, the first entrant against AT&T.Thisgave us a maximallydifferentsecond estimate ofthe rateparameters.In the detailed model, prelaunchmeasures for thedependentvariablesin the flow-level models (Equa-tions (4) to (7)), were operationalized as follows.Optus consideration(yes no on a bipolarscale) wasmeasuredafterexposureto the serviceoffering.Prob-abilityof trial(on an 11-pointJusterscale),continuedconsiderationof Telstra(yes/no on a bipolar scale),and likely call allocationbetween Optus and Telstrafor switchers (as a percentage)were collected aftereach of the four pricing scenarios.Calibrationof theflow rates between states in the detailed model issomewhatmore difficultprelaunch.Data on the evo-lution of consideration, rial,and new-entrant oyaltyin the analogous overseas market were not avail-able. While we asked respondentsabout their over-all expected decision times, we did not think theycould give reliable answers to questions about thetime to consider,the time from consideringto trial,and the time when theirbehaviorwould stabilize. Wehad consumerjudgment of the overall rate, as out-lined in the base model description.We divided itinto its componentflows by using a decision calculusapproach,combining management'sjudgment withthe experienceof theirmarket researchagency (Little1970).Becausethe rateparametersarenot intuitivetomanagers,we asked the Telstraprojectteam to esti-mate the time it would take half of the populationtoflow out of a state after entering it. We pooled theresultantestimatesby averaging.In summary, all data to calibrate both modelsare based on consumer feedback from the mar-ket researchwith two exceptions. First, we use ananalogous marketto provide convergentvalidity ofthe rate parameters n the base model. Second, weuse management judgment to allocate the overallrespondent-sourcedrate parameterinto componentflows between the differentstates.

EstimationEstimating both the base model and the detailedmodel involve calculatingflow levels and flow rates.We estimate these rates separately.Levels and ratesareseparable n the base model (Equation 3)),so theresults will be the same as for a one-step estimator.For the detailed model, we use separateestimationto simplify calibration.Beforeestimatingthe models,we study the structureof respondentattitudesusingprincipal components factoranalysis using Statisticasoftware (de Sa 2003). The logit models of flow lev-els and call allocation(Equations 1), (2), and (4)-(7))relatingthe probabilities o the attitudinal actorsandprice were estimated using a maximum-likelihoodestimationprogram,BLOGIT. ateparameters Equa-tions (3), (8), (9), and (10)) were estimatedusing thenonlinearleast-squaresroutinein Statistica.

ResultsThe Structure and Level of Customer AttitudesPrelaunchcalibrationproceeds by studyingthe struc-ture of respondent attitudes to Telstra relative toOptus, and search and inertia. These attitudes,togetherwith pricing plans, providethe determinantsof flow levels to Optus and, in the base model, theflow rate at which it will be realized. Wereduced the23 attitude items using a principal-componentsac-tor analysis. Based on a scree test, eigenvalues, andinterpretability, e used threefactorsto describehowrespondentsfelt about Telstrarelative to Optus, andfour to embody attitudes to competition, nertia,andsearch costs. After Varimaxrotation,the threefactorsunderlying Telstra attitudes relative to Optus werecalled "strengthof relationship,""servicedelivery,"and "bigand impersonal."The fourfactorsrelating oswitching were "no downside," "restlessness,""highinertia,"and "competition rrelevant."Averageitemlevels arevaluable in theirown right.Telstrawas per-ceived as responsive to customers' needs and easyto contact.However,respondentsbelieved that mostpeople had a complaint about Telstra. With respectto the inertial/searchcost items, on the positive sidemost people had high informationuncertaintyandfelt that Optus would not have a complete range.On the negativeside, theywelcomed competitionandperceivedOptus as low risk.Estimating the Base Model, PrelaunchThe base model has two parts:flow levels (the num-ber of people who will ultimatelyadopt Optus) andflow rates (how quickly adoption will occur). Forboth, we estimate their expected value under themost likelymarketingscenario("thestandarddefenseplan"),and then we examine their determinants.



10/16


Table 1 Base Logit Model Estimated Prelaunch (Equation (1)), 0 in Figure 1Including defensive variables Entrant's marketing mix only

Parameter Standard Parameter StandardVariable value error value errorConstant -0.33 0.07 -0.33 0.07Price of Telstra - price of Optus, P 0.75 0.04 0.74 0.04(Price of Telstra -price of Optus)2, P2 -0.23 0.05 -0.23 0.05Optus price plan, 80 0.20 0.05 0.19 0.05Telstra price plan, 8T -0.16 0.04Strength of relationship, X17- -0.1 1 0.09No downside, X1S 0.32 0.09 0.32 0.09Restlessness, X2S 0.39 0.09 0.42 0.08High inertia, X3S -0.24 0.08 -0.25 0.08Model fit /.(0): -2,216, L(B)\ -1,789, /.(0): -2,216, L(B): -1,803,

*92= 848 *72= 828Test for difference between models xl = 20, p < 0.001

Ultimate Flow Level to Optus and Its Determi-nants. Based on the standard defense-planscenario(providedto respondents n the concept description),respondents provided the chances of eventuallyswitchingto Optus.Theaverageof these probabilities(afterreweightingfor bill size) was 21.9% f Telstracould achieve perceived price parity and 32.7%ifOptuswas perceivedas being5%cheaperon average.As nonlinear effects of price would have importantmanagerial mplicationsfor Telstra,we incorporateda quadraticprice term in the base flow-level model(Equations 1) and (2), flow level in Figure1).Table 1 shows that one relative performancefac-tor (X1T)and three inertial/search cost factors (X1S,X2S,X3S)are significantin determining share loss.4Relative price is significantand nonlinear,suggest-ing thatdecreasingperceivedrelativeprice will havea positive but diminishing return on share. Pricingplans were significantforboth players,with unequaleffects.Optus benefits more from pricing plans thanTelstra.Few diffusion models include the marketingmix of the defender as well as that of the innova-tor.For the sake of comparability,we estimated ourlogit model with only the new entrant'spricing andinertialvariables.In Table1 we see a significant ossof fit according to a x2 test, as well as a loss inthe ability to gauge the effects of Telstra'sdifferentstrategies.The direct effects of Telstra'sdefense (bothprice and strength of relationship)were important,but there is some indication that indirect effectswerealso extremely relevant. The degree of inertia thatrespondents felt depended very much on Telstra'sperceivedperformance.For example,a regressionof

"restlessness" X2S) hows a significantrelationwithattitudes of Telstrabeing "big and impersonal"(t =7.11),having high "servicedelivery"(t = -6.37), anda "strongrelationship" Xir) (t = -10.82). R2was 0.20on individual-leveldata.Flow Rate to Optus and Its Determinants. Hav-ing estimated the determinantsof Optus adoption,we consider how quickly it will be realized. Usingseven years of quarterlyU.S. data fromthe launchofMCI,we estimatedrateparametersn the Bass modelusing nonlinear least squares. In Table 2 we showresults for the Bass model with q = 0, as q was notsignificantwhen estimatedfor the analogousmarket

(or self-stateddecision times), and the comparisonofthe constrainedand unconstrainedmodels shows noloss when q is constrained.The nonsignificantq isnot surprising given the large amount of publicity(externalinfluence) associatedwith the launch of asecond telephonenetwork,and is consistentwith thepostlaunchfindingof Hauserand Wisniewski(1982b,p. 165)with respect to a new public transitsystem.These results suggest that in the analogous market,saturationoccurredwith MCIgaining44%of AT&T'scalls in a duopoly,and a highly significantcoefficientof external nfluence,p, of 0.057.Using five differenttime ranges,we asked respon-dents how long they would require o evaluateOptus.That gave us the number of customers who wouldhave made their decision at five differentpoints intime. Becauseall callerswill eventuallyreacha deci-sion, we set m/M = 1 and estimatedp fromthese self-stated decision times.5 See Table2 for the results offittingthe Bassmodel,with q= 0, to these data.Again

4Variable election was performed by stepwise eliminationhereand for the detailed model. This gave the same result as fittingthe fullmodelsandeliminatingallnonsignificant ariables. deally,historicalcall data would have been used for convergentvalidity,but they providecategory, ather han brandprice,elasticities.

5Whileall respondentswill eventuallyreacha decision,to test therobustnessof ourrate data we estimatedm/M as well. m/M comesout to be 0.8, which is not statisticallydifferent rom 1 given thefew degreesof freedom.



11/16

Roberts, Nelson, and Morrison: A PrelaunchDiffusionModelfor EvaluatingMarketDefenseStrategiesMarketing cience24(1),pp. 150-164,2005 INFORMS 159Table 2 Base Model Flow Rate: Estimation of Bass Model, with q = 0,0 in Figure 1

Analogous market Self-stated decision times

Parameter Standard Parameter Standardestimate error estimate errorm 0.439 0.010 1.000 -p 0.057 0.003 0.063 0.007q 0.000 - 0.000 -R2 adjusted 0.973 0.972n (observations) 28 5

the coefficientof external nfluence,p, is strongly sig-nificant(despite the low power), and the fit is good(even adjusted for degrees of freedom). Based onthese two independentanalyses,we averagedthe esti-mates of p (0.057and 0.063),giving an estimate of theexternal nfluence rate of 0.060.Measuringthe time respondentsthink it will taketo reach a decision enables us to estimate the rateparameterprelaunch,based on the feedback fromthe targetpopulation tself.Additionally,however,wecan model the factors associated with fast and slowdecision making.While the primary challengeto thedefender is to reduce its share of calls lost, the sec-ondary challengeis to slow thatrate. We examine thedeterminantsof a high flow rate by regressing thetime to decision on the attitude items. (See Table 3for these results.)A short time to adoption is associ-ated with negative relative attitudes towards Telstra(e.g.,"notresponsiveto my needs")and a slow rate tounfavorableattitudestowardscompetitionand inertia(e.g., "usingOptuswould be risky").6

Summary of Base Model. Combining the baseflow level with the base flow-ratemodel, the overallmodel fromEquation(3) isMSo,(= (TT^n^)(i--0060% d7)

whereUo-UT = 0.751P- 0.230P2 0.160


12/16

Roberts, Nelson, and Morrison: A PrelaunchDiffusionModelfor EvaluatingMarketDefense Strategies160 Marketing cience24(1),pp. 150-164,2005 INFORMS

Table 4 Detailed Model: Prelaunch Calibration Equilibrium Flow LogitModels(A) Consideration Model: "Iwould consider Optus" (Yes/No), O in

Figure 2Variable Parameter value Standard errorConstant 0.70 0.08Strength of relationship, Xn -0.14 0.09Service delivery, X2T -0.11 0.09No downside, X1S 0.53 0.09Restlessness, X2S 0.84 0.10High inertia, X3S -0.29 0.09Notes. L(0): -555, L(B): -444, xl = 222, p < 0.001 .(B) Trial Model: "I would try Optus" (11-point Juster scale), in

Figure2Variable Parameter value Standard errorConstant 0.73 0.09Price of Telstra- 0.77 0.05price of Optus, P(Price of Telstra- -0.26 0.06

price of Optus)2, P2Optus price plan, 80 0.17 0.06Telstra price plan, 8T -0.16 0.05Restlessness, X2S 0.27 0.10Notes. L(0): -1 ,41 7, L{B): -1,218, xl = 398, p < 0.001 .(C) Optus Loyalty: "I would not continue to use Telstra" (Yes/No), > in

Figure 2Variable Parameter value Standard errorConstant -0.41 0.09Price of Telstra- 0.63 0.06

price of Optus, P(Price of Telstra- -0.10 0.07price of Optus)2, P2

Optus price plan, 80 0.27 0.06Telstra price plan, 8T -0.13 0.06Big and impersonal, X3T 0.32 0.1 1No downside, X1S 0.25 0.13High inertia, X3S -0.32 0.11Competition irrelevant, X4S -0.17 0.11Notes. /.(0): -1,126, L(B): -978, ^| = 296, p < 0.001.(D) Call Allocation to Optus for Switchers (Percentage), in Figure 2Variable Parameter value Standard errorConstant 1.03 0.10Price of Telstra - 0.50 0.06price of Optus, P(Price of Telstra- -0.13 0.07

price of Optus)2, P2Optus price plan, 80 0.25 0.06Telstra price plan, 8T -0.12 0.06Big and impersonal, X3T 0.1 6 0.11No downside, X1S 0.16 0.13High inertia, X3S -0.23 0.12Notes. /.(0): -1,126, L(B): -935, ^| = 382, p < 0.001.

Optuswas considered,the battlegroundwould movemore towardsprice.As with the base model, price issignificant n a nonlinearway.OptusLoyalty(Equation 6)) was related to price,attitudes of Telstra relative to Optus, and inertialvariables.Optus' price plans are more effectivethanTelstra's,suggesting that if trial is achieved, priceplans representa valuable offensive marketingtoolfor Optus,but provide less protectionfor Telstra f itmatches them. An attitude of Telstrabeing "bigandimpersonal" ncreases the chances of consumers ceas-ing to use Telstra,as does a belief that there is "nodownside" to Optus."High nertia" orhabit) s likelyto assist Telstrain staying in the consideration set.(At the time, callers had to dial an extradigit to useOptus.)Those forwhom "competitionwas irrelevant"are also likely to continue to use Telstra.CallAllocation Model (Equation 7)). The relative allo-cationof callsforswitcherswas modeledusing a logitmodel on price and attitudinalfactors. Price was themost significant determinant in call allocation, fol-lowed by the availabilityof pricing plans. If a con-sumer has pricing plans from both companies, anOptus pricing plan will be more effectivein gainingshare than a Telstraplanwill be in stoppingit. FactorsthataffectedOptus'call allocationamongstswitcherswere whether Telstrawas disliked (seen as "bigandimpersonal" elative to Optus)and whetherthere was"no downside" to using the competition(in terms offinancialor psychologicalcost). "Inertia" till contin-ues to favorTelstra.

Flow Rates in the Detailed Model. Consumersreceived their bills on a monthly billing cycle. Asoutlined in the measurementsection,we asked man-agement and the market research agency to esti-mate how manybills consumerswould receive beforethey considered, tried, and stabilized their behaviorwith respect to Optus. Receiptof a bill was seen asan important stimulus because it was at this timethat potential switchers could comparewhat Optus'and Telstra'sprice structures meant for them per-sonally.Telstrahad been trackingconsumers' aware-ness and considerationever since newspaperarticleshad foreshadowed the Optus launch, a period ofapproximately12 months. These data suggested thatapproximatelythree months would be the mediantime to gain consideration.Accordingly,the rate offlow into consideration,pc, was set equal to 0.231,correspondingto 50%of callers making the consid-erationdecision within three billing cycles. The rateat which trial was realized was estimated by man-agement judgment to have a median of two billingcycles, (pT= 0.347) somewhat shorterthan consider-ation. For Optus loyalty, the rate of conversion wasexpected to be slow because, unlike trial, there aresignificantlong-termramifications or the customer.



13/16


Managementestimated that 50%of the Optus userswould decide whether to continue to use Telstrawithin 3.5 billing cycles (pL= 0.198).The convolutionof these management udgmentswas then comparedto the overall flowrate,based on respondentdata andmarketanalogy.Although the base and detailed models haveslightly different time trajectories,we can comparetheir average growth rates. If we compound thedetailedmodel flow rates and estimate the resultwiththe negative exponentialrate of the base model, weobtainan estimateof p, the rate parameter,of 0.062,showing close correspondence o the base model rateparameterof 0.060 (from an analogous market andrespondents'self-reports).While we rely on manage-ment judgment for the allocation of rates betweenstates,we can be more comfortablewith the overallrate that these flows imply.Overall Detailed Model. The logit models inTable4 and the flow rates calibrated above can besubstituted n Equations(4)-(7) and (11)- 16),respec-tively, to give a closed-form representationof thenumber of consumersin any state at any time anal-ogously to Equation(17) for the base model. (It hasnot been included simply in the interestsof preserv-ing journalspace, because it is both straightforwardand cumbersome.)Management Implications andExtensionsThe detailedmodel identifies the driversof consider-ation, trial, Optus loyalty, and call allocation,undercontrolof both Telstraand Optus. It was providedto Telstra op management n the form of workshopsand a report. It was delivered to Telstra market-ing analysts and strategistsas a PC-based decision-supportaid that enabled them to simulate the effectof Optus initiatives and Telstraresponses to blunttheir effect. To reduce Optus' equilibrium appeal,Telstracan improve attitudes towards it (e.g., thestrength of its customer relationships) or increasethe cost of search (e.g., stress the possible downsideinvolved in switching). (See Table1.) Attitude levelswere very diagnostic for Telstra.Telstra'sperceivedresponsivenessand ability to be contacted obviatedthe need for a planned customercontactpoint cam-paign. However,beliefs about Telstraservice (and itseffect on switching levels in Tables 1 and 4) led toa majorservice-level communicationprogram.Oneof the study's key findings was that improving atti-tudes to its service would give Telstra ndirect ben-efits from increasinginertiaas well as direct effects.To slow its rate of share loss, Telstra can appearmore responsive and emphasize that saving moneyis not the only importantattribute n switching now

(see Table3). Based on these findings, Telstra nsti-tuted a micro service delivery program at the cus-tomerinterface.In particular,billing received specialattention.59% of the populationbelieved that "mostpeople had a billing problem with Telstra/' whileless than19%reportedhaving personally experiencedone. This suggests that beliefs about service qual-ity were at least as large a problem as the physicaldelivery of service. As well as focusing managementactions,the model was also used extensivelyfor fore-casting purposes.Inaddition to the insights gainedfromapplyingthebase and detailed models, the flexibilityof the logitchoice framework,combined with negative exponen-tial flow between states,allowedit to be extendedin anumber of ways. These included the incorporationofdifferentprice effects, segmentation,and evaluationof growth in categoryvolume. Postlaunch,the pricevariable was refined by including a "don't know"category."Don't knows" (25%of the trackingsam-ple) had the same probabilityof choosing Telstraasthose who believed thatTelstrawas 10%cheaper hanOptus (less than 1% of the sample). Based on this,Telstraaimed forpricecomparability ather hanpriceequalityor superiority.The desired answer to "Who scheaper?"was not "Telstra"a positionthat was hardto sustain),but "Itdepends."Telstraneeded to ensurethat there were always some routes and times of daywhen it was cheaperthanOptus.Withrespectto seg-mentation,fees for pricing plans were also tested inthe conjointmodel. Fees have the advantagethattheymade pricing plans relatively less appealing to the"valuable,not vulnerables," herebynot giving awaymargin to customers who had a low probabilityofchoosing Optus.Thatis, those who were consideringswitchingwere more engaged in informationsearch,more likely to calculatethe net benefit of a plan, andhence would realize that it had a net benefit. Thoseless likely to switch were unlikelyto calculate he netbenefits of a price plan, and so would avoid pay-ing extra for it, given its uncertain benefits. Finally,the model was extended to examinecategorygrowth.Using a nested logit framework,the inclusive valueof having an extra provider was calculatedand thecategory growth from having two companies, withtheir greater geographiccoverage, lower prices, andincreased marketing activity, was estimated. Whilethe growth in the categorysomewhat mitigated thenegativeeffects of a pricewar, t did not changeany ofthe implicationsfor defensive strategyfromthe shareanalysisdescribedhere.ValidationTelstra share loss was followed by senior manage-ment on a weekly and monthly basis, using both



14/16

Roberts, Nelson, and Morrison: A PrelaunchDiffusionModelfor EvaluatingMarketDefenseStrategies162 Marketing cience24(1),pp. 150-164,2005 INFORMS

sales results and the tracking study.Six months afterthe Optus launch,Telstramanagement requestedtheupdatingof the model to refineits defensive market-ing mix. This enabled us to validate the prelaunchforecasts.Pricing and marketing-mixvariables wereall very close to prelaunch assumptions (reflectingTelstra'sexcellent prelaunchintelligence).Results ofthe forecastvalidation are in Table5.The base model fit well (with mean absolute per-centageerror,MAPE,of 14.8%).The detailed model,while reasonablyaccurateon an overallbasis, revealssome interesting divergences between marketplacerealityand ourforecasts.Considerationand trialwerehigher than we forecast,but this was compensatedforby a decrease n Optus loyalty.One cause for thiscould be the high initial expectationsset by Optus.Another reason, which became clear in postlaunchtracking,was inertia. We expected inertia to be astronginfluencein discouragingconsideration,but itwas more of an influencein gaining continuedusageof Telstra(see Tables4A and 4C). Based on the cali-brationof the prelaunchmodel, we believed thatpre-empting trial was the strongest line of defense forTelstra.While undoubtedly it was the majorline ofdefense (trial was only 27% after six months), thehabit of dialingTelstra or toll callswas so strongthateven afterusing Optus, there was still a very strongtendencyto use Telstra or most calls.

Table 5 Forecast Evaluation: Accuracy of Base and DetailedModels (Calibrated Prelaunch)(A) Base Model: Optus Share Trajectory Over Time

Actual Forecast Forecast MAPEMonth (%) (%) error (%) (%)1 1.1 1.7 +0.6 54.52 3.1 3.3 +0.2 6.53 4.0 4.8 +0.8 20.04 6.1 6.2 +0.1 1.65 8.0 7.5 -0.5 6.36 8.8 8.8 0.0 0.0Mean absolute percentage error (MAPE) +0.37 14.8

(B) Detailed Model: Optus Share by Decision State at Month 6Actual Forecast Forecast MAPE

Decision state (%) (%) error (%) (%)Consideration, 54 52.9 -1.1 2.0

C0(t)Trial,7"0(0 27 25.1 -1.9 7.0Switcher share, 5 4.7 -0.3 6.0

(S0(t)*as)Optus loyalty, 4 4.3 0.3 7.5

L0(t)Overall share, 8.8 8.9 -0.1 1.1MS0(t)

Note. Forecasts and actuals are disguised by the same absolute sharepercentage.

The model was updated by reestimatingthe logitmodels and rate parameters. As anticipated, thelogit flow-level models proved to be reasonablysta-ble. Discrete-choicetheory and conjointanalysis arerobustmethods of estimatingnew productsharepriorto launch.However,the use of prelaunchsurveydatato estimate the rate of diffusion is less well estab-lished. Conversely, n contrastto survey data, earlysales data tell us less about the value of m/M thanthey do aboutdiffusionrates(see Heeler and Hustad1980).There was a high level of uncertainty n ourprelaunch calibration of flow rates (particularlybydecision state), so we wanted to validate these afterconsumershad firsthandexperiencewith Optus.Post-launch,we could do thisby using earlysalesdata andthe monthly tracking study. Sales data enabled us tofit the base model (imposinga value of m/M fromthebase logit model) to test the accuracyof ourprelaunchrate estimatesof p and q. Using six months data,andputting m/M equal to 0.29 from the prelaunch logitmodel, gave a similar fit (R2= 0.973)and a stronglysignificantp of 0.059,with q still insignificant.Similarfits were obtained with weekly data (R2= 0.963).Themonthlytracking tudy collecteddata on Optustrial regularly.Consideration,conversion to Optusloyal, and call allocationwere only measured after sixmonths.Therefore, or detailed model flow-ratevali-dation,a diffusionmodel couldonlybe fit to trialdata.TakingaT from the prelaunch ogit model, fittingsixmonths of trial gave a fitted pTof 0.386 (comparedwith theprelaunchestimate of 0.347),andan R2 n therate model of 0.931.Considerationand Optus loyaltysurvey results after six months enabled estimates ofthese rate parametersto be determined,but not fortheir statisticalaccuracyto be examined. Considera-tion after six months was 0.54,giving pc = 0.242,veryclose to the prelaunchestimate of 0.231. The rate offlow fromtrialto Optus loyalty,pL,was estimatedtobe 0.155, again similar to the prelaunchforecastof0.198.While we have tested our model calibratedpre-launch against actual sales data and behavior,it isdifficult to work out the benchmarkmodels againstwhich it should be compared.Becauseit subsumesastatic choice model, it is easy to observe the cost ofomitting dynamics(seeTable5A).Given the measuresthatwe collected,it is not easy to compare t to otherdynamicbrand-choicemodels.FurtherApplicationsandTransportabilityThe model has been appliedto othertypes of telecom-munications marketsin a number of countries (B2Bwith business toll calls,subscription ervices with cellphone adoption,and global with internationalcalls),



15/16

Roberts, Nelson, and Morrison: A PrelaunchDiffusionModelfor EvaluatingMarketDefenseStrategiesMarketing cience24(1),pp. 150-164,2005 INFORMS 163as well as other industries, including domestic airtravel.Inthe Australiandomesticair travelmarketwewere facedwith two defenders and two new entrants.One advantage of the semi-Markovapproachis itsflexibility.We could use a nested logit model (withone level being choice of established or new carrierand the other choice of airline),calibrating he inclu-sive value of new and established carriers.The otherapproachwas to have multinomial flows from thedynamic equilibriumof the two established airlinesto the four airlines after launch. After examiningthemarketstructure,we chose the former formulation.Thequestionnaturallyarises as to where our modelwould be most useful. Given its prelaunchnature,ability to handle dynamics,and strong choice basis,the approach seems appropriatein any market inwhich marketturbulencedue to new entry (or evenchangingcompetitiveadvantage) is imminent. Obvi-ous examples of this include defense against entrydue to deregulation(e.g., electricitymarkets),entrydue to technology change(e.g.,Intel versus AMD andMicrosoftversus Linux),entry due to changingstan-dards(e.g., Betaversus VHS recordingformats),andentrydue to productscomingoff patent (e.g.,Pfizer sLipitor n the pharmaceuticalndustryand Du Pont'sLycra n the clothingindustry).ConclusionIn contrast to most applications of research onemerging innovations,we have examined the man-agerialproblemfacingthe incumbent ratherthan thenew entrant.We developed a dynamic closed-formsix-state model of choice incorporating defensivemarketing actions as well as the new entrant'smarketing mix. We applied the model to defenseagainstnew entry in an existing category,estimatingprelaunch dynamics based on respondent data.Six months after launch, we validated the model.Challengesfacingthe defenderthat were highlightedin the applicationcan be illustratedby the diagrambelow:

Enhanceand Exploitandcommunicate exposeentrant'sown strengths weaknesses

Reduceequilibriumappeal Positive Negative(Equations1), (2), (4)-(7)) strategies strategiesSlow rate of diffusion Inertial Retarding(Equations3), (8)-(10)) strategies strategiesPositive strategies to enhance the defender'sstrengthsand thus improveequilibrium hare ncludestressingnew featuresand prices,as well as commu-nicationsthat exploit its strengths(e.g., "Telstrahasimproveda lot").Negative trategieshat attackweak-nesses of the new entrant include emphasizing the

new entrant'srisk, because informationuncertaintyis greatest at launch. By talking of trust, other her-itage attributes,and uncertainty,his weaknessof thenew entrantmay be emphasized.Strategiestargetedat the rate of diffusion may include inertialstrate-gies, which strengthenthe incumbent'shold over itscustomers(e.g., contracts, ock-instrategies,and psy-chological appeals). Alternatively,they may involveretardingtrategieso make it harder o choose the newentrant. For example, in the U.S. market,AT&Tsug-gested, when MCI launched its Friends and FamilyProgrampromisinga largesaving, thatpotentialcus-tomers should "get it in writing."This had the effectof making changinga toll-callprovidera hassle, thusretardingconversion. Much of the diffusion of inno-vations literature(e.g., Rogers 1995)suggests inertialways to slow diffusionrate (e.g., the role of compati-bility and complexity).By looking at drivers of flow rates and flow lev-els, our model provides ways of addressingeach ofthese four defensivechallenges.Calibratinghe modelhelps the defender avoid fightingon unwinnable ter-ritory (e.g., it would be pointless to claimthat Optuswill not deliver to this market,given strongand uni-versally accepted attitudes to the contrary).Whilestatic choice models used for defensemay assistwithunderstanding he ultimateappealof the new entrantand how that can be limited, they miss an impor-tant aspect by ignoring the dynamics of share gain.When we move from the base model to the detailedmodel, we identify specificdefensive leverage pointsin the consumer decision process, and thus give themanagerfiner information as to how to conduct thedefense process.The managerialuse of the results ofthis model led Telstra'sCorporateMarketingDirector,CharlieZoi, to say, "This is the single most influen-tialpiece of marketresearch hatthis organizationhasever undertaken."This approach has a number of limitations. Forthe sake of parsimony,we have assumed that flowrates from one state to different destination statesoccur at the same rate. Future researchcould relaxthis assumption.We have assumed that there are nosupply constraints,reasonablein this case, but notalways. We segmented on the basis of usage as aproxy for taste heterogeneity,whereas estimating amodel with unobserved heterogeneity would havebeen preferable.Also, a one-step estimation of thedetailedmodel would have had better statisticalprop-erties than the estimationby decision stage that weundertook.

Finally,while advertisingdata were available dur-ing the first six months, given the short period andthe collinearitybetween advertisingand Optus sharegrowth, it was impossible to calibrate their effect.Clearly, advertising is an important management



16/16


decision variable,and its effects should be incorpo-ratedinto the changingattitudes of both companies.The objectiveof this paper is to demonstratethatdynamic defense models are accessible to managersand to show how they may be calibratedprelaunchin practice. These models provide detailed diag-nostic information to evaluate defensive strategies.When combined with normative models in compet-itive strategy,they provide the pay-off matrix fromwhich game-theoretic olutions may be derived. Thecontributionsof the paperare a closed-formmodel ofdynamic defense, a measurementmethodology thatincludesprelaunchcalibrationof diffusion and choiceparameters, orecastvalidation,and a major ndustryapplicationthat helps in the link between market-ing modeling and marketing management in prac-tice.Thepaper provides a firststep in understandingthe dynamicsof defense in specificmarketsand alsoin incorporatingthe defenders' marketingmix intodynamicchoicemodels.

AcknowledgmentsThe authors thankJohnHauser,JamesLattin,GaryLilien,JordanLouviere,and Seenu Srinivasanfor their construc-tive suggestionswith this paper. They especially acknowl-edge thehelp of theguesteditors,associateeditor,andthreeanonymousreviewers with this paper.ReferencesBass,FrankM. 1969.A new product growthmodel for consumerdurables.Managementci 15(5)215-227.Beardsley, cott,Pedrode Boeck,JacekPoswiata.2002.What'snextfor EasternEuropeanTelcos?McKinseyQuart.38(4)11-13.de Sa, Marques. 2003. Applied Statistics: Using SPSS, Statistica andMatlab.SpringerVerlag,Berlin,Germany.Dipasquale,CaraB. 2002. Six month checkin: Catalina ervices totracknew products.Advertising ge73(38)59.Fader,PeteS.,BruceG.Hardie,RobertZeithammer. 003.Forecast-ing new product rial n a controlled est marketenvironment./. Forecasting2(5)391^10.Federal Communications Commission. 1990. AT&T market share ofinterstate witched accessminutes.IndustryAnalysisDivision,Washington,DC,June22.Gatignon, Hubert, Thomas S. Robertson,Adam J. Fein. 1997.Incumbent defense strategies against new product entry.Internat.J. Res. Marketing14 163-176.

Hauser, John R., Steven P. Gaskin. 1984. Application of theDEFENDERonsumermodel.Marketingci.3(4)327-351.Hauser,John R., Steven M. Shugan. 1983. Defensive marketingstrategy.Marketingci.2(4)319-360.Hauser,JohnR., KennethJ. Wisniewski.1982a.Dynamicanalysisof consumerresponseto marketing trategies.Managementci.28(5)455-486.Hauser,JohnR., KennethJ. Wisniewski.1982b.Application,pre-dictivetest,and strategy mplications or a dynamicmodel ofconsumerresponse.Marketingci.1(2)143-179.Heeler,RogerM., ThomasP. Hustad. 1980. Problemsn predictingnew product growth for consumerdurables.Managementci.26(10)1007-1020.Jeuland,Abel P. 1979. Epidemiologicalmodeling of diffusion ofinnovation:Evaluationand future directionsof research.Proc.Amer.MarketingAssociation Educators'Conf, AMA, Chicago, IL,274-278.Kumar, Ravikumar,D. Sudharsan. 1988. Defensive marketingstrategies: An equilibrium analysis based on decoupledresponsefunctionmodels.Managementci.34(7)805-815.Little,JohnD. C. 1970. Models and managers:The conceptof adecision calculus.Managementci.16 B466-B486.Mahajan, Vijay,Eitan Muller, Yoram Wind. 2001. New ProductDiffusionModels.Kluwer Academic Press, Amsterdam,TheNetherlands.Ramaswamy,Venkatram,Gatignon,Hubert, David J. Reibstein.1994.Competitive marketingbehaviorin industrialmarkets./. Marketing 8(April)45-55.Roberts,JohnH., JamesM. Lattin.1991.Developmentand testingof a model of consideration et composition. . Marketing es.28(November) 29-440.Roberts,John H., Gary L. Lilien. 1993. Explanatoryand predic-tive models of consumerbehavior.JehoshuaEliashberg,GaryL. Lilien, eds. Marketing.Handbooks n OperationsResearchandManagementcience,Vol. 5. North-Holland,Amsterdam,TheNetherlands,27-82.Robertson,Thomas S., Hubert Gatignon.1991. How innovatorsthwartnew entrants nto theirmarket.PlanningRev.19(5)4-12.Rogers,EverettM. 1995.Diffusion fInnovations,th ed. FreePress,New York.Shocker,Allan D., WilliamG. Hall. 1986. Pretestmarketmodels:A critical valuation. . ProductnnovationManagement86-107.Silk, Alvin J., Glen L. Urban.1978. Pretestmarket evaluationofnew packagedgoods: A model and measurementmethodol-ogy./. Marketing es.15(May)171-191.Urban,GlenL.,JohnR. Hauser.1993.DesignandMarketing fNewProducts,nd ed. PrenticeHall,EnglewoodCliffs,NJ.Urban,GlenL.,RichardKarash. 971.Evolutionarymodelbuilding./. Marketing es.8(February)2-66.Wittink,DickR.,TrondBergestuen. 001.Forecastingwith conjointanalysis. J. S. Armstrong, ed. Principlesof Forecasting:A Hand-bookfor Researchers nd Practitioners.Kluwer Academic Publish-ers, Boston,MA.

Model de Marketing exemplu

Documents

Transcript of Model de Marketing exemplu