the impact of sales-practice startup dynamics on sales ... · the impact of sales-practice startup...

TheImpactofSales-practiceStartupDynamicsonSales-forceProductivityJamesHoughton,MichaelSiegel,MohammadJalali1,AllanCampbell,DennisFialho2

Abstract:Theproblemofsalesforceturnoverhasbeenextensivelystudied;itscausesamongstpsychological,managerial,andworkplaceconditionshavebeenexamined.Inthispaper,weexploreastructuralcauseofturnoverrelatedtothestartupdynamicsofasalesagent’spracticeandproposestructuralinterventionsforturnover’samelioration.Ourinterrogatorytechniqueutilizesaformalsimulationmodelofthesalesagent’sstartupdynamics.Withthehelpofthismodel,westudyhowvariancesintheagent’sresourcebase(skill,naturalmarket,andlifestylebuffer)influencetheirthree-yearsurvivalratesandoverallcareerproductivity.Weshowthatapurecommission-basedcompensationpolicyselectsasalesforceoptimizedforsurvivingstartupdynamics,notforlong-termprofitability.Weexamineamixofpolicyinterventionsdesignedtoalignthesalesforcewithacompany’slong-terminterest.

IntroductionTheproblemofsalesforceturnoverhasbeenextensivelystudied;anditscausesamongstpsychological,managerial,andworkplaceconditionshavebeenexamined.Inthispaper,wedescribeapossiblestructuralcauseofturnoverrelatedtothestartupdynamicsofasalesagent’spractice,investigatetheimplicationsofourhypothesis,andproposestructuralinterventionsforturnover’samelioration.Becauseofthemagnitudeofcoststurnoverimposes,awealthofstudieshaveinvestigatedtheproximalcausesofthisturnoverwithaneyetopolicyintervention.Thesestudiestaketwogeneralperspectives:elementsthatvarywiththeworkplace,andelementsthatvarywiththeindividualsalesagent.Wecangetasenseforthediversityofthisliteraturewithabriefsurveyofthecausalmechanismsconsidered.Focusingontheworkplace,Lucasetal.citetheimpactofsupervisoryconsideration,intrinsicandextrinsicjobsatisfaction,andtask-specificself-esteem(Lucas,Parasuraman,Davis,&Enis,1987).SeligmanandSchulmandiscusshowlearned1SloanSchoolofManagementattheMassachusettsInstituteofTechnology.Contact{houghton,msiegel,jalali}@mit.edu2MassachusettsMutualLifeInsuranceCompany

helplessnessencouragesquitting(Seligman&Schulman,1986).Schwepkerdescribestheimpactofthesalesorganization’sethicalclimate(Schwepker,2001).Sagerconsiderstheimpactofjobstressandsatisfaction,perceptionoffairness,andcomparisonwithotherjobs(Sager,1991).Jaramilloexplorestheimpactofwastedtimeonindividualattitudes(Jaramillo,2006),BabakusandCravensinvestigatetheroleofemotionalexhaustion(Babakus&Cravens,1999),whileRobertsandChonkocontinuetheexplorationbylookingatpaysatisfaction(Roberts&Chonko,1996).MorganandInksdescribetheimpactsonturnoverofsalesforceautomation(Morgan&Inks,2001).Concernedwiththeindividual,RussandMcNeillystudycharacteristicsofexperience,gender,andperformance(Russ&McNeilly,1995),whileIngramandLeeconsiderindividualcommitment(Ingram&Lee,1990).ParasuramanandFutrellinvestigatedemographicimpacts(Parasuraman&Futrell,1983).TyagiandWotrubainvestigatedtheimpactofindividualdispositionsontheworkplaceconditionsassociatedwithturnover(Tyagi&Wotruba,1993).Despitesuchexhaustivecharacterization,missingfromthisliteratureisadiscussionofhowthestructureofacommission-basedsalespracticeitselfcreatesdynamicsthatfavorhighratesofturnover.

CaseStudyTomakeourinvestigationconcrete,wefocusourattentiononlifeinsurancesalesagentswhoarepaidoncommission.Thelifeinsuranceindustryhasnotoriouslyhighratesofsalesforceturnover,withestimatesfromtheLifeInsuranceResearchandMarketingAssociationplacingfour-yearretentionratesfornewagentsat16%in1992(Weeks,1995),and13%in2014(Leary,2014),givingourcasestudybothrelevanceandpower.

DataCollectionTounderstandthestartupdynamicsofanewagent,weconductedaseriesofsemi-structuredinterviewswithrepresentativesfromourcasestudy’shomeoffice.During6onetotwo-hourin-personinterviewsconductedoverthecourseofseveralmonths,wemetwith8differentexecutivesresponsibleforInformationTechnology,MarketingandPromotion,DigitalandCustomerService,InsightsandAnalytics,andOperations.Webeganwithopen-endedquestionsdesignedtoelicitanunderstandingofthesalesagents’behavior,drawingoneachorganization’suniqueperspective.Wethenengagedtheintervieweesincollaborativemodel-buildingexercisedesignedtoelicittheirunderstandingofthestructureofinteractionsandfeedbacksthatdrovetheagent’sstartupdynamics.Finally,weaskedspecific,quantitativequestionsaboutvariouscomponentsofourmodel.Weadditionallyconductedapproximately10one-hourinterviewswiththeseexecutivesbyteleconferenceaswerefinedourmodels,seekinginputintovariousstructuralcomponentsofoursimulationandelicitingvaluesforroughparameterization.

Lastly,weconductedasetofone-hourin-personinterviewswitheachoffoursalesagentsatvariousstagesintheircareers,andwithageneralmanageroftheirsalespractice.Intheseinterviewsweaskedaseriesofopen-endedquestionsdesignedtoassessthecongruencebetweenourunderstandingofthesalesagentstartupdynamicsandthatexperiencedbytheagentsthemselves.Wethenaskedspecific,quantitativequestionsdesignedtoelicitparametervaluesanduncertaintiesforoursimulationmodel.

QuestionsandHypothesesArisingfromInterviewsInterviewswithsalesagentsrevealedthatvoluntarydeparturecouldoccurforindividualswhowerenotdissatisfiedwiththeworkitselfbutwereunabletogettheirpracticessuccessfullyestablished.Eachnewsalesagententeredthepracticewithasocialorfinancial‘buffer’thatallowedthemtomeetexpensesuntiltheycouldcountoncommissionfortheirincome.Ifanagentcouldnolongermakethiswork,theymightbeforcedtogiveupthepotentiallyhighrewardsoffuturecommissionsforimmediateincome.Agentswhoremainedtermedthisthe“fail-out”dynamic;wewillconsideritsexistenceasourfirsthypothesisinthispaper.

Giventhishypothesis,managerswereconcernedwiththreequestions:1)howcanweunderstandthedynamicsofstartingupasalespractice?2)whatpoliciescouldbeimplementedtoreduce‘fail-out’?and3)whatimplicationsdothestartupdynamicshaveonthequalityofthesalesforce?Respondingtothisneed,wechosenotattempttodeterminethemagnitudeofthefail-outeffecteitherabsolutelyorincomparisontoothercausesofturnover,butmerelytodeterminetheinternalconsistencyofthehypothesisanditscorrespondencetotheobservedeffect.Tounderstandthisdynamic,wechosetosimulatetheagent’sstartupprocesswithadynamicmodelandtestedvariouspoliciesagainstapopulationofsimulatedagents.

StartupDynamicsInthispaperweconsidersalesagentswhoarepaidcommissionbaseduponthenumberandvalueofthesalestheymakeincategoriesofinsuranceproducts.Whenagentswithnopriorsalesexperienceenterthefirm,theyaregiventraininginthecompany’sofferings,andbasicsalesstrategies.Theyarethensentforthtorecruitcustomers.Agentsbegintheirsaleseffortsbyreachingouttotheir‘naturalmarket’–thenetworkoffriends,family,andcolleagueswithwhomtheyhaveexistingrelationships.Membersofanagent’snetworkwhomayhaveinterestandmeanstopurchasetheproductareconsidered‘qualifiedleads’,towhomtheagentdevotestheirsalesefforttoconverttheminto‘clients’.Agentsmeetwiththeirclientsonan

Hypothesis1:Startupdynamicsforanewsalesagentconstitutearacetoearnsufficientincomebeforerunningoutofstartupbuffer.

approximatelyannualbasisfollowingtheinitialsaletohelpreassesstheclientsappropriateproductmix.Duringthesemeetings,agentssolicit‘referrals’tofriendsandcolleaguesoftheirclients,inanattempttogeneratenewqualifiedleadsforfuturesales.Themakeupofthisnaturalmarketvaries,butbyandlarge,salestothese“Tier1”individualsaresmall,andtheirprimarybenefittotheagentistheirpotentialcontactswithindividualsofamorelucrativemarket.Bychance,asmallfractionofreferralswillbetoatierofindividualswithmoreresourcesattheirdisposal,andgreaterinclinationtobuyinsuranceproducts,asdiagrammedinFigure1.Salestoleadsinthissecondtiercanprovideacomfortablesourceofincometotheagent.

Giventhisstructure,thesurvivalofanagentinthesalesforcedependsupontheirabilitytoleveragetheirTier1‘naturalmarket’togainaccesstoasecondtierofpotentialcustomer,atwhichpointtheyareabletomeettheirexpenses.Oursecondhypothesisfollowsfromthis:

Inourinterviewswithagentsandmanagers,itbecameapparentthatthisprocessof‘naturalselection’wasperceivedasaselectionmechanismthatensuredthatonlyproductive,highlymotivatedindividualswouldremaininthesalesforce.Inthewordsofourinterviewees,tobeasalesagentisto“eatwhatyoukill”.Anewagentmust‘refusetofail’,andbepersistent‘whenyourbackisagainstawallandyou’rescrapingtogetalead’.Ifanagent‘mightnotmakeadollarforayear’they‘havetobeall-in’.

SelltoTier1

GetReferralsfromTier1

SelltoTier2

GetReferralsfromTier2

Tier1toTier1Referrals

Tier2toTier2Referrals

Tier1toTier2Referrals

Figure1:StartupDynamics-Anagentmustusesalestotheirtier1'natural'markettogainaccesstoandjumpstartsalesandreferralsamongsttier2clients.

Hypothesis2:Toearnalivableincomeandavoidfail-out,anagentmustleveragetheir‘naturalmarket’foraccesstoamorelucrativetierofleads.

Whileemphasizingthecharacteristicsofpersonaldeterminationthatwerenecessaryforsuccessasasalesagent,ourintervieweesdescribedtheirownprocessforsurvivingthestartupdynamic.Somedescribedtheabilitytorelyonapersonalfinancialbuffertocovertheirexpenses.Othersdescribedanabilityandwillingnesstolivewithextremefrugalityordependuponfamilyorfriends.Itbecameapparentthatthesebufferscouldvaryinbothtypeandextentfromagenttoagent.Ourdiscussionswiththeagentssuggestedthesecondhypothesisofourpaper:

Finally,inourmeetingswithsalesagentsandhomeofficemanagement,wewereexposedtotherealitythatevenforagentswhosuccessfullynavigatedthestartupdynamic,therecouldbealargedifferencebetweentopperformingagentsandthosewhoearnedamoremodestliving.Ourfourthandfinalhypothesisconcernsthisdisparity:

SimulationModelThroughourinterviewsweidentifiedthefeedbackstructuresresponsibleforcreatingthestartupdynamicbrieflysketchedinFigure1.Foreachagent,weconstructedasystemofdifferentialequationstotrackthestateofanagent’sstocksofleadsandclientsintheirnaturalmarket,asecondtiermarket,andathirdtiermarkettiersuperiortoboth3.Weconstructedamodelthatconformedtotheagent’sownunderstandingandlanguageforthestartupprocess.Insomeplacesfidelitytotheagents’mentalmodeladdsmathematicalcomplexitytotheequationstogainexplanatoryeffect.Baseduponfeedbackfromourinterviews,weconceptualizedtheallocationofanagent’stimeasbeingdistributedfirsttoservicingexistingclients,followedbysalestoleadsindescendingorderofvalue.Weconsideranagent’sbufferatanypointtobedrawnbyongoingexpendituresandbuiltupthroughcommissionsfromsalestoeachtierofleads.

3Whilefiguresareapproximate,onecouldthinkofTier1leadsasthosemaking~$50k/yr,Tier2asthosemaking~$500k/yr,andTier3asthosemaking5$MM/yr.clearlythegroupingsarearbitrarytosomedegreebutarerepresentativeenoughforthismodel.

Hypothesis3:Thestartupdynamicselectspartlyforagentswithskillanddetermination,andpartlyforindividualswithalargestartupbufferorabove-averagenaturalmarket.

Hypothesis4:The‘naturalselection’mechanismisaninferiormethodforoptimizingasalesforceforperformance.

Thesystemofdifferentialequationsmakinguptheheartofthemodelappearsintheequationsbelow.Thefirstequationtrackstherateofsalesasdependentuponthelikelihoodofasuccessfulsaleandtheamountofeffortdevotedtosalesinaparticulartieralongwiththelossrateofclients.Thesecondequationtrackshowleadsareacquiredfromeachtier.Thethirdtrackstheimpactofcommissionandexpensesontheagent’sbuffer.

𝑑𝐶!𝑑𝑡 = 𝑠 ∙

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𝑑𝐿!𝑑𝑡 = 𝐶!

!!!

!!!!!

∙ 𝑟 ∙ 𝑢!" ∙ 𝑞 − 𝑠 ∙𝑒!𝑒!− 𝐿!𝑓

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∙ 𝑛! − 𝑥

Theparametersintheseequationsareasfollows:Ci ClientsinTieriLi LeadsinTieris Salesuccessrateei EffortdevotedtosellingtoleadsinTierieR Effortrequiredtomakeasalel Theaveragelifetimeofaclientq Thequalificationrateofnewleadsr Thereferralrateuij ThechancethatareferralfromTierjwillbeforaclientinTierif TheshelflifeofaleadB Thesalesagent’sbufferni IncomepersaletoaleadinTierix Monthlyexpenses

Weallocatethetimetheagentspendsfirsttoexistingclients,andthentoTier3,2,and1leadsindecreasingorderofpriority,eachuptothepointwheresalesarelimitedbythenumberofleadsavailable.Foramorecompletedescriptionofthetimeallocation,seeFigure13.Forsimplicityinmodeling,wechosetoomitfeedbacksthatencourageasuccessfulagenttobalancetheirworkloadandincomeexpectationsortoincreasetheirmonthlyexpenses.Agentscontinuetoworkatthepacetheysetwhilestrugglingtomakeitthroughthestartupdynamicandtheycontinuetospendasfrugallyastheydidinitially.Clearlythisassumptionisunrealistic,butitdoesnotimpactourdiscussionofagentfail-outrateandisusefulforassessingthepotentialforlong-termagentperformance,withoutintroducingcomplexitytothesystem.Wealsosimplifybymodelingthesizeofthebufferasamultipleofanagent’sconstant

monthlyexpense,inorderavoidneedingtoprovideabsolutefiguresforcompensation.DiagramsofthefullmodelofstartupdynamicsarefoundinAppendixB:ModelStructure.ThefullsetofequationsformalizingthesystemdynamicsmodelisfoundinAppendixC:ModelEquations.Inourinterviewswithsalesagentsandsalesmanagers,weaskedourintervieweestoestimatearangesofvaluesforeachofthefreeparametersinthemodelastheyperceivedthemintheoverallbeginningsalesforce(notonlytheiruniqueexperiences).Intheabsenceofmeasurementsofthesysteminoperation,thesesubjectmatterexpertsgiveusasenseofthelikelyvarianceinbehaviorofthesystem.EstimatesofmodelparameterbaselinesanduncertaintyestimatesobtainedthroughourinterviewsarefoundinAppendixD:EstimatesofModelParameters.

AvoidingFail-outIndividualswithasignificantbuffertobeginwithareatanadvantage,andsomewillsucceedeveniftheirskill,effort,andnetworkresourcesarelow.Overall,therearemultiplewaysinwhichasalesagentcanavoidfailingoutbeforetheirbufferrunsdry:Forexample,individualswhohavebuiltupsavingsinprioroccupations,whoareabletolivewithfamily,orwhoarewillingtoliveextremelyfrugallyforthetimeittakestobuildupaclientbase.Wecansimulatethesehypotheticalindividualsinourmodelbyadjustingtheparametersfortheeffortrequiredtomakeasale(inverselyrelatedtoskill),timespentwithclients(determination),theinitialmixofleads(network)andstartupbuffer.Forexample,Figure2presentstheresultsofasimulationinwhichtheindividual’sbufferissteadilyerodeduntilmonth20,whentheirincomebeginstoexceedtheirexpenses.Figure2displayscompletestateinformationforthesystem,overthecourseofthefirst36monthsoftheagent’scareer.Thetopmostplotshowsthenumberofclientsthatthesimulatedagenthasacquiredineachofthethreetiers.Asreferralsaremodeledascomingfromexistingclients,thisfigureessentiallyrevealstheagent’sinstantaneouscapacitytogeneratenewleadsfromhisexistingnetwork.Weseethatoverthecourseofthesimulation,theagentdevelopsthiscapacityfirstamongstTier1clients,thenamongstTier2clients,andfinallybytheendofthesimulationisbeginningtobuildabaseofTier3clients.Thesecondplotshowsthenumberofleadsthatanagenthasatanygiventime.Thelastplotshowstheinstantaneousbufferthattheagenthasavailableinunitsofmonthsofexpenses.Ifthislinedropsatanypointbelowzero,theagentwillhave‘failedout’.

Figure2:Alow-skill,low-effort,low-networkvaluesalesagentcanavoidfailurebyhavingasubstantial

buffer.45

Asalesagentmayalsoavoidfail-outifheorshecomesintothepositionwithaccesstoamorelucrativesetofleads.Figure3showstheresultsofasimulationinwhichalowskill/effortindividualwithanaveragebuffercanavoidfail-outonthestrengthofsomepreexistingTier2leadsthatallowthemtoworkthroughthestartupdynamicwithasmallnumberofTier2clientsinsteadofhavingtobeginwithalargernumberofTier1clients.

4Forparametervaluesusedintheseexploratorymodelruns,see:Appendix1:Parametervaluesforexplorationandpolicyruns:5PlotsandanalysisinthispaperweregeneratedusingPySD,atoolforconductinganalysisofsystemdynamicsmodelsusingpython.(Houghton&Siegel,2015)

Figure3:Alow-skill,highernetworkvaluesalesagentcanavoidfailurewithanaveragebuffer.Inthis

casetheinitialclientbaseismadeupofTier2individuals.

FindingSuccessInthecasesabove,thesalesagentwasabletoavoidfail-out,buttheiroverallsuccessislimitedincomparisontosimulationswewillseenext.Truesuccessisnotmerelyavoidingfail-out,butdevelopingabaseofhightierclients.Thepathtothissuccessrequiresacombinationofeffortandskill.Figure4showsthestartupdynamicofaskilledindividualwithanaverage(6month)buffer.Weseethatafteravoidingfail-out,thesalesagentgoesontoestablishastrongbaseofclientswithinalltiers;bytheendofthesimulationtheagenthasdevelopedarobustnumberofTier3clientsandastronggrowthtrajectory.

Figure4:Ahighskill,highdeterminationindividualcansucceedwithanaveragebuffer.

Despitebothskillandeffort,inthecaseoftheagentprofiledinFigure4weseethatthesimulatedsalesagentcomesperilouslyclosetofail-outataround9months,dueentirelytothestartupdynamicinvolvedincreatingaclientbase.Indeed,ifwereducethebufferofthisindividualfrom6to5monthsasseeninFigure5,theydoindeedfailoutofthesalesforce,astheirbufferdropsbelowzeroaroundmonth7.

Figure5:Evenhighskillindividualsmaybevulnerabletosmallchangesintheirstartupbuffer.

Thisscenarioisdetrimentaltoallpartiesinvolved:theagent’sexistingclientsarenow‘orphaned’andarestatisticallylikelytoabandonthefirm’sservices.Thefirmincursthecostofhiring,training,andmaintaininganewsalesagentandlosestherevenuethatwouldhavebeenbroughtinhadtheagentremained.Theagenthastoenteranewlineofworkwiththeirsafetybufferdepleted.

PolicyOptionsforSalesManagersFacedwiththerealizationthatanagent’snaturalstartupdynamicsmayeliminatehighachieverswhilelowerperformingagentssurviveonothermerits,whataretheoptionsavailabletothemanagerofsuchasalesforce?Onepolicywouldbetoputinplaceworkaidsdesignedtoimprovetheabilityofanagenttocompletesales.Thesemaybepoliciesthatlocateasalesagentintheheartofamajormetropolitanareatodecreasetraveltimeorprovidecustomerrelationshipmanagementsystemsthatreduceoverheadofdatamanagement.Forexample,ifbysomecombinationofmeans,thesalesmanagercutsoverheadtogivetheagent25%moretimewithleadsandclients,theagentwillnotfailoutandinadditionwillseegainstotheirlong-termperformance,asseeninFigure6.

Figure6:Policiesthatgivetheagentahigherfractionaltime-on-taskmitigatefail-outriskandimprove

longtermperformance.

Anotherpolicyoptionisforthesalesmanagertosubsidizetheincomeoftheagentforsomenumberofmonths.InFigure7,weshowthatifasalesmanagerchoosestosubsidize25%oftheagent’sexpenses(insteadofreducingoverhead),theagentcanmakeitthroughthestartupdynamicandcontributetothefirm.

Figure7:Asmallstartupsubsidycancarryanagentthroughthestartupdynamics.

SalesForcePopulationAnalysisHavingansweredquestionsaboutthenatureofthestartupdynamic,andthegeneralpoliciesavailabletosalesmanagers,wenowturntothequestionofwhatthesedynamicsandpoliciesdotothesalesforceasawhole.Toexplorethis

questionwe’llconstructahypotheticalpopulationof1000individualsidenticalinallbuttworespects.Thefirstdifferencewillbetovaryamongthepopulationthesizeoftheirstartupbufferaccordingtoauniformdistributionfrom0-14months.Theseconddifferencebetweenmembersofthesimulatedsalesforcewillbetovarytheamountoftimethatanagentmustspendwithaparticularlead(onaverage)beforethatleadwillcommittoapurchase.Wewillvarytheeffortrequiredforthemtomakeasaleaccordingtoauniformdistributionfrom0-16hours.Eachindividualcouldthusbeconsideredtooccupyapointinatwo-dimensionalparameterspace,withtheirindividualinitialbufferdefiningtheirlocationalongahorizontalaxis,andtheirskill(asrepresentedbyhowlongittakesthemtomakeeachsale)ontheverticalaxis.Usingourthree-tiermodel,wesimulatethebehaviorofaneachagentfor36months.Iftheagentfailsoutofthesalesforcebeforetheendofthe36months,wecoloramarkerattheirlocationinthetwo-dimensionalparameterspacered.Iftheyavoidfailurefor36months,wecolorthemgreen.Thegeneralpatternshowninouranalysisisthatindividualswitheitherahighinitialbufferoralowamountofeffortrequiredtomakeasale(thereforehighskill)areabletoavoidfailingoutofthesalesforce.Weseethatawholegroupofhighskilledindividuals(lowrequiredeffort)failoutbecausetheydonothaveasufficientbuffertosurvivethestartupdynamic.Separatingtheagentswhofailoutfromthosewhodonotisalinewemightcallthe‘failurefront’.Thegoalofasalesmanagershouldbetoshiftorreshapethisfailurefronttoincludealargerproportionofhighskilledagents.

Figure8:Apopulationof1000agentswithdefaultvaluesforallparametersexceptingtheinitialbuffer

andtimetomakeasale.

InthebaselinecaseseeninFigure8,wesimulatethepopulationwithoutanyaddedmanagementpolicy.Forourtoypopulation,weseeafail-outrateof73%,meaningthat73%ofindividualswhostartworkwillneedtostartoverwithanemptybuffer.

Onaverage,justover11clientsareorphanedforeveryagentwhobeginstheprocess.Fromacustomersatisfactionperspective,thisisanumbertominimize.Lastly,weseeanaverageof16.2non-orphanedTier3clientsforeachagentwhobeginstheprocess.Thisspecificmetricgivesusthebestestimateofthefinancialperformanceofoursalesforce.Implementingpoliciesdesignedtoreduceoverheadandthusincreasingtimeavailabletospendwithleadsorclientsby25%scalesthefailurefrontvertically,asseeninFigure9.Inthiscaseweseeanequalpercentageincreaseinthepopulationoflow-bufferindividualsasofhigh-bufferindividuals.Weseeaslightimprovementinretentionandproductivity,butcounter-intuitively,aslightworseningintheaverageratethatclientsareorphaned.Thisworseningisduetothefactthatagentswhofailoutacquireslightlymoreclientsbeforetheydoso.

Figure9:Improvingtheagent'stime-on-taskscalesthefailurefrontvertically

Ifinsteadofreducingoverhead,thesalesmanagerchoosestoprovideasubsidytonewagentsat30%oftheirmonthlyexpensesfor6monthsasinFigure10,weseethefailurefrontshifttotheleft(essentiallyby6months*25%).Becausethefailurefrontisconcave-up,thisleftwardshiftofthefailurefrontimpliesalargerfractionalincreaseinhigh-bufferindividualsthanlow-bufferindividuals.Whileweseelargerimprovementinagentretention(andcorrespondinglyinthenumberoforphanedclients),smallergainsoccurinproductivityasindicatedbyTier3clients.

Figure10:Providingastartupsubsidytonewagentsshiftsthefailurefronttotheleft.

Thistoypopulationhasbeenhelpfulfordemonstratingthequalitativeimpactofvariouspoliciesontheperformancemetricswehaveidentified.Itisnot,however,representativeoftheactualpopulationofnewagents.Whenwesimulateoverapopulationwithparametersdrawnindependentlyfromthedistributionsforpopulationparametersestimatedfromourinterviewresponses,weseesimilarpatternsofresults,albeitwithdifferentmagnitudes,asseeninTable1.

Table1:Simulatedresultsonapopulationmorerepresentativeoftheobservedsalesforce.

Policy Baseline OverheadReduction

StartupSubsidy

AgentRetention

0.1(100%)

0.13(128%)

.21(206%)

Fail-outRate 0.9(100%)

.87(97%)

.79(88%)

OrphanedClientsperAgentStart

0.8(100%)

1.0(123%)

1.17(148%)

Tier3ClientsperAgentStart

3.5(100%)

5.2(150%)

7.6(218%)

Tier3ClientsperContinuing

Agent

33.8(100%)

39.8(118%)

35.8(106%)

Thegeneralsimilarityinresultsheregivesconfidencethatthelessonsregardingdynamicsandpolicyinterventionsthatwedrewfromourtoypopulationmaybevalidforpopulationswithdistributionsofattributescloser(althoughbynomeansidentical)tothetruepopulationofagents.

DiscussionThehypothesisthatstartupdynamicscontributesignificantlytonewsalesagentfail-outisconsistentwiththementalmodelsofsalesagentsandsales.SimulatingthebehaviorofasinglesalesagentrevealsthattheraceoftheagenttogenerateTier2andTier3leadsbeforefullyexhaustingtheirbufferisareasonableexplanationfortheobservedfail-outbehavior.Increasedefficiencyisshowntoamelioratefail-outandimprovelong-termperformancebyincreasingtherateatwhichtheagentcangeneratenewleads.Startupsubsidiesareshowntoamelioratefail-outbyextendingthetimeavailablefortheagenttomakethetransitiontoaTier2market.Insimulatedagentpools,thefail-outprocessselectsforacombinationofskillandstart-upbufferinwaysthatexcludeanumberofhigh-skilledindividualsandincludeanumberofindividualswhosucceedbyhavingalargestartupbufferorahigh-valuestartingnetwork.Whilecommission-basedcompensationforsalesagentsmayhaveamotivatingeffectforestablishedagents,thestartupdynamicsassociatedwithbuildinganetworkandclientbaseapplyselectionpressuresonthesalesforcethatareonlyweaklyalignedwithselectingforproductivity.Policiesthatsupportindividualagentsthroughthestartupdynamiccanimproveretentionrate,averageskill,andtotalproductivity.

ConclusionThispaperconstructsaplausiblecausalmodelforthesalesagent’sstartupdynamicanditsimpactonsales-agentfailout.Throughsimulation,weshowthatthestructuralunderstandingofthestartupdynamicrevealedbyourinterviewsubjectsleadstoanoutcomethatisconsistentwithobservedfail-outbehavior.Wehavedemonstratedtheimpactoftwopossiblecorrectionstrategiesattheindividuallevel,andontheproductivityofthesales-forceasawhole.Furtherresearchisneededtobuildadditionalconfidenceinthesehypotheses.Ofparticularhelpwouldbealongitudinalstudyofanumberofsalesagentsfromdayonethrougheitherfail-outorafixedfuturetime,trackingtheirleadsandclientsinanumberofdifferenttiers.Whileitwouldbedifficulttoassessanagent’sstartupbuffer,ifconductedcarefully,anexperiementcouldbeconductedtotesttheimpactofsubsidizationorefficiencyimprovingtoolsonimprovingthefail-outrate.

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AppendicesThemodels,data,andanalysisscriptsusedinthispaperareavailableathttps://github.com/JamesPHoughton/sales_agent_startup_dynamics.Thesemodelsarebaseduponthe‘systemdynamics’paradigm,whichformalizesdifferentialequationsandfeedbackstructuresinaformataccessibleandappropriateforuseinsociologicalandstrategicanalysis.Forfurtherdiscussionoftheparadigm,see(Sterman,2000).

Appendix1:Parametervaluesforexplorationandpolicyruns:ThefollowingtableliststheparametervaluesthatwereusedtogeneratesimulationrunsthatweredescribedaboveinthesectionsAvoiding,FindingSuccess,andPolicyOptionsforSalesManagers.ParametersnotlistedarebaselinevaluesasdescribedinAppendixD:EstimatesofModelParameters.Parameter Figure

2Figure3

Figure4

Figure5

Figure6

Figure7

StartupSubsidy 0.0 0.0 0.0 0.0 0.0 0.25LengthofStartupSubsidy 0.0 0.0 0.0 0.0 0.0 6.0FractionofEffortforSales 0.2 0.2 0.3 0.3 0.375 0.3EffortRequiredtoMakeaSale 4.0 4.0 2.0 2.0 2.0 2.0TotalEffortAvailable 200 200 250 250 250 250InitialBuffer 14 6 6 5 5 5InitialTier1Leads 100 90 100 100 100 100InitialTier2Leads 0 10 0 0 0 0

AppendixB:ModelStructureTounderstandthestartupdynamicsofanewagent,weconductedaseriesofinterviewswithrepresentativesfromourcasestudy’shomeoffice.Overthecourseofseveralmonths,werealizedastructurethatincludedthedynamicsofanagent’sclientbaseasdisaggregatedintoanumberof‘tiers’ofclientsthateachrepresentsdifferentincomelevels,salesize,andpayofffortheagent,asseeninFigure11.Wethenmodeledtheagent’s‘startupbuffer’,simplyasanaccountthatisdrawndowncontinuallybymonthlyexpenses,andisrebuiltthroughvariousamountsofcommissionasseeninFigure12.Lastly,wemodeltheallocationoftimethatanagentgivestovarioustasks,prioritizingserviceofexistingcustomersovernewsales,andsalestohighvalueleadsoverlow-valueleads,asseeninFigure13.

Figure11:Thesalesdynamicportionofthemodelformalizestheagents'understandingofhowonetierofleadsandclientscanhelpdevelopanothertier.

Tier 1 Leads Tier 1 ClientsTier 1 SalesTier 1 Lead Aquisition Tier 1 Client Turnover

Tier 1 Referrals

Referrals from Tier1 Clients

Tier 1 LeadsGoing Stale

Tier 2 Leads Tier 2 ClientsTier 2 SalesTier 2 Lead Aquisition Tier 2 Client Turnover

Tier 2Referrals

Referrals from Tier2 Clients

Tier 2 LeadsGoing Stale

Tier 2 Referralsfrom Tier 1

<Up referralfraction>

Tier 3 Referralsfrom Tier 2

Tier 1 Referralsfrom Tier 2

<Flat referral fraction>

<Minimum Time toMake a Sale>

<Effort Required toMake a Sale>

Tier 3 Leads Tier 3 ClientsTier 3 SalesTier 3 Lead Aquisition Tier 3 Client Turnover

Tier 3 Referrals

Referrals from Tier3 Clients

Tier 3 LeadsGoing Stale

Tier 2 Referralsfrom Tier 3

<Minimum Time toMake a Sale>

<Effort Required toMake a Sale>

<Up referral fraction>

<Down referralfraction>

<Down referralfraction>

<Minimum Time toMake a Sale>

<Effort Devoted toTier 1 Leads>

<Effort Required toMake a Sale>

<Effort Devoted toTier 2 Leads>

<Effort Devoted toTier 3 Leads>

<Referrals permeeting>

<Frequency ofMeetings>

<Frequency ofMeetings>

<Frequency ofMeetings>

<Referrals permeeting>

<Referrals permeeting>

<Lead ShelfLife>

<Lead ShelfLife>

<Lead ShelfLife>

<Client Lifetime>

<Client Lifetime>

<Client Lifetime>

<Success Rate>

<Success Rate>

<Success Rate>

<QualificationRate>

<QualificationRate>

<QualificationRate>

Figure12:Thefinancialbufferportionofthemodelformalizesanabstractrepresentationoftheagentsincomeandexpenses.

Figure13:Thepriorityallocationsectionofthemodelformalizesthewayagentsdistributetheirtimeamongstservicingexistingclientsandmakingnewsales.Effortisfirstdedicatedtoexistingclients,andthenapportionedinorderofpriority.

<Tier 1 Sales>

<Tier 2 Sales>

<Tier 3 Sales>

Months of Expensesper Tier 2 Sale

Months of Expensesper Tier 1 Sale

Months of Expensesper Tier 3 Sale

Months ofBufferIncome Expenses

Tier 1 Income

Tier 2 Income

Tier 3 Income

Startup Subsidy

Startup Subsidy Length

<Time>

Still Employed

Initial Buffer

Effort Devoted toTier 1 Leads

Effort Devoted toTier 2 Leads

Effort Devoted toTier 3 Leads

<Sales EffortAvailable>

<Tier 2 Leads>

<Tier 3 Leads>

<Tier 2 Clients>

<Tier 1 Clients>

<Tier 3 Clients>

<Frequency ofMeetings>

<Time per ClientMeeting>

Effort Devoted toTier 3 Clients

Effort Devoted toTier 2 Clients

Effort Devoted toTier 1 Clients

<Time per ClientMeeting>

<Time per ClientMeeting>

<Frequency ofMeetings>

<Frequency ofMeetings>

Time per ClientMeeting

Effort Remainingafter ServicingExisting Clients

<Effort Required toMake a Sale>

Effort Remaining afterServicing Tier 3 Leads

<Effort Required toMake a Sale>

Effort Remaining afterServicing Tier 2 Leads

Sales Effort Available

Fraction of Effortfor Sales

Total EffortAvailable

<Still Employed>

AppendixC:ModelEquationsEffortDevotedtoTier2Leads= MIN(EffortRemainingafterServicingTier3Leads,EffortRequiredtoMakeaSale*\ Tier2Leads/MinimumTimetoMakeaSale) ~ Hours/Month ~ Thisistheamountoftimetheagentspendswithatier2leadinagiven\ year,workingtomakeasale. |EffortDevotedtoTier3Leads= MIN(EffortRemainingafterServicingExistingClients,EffortRequiredtoMakeaSale\ *Tier3Leads/MinimumTimetoMakeaSale) ~ Hours/Month ~ Thisistheamountoftimetheagentspendswithatier1leadinagiven\ year,workingtomakeasale. |QualificationRate= 1 ~ Persons/Referral ~ Whatisthelikelihoodthataleadwillbeworthpursuing?Someleads\ mightnotbeworthyoureffort.Accordingtointerviewees,leadsthatare\ properlysolicitedandintroducedarealmostalwaysworthfollowingup\ with. |Tier1LeadAquisition= QualificationRate*(Tier1Referrals+Tier1ReferralsfromTier2) ~ Persons/Month ~ Howmanynewtier1leadsdoesanagentnet? |Tier2LeadAquisition= QualificationRate*(Tier2Referrals+Tier2ReferralsfromTier1+Tier2ReferralsfromTier3\ ) ~ Persons/Month ~ Howmanynewtier2leadsdoesanagentnet? |Tier3LeadAquisition= QualificationRate*(Tier3Referrals+Tier3ReferralsfromTier2) ~ Persons/Month ~ Howmanynewtier3leadsdoesanagentnet? |SuccessRate= 0.2 ~ Dmnl ~ Whatisthelikelihoodthatagivenleadwillbecomeaclient,ifthe\ agentdevotestheappropriateamountofattentiontothem? |Tier1Sales= SuccessRate*MIN(EffortDevotedtoTier1Leads/EffortRequiredtoMakeaSale,Tier1Leads\ /MinimumTimetoMakeaSale) ~ Persons/Month ~ TherateatwhichTier1leadsbecomeclients.Thisislimitedeitherby\ theeffortoftheagent,orthenaturalcalendartimerequiredtomakea\ sale. |

Tier3Sales= SuccessRate*MIN(EffortDevotedtoTier3Leads/EffortRequiredtoMakeaSale,\ Tier3Leads/MinimumTimetoMakeaSale) ~ Persons/Month ~ TherateatwhichTier3leadsbecomeclients.Thisislimitedeitherby\ theeffortoftheagent,orthenaturalcalendartimerequiredtomakea\ sale. |Tier2Sales= SuccessRate*MIN(EffortDevotedtoTier2Leads/EffortRequiredtoMakeaSale,Tier2Leads\ /MinimumTimetoMakeaSale) ~ Persons/Month ~ TherateatwhichTier2leadsbecomeclients.Thisislimitedeitherby\ theeffortoftheagent,orthenaturalcalendartimerequiredtomakea\ sale. |StillEmployed= IFTHENELSE(MonthsofBuffer<0,0,1) ~ Dmnl ~ Flagforwhethertheagentisstillwiththefirm.Goestozerowhenthe\ bufferbecomesnegative. |Income= Tier1Income+Tier2Income+Tier3Income+IFTHENELSE(Time<StartupSubsidyLength\ ,StartupSubsidy,0) ~ Months/Month ~ Thetotalincomefromcommissionsonsalestoalltiers. |EffortDevotedtoTier1Clients= Tier1Clients*TimeperClientMeeting*FrequencyofMeetings ~ Hours/Month ~ Howmuchtimedoestheagentdevotetomeetingsformaintenanceand\ solicitingreferralsfromTier1Clients. |Tier1Income= Tier1Sales*MonthsofExpensesperTier1Sale ~ Months/Month ~ ThisistheamountofmoneyanagentmakesfromallcommissionsonTier1\ Sales |EffortDevotedtoTier2Clients= Tier2Clients*TimeperClientMeeting*FrequencyofMeetings ~ Hours/Month ~ Howmuchtimedoestheagentdevotetomeetingsformaintenanceand\ solicitingreferralsfromTier2Clients. |EffortDevotedtoTier3Clients= Tier3Clients*FrequencyofMeetings*TimeperClientMeeting ~ Hours/Month ~ Howmuchtimedoestheagentdevotetomeetingsformaintenanceand\ solicitingreferralsfromTier3Clients. |

EffortRemainingafterServicingExistingClients= MAX(SalesEffortAvailable-(EffortDevotedtoTier1Clients+EffortDevotedtoTier2Clients\ +EffortDevotedtoTier3Clients),0) ~ Hours/Month ~ Howmucheffortremainsafterhigherprioritysalesandmaintenance\ activitiesarecomplete? |EffortRemainingafterServicingTier2Leads= MAX(EffortRemainingafterServicingTier3Leads-EffortDevotedtoTier2Leads,\ 0) ~ Hours/Month ~ Howmucheffortremainsafterhigherprioritysalesandmaintenance\ activitiesarecomplete? |EffortRemainingafterServicingTier3Leads= MAX(EffortRemainingafterServicingExistingClients-EffortDevotedtoTier3Leads\ ,0) ~ Hours/Month ~ Howmucheffortremainsafterhigherprioritysalesandmaintenance\ activitiesarecomplete? |FractionofEffortforSales= 0.25 ~ Dmnl ~ Ofalltheeffortdevotedtowork,whatfractionisactuallyspentdoing\ salesandmaintenanceactivities?Thisincludestimespentwithexisting\ clientssolicitingreferrals. |Expenses= 1 ~ Months/Month ~ Howmanymonthsofexpensesareexpendedpermonth.Thisisabitofa\ tautology,butitstherightwaytoaccountfortheagentsincomeand\ spendingwhilepreservingtheirprivacy. |SalesEffortAvailable= FractionofEffortforSales*TotalEffortAvailable*StillEmployed ~ Hours/Month ~ Howmuchtotaltimepermonthcananagentactuallyspendinsalesor\ maintenancemeetings? |InitialBuffer= 6 ~ Months ~ Howlongcantheagentaffordtogowithzeroincome?Thiscouldbemonths\ ofexpensesinthebank,ormonthsof'rentequivalent'theyareableto\ borrowfromfamily,etc. |StartupSubsidyLength= 3 ~ Months ~ Howlongdoesasalesagentrecieveasubsidyfor,beforeitiscutoff? |

TotalEffortAvailable= 200 ~ Hours/Month ~ Thisisthetotalnumberofhourstheagentiswillingtoworkinamonth. |MonthsofBuffer=INTEG( Income-Expenses, InitialBuffer) ~ Months ~ Thisisthestockatanygiventimeofthemoneyinthebank,orremaining\ familialgoodwill,etc. |MonthsofExpensesperTier1Sale= 12/500 ~ Months/Person ~ Incomefromcommissionforasaletoatier1lead.Measuredinunitsof\ monthsofexpenses,topreserveagentsprivacy. |MonthsofExpensesperTier2Sale= 12/50 ~ Months/Person ~ Incomefromcommissionforasaletoatier2lead.Measuredinunitsof\ monthsofexpenses,topreserveagentsprivacy. |MonthsofExpensesperTier3Sale= 12/5 ~ Months/Person ~ Incomefromcommissionforasaletoatier3lead.Measuredinunitsof\ monthsofexpenses,topreserveagentsprivacy. |Tier3Income= MonthsofExpensesperTier3Sale*Tier3Sales ~ Months/Month ~ ThisistheamountofmoneyanagentmakesfromallcommissionsonTier3\ Sales |Tier2Income= MonthsofExpensesperTier2Sale*Tier2Sales ~ Months/Month ~ ThisistheamountofmoneyanagentmakesfromallcommissionsonTier2\ Sales |StartupSubsidy= 0.75 ~ Months/Month[0,1,0.1] ~ Howmuchdoesanagentrecieveeachmonthfromhissalesmanagertohelp\ deferhisexpenses,inunitsofmonthsofexpenses? |TimeperClientMeeting= 1 ~ Hours/Meeting ~ Thisisthenumberofhoursanagentspendswithaclient,maintainingthe\ relationship/accounts,andsolicitingreferrals,inonesitting.

|ClientLifetime= 120 ~ Months ~ Howlong,onaverage,doesaclientremainwithanagent? |Downreferralfraction= 0.2 ~ Dmnl ~ Whatisthelikelihoodthatareferralfromasecondorthirdtierclient\ willbetothetierbelowthem? |EffortDevotedtoTier1Leads= EffortRemainingafterServicingTier2Leads ~ Hours/Month ~ Thisistheamountoftimetheagentspendswithatier1leadinagiven\ year,workingtomakeasale. |Tier2ReferralsfromTier3= ReferralsfromTier3Clients*Downreferralfraction ~ Referrals/Month ~ ThisisthenumberofTier2leadsthatareaquiredthroughreferralsfrom\ tier3. |Flatreferralfraction= 1-Downreferralfraction-Upreferralfraction ~ Dmnl ~ Whatisthelikelihoodthatareferralfromaclientwillbetoaleadin\ theirsametier? |FrequencyofMeetings= 1/12 ~ Meetings/Month/Person ~ Howmanymaintenancemeetingsdoestheagenthavewitheachclientina\ month? |LeadShelfLife= 3 ~ Months ~ Afteracertainamountoftime,leadsgostale.Itgetsawkwardtokeep\ interactingwiththem,andyou'rebetteroffmovingon.Howlongisthat? |ReferralsfromTier1Clients= Tier1Clients*FrequencyofMeetings*Referralspermeeting ~ Referrals/Month ~ Thenumberofreferralscominginfrommaintenancemeetingswithtier1\ clients. |ReferralsfromTier2Clients= Tier2Clients*Referralspermeeting*FrequencyofMeetings ~ Referrals/Month ~ Thenumberofreferralscominginfrommaintenancemeetingswithtier2\

clients. |ReferralsfromTier3Clients= Tier3Clients*FrequencyofMeetings*Referralspermeeting ~ Referrals/Month ~ Thenumberofreferralscominginfrommaintenancemeetingswithtier3\ clients. |Referralspermeeting= 2 ~ Referrals/Meeting ~ Howmanyreferralscananagentcomfortablygatherfromhisclientsina\ givenmaintenancemeeting? |Tier1ClientTurnover= Tier1Clients/ClientLifetime ~ Persons/Month ~ Thisistheflowoftier1clientsleavingthepractice. |Upreferralfraction= 0.15 ~ Dmnl ~ Thelikelihoodthatareferralfromatier1ortier2clientwillbetoa\ leadofthetierabovethem. |Tier1LeadsGoingStale= Tier1Leads/LeadShelfLife ~ Persons/Month ~ Thesearetier1leadsthatgrowoldbeforetheyaresold,andareunable\ tobefollowedupon. |Tier1Referrals= ReferralsfromTier1Clients*(1-Upreferralfraction) ~ Referrals/Month ~ ThisisthenumberofTier1leadsthatareaquiredthroughreferralsfrom\ anytierclient. |Tier1ReferralsfromTier2= ReferralsfromTier2Clients*Downreferralfraction ~ Referrals/Month ~ ThisisthenumberofTier1leadsthatareaquiredthroughreferralsfrom\ tier2. |Tier2ClientTurnover= Tier2Clients/ClientLifetime ~ Persons/Month ~ ThisistheflowofTier2clientsleavingthepractice. |Tier2Clients=INTEG( Tier2Sales-Tier2ClientTurnover, 0) ~ Persons

~ Theseareactiveclientswhoprovidearegularlevelofreturntothe\ company. |Tier2Leads=INTEG( Tier2LeadAquisition+Tier2Sales-Tier2LeadsGoingStale, 0) ~ Persons ~ Theseareindividualswhohavebeenidentifiedastargetsandaresomewhereinthe\ salesprocess,beforeasalehasbeenmade. Theymayormaynothavebeencontactedbytheagentyet.Iftheycanbe\ convertedtoclients,theywillhavearegularlevelofreturnforthe\ company. |Tier2LeadsGoingStale= Tier2Leads/LeadShelfLife ~ Persons/Month ~ Thesearetier2leadsthatgrowoldbeforetheyaresold,andareunable\ tobefollowedupon. |Tier2Referrals= ReferralsfromTier2Clients*Flatreferralfraction ~ Referrals/Month ~ ThisisthenumberofTier2leadsthatareaquiredthroughreferralsfrom\ anytierclient. |Tier2ReferralsfromTier1= ReferralsfromTier1Clients*Upreferralfraction ~ Referrals/Month ~ ThisisthenumberofTier2leadsthatareaquiredthroughreferralsfrom\ tier1. |Tier3LeadsGoingStale= Tier3Leads/LeadShelfLife ~ Persons/Month ~ Thesearetier3leadsthatgrowoldbeforetheyaresold,andareunable\ tobefollowedupon. |Tier3ClientTurnover= Tier3Clients/ClientLifetime ~ Persons/Month ~ Thisistheflowofregularclientsleavingthepractice. |Tier3Clients=INTEG( Tier3Sales-Tier3ClientTurnover, 0) ~ Persons ~ Theseareactiveclientswhoprovidearegularlevelofreturntothe\ company. |Tier3Referrals= ReferralsfromTier3Clients*(1-Downreferralfraction) ~ Referrals/Month ~ ThisisthenumberofTier3leadsthatareaquiredthroughreferralsfrom\

anytierclient. |Tier3Leads=INTEG( Tier3LeadAquisition+Tier3Sales-Tier3LeadsGoingStale, 0) ~ Persons ~ Theseareindividualswhohavebeenidentifiedastargetsandaresomewhereinthe\ salesprocess,beforeasalehasbeenmade. Theymayormaynothavebeencontactedbytheagentyet.Iftheycanbe\ convertedtoclients,theywillhavearegularlevelofreturnforthe\ company. |Tier3ReferralsfromTier2= ReferralsfromTier2Clients*Upreferralfraction ~ Referrals/Month ~ ThisisthenumberofTier3leadsthatareaquiredthroughreferralsfrom\ tier2. |EffortRequiredtoMakeaSale= 4 ~ Hours/Person[0,50] ~ Thisistheamountoftimetheagentmustspend(onaverage)withalead\ (highorlowvalue,fornow)tomakeasale. |MinimumTimetoMakeaSale= 1 ~ Months ~ Whatistheabsoluteminimumcalendartimeitwouldtaketomakeasaleto\ aperson,evenifyouhadallthehoursinthedaytodevotetothem? |Tier1Leads=INTEG( Tier1LeadAquisition+Tier1Sales-Tier1LeadsGoingStale, 100) ~ Persons ~ Theseareindividualswhohavebeenidentifiedastargetsandaresomewhereinthe\ salesprocess,beforeasalehasbeenmade. Theymayormaynothavebeencontactedbytheagentyet.Iftheycanbeconverted\ toclients,theywillhavearegularlevelofreturnforthecompany. Weinitializeto100becauseagentsbegintheirsalescareerswithalist\ of200friendsandfamily,about50%ofwhomtheymightcontact. |Tier1Clients=INTEG( Tier1Sales-Tier1ClientTurnover, 0) ~ Persons ~ Theseareactiveclientswhoprovidearegularlevelofreturntothe\ company. |******************************************************** .Control********************************************************~ SimulationControlParameters |

FINALTIME=120 ~ Month ~ Thefinaltimeforthesimulation. |INITIALTIME=0 ~ Month ~ Theinitialtimeforthesimulation. |SAVEPER=TIMESTEP ~ Month[0,?] ~ Thefrequencywithwhichoutputisstored. |TIMESTEP=0.0625 ~ Month[0,?] ~ Thetimestepforthesimulation. |

AppendixD:EstimatesofModelParametersToidentifyparametersofourmodel,weconductedsemi-structuredinterviewswithavarietyofindividualsthroughoutthesalesorganization,askingthemtoestimatealikelyrangeofvaluesforindividualswhowerejustenteringthesalesforce.Wealsoaskedopenendedquestionsdesignedtoelicittheseindividuals’understandingofthestartupprocess,inordertobuildconfidencethatthemodelstructurewehadconstructedrepresentedtheworldasitwasknowntotheseindividuals.Ourinterviewssurveyedthefollowingindividuals:

• Ayoungsalesagentneartheendofthestartupprocess• Anestablishedsalesagentresponsiblefortrainingnewyoungprofessional

agents• Averyhighperformingsalesagent• GeneralManageroftheSalesAgency• Asalesagentinthemiddleoftheircareer

Therawresponsesofourinterviewsarelistedinbelowintheorderinwhichtheyarelistedabove.Notallindividualswereabletoestimatevaluesforallquestions,andinthesecaseswehavelefttheentryblank.Tosimulatetheoverallpopulation,wehand-fitdistributionsrepresentativeoftheresponsestoourinterviewquestions.Asourobjectisnottomakequantitativepredictionsaboutoutcomes,merelytoshowthatthelessonsdrawnfromourtoypopulationholdforapopulationmorerepresentativeofthatobserved,thissimplificationisappropriate.Parameter Units RawResponses Baseline

ValueEstimatingDistribution

Referralspermeeting

Referrals Min:[1,0,-,-,1]Mean:[2,2.5,-,-,2]Max:[10,30,-,-,-]

2 lognormal(mean=log(2),sigma=.75)

Minimumtimetomakeasale

Months Min:[0.3,0.5,-,-,0.25]Mean:[1.5,1,-,-,1]Max:[2,1,-,-,3]

1 lognormal(mean=log(1),sigma=.3

FrequencyofMeetings*

Meetings/Month

Min:[1,-,-,1,1]Mean:[1,-,-,1,1]Max:[2,-,-,1,1]

1/12 lognormal(mean=log(1.0/12),sigma=.25)

SuccessRate - Min:[0.1,-,-,-,-]Mean:[0.3,0.1,-,-,-]

.2 beta(a=12,b=50)

EffortRequiredtoMakeaSale

Hours Min:[3,2,-,3,3]Mean:[-,3,-,-,-]Max:[5,5,-,5,6]

4 lognormal(mean=log(4),sigma=.15)

Up-ReferralFraction

- Mean:[0.1,0.2,0.1]

.15 beta(a=8,b=40)

Down-ReferralFraction

- Min:[-,0.1,0.1,-,-]Mean:[-,0.2,0.3,-,-]

0.2 beta(a=10,b=40)

LeadShelfLife

Months Min:[2,-,-,-,-]Mean:[3,-,-,-,3]

3 lognormal(mean=log(3),sigma=.1)

ClientLifetime

Months Min:[-,-,-,-,36]Mean:[-,120,-,-,120]

120 lognormal(mean=log(10),sigma=.25)

TotalEffortAvailable**

Hours/Month

Min:[-,-,-,-,160]Mean:[-,-,-,-,200]Max:[-,-,-,-,250]

200 lognormal(mean=log(200),sigma=.2)

FractionofEffortSpentwithclient/lead

- Mean:[0.2,0.25,-,-,0.1] 0.2 beta(a=8,b=20)

TimeperClientMeeting

Hours Min:[0.75,-,-,-,1]Mean:[1,-,-,-,1.25]Max:[-,-,-,-,1.5]

1 lognormal(mean=log(1),sigma=.25)

MonthsofExpensesperTier1Client***

Months/Person

AssumesameratioholdswithTier2/Tier3forestimate

12/500 .1*MonthsofExpensesperTier2Client

MonthsofExpensesperTier2Client

Months/Person

Mean[0.24,-,-,-,-] 12/50 lognormal(mean=log(.24),sigma=.25)

MonthsofExpensesperTier3Client

Months/Person

Min[-,-,1.2,-,-]Mean[-,-,2.4,-,-]

12/5 10*MonthsofExpensesperTier2Client

InitialMonthsofBuffer

Months Min[-,3,-,-,-]Mean[-,-,-,6,-]Max[-,-,-,-,12]

6 lognormal(mean=log(6),sigma=.25)

QualificationRate

- Mean[0.9,1,-,-,-] 1 beta(a=95,b=5)

*PolicysetbySalesManager

**Inferred/Computedfromotherresponses(e.g.residency=1/turnoverrate)***Respondentsunabletoestimate,toosmall.Ourmodelisinsensitivetothisvalue,astheprimaryvalueofTier1clients(accordingtoourinterviews)istoprovidereferralstoTier2leads,notasasourceofdirectincome.Inthiscase,asimpleestimatetakenfromtherelativemagnitudeofTier2andTier3leadsisappropriate.