HowtoDesignProductsthatleadtoLong-TermBehavioralChange

SunilMaulik,Ph.D.sunil.maulik@gmail.com

www.sunilm1.biz

“T-Shaped”ApproachToMyCareer.Deepinscienceforthefirstfifteenyears:

• B.Sc.’sPhysics&Biology,KingsCollege.(MauriceWilkins,NobelLaureate.)

• M.Sc.X-RayCrystallography,Birkbeck College.(SirTomBlundell.)

• Ph.D.Biophysics,BrandeisUniversity(DonaldCaspar&AaronKlug,NobelLaureate.)

Broad forthepastfifteenyears:Sales,Marketing,BusinessDevelopment,Product-MarketFit,Design:

• MiningDataforActionableInsights• Iterating&RefiningHypotheses(“Models”)• ChangingBehaviorsWithImprovedModels

AboutMe:

EachShiftinComputingUnlocksOpportunitiesforInnovationinNewDisciplines

WhyWeBehaveTheWayWeDo

OurSystem1 &System2Minds

“ThinkingFast&Slow”-DanielKahnemann

BasicNeuroscience:

Weight:3lbsO2 consumption:25%oftotalO2CaloricConsumption:20%oftotalcaloriesPowerRequirement:12WCapillaries:400milesNo.ofNeurons:86billionNo.ofConnections:5x1014 (500trillion)Calculations/s:1016 (10quadrillion)No.ActionPotentials/s:17.2x1012 (17.2trillion)SensoryProcessing:40millionbits/sCognitiveProcessing:8000bits/sConscious/SubconsciousProcessing:0.02%!

ExamplesofSystem1 Thinking

•Orienttothesourceofasuddensound.•Completethephrase“breadand...”•Detecthostilityinavoice.•Understandsimplesentences.

ExamplesofSystem2 Thinking

• Focusonthevoiceofaparticularpersoninacrowdedandnoisyroom.•Maintainafasterwalkingspeedthanisnaturalforyou.• Filloutataxform.•Calculate247x183inyourhead

“Theintuitivemindisasacredgiftandtherationalmindisafaithfulservant.Wehavecreatedasocietythathonorstheservantbuthasforgottenthegift.”– AlbertEinstein.

HowWeMakeMostOfOurDecisions

• Mostofthetime,wearenotconsciouslydecidingwhattodonext.• Weoftenactbasedonhabits.• Weoftenmakeintuitive,immediatedecisions basedonpastexperiences.• Whenconsciouslythinking,weoftenavoidhardwork.Weoften“wingit”withroughguesses.• Welooktootherpeople,especiallypeersandexperts.

• ExcessiveOptimism• Overconfidence• ConfirmationBias• Anchoring• Groupthink• Egocentrism• LossAversion• Sunk-costfallacy• Escalationofcommitment• Controllabilitybias• Statusquobias• Presentbias

MakingBetterDecisions:UnconsciousBiases

StudentsprimedwithwordsaboutFloridawalkedslowerthanstudentsprimedwithwordsaboutNewYork.

[Areminderofafeatureteesupbehaviorsreminiscentofthatfeature.]

Priming:

Anchoring:

Shoppersshowed$10,$30and$149bottlesofwineboughtthe$30bottlemoreoftenthanifjustshownthe$10and$30bottles.

[Behaviorsclusteraroundacomparisontosupposed”norms”.]

Framing:

Withasign“limit12percustomer”forsoupcans,peopletake5onaverageratherthan2.

[Decisionsgetframedaroundalinguisticconcept.]

LossAversion:

Traderssellsharesthathavebeengoingupsoonerthansharesthathavebeengoingdown.

[Behaviorstypicallyminimizelossratherthanmaximizegain.]

HabitDesign

“Nevertrustanoverweighthabitdesigner.”– Nir Eyal

Habits:Behaviorsdonewithlittleconsciousthought

“ThePowerofHabit”CharlesDuhigg.

Howdoweturndecisions intohabits?

PersuasiveTechnology

Behavior=Motivation+Ability+Trigger

HookModelofHabitFormationHookModelofHabitDesign

ImplementingtheHookModelImplementing“TheHook”

ExternalTriggers:

Cuesthattelltheuserwhattodonext:• Abutton(clicktoTweet,playorstart)

• Aflashinglightorsound• Aprogress-barorcompletionchart

InternalTriggers:

Internaltriggersareemotions thatweassociatewithanactivity:• Whenwe’relonely,wecheckFacebook

• Whenwe’reuncertain,wecheckGoogle

• Whenwe’rebored,wecheckYouTubeorPinterest

UsingTriggersforBehaviorChange

• Removeuserworkwhereverpossible• Automatetheactionbehindthescenes• Initiatewithameaningful trigger• Provideanimmediate,variablereward• Askforsomeinvestment fromtheuser• Buildnewbehaviorsontopofexistingones.

Behavioral“Lenses”thatTriggerBetterDecisions

"Whatpeoplesay,whatpeopledo,andwhatpeoplesaytheydoareverydifferentthings."- MargaretMead

ArchitecturalLens:Positioning

Positioningpedestrian-crossingpush-buttonunitsatananglemakesitmorelikelythatpedestrianswillturntolookatoncomingtraffic.

Error-ProofingLens:Interlock

ModernATMMachineswon’tdispensecashuntilafteryouremoveyourcard,makingitlesslikelythatyou’llleaveitbehind.(Changedwithnewchip-cards,however!)

InteractionLens:Feedforward/Simulation

savings&loansimulators caninfluenceusers’futurebehavioranddecisions.

Ludic(Playful)Lens:UnpredictableReinforcement

Providingvariable,unpredictably-timedrewardsleadstogreaterconsistencywhenchangingpeople’sbehaviors

PerceptualLens:Transparency

Dyson’stransparentdustcontainerbothdemonstratesthevaccumcleaner’seffectivenessandencouragesuserstoemptyitmoreoften.

PerceptualLens:PerceivedAffordances

Reshapingtheholesontrashbinstomatchthe‘form’ofdifferenttypesofwasteincreasesrecycling.

CognitiveLens:SocialProof

Amazon’srecommendationscanhelpbuyersexpandtheirbuyingdecisions,whilesubtlypressurizingthemtoconformtosocialnorms.

MakingProducts“Scale”intotheirTargetMarkets

“Topredictthebehaviorofordinarypeopleinadvance,youonlyhavetoassumethattheywillalwaystrytoescapeadisagreeablesituationwiththesmallestpossibleexpenditureofintelligence.” - FriedrichNietzsche

Scale:Creatingmassbehaviorchange

• Dynamicallyadjustdata/contentshowntousersbasedonsuccessofpastcontent.

• Explorenewcontentwithanewtargetminority ofusers.

• Addnewcontentifitissuccessfulatelicitingthedesiredbehaviorinthenewtargetmarket.

Scaling:Habittestingforscale

• Identifyareaswherecyclingthroughthehabit-modelbecomesfaster,morefrequent,ormorerewarding.

• Identifynascentbehaviors.Thesearenewbehaviorsthatmayfulfilamass-market need.

• Createnewtriggersthatstimulatethesebehaviors.

Scaling:Makescalingcontagious

• Identifysocialcurrency,suchasremarkability,scarcityorexclusivity.Createanemotionalresponse;eitherawe,anxietyor arousal.

• Enablesocialdisplay e.g.“behavioralresidue”–tracesoftheproductorservice.

• Allowforstorytelling – givetheusereveryopportunitytorelatetheirexperience

Examples:

InConclusion…

“Theonlywayofdiscoveringthelimitsofthepossibleistoventurealittlewaypastthemintotheimpossible.”– ArthurC.Clarke

NeedHelpDesigningYourProducts?– SeeMe!

ThankYou

SunilMauliksunil.maulik@gmail.com