Is tankersley neuro-marketing_slideshare

Neuromarketing

IS12 Psych! Conference: Marketing to the Mind Dharol Tankersley, PhD Cognitive Neuroscience

Data Analyst, Schipul Technologies

©wimeuverman.nl

I.  What is Marketing?

II. What is Neuroscience?

III.  What Neuroscience CAN do for Marketing

IV.  What Neuroscience can NOT do for Marketing

I. What is Marketing? A system to discover and satisfy needs of people (HMT)

Ø  Identify consumer needs Ø  Solution - product/ service

I. W

hat i

s M

arke

ting?

Goal of Marketing

Desire

I. W

hat i

s M

arke

ting?

Goal of Marketing

Purchase

Measure

A system to discover, satisfy and influecne desires of people (HMT)

Traditional Measures of Desire I.

Wha

t is

Mar

ketin

g? What do I desire?

Traditional Measures of Desire

Focus Groups

I. W

hat i

s M

arke

ting?

What do you like?

Focus Groups


Wha

t is

Mar

ketin

g? What do you like?

Focus Groups Questionnaires


Wha

t is

Mar

ketin

g? Which would you choose?


Simulated Choice


Wha

t is

Mar

ketin

g?

Not Consequential!


Simulated Choice


Wha

t is

Mar

ketin

g? What do you want for dinner

tomorrow?

Market Tests


Wha

t is

Mar

ketin

g? What DID you choose?

Fully Consequential!


Simulated Choice


Wha

t is

Mar

ketin

g?

Focus Groups Accuracy

Cos

t

Questionnaires Simulated Choice

Market Tests

Adapted from Ariely & Berns (2010)


Wha

t is

Mar

ketin

g?

Focus Groups Accuracy

Cos

t

Questionnaires Simulated Choice

Market Tests

II. What is Neuroscience?

II. What is Neuroscience? A system to measure the biology of desire

Ø  Predict consumer behavior

Desire

Purchase

Measure

II. What is Neuroscience? A system to measure the biology of desire

Ø  Predict consumer behavior “Hidden Information”

Ø  Emotional Ø  Non-rational Ø  Subconscious

“Hidden” Information II.

W

hat i

s N

euro

scie

nce?

Neuroscience Measures of Desire II.

W

hat i

s N

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scie

nce?

Biometrics

GSR

Temperature

Heart Rate

Pupil Dilation


W

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scie

nce?

Behavioral Physiology

Eye Tracking Facial Coding


W

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nce?

Brain Imaging

Dharol Tankersley Cognitive Affective and Social Neuroscience January 23, 2007

Subject Charity

Com

pute

r S

ubje

ct

Recipient

Pla

yer

Charitable Reward

Personal Reward

Altruistic Action

Selfish Action

Charity Selection Task Survey

You will play for Easter Seals +

Easter Seals

wins a Dukat!

EEG MEG fMRI

III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting NeuroAnatomy of Marketing

(Medial PreFrontal Cortex)

Liking

Disliking Insula Amygdala

Attention/ Arousal Striatum

mPFC

III.  What Neuroscience CAN do for Marketing

Ø Success: Case Studies

Ø Summary: Promising Areas

III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies

What kind of product is being sold?

III.

Wha

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rosc

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e C

AN

do

for M

arke

ting Case Studies


III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies


III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies


III.

Wha

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rosc

ienc

e C

AN

do

for M

arke

ting Case Studies


III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies

Eye Tracking

III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies

Eye Tracking

III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies

Eye Tracking

Website Optimization Packaging

Ad Placement

III.

Wha

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rosc

ienc

e C

AN

do

for M

arke

ting Case Studies

Which design do you prefer?

III.

Wha

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rosc

ienc

e C

AN

do

for M

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ting Case Studies

Biometrics

1 2

0"200"400"600"800"1000"

0" 5" 10"Sales"Ra

nk"

Emo4onal"Engagement"Rank"

III.

Wha

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rosc

ienc

e C

AN

do

for M

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ting Case Studies

Ø Heavy detail that expresses attitude Ø Prominent facial features often with large eyes Ø Bold color palette with high contrast

Biometrics

III.

Wha

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rosc

ienc

e C

AN

do

for M

arke

ting Case Studies

Emotionally Engaging -> Twice the Click Thru Rate

Biometrics

III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies

Which cover design do you prefer?

1 2 3

III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies

Brain Imaging: EEG

Overall effectiveness Attention

Purchase Intent Novelty Awareness

Emotion Emotion Retention

III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies

12% Increase in Sales

Brain Imaging: EEG

2

III.

Wha

t Neu

rosc

ienc

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for M

arke

ting Case Studies

Which advertisement is the most effective?

Campaign A: Coffee Ad

http://www.youtube.com/watch?v=lf01Ti6bH8U

III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies


Campaign B: Jumping Out of Window Ad

http://www.youtube.com/watch?v=dR6odVmNTlw

III.

Wha

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rosc

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ting Case Studies


Campaign C: Puppet http://www.youtube.com/watch?v=weVp5FXVyqM

III.

Wha

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rosc

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ting Case Studies


Campaign C: Puppet

Campaign B: Jumping Out of Window Ad

Campaign A: Coffee Ad

III.

Wha

t Neu

rosc

ienc

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AN

do

for M

arke

ting Case Studies

Brain Imaging: fMRI

0"

2"

4"

6"

8"

10"

A" B" C"

self%report%Expert Predictions

III.

Wha

t Neu

rosc

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for M

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ting Case Studies

Brain Imaging: fMRI

0"

2"

4"

6"

8"

10"

A" B" C"

self%report%

0"

8"

16"

24"

32"

A" B" C"

actual&

2x

10x

30x

Experts & smokers fail to predict.

Calls to 1-800-QUIT-NOW

Expert Predictions

III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

arke

ting Case Studies

Brain Imaging: fMRI

0"

8"

16"

24"

32"

A" B" C"

actual&

2x

10x

30x

Frontal Cortex Predicts Advertisement Effectiveness

Calls to 1-800-QUIT-NOW

Expert Predictions

!0.1%

!0.05%

0%

0.05%

0.1%

A% B% C%

mpfc%Brain: mPFC Activation

III.

Wha

t Neu

rosc

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e C

AN

do

for M

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ting Promising Areas

Ø  Visual – Attention Ø  Advertising Ø  Packaging

III.

Wha

t Neu

rosc

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e C

AN

do

for M

arke


Ø  Emotion - Engagement Ø  Branding Ø  Politics

III.

Wha

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Ø  Consumption - Experience Ø  Beverages Ø  Films

DiscussionThemainhypothesisofthisstudywasthatanincreaseintheperceivedpriceofawineshould,throughanincreaseintasteexpectations,increaseactivityinthemOFC.Theresultsde-scribedaboveprovideevidenceconsistentwiththehypothesis.Thehypothesiswasmotivatedbyseveralpreviousstudies,whichhaveshownthatactivityinthemOFCiscorrelatedwithbehav-ioralpleasantnessratingsforodors(10–13),tastes(6,14,and15),andevenmusic(16).This,togetherwithourbehavioralresultsandtheadditionalimagingresultsdescribedbelow,supporttheinterpretationthat,bymodulatingtheactivityinthemOFC,changesinthepriceofawinemightleadtoachangeintheactualEPderivedfromitsconsumption.

Weperformedtwoadditionalanalysestoprovidefurthersupportforthisinterpretation.First,foreachindividualandwine,wecomputedthechangeinreportedEPbetweenthehighandlowpriceconditions.WealsocomputedtheanalogousdifferenceinparameterestimatesfortheBOLDresponsefromthegenerallinearmodelinanareasurroundingthemOFC.Fig.3Bshowsthattheneuralandbehavioralestimateswerepositivelyandhighlycorrelated(r!0.49,P"0.001).Second,weverifiedthattheresultsofthepreviousliteraturealsoheldinourstudybyestimatingadifferentgenerallinearmodelandlookingforbrainregionswhoseactivitywascorrelatedwithreportedEPfromsamplingthediffer-entstimuli(seeSITextfordetails).Theresultsreplicatedthefindingsofpreviousstudies:activityinthemOFCwascorrelatedwithabsolutereportsofpleasantness(Fig.4).

Importantly,wedidnotfindevidenceforaneffectofpricesonareasoftheprimarytasteareassuchastheinsulacortex,theventroposteriormedialnucleusofthethalamus,ortheprabra-chialnucleiofthepons.Anaturalinterpretationisthatthetop-downcognitiveprocessesthatencodetheflavorexpectan-ciesareintegratedwiththebottom-upsensorycomponentsofthewineinthemOFC,thusmodulatingthehedonicexperienceofflavor,butthattheflavorexpectanciesgeneratedbythechangeinpricesdonotimpactmorebasicsensoryrepresenta-

tions.Interestingly,ananalogousmechanismhasbeenproposedforpainplaceboeffects(7).

Ourresultshaveimplicationsforseveraldisciplines.First,theEPsignalplaysacentralroleinneuroeconomics,becauseitservesasateachingsignalthatguidesfuturebehavior.Unfor-tunately,verylittleisknownaboutthefactorsthataffecttheneuralcomputationofthissignal.Anaturalstartinghypothesisistheeconomicview,whichstatesthatEPdependsonlyonthesensorypropertiesoftheitembeingconsumed(i.e.,itsmolec-ularproperties)andthestateoftheconsumer.OurresultssuggestthatthebrainmightcomputeEPinamuchmoresophisticatedmannerthatinvolvesintegratingtheactualsensorypropertiesofthesubstancebeingconsumedwiththeexpecta-tionsabouthowgooditshouldbe.Itisimportanttoemphasizethatitmightbeadaptiveforthebraintodothis.Tomakegooddecisionsinthefuture,thebrainneedstocarryoutgoodmeasurementsofthequalityofcurrentexperiences.Inaworldofnoisymeasurements,theuseofpriorknowledgeaboutthequalityofanexperienceprovidesadditionalvaluableinforma-tion.Arelatedstudy(13)providesadditionalsupportingevi-denceforthispointbyshowingthatgivingacognitivelabeltoanambiguousodor(‘‘cheddarcheese’’or‘‘bodyodor’’)canaffectbothsubjectivepleasantnessreportsandneuralactivityrelatedtoEP.Unlikethecurrentpaper,however,deAraujoetal.(13)donotprovideevidencethatmarketingactions,suchaspricing,canaffectneuralcorrelatesofEP.

Second,ourfindingsalsohaveimplicationsformarketing.Whereasthereisamplebehavioralevidencethatvariousmar-ketingactionsaresuccessfulininfluencingtheEPofindividuals,thattheycanmodulateneuralrepresentationsofthissignalhadnotbeenreportedbefore.Furthermore,theneuralfindingsalsoprovidesomecluesaboutthemechanismsinvolved.Inparticu-lar,itseemsthatpricechangesmodulatetherepresentationsofexperiencedutilitybutnottheencodingofthesensoryproper-tiesoftasteintheprimarygustatorycortex.

Third,ourresultshaveimplicationsforeconomics.EPisanimportantcomponentofexperiencedutility,whichistheecon-omist’stermforsubjectivewellbeing.Weshowthat,contrarytothestandardeconomicview,EPdependsonnonintrinsicprop-ertiesofproducts,suchasthepriceatwhichtheyaresold.Itthenfollowsthatmarketingmanipulationsmightaffectsubjectiveperceptionsofwellbeing.Thisraisesseveraldifficultquestionsforthefield.Shouldtheeffectofpricesonexperiencedutilitybecountedasrealeconomicwellbeingorasamistakemadebyindividuals?Towhatextentaremeasurabledifferencesinpref-erencesbasedonintrinsicdifferencesbetweenproductsandpriceeffectswehaveidentified?Whathappenstotheefficiencyofcompetitivemarketswhenfirmscaninfluenceexperiencedutilitybychangingthepriceofitems?

AnimportanttaskforfutureresearchistodevelopamorecompletecharacterizationoftherangeofmarketingactionsthatcaninfluencetheneuralcomputationofEP.Weconjecturethatanyactionaffectingexpectationsofproductquality,suchasexpertqualityratings;peerreviews;informationaboutcountryoforigin,store,andbrandnames(especiallythoseassociatedwithluxuryproducts);andrepeatedexposuretoadvertisementsmightleadtoeffectssimilartothoseidentifiedhere.

MaterialsandMethodsSubjects.Twentynormal-weightsubjectsparticipatedintheexperiment(11males,ages21–30;meanage,24.5yr).Oneadditionalsubjectparticipatedintheexperimentbutwasexcludedfromtheanalysis,becausehereportedbeingconfusedaboutthetaskduringadebriefingattheendoftheexperi-ment.Allsubjectswereright-handedandhealthy;hadnormalorcorrected-to-normalvisionandnohistoryofalcoholabuse,psychiatricdiagnoses,orneurologicalormetabolicillnesses;andwerenottakinganymedicationsthatinterferewiththeperformanceoffMRI.Allsubjectswerescreenedforliking,andatleastoccasionallydrinking,redwine.Atthebeginningofeachexper-iment,subjectswererequiredtoshowanofficialformofidentificationto

Fig.4.Neuralcorrelatesoflikingratings.(A)ActivityinthemOFCandthemidbraincorrelatedwiththereportedpleasantnessofthesixliquidsatdegus-tationtime.Forillustrationpurposes,thecontrastisshownbothatP"0.001andP"0.005uncorrectedandwithanextendthresholdoffivevoxels.(B)CorrelationofpleasantnessratingsandBOLDresponses(r!0.593,P"0.000).Eachpointdenotesasubject-pricepair.Thehorizontalaxismeasuresthereportedpleasant-ness.Theverticalaxiscomputesthebetasfromthegenerallinearmodelina5-mmsphericalvolumesurroundingtheareadepictedinA.

1052!www.pnas.org"cgi"doi"10.1073"pnas.0706929105Plassmannetal.

III.

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rosc

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for M

arke


Ø  Virtual Reality Ø  Shopping Display Ø  Architecture

III.

Wha

t Neu

rosc

ienc

e C

AN

do

for M

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ting Summary

Ø  Visual Attention Ø  Advertising Ø  Packaging

Ø  Emotion Ø  Politics Ø  Branding

Ø  Consumption Ø  Beverage Ø  Film

Ø  Virtual Reality Ø  Architecture Ø  In-store marketing

IV. What Neuroscience CanNOT do for Marketing

Ø Cautionary Tales

Ø Methodology

Ø Costs

Ø Context

Cautionary Tales IV

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Facebook Study

Cautionary Tales IV

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Facebook Study

Emotional Engagement

Facebook Yahoo NYTimes

Cautionary Tales IV

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Coke vs Pepsi Taste Test

Blind Taste Test Labeled Taste Test

Cautionary Tales IV

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Coke vs Pepsi Taste Test

Cautionary Tales IV

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NewScience Magazine

Cautionary Tales IV

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Confounds

Cautionary Tales IV

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Confounds

“These methods do not reveal inner truth. Neuroscience techniques need interpretation in light of other information. Real understanding comes from integrating information rather than focusing on only one perspective.” Barbara O’Connell Vice President, Milward Brown

Preference

Methods: Reverse Inference IV

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mPFC Activation

DiscussionThe main hypothesis of this study was that an increase in theperceived price of a wine should, through an increase in tasteexpectations, increase activity in the mOFC. The results de-scribed above provide evidence consistent with the hypothesis.The hypothesis was motivated by several previous studies, whichhave shown that activity in the mOFC is correlated with behav-ioral pleasantness ratings for odors (10–13), tastes (6, 14, and15), and even music (16). This, together with our behavioralresults and the additional imaging results described below,support the interpretation that, by modulating the activity in themOFC, changes in the price of a wine might lead to a change inthe actual EP derived from its consumption.

We performed two additional analyses to provide further supportfor this interpretation. First, for each individual and wine, wecomputed the change in reported EP between the high and lowprice conditions. We also computed the analogous difference inparameter estimates for the BOLD response from the generallinear model in an area surrounding the mOFC. Fig. 3B shows thatthe neural and behavioral estimates were positively and highlycorrelated (r ! 0.49, P " 0.001). Second, we verified that the resultsof the previous literature also held in our study by estimating adifferent general linear model and looking for brain regions whoseactivity was correlated with reported EP from sampling the differ-ent stimuli (see SI Text for details). The results replicated thefindings of previous studies: activity in the mOFC was correlatedwith absolute reports of pleasantness (Fig. 4).

Importantly, we did not find evidence for an effect of priceson areas of the primary taste areas such as the insula cortex, theventroposterior medial nucleus of the thalamus, or the prabra-chial nuclei of the pons. A natural interpretation is that thetop-down cognitive processes that encode the flavor expectan-cies are integrated with the bottom-up sensory components ofthe wine in the mOFC, thus modulating the hedonic experienceof flavor, but that the flavor expectancies generated by thechange in prices do not impact more basic sensory representa-

tions. Interestingly, an analogous mechanism has been proposedfor pain placebo effects (7).

Our results have implications for several disciplines. First, theEP signal plays a central role in neuroeconomics, because itserves as a teaching signal that guides future behavior. Unfor-tunately, very little is known about the factors that affect theneural computation of this signal. A natural starting hypothesisis the economic view, which states that EP depends only on thesensory properties of the item being consumed (i.e., its molec-ular properties) and the state of the consumer. Our resultssuggest that the brain might compute EP in a much moresophisticated manner that involves integrating the actual sensoryproperties of the substance being consumed with the expecta-tions about how good it should be. It is important to emphasizethat it might be adaptive for the brain to do this. To make gooddecisions in the future, the brain needs to carry out goodmeasurements of the quality of current experiences. In a worldof noisy measurements, the use of prior knowledge about thequality of an experience provides additional valuable informa-tion. A related study (13) provides additional supporting evi-dence for this point by showing that giving a cognitive label toan ambiguous odor (‘‘cheddar cheese’’ or ‘‘body odor’’) canaffect both subjective pleasantness reports and neural activityrelated to EP. Unlike the current paper, however, de Araujo etal. (13) do not provide evidence that marketing actions, such aspricing, can affect neural correlates of EP.

Second, our findings also have implications for marketing.Whereas there is ample behavioral evidence that various mar-keting actions are successful in influencing the EP of individuals,that they can modulate neural representations of this signal hadnot been reported before. Furthermore, the neural findings alsoprovide some clues about the mechanisms involved. In particu-lar, it seems that price changes modulate the representations ofexperienced utility but not the encoding of the sensory proper-ties of taste in the primary gustatory cortex.

Third, our results have implications for economics. EP is animportant component of experienced utility, which is the econ-omist’s term for subjective well being. We show that, contrary tothe standard economic view, EP depends on nonintrinsic prop-erties of products, such as the price at which they are sold. It thenfollows that marketing manipulations might affect subjectiveperceptions of well being. This raises several difficult questionsfor the field. Should the effect of prices on experienced utility becounted as real economic well being or as a mistake made byindividuals? To what extent are measurable differences in pref-erences based on intrinsic differences between products andprice effects we have identified? What happens to the efficiencyof competitive markets when firms can influence experiencedutility by changing the price of items?

An important task for future research is to develop a morecomplete characterization of the range of marketing actions thatcan influence the neural computation of EP. We conjecture thatany action affecting expectations of product quality, such asexpert quality ratings; peer reviews; information about countryof origin, store, and brand names (especially those associatedwith luxury products); and repeated exposure to advertisementsmight lead to effects similar to those identified here.

Materials and MethodsSubjects. Twenty normal-weight subjects participated in the experiment (11males, ages 21–30; mean age, 24.5 yr). One additional subject participated inthe experiment but was excluded from the analysis, because he reportedbeing confused about the task during a debriefing at the end of the experi-ment. All subjects were right-handed and healthy; had normal or corrected-to-normal vision and no history of alcohol abuse, psychiatric diagnoses, orneurological or metabolic illnesses; and were not taking any medications thatinterfere with the performance of fMRI. All subjects were screened for liking,and at least occasionally drinking, red wine. At the beginning of each exper-iment, subjects were required to show an official form of identification to

Fig. 4. Neural correlates of liking ratings. (A) Activity in the mOFC and themidbrain correlated with the reported pleasantness of the six liquids at degus-tation time. For illustration purposes, the contrast is shown both at P " 0.001 andP"0.005uncorrectedandwithanextendthresholdoffivevoxels. (B)Correlationof pleasantness ratings and BOLD responses (r ! 0.593, P " 0.000). Each pointdenotes a subject-price pair. The horizontal axis measures the reported pleasant-ness. The vertical axis computes the betas from the general linear model in a5-mm spherical volume surrounding the area depicted in A.

1052 ! www.pnas.org"cgi"doi"10.1073"pnas.0706929105 Plassmann et al.

“Liking”












Preference

Methods: Reverse Inference IV

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mPFC Activation












My product is “liked”

Methods: Multi-voxel Pattern Analysis IV

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Beyond Reverse Inference

Like

Dislike

New Product

Costs IV

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Intern

You are the postdoc. You should know how to make ends meet.

$40 $500 $40 $10,000 $20,000

Data: $17,400 Overhead: $30,0 00

$47,400

Scan Subject Postdoc IT

Costs IV

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Return On Investment?

Data: $17,400 Overhead: $30,0 00

$47,400

“If I can spend $1000 to do a traditional market study that gets me 85% of what a $50,000 fMRI study does then the return on my neuromarketing investment is not great. Thinking about it another way, how much less or more could I get across 50 traditional studies relative to the value of one neuromarketing study.” -Craig Bennett

Context IV

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Ø  Loud

Ø  Claustrophobic

Ø  Stationary

Ø  Repetitive

IV.

Wha

t Neu

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an N

ot d

o fo

r Mar

ketin

g Summary: Limitations

Ø Cautionary Tales

Ø Costs

Ø Methodological

Ø Context

Data: $17,400 Overhead: $30,000

$47,400 My product is “liked”

Neuromarketing: Conclusions

Promise Caution

Data: $17,400 Overhead: $30,0 00

$47,400

Thanks! DHAROL TANKERSLEY, PhD Analyst

[email protected] www.linkedin.com/pub/dir/Dharol/Tankersley

Technology

Is tankersley neuro-marketing_slideshare