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AARHUSUNIVERSITY Innovative Methods in Consumer ResearchInnovative Methods in Consumer Research

88thth International MAPP Workshop on Consumer Behaviour and Food MarketingInternational MAPP Workshop on Consumer Behaviour and Food Marketing88 International MAPP Workshop on Consumer Behaviour and Food MarketingInternational MAPP Workshop on Consumer Behaviour and Food MarketingMiddelfart, Denmark, 4Middelfart, Denmark, 4--5 May 20105 May 2010

Simultaneous segmentation Simultaneous segmentation of products, persons, and of products, persons, and

i ti tenvironmentsenvironments

Joachim ScholdererJoachim Scholderer

ASB AMAPPASB AUMAPP

AARHUSUNIVERSITY

INTRODUCTIONINTRODUCTIONINTRODUCTIONINTRODUCTION

• Segmentation of consumer • In a classical study of intentions to markets is usually based on the assumption that consumers’ preferences are stable across

t t

purchase meat products (Belk, 1974), the variance components were

contexts• Empirically, this assumption tends

to be violated

• Person: 5%• Product: 15%

Situation: 1%• Consumers are often more

different from themselves in other contexts than they are from other

• Situation: 1%• Person × Product: 3%• Person × Situation: 10%contexts than they are from other

consumers in the same context • Product × Situation: 26%

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(Amir & Levav, 2008; Bearden & Woodside, 1976; Belk, 1974a, 1974b, 1975; Bishop & Witt, 1970; Dawar et al., 1992; Inman et al., 2009; Lutz & Kakkar, 1975; Ratneshwar & Shocker, 1991; Ratneshwar et al., 2001; Sandell, 1968; Wakefield & Inman, 2003)

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INTRODUCTIONINTRODUCTIONINTRODUCTIONINTRODUCTION

• A variety of two-way • The aim of the approach segmentation methods have already been developed, based on DeSarbo’s joint-space

h

presented here is to extend DeSarbo’s approach to multi-way data

approach• A scaling technique (MDS, MDU)

maps products and persons into

• Products, persons, and consumption environments are mapped into a joint p p p

a joint, low-dimensional space with a common distance metric

• A segmentation technique

pp jspace by multiple correspondence analysis

• Heterogeneity is modelled by A segmentation technique (clustering, finite mixtures) models unobserved heterogeneity

Heterogeneity is modelled by finite mixtures

ASB AUMAPP(DeSarbo & Hoffman, 1987; DeSarbo et al., 1990, 2001, 2008; Wu & DeSarbo, 2005)

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METHODMETHODMETHODMETHOD

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QPORKCHAINS CONSUMER SURVEYQPORKCHAINS CONSUMER SURVEYQPORKCHAINS CONSUMER SURVEYQPORKCHAINS CONSUMER SURVEY

• Web survey, fieldwork by TNS in • Environmental characteristicsDecember 2007

• Sample sizesBelgium: N 492

• Physical location (home, restaurant, on the go, other location)• Belgium: N = 492

• Denmark: N = 480• Greece: N = 506

location)• Temporal context (weekday,

weekend, special occasions, any day)

• Germany: N = 479• Poland: N = 480

any day) • Social situation (alone, with

family, with friends, other company)• Products

• Thirty pork products consumed in all five countries

company)• Additional person characteristics

• Demographics, FRL

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co su ed a e cou es Demographics, FRL

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DATA STRUCTUREDATA STRUCTUREDATA STRUCTUREDATA STRUCTURE

User status Typical consumption environmentProduct

Physical location

HomeRestaurantOn the go

Consumption frequency = 0

Product j

Consumption

location On the goOther location

TemporalWeekdayWeekendConsumption

frequency > 0Temporalcontext

WeekendSpecial occasionsAny dayAl

Socialsituation

AloneWith familyWith friendsOther company

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Other company

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MODELMODELMODELMODEL

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INDICATOR MATRIX INDICATOR MATRIX ZZINDICATOR MATRIX INDICATOR MATRIX ZZ1 2 … N i 1 2 … N j 1 2 … N k

Person Product

Person Product Environment

Dimensionality:

R×C

Person Product1 11 21 …1 N j

where

R = NiNj

1 N j

2 12 22 … i j

C = Ni+Nj+Nk

2 N j

… …… …… …

Z1 Z2 Z3

… …… …

N i 1N i 2

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N i …N i N j

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MULTIPLE CORRESPONDENCE ANALYSISMULTIPLE CORRESPONDENCE ANALYSISMULTIPLE CORRESPONDENCE ANALYSISMULTIPLE CORRESPONDENCE ANALYSIS

• Bilinear model • Standardised residuals

• Singular value decomposition∑=

⎟⎟⎠

⎞⎜⎜⎝

⎛+= cdrd

D

ddcrrc γφλmmp

11ˆ

12 )()( −−= crcrrcrc mmmmps

• Relative frequencies where • Principal inertias∑∑

−

⎟⎟⎠

⎞⎜⎜⎝

⎛=

R C

rcrcrc zzp1

'VUDS α= IVVUU == ''

• Row and column masses• Standard coordinates

∑

∑∑= =

⎟⎠

⎜⎝

C

r c1 1 ) ... 2, 1, ( 2 Ddαλ dd ==

50and• Principal coordinates

d∑

∑=

=

=

R

rcc

crcr

pm

pm1

UDΦ 5.0−= r VDΓ 5.0−= c

ΦDF ΓDGASB AUMAPP

and∑=r

rcc p1 αΦDF = αΓDG =

(Benzécri, 1979; Gifi, 1981; Greenacre, 1984; Greenacre & Hastie, 1987)

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LATENT CLASS SEGMENTATIONLATENT CLASS SEGMENTATIONLATENT CLASS SEGMENTATIONLATENT CLASS SEGMENTATION

• The principal column coordinate vectors gc from the MCA are treated as realisations of a random variable with dimensionality D

• The distribution of gc is assumed to be an unrestricted finite mixture of multivariate normals. Each segment s (s = 1, 2, ... S) has a class-specific multivariate normals. Each segment s (s 1, 2, ... S) has a class specific mean vector μs and covariance matrix Σs such that

with)|()()( sfsPf c

S

c gg ∑=

( ) ( )⎥⎦⎤

⎢⎣⎡ −−−= −−−

scsscsS

c πsf μgΣμgΣg 12/12/ '21 exp)2()|(

)|()()(1

cs

c gg ∑=

• For given numbers of segments S, the parameters can be estimated via maximum likelihood

⎦⎣ 2

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RESULTSRESULTSRESULTSRESULTS

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MCA RESULTSMCA RESULTSG d f fitG d f fit

Benzécri-adjusted inertia decomposition

Dimension d Principal inertia Adjusted inertia Contribution Cumulative

Goodness of fitGoodness of fit

p j

1 0.431 0.083 31.7% 31.7%2 0.367 0.043 16.6% 48.3%3 0.357 0.039 14.7% 63.0%4 0.335 0.028 10.8% 73.8%5 0.293 0.014 5.2% 79.0%6 0.285 0.011 4.3% 83.2%7 0 278 0 010 3 6% 86 9%7 0.278 0.010 3.6% 86.9%8 0.269 0.007 2.9% 89.7%9 0.256 0.005 1.9% 91.6%10 0.240 0.003 1.0% 92.5%11 0 234 0 002 0 7% 93 2%11 0.234 0.002 0.7% 93.2%12 0.233 0.002 0.7% 93.9%13 0.229 0.001 0.5% 94.4%14 0.228 0.001 0.5% 94.9%

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15 0.228 0.001 0.5% 95.3%

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MCA RESULTSMCA RESULTSi t d ti t d t i t i t Joint productJoint product--personperson--environment spaceenvironment space

2

Alone

OtherCompany

2

Weekday

MC

A 2

(17%

) 1

SalamiS llStuffedMeat

WienersAndFrankfurters

OtherLocation

MC

A 4

(11%

)

1 OtherCompany

M

0

CannedMeat

ColdCutsCollarRoastCookedHamDryCuredHamDryCuredMeat

FreshSausagesGammonRoastLasagne

LiverAndKidneysLiverPâtéMarinatedMedallionsMincedMeat

PizzaPizzaToppings

Ribs

SalamiScallopsShoulderSkewersSmallCutsSpaghettiBologneseTenderloin

HomeOnTheGo

WeekdayWeekend

AnyDay

SpecialOccasionsWithFamily

WithFriendsM

0 CannedMeatColdCutsCollarRoastCookedHam

DryCuredHamDryCuredMeat

FreshSausagesGammonRoast

Lasagne

LiverAndKidneysLiverPâtéMarinated

Medallions

MincedMeatPizza

PizzaToppings

RibsSalamiScallops

ShoulderSkewersSmallCutsSpaghettiBologneseStuffedMeat

Tenderloin

WienersAndFrankfurtersHome

RestaurantOnTheGo

OtherLocation

Weekend

AnyDay

SpecialOccasions

Alone

WithFamily

WithFriends

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

-1 Restaurant

-4 -3 -2 -1 0 1

-1

WithFriends

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MCA 1 (32%) MCA 3 (15%)

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LCA RESULTSLCA RESULTSG d f fitG d f fit

Ratio of change

Segments S Log-likelihood BIC CAIC AWE Classification errors BIC CAIC AWE

Goodness of fitGoodness of fit

g g

1 -41960 84152 84182 84472 0.00%2 -35483 71435 71496 72163 0.68% 1.00 1.00 1.003 -32635 65978 66070 67289 3.13% 0.43 0.43 0.404 -31336 63618 63741 65417 4.31% 0.19 0.18 0.155 -30511 62205 62359 64568 6.53% 0.11 0.11 0.076 -29791 61004 61189 63647 5.93% 0.09 0.09 0.077 -29289 60239 60455 63388 7.43% 0.06 0.06 0.027 29289 60239 60455 63388 7.43% 0.06 0.06 0.028 -28810 59519 59766 63060 8.12% 0.06 0.05 0.039 -28362 58862 59140 62683 7.51% 0.05 0.05 0.0310 -28078 58532 58841 62758 8.01% 0.03 0.02 -0.0111 27794 58202 58542 62857 8 90% 0 03 0 02 0 0111 -27794 58202 58542 62857 8.90% 0.03 0.02 -0.0112 -27359 57570 57941 62514 8.47% 0.05 0.05 0.0313 -27076 57243 57645 62541 8.56% 0.03 0.02 0.0014 -26954 57237 57670 62832 8.25% 0.00 0.00 -0.02

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15 -26595 56758 57222 62697 8.26% 0.04 0.04 0.01

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LCA RESULTSLCA RESULTSS t d d tS t d d t i t i t Segmented productSegmented product--personperson--environment spaceenvironment space

22

1

MC

A 4

(11%

)1

MC

A 2

(17%

)

-10M

-10M

-1 0 1 2-1 0 1 2

-2

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MCA 3 (15%)MCA 1 (32%)

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SEGMENT 1 (19%)SEGMENT 1 (19%)SEGMENT 1 (19%)SEGMENT 1 (19%)

ProfileProfile

• Dominant age group: 20-30• High proportion of single

2

Alone

households (both genders)• Stronger presence in Germany

than in other countriesMC

A 2

(17%

)

1

than in other countries• Less likely to plan meals or use

shopping lists

M

0

CannedMeatLiverAndKidneys

PizzaScallopsSmallCuts

• Stronger snacking habits than other segments

-0.5 0.0 0.5 1.0 1.5 2.0 2.5

-1

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MCA 1 (32%)

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ProfileProfile

• Dominant age group: 40-50• High proportion of couples with

2

children• Weaker snacking habits than

other segmentsMC

A 2

(17%

)

1

other segmentsM

0 Weekend

-0.5 0.0 0.5 1.0 1.5 2.0 2.5

-1

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MCA 1 (32%)

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ProfileProfile

• Dominant age group: 30-40• Couples with children and other

2

multi-person households• More likely to be well-educated

and to live in urban areasMC

A 2

(17%

)

1

and to live in urban areas• Much stronger presence in

Greece than in other countries

M

0

Marinated

SkewersTenderloin

AnyDay

SpecialOccasions

• Interested in product information, specialty shops, organic and natural foods, freshness-0.5 0.0 0.5 1.0 1.5 2.0 2.5

-1 RestaurantWithFriends

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MCA 1 (32%)

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ProfileProfile


2

children• More likely than other segments

to live in rural areasMC

A 2

(17%

)

1

St ff dM tienersAndFrankfurters to live in rural areas

• Stronger presence in Belgium than in other countries

M

0

ColdCutsCollarRoastCookedHamDryCuredHamDryCuredMeat

FreshSausagesGammonRoastLasagne

LiverPâtéMedallionsMincedMeatPizzaToppings

Ribs

SalamiShoulderSpaghettiBologneseStuffedMeat

HomeWeekday

WithFamily

-0.5 0.0 0.5 1.0 1.5 2.0 2.5

-1

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MCA 1 (32%)

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ProfileProfile


2

children• Stronger presence in Denmark

and Poland than in other MC

A 2

(17%

)

1

and Poland than in other countries

• Less social, less likely to dine in restaurants than other segments

M

0

restaurants than other segments

-0.5 0.0 0.5 1.0 1.5 2.0 2.5

-1

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MCA 1 (32%)

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ProfileProfile

• Dominant age group: 20-30• Single households (both genders)

2

OtherCompany

and shared flats• More likely to be well-educated

and to live in suburban areasMC

A 2

(17%

)

1

OtherLocation

and to live in suburban areas• Stronger presence in Denmark

and Greece

M

0

OnTheGo

• Low involvement in food, high convenience orientation, strong snacking habits-0.5 0.0 0.5 1.0 1.5 2.0 2.5

-1

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MCA 1 (32%)

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ProfileProfile

• Dominant age group: 50-60• Very high proportion of single

2

households (particularly single men)

• Unlikely to use a shopping listMC

A 2

(17%

)

1

Unlikely to use a shopping list• Negative attitudes towards

advertising

M

0

-0.5 0.0 0.5 1.0 1.5 2.0 2.5

-1

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MCA 1 (32%)

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DISCUSSIONDISCUSSIONDISCUSSIONDISCUSSION

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DISCUSSIONDISCUSSIONDISCUSSIONDISCUSSION

• The multi-way segmentation • Only modest data requirements. approach developed here has clearly distinct features:• Low overlap with seven-

Model can be estimated with easily available software (e.g., SAS and LatentGold; R)Low overlap with seven

segment solutions obtained with a variety of distance-based clustering methods

• Open problems:• The finite mixture part of the

model is parametric whereas gwhen the MCA coordinates were used as input data

• No overlap with 7-segment

model is parametric, whereas the scaling part is not. Little is known about the sampling properties of MCANo overlap with 7 segment

solutions when lifestyle dimensions (FRL) were used as input data

properties of MCA• Difficult to formally justify the

choice of a particular finite i t d l

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input data mixture model

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THANK YOU FOR YOUR ATTENTIONTHANK YOU FOR YOUR ATTENTIONTHANK YOU FOR YOUR ATTENTIONTHANK YOU FOR YOUR ATTENTION

Further informationFurther information AcknowledgementAcknowledgement

Prof Joachim ScholdererProf. Joachim ScholdererMAPP, Aarhus UniversityHaslegaardsvej 10

82 0 h DK-8210 Aarhus [email protected] www.q-porkchains.org

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