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FRANKM. BASS,DOUGLASJ. TIGERT,
and RONALDT. LONSDALE*
The argumentthat socioeconomicvariables do not provide an adequate basis
for marketsegmentationof grocery productsis disputed. A theoretical frameworkfor segmentationmeasurementin termsof group behavioris developed and
applied to surveydata.
Market
Segmentation:
Group
Individual
Behavior
This articleis about marketsegmentation.Although
most of this articleis aboutthe analysisof data and the
measurementof market segments, it also focuses on
those aspectsof measurementthathave a practicalbearing on the managerialstrategyof marketsegmentation.
Thoughthere are severalpossiblebases for application
of the selectivityprinciplein marketing[11], the specific
concernhere is the allocationof marketingeffort,particularly advertising,to selective groups of potential
customers.In currentmarketingmanagementpractice,
there is probablyno problemarea of greaterpractical
consequencethan the questionof how to definemarket
segments.Despite extensiveresearchin marketsegmentation, there is some uncertaintyabout the practical
feasibilityof definingmarketsegmentsby socioeconomic
measurements[15]. The authors will argue that such
measurementsare operationallyfeasible and demonstratewith empiricaldata.
SEGMENTATIONSTUDIES
Frank [4] reviewed the research literatureon economic, demographic,personality,and purchasingcharacteristicsas basesfor segmentingthe marketfor grocery
products. His synthesis of the literaturehas yielded
highly significantconclusions.In severalcases reviewed
by Frank, the almost universalconclusion appearsto
Versus
be that regressionanalysis-or some variantof regression such as discriminantanalysis-of the quantityof
groceryproductspurchasedby individualfamiliesyields
low R2's when the independentvariablesare socioeconomic.Thusit wouldappearthatthesevariablesexplain
only a small part of the variancein purchasingrates by
individualfamilies.To illustrate,in the studyby Frank,
Massy, andBoyd [5] using ChicagoTribunepanel data,
quantitiesof 57 groceryproductspurchasedby individual families were analyzedin relation to 14 socioeconomic variables.The highestR2 obtainedfor any product category was .29, and about 50 percent of the
regressionsproducedR2'sof less than.10.
There is evidencethat the additionof psychological
and sociological variables increases the "explained"
variance somewhat [10, 13] but, even so, the unexplainedvarianceremainshigh.The verylow goodnessof
fit in cross-sectionalstudies of groceryproductsis not
surprising,particularlyconsideringthe resultsof crosssectional studies of consumerexpendituresreportedin
economicsjournals.In a survey articlein 1962, Ferber
[3] indicatedthat in expenditurestudiescoveringa wide
rangeof products,includingdurables,the proportionof
variancein individualhouseholdexpendituresexplained
by socioeconomicvariablesis small,frequentlyless than
.3. An extensivecollectionof cross-sectionalexpenditure
studies is included in Consumption and Savings [6]. The
evidenceis overwhelmingthatR2 is low whenindividual
householdpurchaserates are relatedto socioeconomic
variables.
The intuitiveconclusion,perhaps, suggestedby the
evidenceis that marketsegmentationbasedon socioeconomicmeasurementsis infeasible.This is the conclusion
of Twedt [14], Frank [4] and others. Frank concludes,
"Based on the researchreportedin the precedingsections for the most partsocioeconomiccharacteristicsare
* Frank M. Bass is professor of industrial administration,
Purdue University. Douglas J. Tigert is assistant professor of
marketing,University of Chicago. Ronald T. Lonsdale is operations researchmanager, G. D. Searle and Company.The authors
are indebted to their colleagues E. A. Pessemier, C. W. King,
D. L. Weiss, D. E. Schendel, J. O. Summers and to numerous
graduate students for their participation in discussions that
sharpened their views about the issues involved in the measurement of market segments.
264
Journal of Marketing Research,
Vol. V (August 1968), 264-70
MARKET
SEGMENTATION:
GROUPVERSUSINDIVIDUAL
BEHAVIOR
not particularlyeffectivebases for segmentationeither
in termsof their associationwith householddifferences
in average purchase rate or response to promotion."
Twedt said, "the heavy users usually accountfor 7-10
times the volume of light users. They buy more often
and they buy more differentbrands.Furthermore,the
heavy user is not readily identifiablein terms of any
othercharacteristics."
Though we agree that multiple regressioninvolving
socioeconomic variables and quantities purchasedby
individualhouseholdsof groceryproductsresults in a
low proportionof explainedvariance,we disagreewith
the conclusionthat socioeconomicvariablesdo not provide measurementsthat can be effectivelyappliedin a
strategyof market segmentation.In other words, the
evidence is not disputed, but we argue that the conclusion does not follow from it. This shall be demonstrated with empirical illustrations, but before the
analysis, some propositions about measurementand
marketsegmentationwill be examined.
MEASUREMENT THEORY AND MARKET
SEGMENTATION
The two most importantideas to considerin market
segmentationare the following fundamentalpropositions:(1) marketsegmentationis a managementstrategy
and (2) implementationof the strategyof market segmentationinvolves postulatesabout the characteristics
and the behaviorof groups, not persons. Smith, in his
pioneeringarticle[11], expresslyincludedthese propositions in his definitionof market segmentation.Smith's
definitionwas "marketsegmentationconsistsof viewing
a heterogeneousmarketas a numberof smallhomogeneous marketsin responseto differingproductpreferences
among importantmarketsegments.It is attributableto
the desiresof consumersor usersfor more precisesatisfaction of their varyingwants. Segmentationoften involves the use of advertisingand promotion. It is a
merchandisingstrategy"[11]. Much of the confusion
about market segmentation,we believe, stems from
failure to recognizethe two fundamentalpropositions.
The absence of a satisfactorytheory of individualbehavior does not necessarilyimply the absence of valid
propositionsabout the groups'behavior.For marketing
strategy,it is the behaviorof groups,not persons, that
is primarilyimportant.
A hypotheticalexampleillustratesthe basic structure
of the measurement problem in market segmentation as
Table 1
PROBABILITY
OF LOW,MEDIUM,
AND HIGHUSAGERATES
Usage rates
Probability
Low (amount purchased = xi)
Medium (amount purchased = x2)
High (amount purchased = x3)
PI
P2
P3
265
it applies to quantitiespurchasedor usage rates. The
analysisof segmentationconsideredhere is restrictedto
usage rates although the principles generally apply
equally well to other segmentationmeasurementslike
promotionalelasticityandbrandloyalty.
Table 1 shows the probabilitiesor proportions of
families purchasing,on average,differentquantitiesof
some product. For market segmentation,the essential
questionis whetherit is possible to identify groups of
consumerswith differentmeanpurchaseratesdependent
on certainvariables,such as income, age, and occupation.
Table 2 shows the conditionalprobabilitiesof purchasing differentquantities of a product given some
measurementon a variable.If in each row of the matrix
thereis one numberthatis nearone, maybe.8 or greater,
it will be possible to predictfairly accuratelythe usage
rates for individualconsumers.The evidence from the
regression and discriminantstudies clearly indicates
that it is impossibleto predictusage rates very well on
an individualbasis with socioeconomicvariables.For
purposesof marketsegmentation,however,it is sufficient
that the variablesyield large differencesin mean purchase rates.Thus in Table 2, if E(x I Z,) > E(x i Z1), it
may be possible to segment the market with this information.Even though the within-groupvariance is
great, the fact that the choice is between groups not
personspermitssegmentationby groupmeans.
The regressionmodels applied in several studies of
marketsegmentationhave tendedto focus on individual
behavior,resultingin misleadingconclusions.The propriety of the linearity assumptionand noncontinuous
observationson the dependentand independentvariables bring into questionthe meaningof the regression
results. This was essentially the point which Kuehn
[8] made in his debatewith Evans [1, 2]. Kuehnargued
that simplecross-classification
analysisof a single variable with the probabilityof ownershipwas more revealing than discriminantanalysis.
It is not suggestedthat regressionmodels are necessarily inappropriatefor analysis of market segments.
However, the results of these models should be interpretedin terms of group means. In succeedingsections of this article,these ideas will be illustratedwith
an analysisof survey data.
DATA SOURCE, MEASUREMENTS, AND
PRODUCTS STUDIED
The grocery product data analyzed here were obtained by the Milwaukee Journal in its annual survey of
consumer-purchasing behavior. The survey was conducted by mail in the Milwaukee area on a probability
sampling basis in October, 1964. The sample return for
two questionnaires in separate samples of 4,000 each
was 6,264 or 81.2 percent [9]. The analysis here is
restricted to the following product categories: catsup,
frozen orange juice, pancake or waffle mix, candy bars,
AUGUST1968
OF MARKETING
JOURNAL
RESEARCH,
266
cake mix, beer, creamshampoo,hair spray,toothpaste,
mouthwashor oral antiseptic.For the first six products,
consumerswere asked to state the quantitiespurchased
in the past 30 days, but for the remainingfour products
the purchaseperiod coveredwas 60 days.1
The socioeconomicvariablesanalyzedand the measurementcategoriesare:
Table 2
ANDHIGH
CONDITIONAL
PROBABILITY
OF LOW,MEDIUM,
Z
USAGERATESGIVENMEASUREMENT
Variable
Variable
measurements
Low
Z1
Age of male head
Numberof children
under 18 years
No man in household
18-24
0
1
25-34
35-49
50-64
65 and over
2
3
4
5
6 or more
Family income
Education of head
Under$3,000
$3,000 -$4,999
$5,000 -$7,999
$8,000 -$9,999
Gradeschool or less
1-3 yearsof high school
Graduatedhigh school
1-3 yearscollege
$10,000-$14,999
$15,000and over
Occupationof head
All other
Sales
Clerical
Professional
Manager
Graduated college
Television viewing
yesterday, by head
0
0-1Y2hours
hours
1-3
Over3%12
hours
REGRESSION ANALYSIS
The relationshipbetween purchaserates and socioeconomic variablesis analyzedby multiple regression,
among other statistical techniques. To avoid the assumptionsof linearityand continuity,dummyvariables
areused for the socioeconomicmeasurements[7]. Thus,
is the value for the kth householdon the jth disXi•,
crete variableof the ith discreteclassificationand
is one if the householdis in the jth class, otherXi•k
wise zero.
Therefore,least-squaresregressionprovidesestimatesof
table using
cell meansfor a multiplecross-classification
all of the socioeconomicvariables.
If there are N classes of the ith discreteclassification
and if a household is not in the first N - 1 of these
classes, then the household must necessarily be in the
Nth class. In fact, the solution is indeterminate if all N
variables are included [12]. Therefore, the first classification is omitted for each variable.
The Weighted Regression Analysis Program (WRAP),
of the reliabilitywith which
1 Although there is some question
consumers report quantity purchased and some bias in the unit
of measurement for products in which there are a variety of
sizes (the questionnaire calls only for a report on units, not
sizes), it is not believed that either of these weaknesses is severe
enough to destroy the usefulness of the data for the present
purposes.
Z2
71"1
7121
Zn
7rnl7r1n2
Medium
71 2
7122
High
7'13
7223
7n3
used to obtain the regressionestimates, performs the
multiple-regressioncalculationson all of the variables
and then in a stepwiseway deletes nonsignificantvariables by a fixed F ratio or a fixed probabilitylevel. For
the presentanalysisthe probabilitywas chosensuch that
all variablesaresignificantat the .10 level.
The R2 values are uniformlylow-also true of previous studies-but the essentialpoint is that all of the
variablesincludedin the final stage are significantby
definition.The low R2 values imply only that the variance withincells is great,not that the relationshipsare
weak.
Table 3 shows the numberof classificationsfor each
variableand each productincludedin the final stage of
the regression.Every variableappearsin the final stage
for some group of products.Numberof childrenis includedin nine of ten productcategories,and age andincome are includedin eight of the ten. Occupationappearsto be the weakestmeasurementsince it is included
only threetimes,andthenwithonly one classification.
The crucialtest of whetherit is possible to segment
the marketby socioeconomicmeasurementsis whether
segmentscan be describedby socioeconomicmeasurements with widely varying mean purchase rates. For
such a test, finalstage regressionestimateswere used to
estimatemean purchaserates for differentsegmentsby
summingthe appropriateregressioncoefficientsusing
the vector descriptionof socioeconomicmeasuresfor
groupsof householdscomprisingthat segment.Table 4
summarizesthese estimatesfor various groups of segments, collectedinto light-buyerand heavy-buyercategories.The mean consumptionrate ranges(see Table 4)
have been computedfromthe finalstageregressionestimates by summingthe regressioncoefficientsincluded
in the segmentdescriptionand then summingover variablesnot included.It is clearfromthe rangeof variation
of meanpurchaseratesbetweenthe segmentsthat socioeconomicmeasurementsprovidea meaningfulbasis for
segmentation.For example,marriedhouseholdswhose
heads are over 50 with no childrenunder 18; the husbands are college graduates,watch televisionless than
one and a half hoursa day, have an estimatedmeanpurchase rate of .74 bottles a month for catsup.However,
if heads are between 35 and 49, are not high-school
BEHAVIOR
GROUPVERSUSINDIVIDUAL
SEGMENTATION:
MARKET
267
Table 3
FOREACHVARIABLE
FOREACHPRODUCTIN THEFINALREGRESSION
CLASSIFICATIONS
STAGE
Product
Catsup
Frozen orange juice
Pancake or waffle mix
Candy bars
Cake mix
Beer
Cream shampoo
Hair spray
Toothpaste
Mouthwash or oral antiseptic
Age
Children
Income
Education
Occupation
T. V. viewing
R2
2
2
0
2
4
2
0
4
4
4
6
3
4
6
6
1
2
2
4
0
0
2
0
2
4
5
4
2
3
1
2
1
2
0
0
4
2
1
0
2
0
1
0
1
0
1
0
0
0
0
1
1
0
1
3
3
0
1
1
1
.081
.072
.037
.080
.082
.092
.017
.058
.093
.032
graduates,have five children,and watch televisionbetweenone anda half andthreeand a half hoursper day;
the householdshave an estimatedmean purchaserate
of 5.78 bottlesper monthfor catsup, almosteight times
as much.
The fact that the R2 values are low impliesonly that
the variance within segments is great, not necessarily
thatthe differencesin meanvaluesbetweensegmentsare
not significant.Of course,even for largesamplessuch as
this one, samplemeasurementsare smallenoughto raise
questions about the reliability of estimates for cells
sparselyrepresentedin the sample.Later, the reliability
of regressionestimateswill be examinedin greaterdetail, and the possibleapplicationof regressionmeasures
of mean purchaserates to media analysis will be explored.
SIMPLE CROSS-CLASSIFICA
TION ANALYSIS
For greaterfocus on the variationin groupbehavior
associated with socioeconomiccharacteristics,contingency tables were developedfor each of the six socioeconomicvariableswith each of the ten products.This
analysisthus providesa basis for measuringthe ability
Table 4
RATESFORDIFFERENT
LIGHTAND HEAVYBUYERSBY MEANPURCHASE
SOCIOECONOMIC
CELLS
Mean consumption
rate ranges
Light buyers
Heavy buyers
Light
buyers
Heavy
buyers
Ratio of
highest
to
lowest
rate
Unmarried or married over
age 50 without children
Under 35 or over 65, income
less than $10,000, not college
grads, 2 or less children
Some college, 2 or less children
Under 50, 3 or more children
.74-1.82
2.73-5.79
7.8
College grads, income over
$10,000, between 35 & 65
1.12-2.24
3.53-9.00
8.0
3 or more children, high school
or less ed.
35 or over, 3 or more children
35 or over, 3 or more children,
income over $10,000
.48-.52
1.10-1.51
3.3
1.01-4.31
.55-1.10
6.56-22.29
2.22-3.80
21.9
6.9
0-12.33 17.26-40.30
oo
Description
Product
Catsup
Frozen orange
juice
Pancake mix
Candy bars
Cake mix
Beer
Cream shampoo
Hair spray
Toothpaste
Mouthwash
Under 35, no children
Not married or under 35,
no children, income under
$10,000, T.V. less than 3/2
hrs.
Under 25 or over 50, college ed.,
nonprofessional, T.V. less
than 2 hrs.
Income less than $8,000, at least
some college, less than 5 children
Over 65, under $8,000 income
Over 50, less than 3 children, income less than $8,000
Under 35 or over 65, less than
$8,000 income, some college
Between 25 & 50, not college
grad., T.V. more than 3/2
hrs.
Income $10,000 or over with
high school or less ed.
Under 65, over $10,000 income,
not college grad.
Under 50, 3 or more children
over $10,000 income
Between 35 & 65, income over
$8,000, high school or less ed.
.16-35
0-.41
1.41-2.01
.46-.85
.44-.87
5.5
.52-1.68
oo
2.22-4.39
3.1
.98-1.17
2.5
AUGUST1968
OF MARKETING
JOURNAL
RESEARCH,
268
of each variablesinglyto discriminateamonggrouppatterns of behavior for quantity purchased.Chi-square
values have been calculatedfor each contingencytable.
The tablessignificantat the .05 level or less are listed in
Table 5. Again socioeconomicmeasureseffectivelydiscriminatebetweenpatternsof group behavior.Number
of childrenis significantfor all ten products;age and income aresignificantfor eightof ten products.
Althoughcross-classificationanalysisof a single independentvariablewith the dependentvariablehas the
disadvantagerelative to regression and other multivariatetechniquesof failingto measurethe joint effects
of several variables,it helps greatly in demonstrating
the natureof the variation.In principle,of course,multiple cross classificationis possibleand desirable;but as
the numberof variablesincreases,the numberof cells
becomes large enough to overwhelmeven vary large
samples. However, several multiple cross-classification
analyses (cross classificationwith variable stacking)
wereperformed,one of whichis reportedlater.
It has been contendedthat the low R2's obtainedin
regressionanalysishave led to false conclusionsabout
the ability of socioeconomicvariablesto segment the
marketsince R2 is a measureof the model's ability to
predictindividualratherthan group behavior.In addition, it has been arguedthat the relationshipsmay be
nonlinear.These pointsare demonstratedby conditional
probabilitymatricesof the charactershown in Table 2.
Developmentof these tables should be the first step in
any segmentationstudy. To conserve space, only 4 of
42 significanttables for the ten productsare presented
here.Table 6 illustratesconditionalprobabilities,means,
and standarderrors.These conditionalprobabilitiesindicate clearly the strengthof the socioeconomicvariables. For example, the probabilitythat a household
with five or morechildrenwill buy threeor morebottles
of catsupin a month is more than three times as great
as the correspondingconditionalprobabilityfor a household with no children.There is a similardifferencein
the probabilitiesfor toothpaste.The probabilitythat a
householdin which the head is a college graduatewill
buy five or morecans of frozenorangejuice in a month
is about twice the probabilityfor a householdin which
the headis a high schoolgraduate.
The nonlinearityof beer consumptionrelativeto age
is also demonstratedin Table 6. When the analysisis
extendedto includetwo or more independentvariables,
the differencesin conditionalprobabilitiesand means
are further increased.
CROSS CLASSIFICATION WITH
VARIABLE STACKING
To study the simultaneous effects of two variables on
mean usage rates, two variables were cross-classified
with purchase rates for a subsample of 1,400 households. In addition, a regression analysis was done using
Table 5
SIGNIFICANT(S) AND NONSIGNIFICANT
(NS) CONFOR
TINGENCYTABLES
OF AMOUNTSPURCHASED
AND SOCIOEACHPRODUCT
ECONOMICMEASUREMENTS
Product
Age
S
Catsup
S
Frozenorange
juice
Pancakeor waffle NS
mix
S
Candybars
S
Cake mix
Beer
S
Creamshampoo NS
S
Hair spray
S
Toothpaste
S
Mouthwash
ChiPdren
T. V.
Income Educa- Occu- viewtion pation
ing
ing
S
S
S
S
S
S
S
S
S
S
S
S
S
S
NS
S
S
S
S
S
S
S
S
S
S
NS
S
S
NS
S
NS
NS
NS
S
S
S
NS
NS
NS
NS
NS
S
S
NS
NS
S
NS
NS
S
NS
only two variables,similarto the analyseswith several
variablesdiscussedpreviously.This permitscomparison
of regression estimates of segment means with the
sampleestimatesof these means determinedfrom cross
classification.Table 7 shows the segment means, the
standarderrorof these means, the regressionestimates
of segmentmeans, and the samplesizes cross-classified
into each segmentfor an analysisof educationand income on beerpurchases.
The differencesin the segmentmeans are again substantial.The mean consumptionrate for householdsin
whichthe husbandis a high-schoolgraduateand has an
annualincomebetween$8,000 and$10,000 is fivetimes
as great as for householdswith the same educationand
less than $3,000 income. Furthermore,this group of
householdsbuys, on average, about 50 percent more
beer than householdswith the same income but a college-graduatehead.
In general, the regressionestimates are fairly good.
In a few instancesthey are substantiallyinaccurate,but
the largererrorstend to occur in those segmentswith
a small sample.Fortunately,these segmentsare a small
part of the populationand representa small proportion
of the potential market. In developing segmentation
strategy, regression analysis should be performedon
severalvariablesto determinewhichtwo or threeare the
more powerful.Estimatesof segment means can then
be made from cross classification of the smaller number
of variables. In practical terms these two or three variables will be sufficient to permit discrimination among
groups.
To study the overlap of the effectiveness of the seg-
mentationstructuresuggestedby Table 7, a weighted
regressionwas performedin which the segment mean
is the dependentvariableand educationand family in-
GROUPVERSUSINDIVIDUAL
BEHAVIOR
MARKET
SEGMENTATION:
269
Table 6
CONDITIONAL
PROBABILITIES
OF PURCHASES
OF FOURPRODUCTS
BY SELECTED
CHARACTERISTICS
Variable
Mean
Standard
error
3 or more
.16
.30
.30
.42
.39
.54
1.76
2.45
2.47
3.06
2.95
3.36
.06
.12
.11
.13
.17
.19
5 or more
.07
.09
.12
.11
.23
1.18
1.37
1.65
1.83
2.87
.13
.15
.13
.20
.27
3 or more
.16
.28
.36
.36
.37
.57
1.77
2.36
2.65
2.68
2.79
3.35
.07
.13
.12
.14
.19
.19
25 or more
.24
.35
.33
.27
.13
19.52
27.05
25.50
21.80
12.89
2.05
1.50
1.10
1.31
1.53
Conditional probability
Category
Catsup
Children
Education
Children
0
1
2
3
4
5 or more
0
.20
.11
.13
.04
.05
.08
1
.32
.23
.17
.12
.14
.09
2
.32
.36
.40
.42
.43
.29
6 yrs.
10
12
14
16
0
.67
.62
.61
.51
.40
Frozen orangejuice
1
2-4
.12
.14
.16
.13
.13
.14
.16
.22
.17
.20
0
1
2
3
4
5 or more
0
.15
.06
.05
.04
.01
.04
Toothpaste
1
2
.33
.35
.26
.40
.20
.39
.19
.41
.19
.44
.08
.31
18-24
25-34
35-49
50-64
65 or over
0
.45
.33
.36
.42
.61
Beer
Not married
or agea
a
1-18
.15
.08
.09
.07
.04
19-24
.17
.24
.22
.23
.22
Of head of household.
come are independentvariables.In this regression,R
is equal to .65, and the regressioncoefficientsfor both
variables are substantiallylarger than their standard
deviations.Thus when the noise is eliminatedfrom the
data, it is even more obvious that variableseffectively
discriminatebetween groups with differentmean purchasing rates.
MARKET POTENTIAL AND MEDIA
SELECTION
In allocatingmarketingeffortto marketsegments,the
size and the mean usage rate of the segment must be
considered.Other thingsbeing equal, a mediumwith a
higherproportionof its audiencein those segmentswith
a largemarketpotentialrelativeto the numberof households would be more valuable than a medium with a
smaller proportionof its audience in these segments.
If Wmiis the relativesize of segmenti in the total audience, Am, of mediumm, then
X, =
Z wmixiis the meanusagerate of medium
m and,
AmXm is the marketpotentialof mediumm in units of
the product. Media may then be ranked by order of
cost per unit of marketpotentialwhichthey deliver.
An interestingarea for future researchwould be a
comparisonof the mean usage rates for the audience
of differentmedia with the mean usage rates to be expectedfrom the audiencecompositionin socioeconomic
segments.This comparisonwould provide a basis for
evaluationof the media effect.
SUMMARY AND CONCLUSION
Althoughthere is not yet a satisfactorytheoryto explain variationsin usage rates of individualsof grocery
products,this deficiencydoes not imply that a strategy
of market segmentationis infeasible. The inability of
socioeconomicvariables to explain a substantialpart
270
JOURNALOF MARKETING
AUGUST1968
RESEARCH,
Table 7
CROSS-CLASSIFICATION
ANALYSISOF BEERPURCHASE
BY INCOME
OF 1,400 HOUSEHOLDS,
EDUCATION
Annual
family
income
Years of education
6a
10
12
14
16
~a 10.01
1.64
a
Xi 12.18
n 124
6.53
2.38
9.03
36
6.18
1.74
5.78
38
12.27
7.78
4.67
7
15.21
13.17
0.00
4
Xa 27.74
3.93
Xi 23.33
n 48
20.27
4.03
17.12
38
11.70
2.98
16.93
45
17.40
9.51
15.82
14
1.79
1.65
8.25
7
$5,0007,999
25.23
ax 2.62
Xe 31.81
n 115
26.03
2.45
28.66
122
22.63
1.85
25.41
196
24.27
3.48
24.30
57
16.80
3.85
18.73
35
$8,0009,999
Xa 27.72
ax 4.62
X, 37.00
n 30
24.21
3.38
33.85
56
32.14
2.13
31.40
88
21.78
4.41
29.49
32
23.23
4.93
23.82
30
$10,00014,999
Xa 34.24
6.47
ar
X, 33.79
n 15
24.05
4.51
30.64
37
21.54
3.07
27.30
61
20.63
3.92
26.28
45
24.18
3.78
20.71
50
$15,000
and over
X, 36.58
ax 10.68
X, 34.85
7
n
12.50
0.00
31.70
1
28.49
6.93
28.45
15
34.17
8.52
27.34
10
17.86
3.80
21.75
37
Under
$3,000
$3,0004,999
SXa.
Si
Xa
is the segment mean estimated by cross classification,
ax is the standard error of the mean,
X, is the regression estimate of the segment mean,
n is the sample size in the segment.
of the varianceof usage rates of personsdoes not necessarilyimply that there are not substantialdifferences
in the mean usage rates for different socioeconomic
marketsegments.Differencesin meanusageratesamong
segmentsis sufficientconditionfor the developmentof
a strategyof marketsegmentation.
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the Prediction of Brand Choice: Ford versus Chevrolet,"
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