FRANK M. BASS, DOUGLAS J. TIGERT, and RONALD T. LONSDALE*
The argument that socioeconomic variables do not provide an adequate basis
for market segmentation of grocery products is disputed. A theoretical framework for segmentation measurement in terms of group behavior is developed and
applied to survey data.
Market Segmentation: Group Versus
Individual Behavior
[
J , /
be that regression analysis—or some variant of regression such as discriminant analysis—of the quantity of
grocery products purchased by individual families yields
low R-'s when the independent variables are socioeconomic. Thus it would appear that these variables explain
only a small part of the variance in purchasing rates by
individual families. To illustrate, in the study by Frank,
Massy, and Boyd [5] using Chicago Tribune panel data,
quantities of 57 grocery products purchased by individual families were analyzed in relation to 14 socioeconomic variables. The highest R^ obtained for any product category was .29, and about 50 percent of the
regressions produced R^'s of less than .10.
There is evidence that the addition of psychological
and sociological variables increases the "explained"
variance somewhat [10, 13] but, even so, the unexplained variance remains high. The very low goodness of
fit in cross-sectional studies of grocery products is not
surprising, particularly considering the results of crosssectional studies of consumer expenditures reported in
economics journals. In a survey article in 1962, Ferber
[3] indicated that in expenditure studies covering a wide
range of products, including durables, the proportion of
variance in individual household expenditures explained
by socioeconomic variables is small, frequently less than
.3. An extensive collection of cross-sectional expenditure
studies is included in Consumption and Savings [6]. The
evidence is overwhelming that R^ is low when individual
household purchase rates are related to socioeconomic
variables.
The intuitive conclusion, perhaps, suggested by the
evidence is that market segmentation based on socioeconomic measurements is infeasible. This is the conclusion
of Twedt [14], Frank [4] and others. Frank concludes,
"Based on the research reported in the preceding sections for the most part socioeconomic characteristics are
This article is about market segmentation. Although
most of this article is about the analysis of data and the
measurement of market segments, it also focuses on
those aspects of measurement that have a practical bearing on the managerial strategy of market segmentation.
Though there are several possible bases for application
of the selectivity principle in marketing [11], the specific
concern here is the allocation of marketing effort, particularly advertising, to selective groups of potential
customers. In current marketing management practice,
there is probably no problem area of greater practical
consequence than the question of how to define market
segments. Despite extensive research in market segmentation, there is some uncertainty about the practical
feasibility of defining market segments by socioeconomic
measurements [15]. The authors will argue that such
measurements are operationally feasible and demonstrati^ with empirical data.
SEGMENTATION STUDIES
Frank [4] reviewed the research literature on economic, demographic, personality, and purchasing characteristics as bases for segmenting the market for grocery
products. His synthesis of the literature has yielded
highly significant conclusions. In several cases reviewed
by Frank, the almost universal conclusion appears to
* 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 research manager, 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 mimerous
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: GROUP VERSUS INDIVIDUAL BEHAVIOR
not particularly effective bases for segmentation either
in terms of their association with household differences
in average purchase rate or response to promotion."
"Twedt said, "the heavy users usually account for 7-10
: times the volume of light users. They buy more often
and they buy more different brands. Furthermore, the
heavy user is not readily identifiable in terms of any
other characteristics."
Though we agree that multiple regression involving
socioeconomic variables and quantities purchased by
individual households of grocery products results in a
low proportion of explained variance, we disagree with
the conclusion that socioeconomic variables do not pro, vide measurements that can be effectively applied in a
strategy of 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 measurement and
market segmentation will be examined.
MEASUREMENT THEORY AND MARKET
SEGMENTATION
The two most important ideas to consider in market
segmentation are the following fundamental propositions: (1) market segmentation is a management strategy
and (2) implementation of the strategy of market segmentation involves postulates about the characteristics
and the behavior of groups, not persons. Smith, in his
pioneering article [11], expressly included these propositions in his definition of market segmentation. Smith's
definition was "market segmentation consists of viewing
a heterogeneous market as a number of small homogeneous markets in response to differing product preferences
among important market segments. It is attributable to
the desires of consumers or users for more precise satisfaction of their varying wants. Segmentation often involves the use of advertising and promotion. It is a
merchandising strategy" [11]. Much of the confusion
about market segmentation, we believe, stems from
failure to recognize the two fundamental propositions.
The absence of a satisfactory theory of individual behavior does not necessarily imply the absence of valid
propositions about the groups' behavior. For marketing
strategy, it is the behavior of groups, not persons, that
is primarily important.
A hypothetical example illustrates the basic structure
of the measurement problem in market segmentation as
Table 1
PROBABILITY OF LOW, MEDIUM, AND HIGH USAGE RATES
Usage rates
Low (amount purchased = xi)
Medium (amount purchased =
High (amount purchased = x{)
Probability
Pi
Pi
Pi
265
it applies to quantities purchased or usage rates. The
analysis of segmentation considered here is restricted to
usage rates although the principles generally apply
equally well to other segmentation measurements like
promotional elasticity and brand loyalty.
Table 1 shows the probabilities or proportions of
families purchasing, on average, different quantities of
some product. For market segmentation, the essential
question is whether it is possible to identify groups of
consumers with different mean purchase rates dependent
on certain variables, such as income, age, and occupation.
Table 2 shows the conditional probabilities of purchasing different quantities of a product given some
measurement on a variable. If in each row of the matrix
there is one number that is near one, maybe .8 or greater,
it will be possible to predict fairly accurately the usage
rates for individual consumers. The evidence from the
regression and discriminant studies clearly indicates
that it is impossible to predict usage rates very well on
an individual basis with socioeconomic variables. For
purposes of market segmentation, however, it is sufficient
that the variables yield large differences in mean purchase rates. Thus in Table 2, if E(x \ Z«) > E(x \ Zi), it
may be possible to segment the market with this information. Even though the within-group variance is
great, the fact that the choice is between groups not
persons permits segmentation by group means.
The regression models applied in several studies of
market segmentation have tended to focus on individual
behavior, resulting in misleading conclusions. The propriety of the linearity assumption and noncontinuous
observations on the dependent and independent variables bring into question the meaning of the regression
results. This was essentially the point which Kuehn
[8] made in his debate with Evans [1, 2]. Kuehn argued
that simple cross-classification analysis of a single variable with the probability of ownership was more revealing than discriminant analysis.
It is not suggested that regression models are necessarily inappropriate for analysis of market segments.
However, the results of these models should be interpreted in terms of group means. In succeeding sections of this article, these ideas will be illustrated with
an analysis of survey data.
DATA SOURCE, MEASUREMENTS,
PRODUCTS STUDIED
AND
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 wafiEle mix, candy bars.
JOURNAL OF MARKETING RESEARCH, AUGUST 1968
266
cake mix, beer, cream shampoo, hair spray, toothpaste,
mouthwash or oral antiseptic. For the first six products,
consumers were asked to state the quantities purchased
in the past 30 days, but for the remaining four products
the purchase period covered was 60 days.^
The socioeconomic variables analyzed and the measurement categories are:
Age of male head
No man in household
18-24
25-34
35-49
50-64
65 and over
Family income
Under $3,000
$3,000 -$4,999
$5,000 -$7,999
$8,000 -$9,999
$10,00O-$14,999
$15,000 and over
Occupation of head
All other
Sales
Clerical
Professional
Manager
REGRESSION
Number of children
under 18 years
0
1
2
3
4
5
6 or more
Education of head
Grade school or less
1-3 years of high school
Graduated high school
1-3 years college
Graduated college
Television viewing
yesterday, by head
0
0-13^ hours
l>S-3}4 hours
Over 3}^ hours
ANALYSIS
The relationship between purchase rates and socioeconomic variables is analyzed by multiple regression,
among other statistical techniques. To avoid the assumptions of linearity and continuity, dummy variables
are used for the socioeconomic measurements [7]. Thus,
Xijh is the value for the kth household on the fth discrete variable of the ith discrete classification and
Xijic is one if the household is in the jth class, otherwise zero.
Therefore, least-squares regression provides estimates of
cell means for a multiple cross-classification table using
all of the socioeconomic variables.
If there are A^ classes of the ith discrete classification
and if a household is not in the first N — 1 oi 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),
'• Although there is some question of the reliability with which
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.
Table 2
CONDITIONAL PROBABILITY OF LOW, MEDIUM, AND HIGH
USAGE RATES GIVEN MEASUREMENT Z
Variable
measurements
Low
Medium
High
used to obtain the regression estimates, performs the
multiple-regression calculations on all of the variables
and then in a stepwise way deletes nonsignificant variables by a fixed F ratio or a fixed probability level. For
the present analysis the probability was chosen such that
all variables are significant at the .10 level.
The R2 values are uniformly low—also true of previous studies—but the essential point is that all of the
variables included in the final stage are significant by
definition. The low R^ values imply only that the variance within cells is great, not that the relationships are
weak.
Table 3 shows the number of classifications for each
variable and each product included in the final stage of
the regression. Every variable appears in the final stage
for some group of products. Number of children is included in nine of ten product categories, and age and income are included in eight of the ten. Occupation appears to be the weakest measurement since it is included
only three times, and then with only one classification.
The crucial test of whether it is possible to segment
the market by socioeconomic measurements is whether
segments can be described by socioeconomic measurements with widely varying mean purchase rates. For
such a test, final stage regression estimates were used to
estimate mean purchase rates for different segments by
summing the appropriate regression coefficients using
the vector description of socioeconomic measures for
groups of households comprising that segment. Table 4
summarizes these estimates for various groups of segments, collected into light-buyer and heavy-buyer categories. The mean consumption rate ranges (see Table 4)
have been computed from the final stage regression estimates by summing the regression coefficients included
in the segment description and then summing over variables not included. It is clear from the range of variation
of mean purchase rates between the segments that socioeconomic measurements provide a meaningful basis for
segmentation. For example, married households whose
heads are over 50 with no children under 18; the husbands are college graduates, watch television less than
one and a half hours a day, have an estimated mean purchase rate of .74 bottles a month for catsup. However,
if heads are between 35 and 49, are not high-school
MARKET SEGMENTATION: GROUP VERSUS INDIVIDUAL BEHAVIOR
267
Table 3
CLASSIFICATIONS FOR EACH VARIABLE FOR EACH PRODUCT IN THE FINAL REGRESSION 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
2
2
0
6
3
4
6
6
1
2
2
4
0
0
2
1
2
0
0
4
2
1
0
2
0
1
1
2
4
2
0
4
4
4
2
0
2
4
5
4
2
3
1
graduates, have five children, and watch television between one and a half and three and a half hours per day;
the households have an estimated mean purchase rate
of 5.78 bottles per month for catsup, almost eight times
as much.
The fact that the R^ values are low implies only that
the variance within segments is great, not necessarily
that the differences in mean values between segments are
not significant. Of course, even for large samples such as
this one, sample measurements are small enough to raise
questions about the reliability of estimates for cells
sparsely represented in the sample. Later, the reliability
1
0
1
0
1
0
0
0
0
.081
.072
.037
.080
.082
.092
.017
.058
.093
.032
0
1
3
3
0
1
1
1
of regression estimates will be examined in greater detail, and the possible application of regression measures
of mean purchase rates to media analysis will be explored.
SIMPLE CROSS-CLASSIFICATION
ANALYSIS
For greater focus on the variation in group behavior
associated with socioeconomic characteristics, contingency tables were developed for each of the six socioeconomic variables with each of the ten products. This
analysis thus provides a basis for measuring the ability
Table 4
LIGHT AND HEAVY BUYERS BY MEAN PURCHASE RATES FOR DIFFERENT SOCIOECONOMIC CELLS
Mean consumption
rate ranges
Heavy
buyers
Ratio of
highest
to
lowest
rate
.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
Description
Product
Catsup
Frozen orange
juice
Pancake mix
Candy bars
Cake mix
Beer
Cream shampoo
Hair spray
Toothpaste
Mouthwash
Light buyers
Heavy buyers
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
Under 35, no children
Not married or under 35,
no children, income under
$10,000, T.V. less than 334
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 33^
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.
Light
buyers
0-12.33 17.26^W.3O
.16-35
.44-.87
5.5
0-.4I
.52-1.68
00
1.41-2.01
2.22-4.39
3.1
.46-.85
.98-1.17
2.5
JOURNAL OF MARKETING RESEARCH, AUGUST 1968
268
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
Product
Age
Catsup
Frozen orange
juice
Pancake or waffle
mix
Candy bars
Cake mix
Beer
Cream shampoo
Hair spray
Toothpaste
Mouthwash
S
T.V.
Chil- Income Educa- Occti- viewdren
tion pation ing
S
S
S
S
S
NS
S
S
S
s
s
s
s
s
S
NS
s
s
s
s
s
s
s
s
s
NS
NS
S
S
s
s
s
NS
s
s
NS
NS
S
s
s
S
t/5
/
SIGNIFICANT (S) AND NONSIGNIFICANT (NS) C O N TINGENCY TABLES OF AMOUNTS PURCHASED FOR
EACH PRODUCT AND SOCIOECONOMIC MEASUREMENTS
t/5
CROSS CLASSIFICATION WITH
VARIABLE STACKING
Table 5
t/5
of each variable singly to discriminate among group patterns of behavior for quantity purchased. Chi-square
values have been calculated for each contingency table.
The tables significant at the .05 level or less are listed in
Table 5. Again socioeconomic measures effectively discriminate between patterns of group behavior. Number
of children is significant for all ten products; age and income are significant for eight of ten products.
Although cross-classification analysis of a single independent variable with the dependent variable has the
disadvantage relative to regression and other multivariate techniques of failing to measure the joint effects
of several variables, it helps greatly in demonstrating
the nature of the variation. In principle, of course, multiple cross classification is possible and desirable; but as
the number of variables increases, the number of cells
becomes large enough to overwhelm even vary large
samples. However, several multiple cross-classification
analyses (cross classification with variable stacking)
were performed, one of which is reported later.
It has been contended that the low R^'s obtained in
regression analysis have led to false conclusions about
the ability of socioeconomic variables to segment the
market since R^ is a measure of the model's ability to
predict individual rather than group behavior. In addifXion, it has been argued that the relationships may be
I nonlinear. These points are demonstrated by conditional
probability matrices of the character shown in Table 2.
Development of these tables should be the first step in
any segmentation study. To conserve space, only 4 of
42 significant tables for the ten products are presented
here. Table 6 illustrates conditional probabilities, means,
and standard errors. These conditional probabilities indicate clearly the strength of the socioeconomic variables. For example, the probability that a household
I with five or more children will buy three or more botdes
I of catsup in a month is more than three times as great
I as the corresponding conditional probability for a house; hold with no children. There is a similar difference in
the probabilities for toothpaste. The probability that a
household in which the head is a college graduate will
buy five or more cans of frozen orange juice in a month
is about twice the probability for a household in which
the head is a high school graduate.
The nonlinearity of beer consumption relative to age
is also demonstrated in Table 6. When the analysis is
extended to include two or more independent variables,
the differences in conditional probabilities and means
are further increased.
NS
NS
NS
NS
NS
NS
S
NS
NS
S
NS
NS
S
s
NS
only two variables, similar to the analyses with several
variables discussed previously. This permits comparison
of regression estimates of segment means with the
sample estimates of these means determined from cross
classification. Table 7 shows the segment means, the
standard error of these means, the regression estimates
of segment means, and the sample sizes cross-classified
into each segment for an analysis of education and income on beer purchases.
The differences in the segment means are again substantial. The mean consumption rate for households in
which the husband is a high-school graduate and has an
annual income between $8,000 and $10,000 is five times
as great as for households with the same education and
less than $3,000 income. Furthermore, this group of
households buys, on average, about 50 percent more
beer than households with the same income but a college-graduate head.
In general, the regression estimates are fairly good.
In a few instances they are substantially inaccurate, but
the larger errors tend to occur in those segments with
a small sample. Fortunately, these segments are a small
part of the population and represent a small proportion
of the potential market. In developing segmentation
strategy, regression analysis should be performed on
several variables to determine which two or three are the
more powerful. Estimates of 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 segmentation structure suggested by Table 7, a weighted
regression was performed in which the segment mean
is the dependent variable and education and family in-
MARKET SEGMENTATION: GROUP VERSUS INDIVIDUAL BEHAVIOR
269
Table 6
CONDITIONAL PROBABILITIES OF PURCHASES OF FOUR PRODUCTS BY SELECTED CHARACTERISTICS
Variable
Category
Conditional probability
Mean
Standard
error
1.76
2.45
2.47
3.06
2.95
3.36
.06
.12
.11
.13
.17
.19
1.18
1.37
1.65
1.83
2.87
.13
.15
.13
.20
.27
1.77
2.36
2.65
2.68
2.79
3.35
.07
.13
.12
.14
.19
.19
19.52
27.05
25.50
21.80
12.89
2.05
1.50
1.10
1.31
1.53
1Catsup
0
1
2
3 or more
5 or more
.20
.11
.13
.04
.05
.08
.32
.23
.17
.12
.14
.09
.32
.36
.40
.42
.43
.29
.16
.30
.30
.42
.39
.54
6 yrs.
0
.67
1
.12
.62
.16
.61
.51
.40
.13
.16
.17
5 or more
0
.15
.06
.05
.04
.01
.04
1
.33
.26
.20
.19
.19
.08
18-24
25-34
35-49
50-64
65 or over
0
.45
.33
.36
.42
.61
Children
0
1
2
3
4
Frozen• orange juice
Education
10
12
14
16
5 or more
2-4
.14
.13
.14
.22
.20
.07
.09
.12
.11
.23
Toothpaste
Children
0
1
2
3
4
2
.35
.40
.39
.41
.44
.31
3 or more
7-75
19-24
25 or more
.15
.08
.09
.07
.04
.17
.24
.22
.23
.22
.24
.35
.33
.27
.13
.16
.28
.36
.36
.37
.57
Beer
Not married
or age"
» Of head of household.
come are independent variables. In this regression, R
is equal to .65, and the regression coeffieients for both
variables are substantially larger than their standard
deviations. Thus when the noise is eliminated from the
data, it is even more obvious that variables effectively
discriminate between groups with different mean purchasing rates.
MARKET POTENTIAL AND MEDIA
SELECTION
In allocating marketing effort to market segments, the
size~and the mean usage rate of~IEe segment must b£
^considered. Other things being equal, a medium with a
higher proportion of its audience in those segments with
a large market potential relative to the number of households would be more valuable than a medium with a
smaller proportion of its audience in these segments.
If W,ni is the relative size of segment / in the total audience. Am ,oi medium m, then
= ^
WmiXi is the mean usage rate of medium
i
m and,
is the market potential of medium m in units of
the product. Media may then be ranked by order of
cost per unit of market potential which they deliver.
An interesting area for future research would be a
comparison of the mean usage rates for the audience
of different media with the mean usage rates to be expected from the audience composition in socioeconomic
segments. This comparison would provide a basis for
evaluation of the media effect.
SUMMARY AND CONCLUSION
Although there is not yet a satisfactory theory to explain variations in usage rates of individuals of grocery
products, this deficiency does not imply that a strategy
of market segmentation is infeasible. The inability of
socioeconomic variables to explain a substantial part
270
JOURNAL OF MARKETING RESEARCH, AUGUST 1968
Table 7
CROSS-CLASSIFICATION ANALYSIS OF BEER PURCHASE
OF 1.400 HOUSEHOLDS, EDUCATION BY INCOME
REFERENCES
Years of education
Annual
famity
income
10
12
14
16
X. 10.01
^x
1.64
X. 12.18
rt 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
21.lA
3.93
23.33
48
20.27
4.03
Yl.Yl
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
Xa 25.23
£^
2.62
X. 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
X« 27.72
£x 4.62
X. 37.00
n 30
24.21
3.38
33.85
56
32.14
2.13
31.40
21.78
4.41
29.49
32
23.23
4.93
23.82
30
$10,00014,999
^a
«
X.
n
34.24
6.47
33.79
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
Xa 36.58
crj? 10.68
X, 34.85
n
7
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
segments is sufficient condition for the development of
a strategy of market segmentation.
A.
3.
4.
$3,0004,999
X,
n
» Xa is the
ax is the
Xt is the
« is the
segment mean estimated by cross classification,
standard error of the mean,
regression estimate of the segment mean,
sample size in the segment.
9.
10.
11.
13.
14.
/ of the variance of usage rates of persons does not necessarily imply that there are not substantial differences
in the mean usage rates for different socioeconomic
market segments. Differences in mean usage rates among
15.
Franklin B. Evans, "Psychological and Objective Factors in
the Prediction of Brand Choice: Ford versus Chevrolet,"
Journal of Business, 32 (October 1959), 340-69.
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