An executive summary for
managers and executive
readers can be found at the
end of this article
Segmenting customer brand
preference: demographic or
psychographic
Chin-Feng Lin
Associate Professor, Department of Business Administration, National
Chin-Yi Institute of Technology, Taiwan
Keywords Brands, Market segmentation, Demographics, Psychographics
Abstract A multi-segmenting methodology is proposed for comparing the segmenting
capabilities of segmentation variables and providing complete market segmentation
information. Demographic and psychographic variables based on the differentiation of
consumer brand preference were used to elicit the characteristics of market segments. In
a comparative evaluation, the multi-combination variables of demographic segmentation
exhibited market-segmenting capabilities equivalent to those of psychographic
segmentation. The purpose of this research is utilizing multiple segmentation variables to
identify smaller, better-defined target sub-markets for enhancing business competitive
advantages.
Taxonomy of consumption
patterns
Concept of market
segments
Introduction
The purpose of market segmentation is to identify the taxonomy of
consumption patterns by dividing a market into several homogeneous submarkets. Marketers can formulate product strategies, or product positions,
tailored specifically to the demands of these homogeneous sub-markets.
Homogeneous sub-markets are defined by predetermined segmentation
variables. Traditional demographic variables, such as gender, age, income,
and education, can be used to explain the characteristics of the sub-markets
and classify the key factors of a market segment. Traditional demographic
variables, however, cannot identify the complete characteristics of the submarkets because consumers in the same demographic group have very
different psychographic makeups (Kotler and Armstrong, 1999). Based on
the differentiation of consumer's brand preference, this study divides
consumers into homogeneous groups using psychographic variables through
the classification in VALS2 (values and lifestyles) and LOV (list of values)
systems and demographic variables and then compares the relative
usefulness these two different segmentation variables to marketers.
Literature review
Smith (1956) first introduced the concept of market segments, which has
become an integral part of modern marketing. A market segment is a group
within a market that is clearly identifiable based on certain criteria.
Consumers within such a sub-market are assumed to be quite similar in their
needs, characteristics and behaviors.
Pride and Ferrell (1983) devised the market segmentation process of dividing
a market into several market groups. Consumers in each market segment
have similar product needs. Each segment requires a different mix of
marketing strategies to satisfy its special consumer needs. McCarthy (1981)
explained that the purpose of dividing a market into several homogeneous
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249
markets is so that marketers can aim to satisfy the specific needs of any
target market. The idea of designing marketing strategies for market
segments is based on consumers' wants and interests. The purpose of market
segmentation is two-fold: to divide a market into several homogeneous submarkets and to formulate a proper marketing-mix strategy for the sub-market
(McCarthy, 1981).
Effective segmentation
techniques
Market segmentation variables
In a multi-dimensional market, companies can increase profitability by
utilizing market segmentation. An effective market segmentation technique
depends on selecting the relevant segmenting bases and descriptors (Wind,
1978). Segmentation variables must be considered in light of their
measurability, availability, reliability and ability to uncover the
characteristics of each market segment. Researchers (Becker et al., 1985;
Becker and Conner, 1981) have tried to divide consumer markets by looking
at a consumer's ``personality''. Jain (1993) analyzed markets through social,
economic, and special segmentation variables such as brand loyalty and
consumer attitude. Kotler (1997) has proposed that consumer markets should
be divided according to geographic, demographic, psychographic, and
behavioral variables.
In psychographic segmentation, consumers are divided into different groups
on the basis of lifestyle and personality (Kotler, 1997). Customers within the
same demographic group can exhibit very different psychographic profiles.
Therefore, enterprises making different consumer goods can seek marketing
opportunities in lifestyle/personality segmentation (Kim, 1993; Lee and
Ferber, 1977).
Lifestyle and persoanlity
Statistical analysis
Products are the building blocks of lifestyles (Solomon, 1999). Customers
define their lifestyles by the consumption choices they make in a variety of
product categories. Lifestyle can therefore be defined quantitatively and used
as a group identity for market segmentation. In addition, the brand
characteristics with which marketers endow their products correspond to
consumer personalities. These inferences about a product's characteristics
are an important part of brand equity, which refers to the extent to which a
consumer holds strong, favorable, and unique associations with a brand in
memory (Keller, 1993). Thus, lifestyle and personality variables are effective
segmentation variables for identifying sub-market profiles and targeting
consumers.
Brand preference and segmentation
Markets may be effectively segmented through statistical analysis of brand
preference and selection (Henderson et al., 1998). Single brand preference
can be regarded as a measure of loyalty, which also provides valuable
information for customer management and market segmentation (Gralpois,
1998). Market classification can be obtained by using the Logit regression
(Guadagni and Little, 1983). Several researchers (Bucklin et al., 1998), using
the decision variables of consumers' brand preference, utilized a joint
estimation approach to identifying sub-markets. Consumer values give
marketers a direction on how best to satisfy their customer needs and
increase brand preference (Chudy and Sant, 1993).
Personal value system
Personal value or characteristic classifications in LOV, VALS2 and RVS are
often used to develop effective marketing strategies. Below are descriptions
of the three taxonomic models.
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JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
Associated pattern
technique
Self-orientation and
resources
(1) Researchers (Rallapalli et al., 2000) used a scale developed by Kahle
(1986) called ``list of values'' (LOV) to discuss marketers' norms.
Results from canonical correlation analysis generally indicated that
marketers' norms could be partly explained by personal values. Hofstede
and Steenkamp (1999) developed an integrated methodology called
association pattern technique (APT) based on consumer means-end
chains to identify segments in international markets. The means-end
methodology also utilized LOV to analyze consumers' value. The value
variables used in APT are those from the LOV inventory (Kahle, 1986);
that is, several researchers think nine ``value'' items in LOV are suitable
as segmentation variables. LOV classified the terminal values of what
Americans pursue into eight groups:
.
self-respect;
.
security;
.
warm relationships with others;
.
sense of accomplishment;
.
self-fulfillment;
.
being well respected;
.
sense of belonging; and
.
enjoyment in life (Schiffman and Kanuk, 1994).
(2) Several research firms have developed lifestyle classification systems.
The most widely used is SRI consulting's values and lifestyles (VALS2)
typology (Kotler, 1997; Loudon and Della Bitta, 1993; Kotler and
Armstrong, 1999). VALS2 is a psychographic system that links
demographics and purchase patterns with psychological attitudes. Using
this technique, the American market was classified into eight categories
(Loudon and Della Bitta, 1993). This classification considers the time
and money consumers spend. Self-orientation and resources are used as
the basis for the vertical-horizontal axes to classify consumers into eight
groups:
.
fulfilled;
.
believers;
.
achievers;
.
strivers;
.
experiencers;
.
makers;
.
actualizers; and
.
strugglers.
Winters (1992) followed the psychographic classifications of VALS to
divide Japanese consumers into five dimensions and ten classes:
.
exploration (integrators, sustainers);
.
self-expression (self-innovators, self-adapters);
.
achievement (Ryoshiki ``social intelligence'' innovators, Ryoshiki
adapters);
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
251
Personal values
.
tradition (tradition innovators, tradition adapters); and
.
realist orientation (high pragmatics, low pragmatics).
(3) Marketers need to identify segmentation variables based on demographics, lifestyles, and values. Personal values can be an important
basis for segmentation because values differ due to age, income,
education, gender and social class (Rokeach, 1973). A popular
methodology, Rokeach Value Survey (RVS), consists of 18 terminal
values and 18 instrumental values. Prakash's (1986) discussion on
women's segmentation by value structure is mainly based on the RVS
system (Bartos, 1977, 1978; Rokeach, 1973; Coleman, 1983).
Research method
Research variables
VALS2 and LOV are used as the theoretical bases in this study. The
psychographic description variables in the questionnaire were derived from
VALS2 and LOV inventories. The researcher focused on nine product
categories and collected 67 well-known brands in the market.
Three categories of
questions
The questions in the questionnaire fell into three categories. The first
category concerned respondents' degree of agreement (scale: 1-5 on the
Likert scale). Tables I and II list partial questionnaire variables. A total of 35
and 32 items were selected from VALS2 and LOV measurements,
respectively. The second category concerned gender, age, education and
monthly family income of the respondents. The third category concerned
Groups
Actualizers
Fulfilleds
Believers
Achievers
Psychographic
categories of VALS2
Category
Interested in growth and Personality
seek to develop, explore
in a variety ways
Personality
Well informed about
world and national
events
Looks for functionality,
value and durability in
the products they buy
Personality
Has same preference
and like American
products/established
brands
Conservative and
predictable
Follows established
routines
Values stability over
risk
Lives conventional life
Respects authority
Accepts the status quo
Consumption
Consumption
Consumption
Consumption
Personal value
Life style
Personality
Personality
Personal value
Personality
Descriptive sentences
I am enthusiastic about
seeking growth
I often seek to develop
and explore in my life
I am concerned about
national events
I spend a constant
amount of money every
month
I will consider product
value when I buy it
I usually buy wellknown brands
I will think things over
before I buy a product
I am a frugal person
I like a routine life
I do not like to take
risks
I live a conventional life
I respect authority
I usually accept the
status quo
Table I. Variables based on VALS2 system
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Groups
Security
Psychographic
categories of LOV
Endorsed by people who Personal value
lack economic security
Warm
relationship
with other
Experiences nightmares
but has good social
support networks and
families
Has a lot of friends and
is friendly
Cares about others
Sense of
Does not like to watch
accomplishment TV
Likes conspicuous
consumption
Self-fulfillment
Category
Likes the movies more
than TV
Well fulfilled
economically,
educationally, and
emotionally
Life style
Descriptive sentences
I feel secure because of
my current economic
situation
My work-emotion will
not affect my family
Personality
I have a lot of friends
Personality
Life style
I often care about others
I am influenced by TV
Consumption
I like to buy something
that can express my
status
I like to watch movie
more than TV
I can usually achieve
my goals
Personality
Personality
Personality
Personality
I am fulfilled
economically
I am emotional
Table II. Variables based on LOV system
consumer brand preference. Respondents could choose one, two or three
brands they used most from each product category.
Personal interviews
Sampling and statistical methods
Personal interviews based on the questionnaire were conducted to collect the
data for the study. A total of 1,000 questionnaires were completed but
because 293 were eliminated for missing values, 707 questionnaires were
used for the analysis.
The researcher used mean analysis, analysis of variables (ANOVA), factor
analysis and cluster analysis to explore the capability of segmenting
variables. Factor analysis in this research was conducted using the Varimax
Method. K-means of Nonhierarchical Method was used in cluster analysis.
Consumer brand preference
Interviewers asked respondents to select one, two or three brands they used
most from each product category (with a total of nine product categories and
67 brands). According to respondents' selections, the researcher calculated
the preference rate for each brand listed in Table III.
Consumer preference rate
For example, Jif and Attack (laundry powder) are well-known brands in
Taiwan so consumer preference rate is higher than that of the others (see
Table III). Scott (toilet paper) and Darling (toothpaste) are two leading
brands, so the consumer preference rates are naturally higher than the others.
Lux (soap) uses the penetrative strategy to obtain higher preference. For
shampoo and shower gel, brand preference is not a significant factor because
of the high number of brands available. Clearly, the preference rate was
affected by brand image and brand volume. Therefore, to enhance the
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
253
Percent of usage
Preference ratio (%)
A. Shampoo (699)
1. Sassoon
2. Lux
3. Pert
4. Pantene
5. Sifone
6. Johnson's
7. Organics
8. Others
33.19
42.20
31.47
31.23
14.02
11.87
14.74
32.81
15.69
19.95
14.88
14.76
6.63
5.61
6.97
15.51
B. Laundry powder (695)
1. Jif
2. Attack
3 Amway
4. Lamboo
5. Others
63.60
65.47
16.47
26.76
23.34
32.51
33.46
8.42
13.68
11.93
C. Dishwashing liquid (700)
1. Paos
2. White Bear
3. Jif
4. Baigo
5. Shingi
6 Swililon
7. Others
50.43
41.83
36.86
10.19
22.06
5.89
26.72
26.00
21.56
19.00
5.25
11.37
3.04
13.77
D. Facial tissue (695)
1. Scott
2. Tender
3. May
4. Sunti
5.Yungli
6. Kanboo
7. Others
76.98
56.63
39.42
33.38
3.31
5.93
10.39
34.06
25.05
17.44
14.77
1.46
2.62
4.60
E. Toilet tissue (699)
1. Scott
2. Tender
3. May
4. Kleenex
5. Andante
6. Paichi
7. Others
73.25
41.77
36.71
32.90
15.45
11.40
11.16
32.90
18.76
16.49
14.78
6.94
5.12
5.01
F. Washing cream (684)
1. Musk
2. Phisoderm
3. Pond's
4. Hito
5. Biore
6. Samsara
7. Shiseido
8. Others
14.91
13.62
39.47
9.50
23.10
9.06
22.22
45.16
8.42
7.69
22.29
5.37
13.05
5.12
12.55
25.51
G. Shower gel (677)
1. Lux
2. Pon Pon
3. Snow White
42.31
39.14
14.24
23.13
21.39
7.78
(continued)
Table III. Product categories, brands and brand preference rates
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JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
Percent of usage
Preference ratio (%)
16.47
11.54
16.86
6.95
35.45
9.00
6.31
9.22
3.80
19.38
H. Toothpaste (701)
1. Darline
2. Whiteman
3. Colgate
4. Cleardent
5. Aquafresh
6. Smiling
7. Kolynos
8. Others
81.17
25.71
47.43
16.86
7.43
10.27
13.73
12.70
37.70
11.94
22.03
7.83
3.45
4.77
6.38
5.90
I. Soap (702)
1. Lux
2. Majestic
3. G.reen
4. Palmolive
5. Dove
6. Wanwan
7. Kao
8. Others
71.23
17.52
15.53
34.66
14.00
21.11
7.41
26.92
34.18
8.41
7.45
16.63
6.72
10.13
3.56
12.92
4.
5.
6.
7.
8.
Johnson's
Lu la la
Pink lady
Kao
Others
Notes: ``Percent of usage'' indicated the ratio that respondents had used the given
product; ``Preference ratio'' represents relative percentage of usage on each product
category; The figures in parentheses represent the valid sample size of each product
category
Table III.
competitive advantages of brand recognition, the market segments within
each product category should be analyzed.
Eight clusters
Factor-cluster relationships
Brand preference and segmenting variables
The factor analysis through the varimax method classified 67 variables (35
items from VALS2 and 32 items from LOV) into 17 factors when the cutoff
value was greater than 1 for the eigenvalue. The cumulative percent of
variance for factor analysis was 71.2 percent. In this research, all respondents
were classified into eight clusters. Applying discriminant analysis, a high
degree of ``clustering'' classification accuracy was obtained. The percent of
``clustering'' correctly classified (hit ratio) was 95.1 percent. Appendix 1 lists
the eight clusters and factors within each cluster. Appendix 2 represents cluster
indicators concerning consumer product ownership and demographics.
Table IV exhibits the factor-cluster relationships in terms of factor scores.
The figure with maximum factor score in each row of Table IV signifies that
respondents in the particular cluster have the characteristics belonging to the
factor. The largest factor score in the first row of Factor 1, for example, is
0.9, which means respondents in cluster 5 have the characteristic of
``enthusiasm'' (Factor 1). The minimum factor score in each row, by
contrast, signifies that the characteristics of cluster respondents are not
similar to the factor. Scheffe test was used to verify the cluster-cluster
relationships for each factor.
The researcher used four demographic variables, gender, age, education and
monthly family income, to obtain 11 combinations of market segmenting
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
255
256
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
0.09
±0.31
0.22
±0.10
6. Comfort
7. Free-living
8. Conservatism
9. Pessimism
±0.67
±0.48
0.53
±0.08
±0.13
0.48
±0.67
±0.55
0.59
±0.82
±0.26
±0.79
0.52
±0.38
0.02
0.58
±0.77
0.79
0.47
±0.43
±0.21
0.27
0.45
0.72
±0.11
0.20
0.39
±0.72
±0.43
±0.21
1.18
0.71
±0.39
0.42
±0.01
0.90
Cluster5
(47)
Table IV. Each cluster classification based on VALS2 and LOV system
±0.67
5. Independence
0.65
3. Home-life
±0.24
0.05
2. Solipsism
4. Well-known brand pursuer
0.26
Cluster1 Cluster2 Cluster3 Cluster4
(67)
(63)
(37)
(86)
1. Enthusiasm
Factor
±0.09
0.64
0.15
±0.41
±0.76
0.27
±0.18
0.42
±0.05
Cluster6
(108)
0.32
0.41
±0.65
0.13
0.28
±0.74
0.59
0.03
±0.56
Cluster7
(88)
0.49
±0.20
0.76
±0.59
0.47
±0.20
±0.60
±0.01
±0.94
Cluster8
(112)
24.075
18.569
29.611
25.980
33.876
23.691
28.767
9.947
50.553
1>3. 4>1. 4>2. 4>3. 4>5. 4>6. 6>2. 6>3. 6>5. 7>2.
7>3. 7>5. 8>1. 8>2. 8>3. 8>5. 8>6
(continued)
1>2. 1>4. 1>5. 6>2. 6>3. 6>4. 6>5. 6>8. 7>2. 7>4.
7>5. 7>8
2>1. 2>3. 2>4. 2>5. 2>7. 6>3. 6>7. 8>1. 8>3. 8>4.
8>5. 8>6. 8>7
1>8. 3>6. 3>8. 4>6. 4>8. 5>1. 5>2. 5>4. 5>6. 5>7.
5>8. 7>6. 7>8
2>6. 4>1. 4>2. 4>3. 4>6. 5>1. 5>2. 5>3. 5>6. 7>1.
7>3. 7>6. 8>1. 8>2. 8>3. 8>6
2>1. 2>5. 2>7. 2>8. 3>7. 4>1. 4>3. 4>5. 4>7. 4>8.
6>5. 6>7. 6>8. 8>7
1>2. 1>6. 1>8. 3>2. 3>4. 3>6. 3>8. 4>2. 4>8. 5>2.
5>6. 5>8. 7>2. 7>4. 7>6. 7>8
1>3. 4>2. 4>3. 6>2. 6>3. 7>3. 8>3
1>7. 1>8. 2>6. 2>7. 2>8. 3>6. 3>7. 3>8. 4>6. 4>7.
4>8. 5>1. 5>6. 5>7. 5>8. 6>7. 6>8
F value Scheffe test
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
257
±0.85
±0.15
0.64
0.08
1.04
±0.22
±0.30
11. Individualism
12. Anti-authority
13. Traditionalism
14. TV-dislike
15. Hedonism
16. Subjectivity
17. Self-confidence
Table IV.
0.29
±0.31
±0.42
0.10
0.28
0.48
0.28
0.21
0.17
±1.20
±0.13
±0.70
±1.26
0.29
±0.24
±1.16
±0.08
±0.10
0.01
0.35
±0.17
±0.01
±0.86
0.12
±0.04
Cluster1 Cluster2 Cluster3 Cluster4
(67)
(63)
(37)
(86)
10. Saving
Factor
±0.81
0.81
±0.07
0.40
±0.39
0.69
±0.06
0.81
Cluster5
(47)
0.44
±0.59
0.04
±0.35
±0.63
±0.32
0.22
0.11
Cluster6
(108)
0.03
0.36
±0.92
0.19
0.38
0.26
0.22
0.23
Cluster7
(88)
0.71
0.35
±0.00
0.36
±0.27
0.51
0.332
±0.83
Cluster8
(112)
34.698
18.762
34.956
17.799
20.213
25.563
21.940
21.595
1>3. 2>3. 4>3. 4>5. 6>1. 6>2. 6>3. 6>4. 6>5. 7>3.
7>5. 8>1. 8>2. 8>3. 8>4. 8>5. 8>7
4>6. 5>1. 5>2. 5>3. 5>4. 5>6. 7>1. 7>2. 7>6. 8>1.
8>2. 8>6
1>2. 1>3. 1>4. 1>5. 1>6. 1>7. 1>8. 2>3. 2>7. 4>3.
4>7. 5>7. 6>7. 8>3. 8>7
1>3. 2>3. 2>6. 4>3. 5>3. 5>6. 6>3. 7>3. 7>6. 8>3.
8>4. 8>6
1>4. 1>5. 1>6. 1>8. 2>5. 2>6. 2>8. 3>6. 4>6. 7>5.
7>6. 7>8
1>4. 2>4. 2>6. 5>1. 5>3. 5>4. 5>6. 6>4. 7>4. 7>6.
8>1. 8>3. 8>4. 8>6
2>1. 2>3. 4>1. 4>3. 5>1. 5>3. 6>1. 6>3. 7>1. 7>3.
8>1. 8>3
1>8. 2>8. 3>8. 4>8. 5>3. 5>4. 5>6. 5>8. 6>8. 7>8
F value Scheffe test
variables such as gender and age (ga) and gender and education (gd). A total
of 15 demographic variables were used to compare the difference of
consumer brand preference with the psychographic variable. The
psychographic variable was based on the eight clusters derived from using
factor analysis to classify VALS2 and LOV inventories. Utilizing ANOVA
analysis to analyze nine product categories (67 brands) produced the results
shown in Appendix 3.
Significant brand
preference differentiation
Through ANOVA analysis, the results showed that ten brands (see the sum
row of Appendix 3) exhibited significant brand preference differentiation in
the demographic combination of gender, age and monthly family income
(gai) variables, same as the combination variable of gender and age (ga).
Thus, the demographic combination variables exhibit better brand preference
differentiation than the single variable of traditional demographic
segmentations. In the psychographic segmentation, nine brands had
significant preference differentiation, which can be considered a proper
measurement of consumers' brand preference differentiations.
Demographic segmentation
variables
Figures 1 and 2 were derived from the ``sum'' row and ``total'' column of
Appendix 3, respectively. In Figure 1, the one demographic segmentation
variable identified seven brands that exhibited brand preference
differentiations. The average of 6.67 items of brands represents the
differentiations of brand preference obtained using the combination variable
of two demographic segmentations. Averages of six and three items showed
the differentiations in utilizing the combination of three and four
demographic variables, respectively. Using a total of 15 demographic
variables averaged 6.33 items of brand preference differentiations. From
these results, it is clear that the measurements of psychographic variables
through cluster analysis were useful in identifying differentiations in
customer brand preference.
Figure 2 showed that only one brand item could use ten segmenting variables
to measure its differentiations of brand preference. Two brand items could
create their sub-markets by using seven segmenting variables. In all, 10.5
percent of 67 brands can examine the differentiations of brand preference by
Figure 1. The number of brand preference differentiations in
multi-segmentations
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JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
Figure 2. The number of segments in each brand
using four or more segmenting variables. An average of 1.55 segmenting
variables can be utilized in each brand item to examine consumer brand
preferences.
Cluster analysis
Key factor in resource
allocation
Cluster analysis based on VALS2/LOV systems to group customers is used
to understand the differentiation of consumers' characteristics. In that eight
cluster classifications of this research, five product categories, including nine
brands, show brand preference differentiation (see Appendix 3: the ``cluster''
column). The results of cluster analysis revealed the distinctions of
consumer's psychographics. Marketers can use psychographic variables to
divide the market, give their brand the characteristics that correspond to
specific consumer personalities, and develop the relevant marketing
strategies for each cluster.
Conclusion
The relation between consumer brand preference and the characteristics of a
sub-market is the key for marketers to develop effective sub-marketing
strategies. Utilizing different market segmentation variables can provide
more valid information to understanding the brand preference of each
segment. This study used the psychographic classification and the traditional
demographic segmentation to analyze consumer's preferences within the
sub-markets, which can provide marketers new insights into the brand
preference of each targeted segment and the preferences of the same product
brands. Businesses that want to develop new brands or expand their product
lines can use brand preference as a key factor in allocating resources to
develop effective product strategies.
Using demographic variables to identify market segments is commonly used,
but researchers adopted different segmentation variables to divide a market
(Bucklin and Gupta, 1992). In Appendix 3, psychographic variables through
clustering classifications can provide more information than market
segmentations based on traditional demographic variables alone. Traditional
demographic segmentation can only provide marketers with customers'
demographic data such as age, gender, income, etc. Psychographic segments,
on the other hand, can clearly describe lifestyle and personality of
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
259
consumers, explore consumption models, and identify relevant brand
characteristics.
Enhancing competitive
advantage
Traditional demographic segmentation identifies the brand preference of a
sub-market necessary to marketers for developing effective product
strategies. Multi-segmenting method of adopting both psychographic and
demographic segmentations, on the other hand, provides complete marketing
segmentation information useful for deciding product positioning and
increasing target market share. This study shows how to use multisegmentation variables to divide a market into several sub-markets and
interpret sub-market brand preferences for satisfying consumer demands,
which are invaluable to developing marketing strategies that enhance
business competitive advantages.
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Appendix 1
Cluster
Factor
VALS2/LOV variables
Cluster 1 Home-life
Cluster
Cluster
Cluster
Cluster
Loves home (0.80), Loves kids (0.75), Routine life (0.65),
Plain life (0.56), Without work emotion (0.46)
Traditionalism Healthy (0.57), Conventional life (0.50)
Hedonism
Epicurean (0.73), Seeks the wrong aim (0.45), Seldom
helps others (0.45)
2 (Home-life)
Loves home (0.80), Loves kids (0.75), Routine life (0.65),
Plain life (0.56), Without work emotion (0.46)
(Conservatism) Risk averse (0.68), Passive disposition (0.61), Accepts the
status quo (0.56), Less active (0.51)
3 (Solipsism)
Concerned about national events (±0.45)
(Free-living)
Thinks things over when buying a product (±0.84),
Considers product value (±0.77), Constant expenditures (±
0.37)
(Pessimism)
Has a lot of friends (±0.67), Gets along with others (±
0.67), Cares about others too much (±0.53), Optimistic (±
0.51)
(TV-dislike)
Influenced by TV (±0.72), Likes TV more than movie (±
0.71)
(SelfSelf-confident (±0.74), Achieve one's goals (±0.74),
confidence)
Economically fulfilled (±0.64), For myself (±0.56), Have a
respected occupation (±0.51), Emotional (±0.48), Likes
one's own job (±0.46)
4 Well-known
Buys well-known brands (0.81), Brand loyal (0.72), Buys
brand pursuer
clothes often (0.70), Buys something to represent one's
position (0.66), Often follows the fashion (0.53), Usually
goes out to eat (0.42)
(Anti-authority) Money represents status (±0.78), Respects authority (±
0.62), Willing to overwork for money (±0.56)
5 Enthusiasm
Likes novelty (0.76), Enthusiastic about seeking growth
(0.86), Seeks to develop and explore (0.81), Accepts new
concepts (0.72), A vital man (0.54), Makes life special
(0.62), Pursues a spiritual life (0.56), Likes a person who
can express one's opinion (0.39), Sensitive to surroundings
(0.38)
(continued)
Table AI. Classification of respondents based on cluster analysis
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
261
Cluster
Factor
VALS2/LOV variables
Independence
Usually needs someone's concern (±0.78), Usually needs
someone's help (±0.72), Dependent (±0.66), Cares about
other's opinions (±0.52), Influenced by others (±0.52),
Seeks more security (±0.44)
Comfort
Satisfied (0.81), Goes home from work without workemotion (0.78), Feels secure because of current economic
situation (0.55)
Saving
Shops at the same place (0.70), Likes to buy durable
products (0.66), Thrifty (0.41)
Anti-authority
Money can represent status (±0.78), Respects authority (±
0.62), Willing to overwork for money (±0.56)
TV-dislike
I Influenced by TV (±0.72), Likes TV more than movie (±
0.71)
Subjectivity
Impulsive (±0.73), Nervous (±0.70), Sometimes buys
cheap but non-suitable goods (±0.44)
Cluster 6 Solipsism
Concerned about national events (±0.45)
(Independence) Usually needs someone's concern (±0.78), Usually needs
someone's help (±0.72), Dependent (±0.66), Cares about
other's opinions (±0.52), Influenced by others (±0.52),
Seeks more security (±0.44)
Conservatism
Risk averse (0.68), Passive disposition (0.61), Accepts the
status quo (0.56), Less active (0.51)
(Traditionalism) Healthy (0.57), Conventional life (0.50)
(Subjectivity)
Impulsive (±0.73), Nervous (±0.70), Sometimes buys
cheap but non-suitable goods (±0.44)
Cluster 7 (well-known
Buys well-known brands (0.81), Brand loyalty (0.72),
brand pursuer) Buys clothes often (0.70), Buys something to represent
one's position (0.66), Often follows the fashion (0.53),
Usually goes out to eat (0.42)
(Hedonism)
Epicurean (0.73), Seeks the wrong aim (0.45), Seldom
helps others (0.45)
Cluster 8 (Enthusiasm)
Likes novelty (0.76), Enthusiastic about seeking growth
(0.86), Seeks to develop and explore (0.81), Accepts new
concepts (0.72), A vital man (0.54), Makes life special
(0.62), Pursues a spiritual life (0.56), Likes a person who
can express one's opinion (0.39), Sensitive to surroundings
(0.38)
(Comfort)
Satisfied (0.81), Goes home from work without workemotion (0.78), Feels secure because of current economic
situation (0.55)
Free-living
Thinks things over when buying a product (±0.84),
Considers product value (±0.77), Constant expenditures (±
0.37)
Pessimism
Has a lot of friends (±0.67), Gets along with others (±
0.67), Cares about others too much (±0.53), Optimistic (±
0.51)
(Saving)
Shops at the same place (0.70), Likes to buy durable
product (0.66), Thrifty (0.41)
Individualism
Religious (±0.75), Approves of government policies (±
0.71), Approves of current social value (±0.48)
Self-confidence Self-confident (±0.74), Achieves one's goals (±0.74),
Economically fulfilled (±0.64), For myself (±0.56), Has a
respected work (±0.51), Emotional (±0.48), Likes one's
own job (±0.46)
Notes: Each factor in the sign ( ) represents the opposite mean. Figures mentioned for
each variable represent standard scores; larger negative figures have smaller
characteristic of variable exhibited. Conversely, the larger the figure, the more the
characteristic of variable exhibited
Table AI.
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JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
Appendix 2
Indicator
Cluster
Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7 Cluster8
Product ownership
Computer (%)
52.2
Imported car (%)
29.9
58.7
47.6
61.1
41.7
66.3
33.7
72.3
40.4
62.0
40.7
63.6
21.6
58.9
26.8
Demographic variable
Average age
36.0
34.2
41.5
33.0
35.0
30.4
39.4
34.8
Male sex (%)
55.2
49.2
56.8
61.6
55.3
34.3
34.1
45.9
College
education (%)
35.8
52.3
43.2
58.8
63.8
56.4
38.6
36.0
Married (%)
68.7
50.8
83.8
42.4
61.7
35.2
85.2
54.5
Personal
monthly
income
($USD)
978 1,019 1,363
932 1,175
681
871
708
Family monthly
income
($USD)
1,812 2,236 2,058 2,143 2,531 1,886 1,961 1,530
Table AII. Representative cluster indicators: product ownership and
demographics
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
263
264
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*
**
*
Gender
*
*
**
**
*
*
*?*
*
*
**
*
**
**
*
**
**
Demographic combination variables
ad
ai
di
gad
gai
**
**
**
*
**
gi
**
*
*
*
*
*
gd
*
*
*
*
**
**
**
*
ga
**
*
Demographic variables
Age
Education Income
*
gdi
Table AIII. Comparison of demographic and psychographic segmentation: the differences of customers' brand preference
A1
A2
A3
A4
A5
A6
A7
A8
B1
B2
B3
B4
B5
C1
C2
C3
C4
C5
C6
C7
D1
D2
D3
Brands
Appendix 3
*
**
*
adi
*
gadi
**
**
Psychographic
Cluster
3
0
1
3
0
3
0
11
0
2
7
1
2
3
2
0
0
2
2
0
0
7
0
(continued)
Total
Total
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
265
**
*
**
**
Gender
Table AIII.
D4
D5
D6
D7
E1
E2
E3
E4
E5
E6
E7
E8
F1
F2
F3
F4
F5
F6
F7
F8
G1
G2
G3
G4
Brands
*
**
**
**
*
*
**
**
Demographic variables
Age
Education Income
**
**
**
ga
**
*
**
**
gd
gi
*
**
*
*
**
**
*
*
**
**
Demographic combination variables
ad
ai
di
gad
gai
**
*
gdi
*
*
adi
*
gadi
**
**
*
Psychographic
Cluster
2
3
0
3
1
0
0
5
0
0
0
1
2
3
0
1
1
2
1
1
4
6
1
0
(continued)
Total
Total
266
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
3
6
9
**
**
Gender
3
5
8
4
3
7
2
2
4
*
Demographic variables
Age
Education Income
7
3
10
*
*
ga
4
4
8
**
gd
1
5
6
**
**
gi
3
4
7
**
3
2
5
2
2
4
*
3
3
6
**
*
4
6
10
**
Demographic combination variables
ad
ai
di
gad
gai
2
1
3
gdi
4
1
5
adi
3
0
3
*
gadi
*
4
5
9
*
**
*
Psychographic
Cluster
0
0
1
0
0
0
1
1
3
1
4
3
0
0
0
0
1
0
2
1
48
53
101
Total
Total
Table AIII.
Notes: * p < 0.05; ** p < 0.01; g: gender; a: age; d: education; i: family income; A, B, C, D, E, F, G, H and I represent nine product categories (see Table III); A1, A2, . . . , I8
represent the brands in the ``A'' product category
G5
G6
G7
G8
H1
H2
H3
H4
H5
H6
H7
H8
I1
I2
I3
I4
I5
I6
I7
I8
*Total
**Total
Sum.
Brands
This summary has been
provided to allow managers
and executives a rapid
appreciation of the content
of this article. Those with a
particular interest in the
topic covered may then read
the article in toto to take
advantage of the more
comprehensive description
of the research undertaken
and its results to get the full
benefit of the material
present
Executive summary and implications for managers and
executives
Demographics and lifestyle ± use them both to target and segment
In some ways it is unfortunate that some marketers perceive a great debate
over the value and effectiveness of market segmentation models using either
demographic variables or psychographic variables. First, some
demographic variables give clues as to psychological or behavioural traits.
And second, there is no earthly reason why ± as Lin shows here ± marketers
cannot use and apply both types of segmentation tool.
The issues for marketers involved in developing market segmentation models
relate as much to applicability and functionality as they do to the intellectual
debate about which approach is ``best''. There are occasions when academic
research has shown how one or other factor influences consumer purchase
behaviour only for practising marketers to ask how they are supposed to
collect the data needed to allow segmentation into market sub-sections.
Demographics ± the building blocks of market segmentation
The variables typically described as demographic ± age, gender, location,
employment, income and education level ± provide the basis for
segmentation. We select one or other magazine for our advertising by
looking (among other considerations) at the demographics of that
magazine's readers. And the same goes for TV advertising, the design of
sales areas and the targeting of direct mail.
However, we all recognise that demographics remain a blunt instrument.
They tell us something about a person but not enough to know that person.
We can identify that Fred Smith is aged between 35 and 45, male, lives in
New Jersey, has an income of $80,000-100,000 and a college degree. But we
do not know a great deal about Fred's outlook on life or his lifestyle.
These lifestyle questions can be addressed and used to enhance segmentation
and targeting but throughout this process we have to remember than the
demographic data is crucial to a successful segmentation strategy even if it
is clearly insufficient on its own. Income level is important ± we may want
better off folk because we are selling an expensive, upscale brand. Education
is important ± we may be looking to recruit subscribers to a literary
magazine. And we can see how gender, age and location can also prove
significant to our targeting and segmentation.
Lifestyle variables and how to use them
There are two ways to apply lifestyle variables to our marketing ± one
strategic and the other tactical. Lin is concerned with the strategic, with the
application of psychographic variables to brand strategies and our
understanding of customer brand preference. Before discussing this strategic
application we should note the value of the tactical.
At the tactical level, lifestyle variables can be applied to the design and
presentation of advertising, the selection of direct mail targets and the
segmentation of large databases. Each of these applications ± often using the
big ``lifestyle databases'' developed by commercial organisations ± provide
a secondary input into strategy but primarily deliver better targeted
marketing communications through improved media selection or better copy
treatment.
Lin's work is more thoughtful in that the question of how we apply the
lifestyle variable is subsumed in the more important question of where we
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 4 2002
267
apply such analysis. Market segmentation is not just a clever database
technique but a means of establishing the right brand position and brand
communication so as to make the most of investments in the brand and
thereby enhance the brand's real value.
Brand marketers should consider using bespoke research applying the
concepts and techniques described and tested here by Lin. The methods
applied are easily transferred to a commercial environment and can be
linked into brand develop processes without compromising the overall
objectives of such research.
Psychographics give better research outcomes
Market research tends to use demographics as the basis for analysis rather
than ``lifestyle'' issues. While demographics remain a crucial link to the
``real'' world of targeting and segmentation and cannot be ignored,
researchers engaged in commercial work should begin to make more use of
psychographic variables alongside the demographics.
It seems self-evident that aspects of consumer behaviour relating to values
such as self-respect, security and a sense of belonging will have a closer
correlation with our brands and their development than will how old
someone is or how much money they earn. But marketers (being practical
minded folk and a tad prejudiced on this subject) continue to view
psychographics with a healthy suspicion if not a downright dislike.
The reason for this prejudice lies in the somewhat nebulous nature of
psychographic variables and the fact that, taking the List of Values as
illustrative, most people have some degree of each of the variables. It is the
relative emphasis within these variables that provides the psychographic
categorisation. We all want some enjoyment out of life but for some this is
more important than security or a sense of belonging and vice versa.
What Lin has done is to present an approach that categorises people
according to scores on the lifestyle variables. The result is a method that
provides understandable segmentations within a marketplace ± not without
the potential for overlap and exception ± but with the possibility of applying
psychographics to the strategic development of brands and to the targeting
of brand communications. More work may be needed to translate this work
into an easily usable marketing and market research tool, but the basis for
an effective method exists.
The combination of demographics (necessary for targeting) with
psychographics (necessary for understanding) represents an important step
forward and, I hope, puts to bed the argument over whether demographics or
lifestyle are more important. Finally, I am sure marketers involved in the
development and management of brands will welcome the chance to get
deeper into their customers' psyche without losing site of practical
marketing issues such as how we reach the segments once we have identified
them.
(A preÂcis of the article ``Segmenting customer brand preference:
demographic or psychographic''. Supplied by Marketing Consultants for
Emerald.)
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