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Lifestyle Segmentation 277

of general observable segmentation bases, such as geographic, demographic or
socioeconomic variables. How do our typologies perform? To test the significant
differences among the clusters on product attribute importance, a one-way
ANOVA was performed (Kaynak and Kara, 2001: 468, Kahle et al., 1992: 347).
The next table summarizes our findings, by indicating how many product
attributes (total = 85) proved to be significant (respectively at the .05 and the .01
level) or not, within the different product categories and in total. The overall
lifestyle typology (V-L-A) combines values, life visions and aesthetic style prefer-
ences. The global lifestyle typology (V-L-A-M) combines values, life visions,
aesthetic style preferences and media preferences. The demographic variables are
sex, age (three segments: 18–30 years old, 31–45 years old and 46–65 years old),
social groups (highest, high, low, lowest) and (nine) stages of life (from ‘young –
living with their parents’ to ‘elderly parents – most children have left home’).
Table 20.3 reads as follows: if we look at the product category ‘cars’, we find,
for example, that the consumer typology based on the values dimension results
on all 14 car benefits in significant differences below the .01 level, while a seg-
mentation based on the sex of the consumer only results in such significant
differences on seven attributes, besides three attributes that score at the .05 level
of significance and four attributes yielding no significant differences.
Notice that all psychographic segmentations perform extremely well compared
to the much weaker performance of demographic and socioeconomic segmenta-
tions (which yield much larger numbers of nonsignificance), and this in all markets
analysed here. The lowest number of significant differences is provided by the
social class concept. One can refer here to the debates over ‘the death of class’
and so on, but this would take us too far from our subject.
To further distinguish the discriminative power of the different psychographic
lifestyle typologies developed here, we also computed measures of association
between the different typologies and the respective product attributes or bene-
fits. Since we are combining nominal and interval data here, we chose to calcu-
late eta (with the cluster variable as independent and the benefit measure as
dependent variable). To summarize our findings, we calculated an averaged eta
(over all benefits or attributes) for all markets under scrutiny, and compared the
different cluster typologies on how they perform (see Table 20.4). One demo-
graphic variable – sex (which was one of the relatively better performing demo-
graphic variables in Table 20.3) – was included for reasons of comparison.
First, notice that of the three single-dimension typologies, the value typology
performs better than the typologies based on either life visions or aesthetic style
preferences (which both yield similar results). Moreover, adding life visions and
aesthetic style preferences to the value-based research instrument, in order to create
the overall V-L-A typology, hardly raises the average eta-value of the typology
based on values alone. In 1978, Clawson and Vinson suggested that values perhaps
equal or surpass the contribution of other major psychographic constructs in
understanding consumer behaviour. Nevertheless, for communication strate-
gists, adding life visions and aesthetic styles to the value dimension of course
increases the richness of the lifestyle profiles obtained.
Second, notice that adding a section on media preferences to develop the global
V-L-A-M typology does improve the average eta-value of the overall V-L-Atypology