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In
our last issue, we told our readers about the startling
finding that marriage doesn't make people happier. For
thirty years, academic researchers had published over
60 studies on the relationship between marital status
and personal happiness. The conclusion? Marriage works
wonders.
Since
married people have been found to be happier than singles,
the state of marriage must be a "causal agent"
contributing to a couple's bliss. But along came a large-scale,
15-year longitudinal study involving more than 24,000
people that found most married people were happy and
satisfied with their lives long before they actually
got married.
Now
some might question overturning a long-held belief supported
by over 60 studies based on the results of one opposing
study, but we wouldn't. You see, those 60 studies were
driven primarily by cross-sectional correlation analysisessentially
comparing all marrieds versus all singles at a single
point in time and observing who's happier. Seeing a
high correlation between marriage and happiness, researchers
"derived" the importance of marriage to happiness
and incorrectly concluded that marriage lead to happiness.
But
as anyone who has ever taken an introductory statistics
class will tell you (because professors drill it into
their head), correlation does not equal causation. It's
a plain fact that evidence of a relationship reveals
nothing about the cause of the relationship; derived
importance is by no means an exception to this inescapable
truth. Nevertheless, researchers not only in the social
sciences, but also in marketing, continue to act like
it is, particularly in studies of brand positioning
and of customer satisfaction.
Too
many marketing researchers have been telling companies
that they don't have to query respondents to find out
what's really motivating: all positioning and satisfaction
research requires is correlation analysis. As part of
a sample survey, researchers pull out a list of 30 or
more product and/or service attributes and benefits
and ask respondents to rate different brands on each
dimension. For a soft drink, as an example, a researcher
might ask how satisfied a respondent is with taste,
carbonation, youthfulness, etc. The survey then goes
on to ask about the likelihood of purchase and/or overall
preference for each of the same set of brands.
The
next step is to run correlations between each attribute/benefit
(or factor) and the dependent variable of purchase interest
or behavior.
Ceteris
paribus, the attributes and benefits that yield
the highest correlations with likelihood of purchase
and/or overall preference (positive or negative) are
labeled the "Most Important." Thus, they've
derived the importance of the attributes and benefits.
Yet
the winning attributes from this analysis may really
be losers and vice versa. See the six illustrations
from the soft drink category below for further insight
into this problem. But as an example, for a soft drink,
the presence or absence of carbonation or an energy
stimulant such as taurine, the image of youthfulness
or a health claim might be revealed to be unimportant
(when, in fact, they can be very powerful), while a
red and blue can and a consumer hotline might be (incorrectly)
revealed to be the drivers of preference.
Conclusion:
Beware of traditional correlation-based measures of
derived importance. They're liable to bring you toward
a contrived disaster. It can be a path to the wrong
strategy.
Six
Illustrative Problems with Derived Importance
A
Soft Drink Example
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Price
of Entry Example
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Reverse
Causality Example
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Irrationality
Example
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Unfamiliarity
Example
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Spurious
Correlation Example
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Innovation
Example
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If
an attribute or benefit is so important that everyone
wants it and every brand delivers it (e.g., carbonation
in a soft drink)
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If
an attribute or benefit is associated with market
leadership because consumers believe the best
must offer it (e.g., a global consumer soft drink
hotline which offers advice on ingredients and
recipes)
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If
an attribute or benefit is intangible (e.g. youthfulness
in a soft drink), some consumers have trouble
rating brands high or low on this dimension -
in part because it's irrational to apply animate
descriptions to inanimate objects
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If
an attribute or benefit is relatively unfamiliar,
never heard of before (e.g. the "Taurine" in Red
Bull)
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If
an attribute or benefit is not important but big
brands have it (e.g. a red and blue can for a
soft drink) and small brands don't
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If
an attribute or benefit is breakthrough, innovative
and, therefore, no brand currently delivers it
(e.g. a health claim for a soft drink)
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| All
companies achieve high scores |
Big
brands get high scores; small brands low scores
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Scores
are low and randomly distributed across brands
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All
companies achieve low scores
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Big
brands get high scores; small brands low scores
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All
companies achieve low scores
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| Correlation
will be zero: implication is no importance |
Correlation
will be high; implication is high importance
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Correlation
is weak; implication is no importance
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Correlation
will be zero; implication is no importance
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Correlation
will be high; implication is high importance
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Correlation
will be zero: implication is no importance
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| Yet
if you strengthened a brand on a "price of
entry" dimension, the brand might perform
better; take it away and the brand would suffer
badly |
Yet
if you took this characteristic away, no one would
care and monies could be better spent on something
else
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Yet
if the dimension is highly appealing, as is the
case with youthfulness, and the brand is positioned
as delivering on this dimension, the brand would
benefit.
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Yet
if the dimension is or could be highly appealing,
and the brand offered it, the brand would benefit
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Yet
if you strengthened the big brand on this dimension,
nothing would happen; strengthen the small brand
and you would create brand confusion
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Yet
if the dimension is highly appealing, and the
brand offered it, the brand would benefit - it
would be a successful new product
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