redefining their world in more granular detail, creating ever more specific sub‐
segments. Although this approach does solve
some of the problems of an industry sector
structure it is inherently inefficient, undermining economies of scale. Excruciatingly
long business review meetings of dozens of
important sub‐segments fail to inspire
senior corporate leaders to invest in growing specific niches because of perceived low
business impact.
Need‐based segmentation represents
an alternative if we agree to reject the traditional paradigm. A variety of approaches may be employed to redraw
market segment boundaries to create a
new structure based on customer preferences. A more formal but fairly common
methodology utilizes statistical tools from
the marketing science discipline including
conjoint and cluster analysis. In a less
complex business context, individual
clients can be grouped by team consensus
based on similarities in preference data.
Regardless of the approach employed,
the re-segmentation effort must drive
from sound current customer preference
data—garbage in leads to garbage out.
The importance individual clients ascribe
to specific product and service attributes
forms the foundation for further analysis.
Before embarking on a project of this import, marketers must determine what benefit attributes to include in their query. A
preliminary listing of attributes can be assembled through multiple thoughtfully
placed and well-executed focus group sessions. Respondents from the survey population can then score the importance of
these attributes by completing either a
simple survey (Likert scale), or through
ranking hypothetical benefit combinations
in an orthogonal array experimental design (conjoint analysis). The later approach may provide a more robust
assessment of the trade‐offs customers
make when presented with multiple benefit combinations.
Regardless of the scoring methodology,
the preference data once gathered is compared in order to establish a manageable
number of groupings (ideally five or less).1
Using a multivariate statistical analysis
technique known as cluster analysis, respondents are clustered by calculating the
minimum squared Euclidean distance between all clustering variable.2 Likewise, a
simpler approach is possible in less complex businesses. The illustration above details a simple example.
For sake of illustration, call the sectors
in the top table anything you like.
Nonetheless, keep in mind these are typical industry sectors such as automotive,
petrochemical, building and construction,
etc. In this illustration very simplistic preference data was scored based on importance—high, medium, low—for three
preference attributes identified as important in our focus groups.
Looking only at the top table, imagine
you were the marketing director for one
of those sectors. What would you do to
craft and execute a game changing strategy? Go ahead and take your time.
Now turn your attention to the bottom
table. If we abandon our previous paradigm, creating new segments based only
on clustering the preference data we come
up with an alternative segment that is actionable and supports specific strategies
that speak to the needs of the clients
within the segment. My non‐traditional
segment names may sound funny, as was
my intent, but the point is segmented in
this way you get a much clearer picture of
how to address these customers in a way
that creates competitive advantage and
supports market share expansion.
The illustration was highly simplified,
intended only to paint the most general picture of the concepts I have discussed. In a
technology driven context like the coatings
industry, a rigorous assessment should include both product and service attributes.
I want to acknowledge Professor
David Reibstein, of the Wharton Business
School for opening my eyes to these ideas
in an Executive Education program at the
University of Pennsylvania. CW
References
1. May be more or less depending on
the actual data.
2. Quick Cluster a SPSS statistical clustering program simplifies this work.
Thomas P. Frauman is a member of the
Coatings World editorial advisory board
and independent coatings industry consultant. Frauman has more than 20 years
experience in senior leadership roles developing and executing strategy. He is a respected leader in the coatings industry and
an associate of the Chemark Consulting
Group. Frauman can be reached at tfrauman@yahoo.com.
