Market segmentation 277
1 Industry competitors and the threat of
segment rivalry.
2 Potential entrants to the market and the threat
of mobility.
3 The threat of substitute products.
4 Buyers and their relative power.
5 Suppliers and their relative power.
Having measured the size, growth rate and
structural attractiveness of each segment, the
marketing manager needs to examine each
one in turn against the background of the
organization’s objectives and resources. In
doing this, the marketing manager is looking
for the degree of compatibility between the
segment and the long-term goals of the organ-
ization. It is often the case, for example, that a
seemingly attractive segment can be dismissed
either because it would not move the organi-
zation significantly forward towards its goals,
or because it would divert organizational
energy. Even where there does appear to be a
match, consideration needs to be given to
whether the organization has the necessary
skills, competences, resources and commit-
ment needed to operate effectively. Without
these, segment entry is likely to be of little
strategic value.
The company has also to decide on how
many segments to cover and how to identify
the best segments. There are four market
coverage alternatives:
1 Undifferentiated marketing (marketing mix for
the mass).
2 Differentiated marketing (separate mixes for
each segment).
3 Concentrated marketing (separate mix but
only for those segments selected).
4 Custom marketing (separate mix for each
customer).
Traditional marketers might target all custom-
ers within selected segments, but data-driven
marketers can choose how many and which
customers to target, as indicated in the cov-
erage of segmentation metrics. Data-driven
marketers are increasingly striving to select
those segments that are likely to contribute
more financially to the organization. This in
turn is driven by the need for marketing
activity to be more accountable. Some com-
panies, such as a leading electronics retailer in
the UK, track the sales of newly launched
products, and those customers who purchase
within the first few months of launch are then
targeted for new complementary products on
the basis of the conceptual framework outlined
earlier, namely the discussion of early adopters
and opinion leadership.
One of the important metrics in data
mining which segments to select for targeting
purposes is ‘RFM analysis’. Here, companies
identify the ‘recency, frequency and monetary
value’ of customers; those with the highest
rating according to this measure could be
selected for targeting and those with poorer
RFM scores might be ignored or even dese-
lected (it is interesting to note this development
of rejecting customers even if they do not want
to be rejected).
Just knowing that a customer has pur-
chased from the organization in the past is
important but not sufficient. Marketers are
clearly more interested in a customer who has
purchased in the last 6 months than a customer
who last bought from the organization in 1984.
Similarly, a one-off purchase may also make a
customer less attractive (depending, of course,
on the product-market in which we operate). So
knowing how often they buy from the organi-
zation is an important measure. The value of
orders from the customer hardly requires fur-
ther explanation, but the combination of these
factors clearly could identify the better groups
of customers to target.
A simple example of the value of this
analysis is afforded by a small mail order wine
business. It firstly scored its customers accord-
ing to RFM criteria and from this identified its
‘best’ customers. It then approached a geode-
mographics company and paid for a profiling
of these best customers into geodemographic
groups. Four groups emerged as representing