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Unit 5: Closed Loop Marketing

of the enterprise to deliver more tailored marketing programmes, to identify segments that are Notes
more important to the business, to identify segments that have been neglected and to become
more attentive to previously unrecognized consumer needs.
Increasingly companies realise that consumers differ in their needs, preferences, sensitivities,
opinions and behaviours. The transition from mass marketing to target marketing that is currently
in progress creates demands for more sophisticated customer segmentation techniques. The key
enabler of any segmentation strategy is customer data. Customer data are the raw material that
must be captured, integrated and effectively analysed in order to achieve the goal of profiling
customers. Before customer data can be integrated they must first be assessed for quality.
Inconsistencies in semantics (what the data mean) and the occurrence of null fields are encountered
as well as incorrect data. These so-called ‘noisy’ data must be conditioned and cleansed before
proceeding to uncover meaningful patterns.
Once the data are cleansed and integrated they can be interrogated to discover groups of customers
sharing the same characteristics and needs. Market segmentation is the process of partitioning
the heterogeneous market into separate and distinct homogeneous segments. A segment consists
of a group of consumers who react in a similar way to a given set of marketing stimuli. Usually
the enterprise defines a segmentation matrix and then, based on the data, would allocate customers
to segments. This a priori approach to segmentation defines, in advance, a framework or system
that describes characteristics of customers or prospects based on information that is known
about those individuals. Some common a priori approaches to looking at segments include
loyalty, profitability, sensitivity, usage, demographics, psychographics and attitude. Table 5.1
provides an overview of the common a priori approaches to segmenting customers.
In addition to the a priori approach, data-mining techniques make possible a different approach
to segmentation— namely cluster segmentation. The cluster segmentation approach, in direct
contrast to the a priori method, seeks to discover naturally occurring clusters of customers who
share common characteristics or behave in the same way.
Regardless of the segmentation technique used, the starting point is the collection of the data
that provide the variables to construct the segments.

Table 5.1: Common a Priori Customer Segmentation Categories

Segmentation type Segment definition
Buyer-readiness segmentation The division of prospects and customers into groups reflecting the
different stages which consumers normally pass through during the
purchase process. These usually comprise ignorance, awareness,
knowledge, preference and conviction.
Benefit segmentation Dividing the market into groups according to the different benefits
that consumers seek from the product.
Occasion segmentation The division of customers into groups which consume a product or
service at particular times, in certain situations, in response to
particular events or according to seasonal or cyclical times.
Psychographic/lifestyle The division of customers into groups based on lifestyle, social
segmentation behaviour, values, sensitivities and personality characteristics.

Demographic segmentation The division of customers into different groups based on
demographic variables such as age, gender, family size, income,
occupation, education, language, religion, race and nationality.

Life-cycle segmentation The division of customers into different groups that recognise the
different needs of consumers at different stages in their life.
Geographic segmentation The division of customers into different groups based on countries,
Contd...
regions, climate and population density.
Loyalty segmentation The division of customers into different groups based on different
degrees of loyalty to supplier or brand. 127
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Product segmentation The division of customers into different groups based on levels and
type of usage of the product or service.
Profitability segmentation The division of customers into different groups based on the
different levels of value or profitability of the customers.

Interaction segmentation The division of customers into different groups based on their
preferences regarding channels, payment method, promotions and
communications.

Satisfaction segmentation The division of customers into different groups based on their
recorded satisfaction levels, complaint history, fault history and
upgrade history.