The four different traditional market segmentation approaches are frequently used in marketing research. These segmentation variables are referred to as geographic, demographic, psychographic, and behavioral variables and consumers can be segmented according to them.
Geographic variables are such variables as country, city, locality and density.
Demographic variables involve dividing consumers with regard to their age, sex, life cycle, income and occupation.
The psychographic variables cover social class, lifestyle and personality.
Behavioral variables consist of benefits sought, usage rate and purchase occasion. (Kotler et al 2005)
Critique of the traditional approach:
Geographic variables are not good predictors of consumer behavior. In present time, the choice of consumers from the rural and urban areas does not vary much. It cannot predict future buying behavior within consumers. Moreover, geographic segmentation variables are based on ex-post factor analysis of consumers in different market segments, which rely on explanatory features. (Haley 1968) The capability of geographical variables has also been questioned due to their lack in offering an understanding of target markets (Schoenwald 2001).
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In case of demographic variables, problem arises when marketers try to segment the whole market. If the segment is not clear, demographic variables can not describe the segment. Neither can they predict future buying behavior of consumers. Moreover, demographic variables can not capture the drivers of consumers’ behavior. Tynan and Drayton (1987) argue that demographic variables are weakly related to product choice. Additionally these variables are not efficient to use due to that even if people share the same age, sex and so forth, they do not inevitably have common values, motivations and beliefs (Morgan et al 2003). Accordingly, it is difficult for a company to act on demographics (Winter 1984).
The psychographic variables do not always accurately forecast consumer’ behavior. Additionally, the approach has also received critique as it is perceived as being too explorative in its research process. (Lesser and Hughes 1986) Yankelovich and Meer (2006) also claim that the weakness with psychographic variables is that they do not well forecast what consumers will buy. These variables are thus not good at helping marketers to decide which market to enter nor do they focus on one of the most important determining factor; the dissimilarities between different customers’ needs (Mitchell 2006).
Behavioral segmentation variables do also have difficulties in understanding consumers’ behavior (Schoenwald 2001). Not all heavy users are exploiting the same brand since they are not looking for the same product benefits.
(Traditional market segmentation – an evaluating approach
Authors: Sara Ahlm
Maria Holmström, Victoria Stenman Supervisors: Ulf Johansson Sofia Ulver Sneistrup)
According to Schiffman and Kanuk (2004), large population, sufficient disposable income and diversity to partition are three essential conditions for successful segmentation of a market into sizable market segments according to geography, demographic, psychological and strategic variables.
To determine the appropriate bases for segmenting market, the very beginning task is to find the variables out that divide the heterogeneous market into some homogenous segments of similar needs and wants.
There are two types of segmentation approaches:
(1) Needs based approach
(2) Profilers approach
Under the need based approach, market is segregated into different segments according to similar nature of needs. Customer needs are main tools of dividing the market. Market research is to uncover the needs of consumers in a market.
Profilers are the descriptive in nature and measurable in terms of customer characteristics (such as location, nationality, age, sex, income) which can be used to update a segmentation exercise. Generally geographic, demographic, psychographic and behavioral variables are used under profiler segmentation bases which have already briefly described in traditional segmentation approaches above.
Data for segmentation:
According to Malcolm & Ian (2004) , for segmentation a broad range of data is required. These data could range from the company internal data to segment data. Company’s sales and financial data along with the data generated from market research findings are the main input in this process. It is beyond to say the quality of output depends on the quality of inputs. Hence the following data are generally used in market segmentation:
The underlying intentions of customers to enter the market;
An understanding of how market performs;
Market size and its division among competing products and services.
Characteristics, used to identify the different customers, found within the market;
The key requirement of product, service, channel, frequency and method of purchase according to customer’s view point;
The delivered benefits by these requirements, also according to customer’s view point;
The relative importance of these benefits to the different customers found within the market.
Market segmentation process starts from market mapping (step 1) and ends in segment checklist (step 7) in three distinctive but related stages. Following figure depicts the whole segmentation process. A summary of process is shown below with a diagram:
1. Market definition – ‘A customer need that can be satisfied by the products or services seen as alternatives’. It is based around what the customers perceive as distinct activities or needs they have which different customers could be satisfying by using alternative products or services.
2. The distribution and value added chain that exists for the defined market.
3. The decision makers in that market and the amount of product or service they are responsible for in their decision making.
1. Recording information about the decision makers in terms of who they are – Customer Profiling. Demographics, geographic etc.
2. Testing a current segmentation hypothesis to see if it stacks up – Preliminary Segments.
Who buys what*
1. Building a customer ‘model’ of the market – based on either the different combinations of KDFs customers are known to put together, or derived from the random sample in a research project. Can be constructed by Preliminary Segment. Each customer in the model (sample) is called a Micro-segment.
2. Each micro-segment is profiled using information from the data listed in ‘Who buys’.
3. Each micro-segment is sized to reflect the value or volume they represent in the market.
1. Is each cluster big enough to justify a distinct marketing strategy?
2. Is the offer required by each cluster sufficiently different?
3. Is it clear which customers appear in each cluster?
If all ‘yes’,
clusters = segments.
4. Will the company change and adopt a segment focus?
1. By attributing a ‘score’ to all the CPIs for each micro-segment, the similarity between micro- segments can be determined.
2. Micro-segments with similar requirements are brought together to form clusters
3. Clusters are sized by adding the volumes or values represented by each micro-segment.
1. As customers only seek out features regarded as key because of the benefit(s) these features are seen to offer them, the benefits delivered by each KDF should be listed. For some customers it is only by combining certain KDFs that they attain the benefit(s) they seek – benefits should also be looked at from this perspective. These benefits are Critical Purchase Influences CPIs.
2. For thoroughness, benefits can be looked at from the perspective of each Preliminary Segment.
3. Once the CPIs for the market have been developed their relative importance to each micro-segment is addressed (by distributing 100 points between the CPIs).
What* is bought
1. Listing the features customers look for in their purchase – what, where, when and how.
2. Focusing in onto those features customers use to select between the alternative offers available – Key Discriminating Features KDFs.
The Market Segmentation Process – Summary
Segmentation step by step
Stage 1 – the market and how it operates
Step 1 – market mapping
This part of the process tackles the first requirement of segmentation, which is to determine who the decision makers are. A prerequisite is, however, to define the market about to be segmented according to need based point of view. Once the definition is clear, it is essential that the market is mapped out in a way which illustrates where the decision-makers are to be found, as it is these individuals whose needs must be understood and around whom the segments will be built.
TYPES OF SEGMENTATION
The segmentation of a market needs criteria or variables to meaningfully differentiate fundamentals in the population. There are two approaches for segmenting markets, a priori and clustering. A priori segmentation design uses the knowledge of management of a market to select criteria or variables that can be used to sub-divide a population. These variables can be product based or customer characteristics. Marketers usually do this intuitively. For example, younger individuals are less likely to vote, so the criteria chosen may be age. In other words the criteria are deduced prior to segmentation using knowledge of cause and effect.
There are 7 stages to a priori segmentation:
1. Selection of the priori basis of segmentation
2. Selection of a set of segment descriptors
3. Sample design
4. Data collection
5. Formation of the segments on a sorting of respondents into
6. Establishment of the segment profile using analytical procedure
like multiple regression analysis, multiple discriminant analysis
and so on.
7. Develop specific marketing strategies for each segment (Wind,1978).
Clustering (post hoc) segmentation selects the relevant criteria for segmenting the market from research conducted in the market being segmented. In certain cases a mixture of a priori and clustering approaches may improve the criteria selected (Baker, 2000; Green et al 1998; Myers, 1996; Smith & Hirst, 2001; Wind, 1978). Cluster analysis is potentially a very useful technique; however it can prove difficult in its application (Everitt, 1974).
Ball (1971) identifies 7 possible uses for clustering techniques:
1. Finding a true typology
2. Model fitting
3. Prediction based on groups
4. Hypothesis testing
5. Data exploration
6. Hypothesis generating
7. Data reduction
Preferably, clusters should be understandable simply by reviewing the data set and distinguishing usual groupings within it. However the judgment and strength of the criteria selected can be subjective and thus potentially misleading. The need for refinement of segments has led to the development of 5 types of clustering technique as identified by Everitt (1974):
2. Optimization partitioning
3. Density or mode seeking
Hierarchical clustering techniques are agglomerative (collect into a mass) and are divisive in nature. The former is typical of the approach of behaviourist who would seek to cluster individuals based on an understanding of each individual. Economists would use a divisive approach to segmenting the undifferentiated demand schedule. Partitioning techniques differ from hierarchical in that they allow for adjustment of the original clusters, created on the basis of a predetermined criterion. This technique allows the fine tuning of a priori cluster segments until an optimum segment is achieved.
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Density techniques attempt to differentiate between clusters of high and low density based on meaningful parameters of differentiation. Clumping identifies overlapping clusters as desirable segments for targeting. The others category covers a multitude of techniques that do not comfortably fit with the previous 4 categories e.g. Q factor analysis, latent structures (Everitt, 1974). The fact that so many factors can be used to segment markets is problematic. There is no clear best method of cluster segmentation.
Marketing Segmentation and Political Marketing
Declan P Bannon (2004)
REQUIREMENTS OF SEGMENTATION
Before targeting a specific segment, it is important to evaluate the effectiveness of a targeting strategy and the practicability of the segment. The market which is segmented must meet the following criteria:
Measurability of segment: It should be measurable in terms of size and growth. In the UK the DVD market is growing at an extremely fast pace. For example, year-over-year the number of mobile video audience grew 51.2% which for the first time surpassed 20 million users.
Accessibility of segment: It should be trouble-free to target and to reach. It must be reached using general communication tools such as TV advertising as well as radio. It will not be viable if it cannot be effectively targeted with marketing communication.
Suitability of segment: the segment must have enough spending power for the company to sustain itself.
Actionability of segment: the organization must have sufficient resources to reach targeted segments. Targeting a segment is worthless if the organization can not support implementing strategy due to insufficient resources to implement strategy.
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