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Market Segmentation
Targeting a segment of the market can be a powerful strategy.
It’s the concentration of marketing effort to dominate a market niche.
Market segmentation is the process of identifying and targeting groups of
individuals who are similar to one another. Markets can be segmented in many
different ways: by product or service needs, by sensitivity to price, by
geographic area, by demographic segment, or by psychographics and lifestyles.
Successful segmentation depends on understanding what consumers need, how
groups of consumers differ from one another, and how consumers decide among
products.
Decision Analyst’s Advanced Analytics Group searches for and
identifies patterns in the data. Rigorous analytic techniques (including factor
analysis, discriminant analysis, k-means and hierarchical clustering, latent
class segmentation, and Factor Segmentation™) are used to organize
consumers into groups with similar attitudes, needs, and desires. The size and
market potential of each psychographic segment is determined, along with the
positioning and appeals that should be employed to reach each segment.
Segmentation Methods
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Factor Segmentation™. Factor
Segmentation™ begins with factor analysis (hence, the name). The model
segments the respondents on a mutually exclusive basis (i.e., each respondent
is assigned to one segment only) and may be followed by segmenting on a
nonmutually exclusive basis to examine the overlap among segments. Factor
Segmentation™ yields coherent clusters of respondents with very similar
attitudes and perceptions, and is an important technique in developing
targeting, positioning, and marketing strategies.
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K-means Cluster Analysis. K-means cluster analysis
attempts to identify relatively similar groups of respondents based on selected
characteristics, using an algorithm that can handle large numbers of
respondents. This procedure attempts to identify similar groups of respondents
based on selected characteristics.
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TwoStep Cluster Analysis. This procedure is
relatively new. It uses hierarchical cluster analysis and is designed to handle
very large data sets. The algorithm employed by this procedure has several
desirable features that differentiate it from traditional k-means clustering
techniques: the handling of categorical and continuous variables, and automatic
selection of the number of clusters. By comparing the values of a model-choice
criterion across different clustering solutions, the procedure can
automatically determine the optimal number of clusters.
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Latent Class Cluster Analysis. Latent class cluster
analysis produces an objective segmentation solution that optimizes the number
of clusters and the fit of the segmentation model to the data. This model can
predict patterns in multiple dependent variables (such as attitudes, needs, and
behaviors) as a function of segment membership. It easily incorporates data
from different types of questions and different types of scales (e.g., yes/no
answers, multiple choice questions, various rating scales, and even volumetric
data) without the need for rescaling or normalizing the data. Latent class
cluster analysis can introduce secondary variables (brand usage, demographics,
etc.) as covariates that correspond with needs, attitudes, and behaviors.
Respondents are assigned to the cluster to which they have the highest
probability of belonging.
Scoring Model
Once a segmentation model has been produced, a scoring model (a set
of equations) may be developed to allow additional respondents to be classified
using the same segmentation scheme. Discriminant analysis is usually used to
develop the model, although other forms of regression may also be employed.
This analysis identifies the questions that are the most important in
determining segment membership.
Database Customer Segmentation
Most companies have multiple databases containing information about
their customers’ attitudes, preferences, and buying behaviors, but rarely
are these databases fully linked and integrated. Linking the information in
these various databases enriches the value of each database. Our Advanced
Analytics Group has developed unique ways of linking segmentation solutions
based on attitudinal and/or preference data with transactional databases. They
“bend” the segmentation solution in a way that optimally finds and
creates “hooks” into each database, while maintaining the basic
structure of the segments. We can apply these “bending” techniques
to identify high-potential customer segments that merit special marketing
attention.
Other Advanced Analytic Services include:
Additional Resources from Decision Analyst
If you would like more information on
Advanced Analytics Services,
please contact Dr. John Colias by
email or call 1.800.ANALYSIS (262.5974).
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