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Segmentation
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
- 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.
- 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.
- 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.
- 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.
- Latent Class Choice Modeling. Survey respondents
select their most and least preferred sets of product benefits and rate the
influence of these benefits on the purchase decision. Latent class choice
modeling classifies customers into segments based on their preferred product
benefits. This type of segmentation is ideal for customizing product offerings
or bundles to match segment preferences, enabling the firm to maximize business
performance.
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.
Insight Into Target Segments
Segmentations are only useful if they can be applied. Once a few target segments have been identified, further analytic work can answer these strategic questions for each segment:
- What factors (drivers) impact the outcome of interest, such as purchase intent, intent to prescribe, or intent to use? Key Driver Analysis
- Which key drivers are important? Strategic Attribute Mapping
- Which important drivers are opportunities or risks for the brand? Opportunity/Risk Analysis
- What is the market potential? Volumetric Analysis
Key Driver Analysis
This analysis measures and orders attributes based on their influence on the outcome of interest, such as likelihood to purchase or likelihood to subscribe. The attributes included in the analysis frequently arise from these areas:
- Opinions of the brand (image)
- Cost relative to other brands
- Experience relative to expectations
- Value for the money
- Perceived availability (location)
In the example chart below, we can see that among the members of the On-The-Go
segment, "Offers good deals" and "Service is friendly" were
considered the most important drivers of likelihood to purchase.
Strategic Attribute Mapping
These graphical displays use importance and performance to identify strategically important attributes that are:
- High in importance and high in performance: These are attributes that are
important and the brand delivers.
- High in importance and low or average in performance: These are attributes
that are important yet the brand fails to deliver.
In the following example map, "Offers good deals" and "Check
out lines are short" are considered strengths. "Store is clean"
and "Service is friendly" are opportunities for improvement.
Opportunity and Risk Analysis
This analysis quantifies the degree of opportunity or risk for each attribute. That way, effort in maintaining and improving product or service attributes can be optimally allocated.
- Risk drivers are those attributes that must be monitored to maintain purchase
likelihood levels.
- Opportunity drivers are those attributes that represent prospects for improvement
to increase purchase likelihood.
In the example chart below, among members of the On-The-Go Segment, failing
to maintain "Offers good deals" would result in an 8% decline in likelihood
to purchase. Increasing perceptions of "Store is clean" would result
in a 12% increase in likelihood to purchase.
Volumetric Analysis
When asked of a representative set of competitive brands or product types, the results of purchase behavior measures can be translated into a very accurate estimate of aggregate category volume and share. This type of consumer-driven approach to sales analysis has many applications and is particularly useful for new categories or those categories for which there is little reliable sales data available.
Potential volume can be calculated using purchase frequency, purchase volume,
and products purchased along with their associated prices. Sales volume is extrapolated
using competitive set incidence information and calibrated using known sales
data (if available). The projection can then be cross-tabulated by consumer
segments and reported in dollar volume, units, or market size in terms of number
of consumers.
The example volume analysis below shows dollar volume for the total convenience
store category (minus gas sales), along with the potential volume contributed
by each market segment.
Segment |
Market Size |
Annual Purchase Frequency |
Average Spend Per Transaction |
Volumetric Projection |
Per Capita Expenditure |
Segment 1 |
4,234,014 |
38.0 |
$35.00 |
$5,631,238,620 |
$1,330.00 |
Segment 2 |
20,081,049 |
15.0 |
$11.00 |
$3,313,373,085 |
$165.00 |
Segment 3 |
1,686,126 |
24.0 |
$25.00 |
$1,011,675,600 |
$600.00 |
Segment 4 |
8,360,667 |
10.0 |
$8.00 |
$668,853,360 |
$80.00 |
Segment 5 |
90,979,686 |
3.0 |
$23.00 |
$6,277,598,334 |
$69.00 |
Segment 6 |
2,615,748 |
27.0 |
$67.00 |
$4,731,888,132 |
$1,809.00 |
Total |
127,957,290 |
19.5 |
$28.17 |
$21,634,627,131 |
$169.08 |
Target Segments |
8,535,888 |
29.7 |
$42.33 |
$10,720,126,896 |
$1,255.89 |
Analytical Consulting Services
Decision Analyst is a leading international marketing research and analytical
consulting firm with over 30 years of experience in state-of-the-art modeling,
simulation, and optimization. A team of Ph.D's heads up Decision Analyst’s
choice-modeling work. They publish many white papers on advanced analytical
methods and speak frequently at marketing research industry conferences.
Decision Analyst’s strengths in statistics and mathematics, simulation,
modeling, and optimization provides the analytical foundation to address complex
business and strategy issues. If you would like more information or would like
to discuss a possible project, please contact Jerry W. Thomas,
President/CEO (jthomas@decisionanalyst.com),
or John Colias, Ph.D. (jcolias@decisionanalyst.com)
at 1-800-ANALYSIS (262-5974) or 1-817-640-6166.
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