Transcript marketsegments

Agenda
1.
2.
3.
4.
5.
Housekeeping: readings, team name &
e-e-mail,
etc.
News
1. Surveillance data mining
2. Shopping basket analysis
Segmentation marketing
1. Basics of segmentation
2. Geographics
3. Demographics
4. Lifecycle
5. Cohorts
6. Psychographics & behavior
Team discussion questions
1. Profile yourself as consumer
2. Profile your customers: How does this enable you to
respond to their needs better
Next week: consumer behavior– why we buy what we buy
Group discussion
questions for tonight
Use the various consumer
profiling methods to:
1.
Profile yourself as a consumer (use VALS-2, Prism, and other
demographic, psychographic, and lifestyle descriptors).
2.
What are the implications for marketers (e.g., how is this reflected in how
they do/can market to you more effectively)?
3.
Profile the customers in your business (or department).
4.
How does this information about your customers enable you to provide
better products/services to them?
5.
What more do you need to know? How could you find out?
What is
?
You might understand the parts, but might miss the whole chicken
Methods of
Seg-men-ta-tion
Demographic/Geographic refers to age, sex, income, education, race,
martial status, size of household, geographic location, size of city, and
profession.
Life stage refers to chronological benchmarking of people's lives at
different ages (e.g., pre-teens, teenagers, empty-nesters, etc.).
Lifestyle refers to the collective choice of hobbies, recreational
pursuits, entertainment, vacations, and other non-work time pursuits
Psychographics refers to personality and emotionally based behavior
linked to purchase choices; for example, whether customers are risktakers or risk-avoiders, impulsive buyers, etc.
Belief and value systems includes religious, political, nationalistic,
and cultural beliefs and values.
Behavior analysis includes what behaviors consumers actually
engage in (after all is said and done)
Requirements for segmentation
Identifiable: the differentiating attributes of the
segments must be measurable so that they can be identified.
Relevant/Accessible: the segments must be reachable through
communication
and distribution
channels.
Question:
What are
some criteria that could be
used to ensure that a segmentation has utility?
Substantial: the segments should be sufficiently large to justify the
resources required to target them.
Unique needs: to justify separate offerings, the segments must respond
differently to the different marketing mixes.
Durable: the segments should be relatively stable to minimize the cost of
frequent changes.
Pitfalls of Segmentation
• appeal to segments that are too small
• misread consumer similarities and differences
• become cost inefficient
• spin off too many imitations of their original products or brands
• become short-run rather than long-run oriented
• unable to use certain media (due to small segment size)
• compete in too many markets
• confuse people
• become locked in to a declining market
• too slow to seek innovation possibilities for new products
Demographic Profile
Business segmentation can
help companies align their
sales territories based on
the opportunities
on the ground. The
BEFOREmap shows
territories determine by
geometry—four quadrants
dividing the central area—
while the AFTERmap shows
territories that vary in size
based on the number and
potential value of target
businesses (the red dots
indicating the locations of
target businesses). By
mapping its business
prospects by size and
industry type in Lexington,
Kentucky, a company can
better realign its sales
territories based on the
concentrations of its highquality prospects.
Social Network Analysis
In online communities, who are the influencers?
The Hypernetworked World
Profile of Motor Boat Owner Segmentation
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Chapter Twenty-one
Factor and Cluster Analysis
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Factor Analysis
• Combines questions or variables to create new
factors
• Combines objects to create new groups
Uses in Data Analysis
▫ To identify underlying constructs in the data from the groupings of
variables that emerge
▫ To reduce the number of variables to a more manageable set
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Factor Analysis (Contd.)
Methodology
• Principal Component Analysis
▫ Summarizes information in a larger set of variables
to a smaller set of factors
• Common Factor Analysis
▫ Uncovers underlying dimensions surrounding the
original variables
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Factor Analysis - Example
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Export Data Set - Illustration
Respid
Will(y1)
Govt(y2) Train(x5) Size(x1)
Exp(x6)
Rev(x2)
Years(x3) Prod(x4)
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Description of Variables
Variable Description
Corresponding
Name in Output
Scale Values
Willingness to Export (Y1)
Will
1(definitely not interested) to 5
(definitely interested)
Level of Interest in Seeking Govt
Assistance (Y2)
Govt
1(definitely not interested) to 5
(definitely interested)
Employee Size (X1)
Size
Greater than Zero
Firm Revenue (X2)
Rev
In millions of dollars
Years of Operation in the Domestic
Market (X3)
Years
Actual number of years
Number of Products Currently
Produced by the Firm (X4)
Prod
Actual number
Training of Employees (X5)
Train
0 (no formal program) or 1 (existence
of a formal program)
Management Experience in
International Operation (X6)
Exp
0 (no experience) or 1 (presence of
experience)
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Factors
Factor
▫ A variable or construct that is not directly observable but
needs to be inferred from the input variables
▫ All included factors (prior to rotation) must explain at least as
much variance as an “average variable”
Eigenvalue Criteria
▫ Represents the amount of variance in the original variables
that is associated with a factor
▫ Sum of the square of the factor loadings of each variable on a
factor represents the eigenvalue
▫ Only factors with eigenvalues greater than 1.0 are retained
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How Many Factors - Criteria
Scree Plot Criteria
▫ A plot of the eigenvalues against the number of factors,
in order of extraction.
▫ The shape of the plot determines the number of factors
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How Many Factors: Criteria (Contd.)
Percentage of Variance Criteria
▫ The number of factors extracted is determined so
that the cumulative percentage of variance
extracted by the factors reaches a satisfactory level
Significance Test Criteria
▫ Statistical significance of the separate eigenvalues is
determined, and only those factors that are
statistically significant are retained
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Extraction using Principal Component Method
- Unrotated
Total Variance Explained
Initial Eigenvalues
Component
1
Extraction Sums of Squared Loadings
Total
2.326
% of Variance
38.761
Cumulative %
38.761
Total
2.326
% of Variance
38.761
Cumulative %
38.761
2
1.567
26.109
64.870
1.567
26.109
64.870
3
.918
15.306
80.175
4
.594
9.894
90.069
5
.362
6.035
96.104
6
.234
3.896
Extraction Method: Principal Component Analysis.
100.000
Component Matrix(a)
Component Score Coefficient Matrix
Component
1
x5
x1
x6
x2
x3
x4
Component
1
2
.566
.880
.695
-.100
-.297
.806
.724
.022
-.344
.503
.809
.124
x5
x1
x6
x2
x3
x4
2
.244
.378
.299
-.043
-.128
.347
.462
.014
-.220
.321
.517
.079
Extraction Method: Principal Component Analysis.
Component Scores.
Extraction Method: Principal Component Analysis.
a 2 components extracted.
Factor Loadings
Factor Score Coefficient
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Extraction using Principal Component Method Factor Rotation
Total Variance Explained
Initial Eigenvalues
Component
1
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Total
2.326
% of Variance
38.761
Cumulative %
38.761
Total
2.326
% of Variance
38.761
Cumulative %
38.761
Total
2.309
% of Variance
38.479
Cumulative %
38.479
2
1.567
26.109
64.870
1.567
26.109
64.870
1.583
26.391
64.870
3
.918
15.306
80.175
4
.594
9.894
90.069
5
.362
6.035
96.104
6
.234
3.896
100.000
Extraction Method: Principal Component Analysis.
Not significantly different
from unrotated values
Rotated Component Matrix(a)
Component Score Coefficient Matrix
Component
Component
1
1
2
2
x5
.668
.632
x5
.310
.421
x1
.873
-.110
x1
.376
-.043
-.444
x6
.263
-.262
.512
x2
.006
.324
x3
-.049
.530
x6
x2
.636
-.023
x3
-.173
.844
x4
.816
.002
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 3 iterations.
x4
.355
.027
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Component Scores.
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Common Factor Analysis
▫ The factor extraction procedure is similar to that of principal
component analysis except for the input correlation matrix
▫ Communalities or shared variance is inserted in the diagonal instead
of unities in the original variable correlation matrix
▫ The total amount of variance that can be explained by all the factors in
common factor analysis is the sum of the diagonal elements in the
correlation matrix
▫ The output of common factor analysis depends on the amount of
shared variance
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Common Factor Analysis – Results (Contd.)
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Common Factor Analysis - Results
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Common Factor Analysis – Results (Contd.)
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Cluster Analysis
• Technique for grouping individuals or objects into unknown groups.
• The typical criterion used in cluster analysis is distance between
clusters or the error sum of squares.
• The input is any valid measure of similarity between objects, such as:
▫ Correlations
▫ Distance measures (Euclidean distance)
▫ Association coefficients
▫ The number of clusters or the level of clustering
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Steps in Cluster Analysis
Define the
problem
Decide on
the
appropriate
similarity
measure
Decide on
how to
group the
objects
Decide the
number of
clusters
Interpret,
describe,
and validate
the clusters
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Cluster Analysis (Contd.)
Hierarchical Clustering
▫ Can start with all objects in one cluster and divide and subdivide them until all objects
are in their own single-object cluster ( ‘top-down’ or decision approach)
▫ Can start with each object in its own single-object cluster and systematically combine
clusters until all objects are in one cluster (‘bottom-up’ or agglomerative approach)
Non-hierarchical Clustering
▫ Permits objects to leave one cluster and join another as clusters are being formed
▫ A cluster center is initially selected and all the objects within a pre-specified threshold
distance are included in that cluster
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Hierarchical Clustering
• Single Linkage
▫ Clustering criterion based
on the shortest distance
• Complete Linkage
▫ Clustering criterion based
on the longest distance
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Hierarchical Clustering (Contd.)
• Average Linkage
▫ Clustering criterion based
on the average distance
• Ward's Method
▫ Based on the loss of
information resulting from
grouping of the objects into
clusters (minimize within
cluster variation)
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Hierarchical Clustering (Contd.)
• Centroid Method
▫ Based on the distance between the group centroids
(the point whose coordinates are the means of all the
observations in the cluster)
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Hierarchical Cluster Analysis - Example
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Hierarchical Cluster Analysis (Contd.)
A dendrogram for hierarchical clustering of bank data
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Hierarchical Cluster Analysis (Contd.)
▫ Number of clusters is specified by the analyst for theoretical or
practical reasons.
▫ Level of clustering with respect to clustering criterion is specified.
▫ Determine the number of clusters from the pattern of clusters
generated. The distances between clusters or error variability
measure at successive steps can be used to decide the number of
clusters (from the plot of error sum of squares with the number of
clusters).
▫ The ratio of total within-group variance to between group variance
is plotted against the number of clusters and the point at which an
elbow occurs indicates the number of clusters.
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
Assumptions
◦ The basic measure of similarity on which the clustering is
based is a valid measure of the similarity between the
objects.
◦ There is theoretical justification for structuring the objects
into clusters

Limitations
◦ It is difficult to evaluate the quality of the clustering
◦ It is difficult to know exactly which clusters are very similar
and which objects are difficult to assign.
◦ It is difficult to select a clustering criterion and program on
any basis other than availability.
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